Development of water quality index (WQI) for the Juskei River catchment, Johannesburg.

Water quality monitoring is a key component of integrated water resources management. Information generation from the data produced during this monitoring exercise is therefore critical in the process of deciding which rehabilitation or pollution control measures need to be undertaken. Water quality index (WQI) is useful in achieving this through simplifying complex water quality data into a single value that can therefore be classified to indicate the water quality. The objectives of the research were as follows: To evaluate water quality data analysis and interpretation methods being employed in the City of Johannesburg (COJ), To develop a water quality index (WQI) for Jukskei catchment in the COJ as a practical method of presenting complex water quality data simply, To apply the developed index to evaluate the water quality data, To determine the levels of pollution in the Jukskei catchment using the index and identify the highly polluted locations, To determine the water quality trends in the Jukskei catchment using the WQI. The methodologies used to achieve the above objectives consisted of literature review, data analysis and determination of appropriate water quality index and determination of trend on highly polluted areas identified using the water quality index determined. The current data analysis methods being employed by the City of Johannesburg and associated problems were discussed. The study also brings to the fore the benefits of using the water quality index in analysing the data and producing the simple water quality status report on monthly and quarterly basis to align it with City of Johannesburg reporting periods. The study recommends that the City of Johannesburg employs the proposed water quality index to complement existing methods of analysing and interpreting water quality data and reporting this information. This could improve the understanding of surface water quality conditions and decision making

Experiencing algorithms:How Young People Understand, Feel About, and Engage With Algorithmic News Selection on Social Media

The news that young people consume is increasingly subject to algorithmic curation. Yet, while numerous studies explore how algorithms exert power in citizens’ everyday life, little is known about how young people themselves perceive, learn about, and deal with news personalization. Considering the interactions between algorithms and users from an user-centric perspective, this article explores how young people make sense of, feel about, and engage with algorithmic news curation on social media and when such everyday experiences contribute to their algorithmic literacy. Employing in-depth interviews in combination with the walk-through method and think-aloud protocols with a diverse group of 22 young people aged 16–26 years, it addresses three current methodological challenges to studying algorithmic literacy: first, the lack of an established baseline about how algorithms operate; second, the opacity of algorithms within everyday media use; and third, limitations in technological vocabularies that hinder young people in articulating their algorithmic encounters. It finds that users’ sense-making strategies of algorithms are context-specific, triggered by expectancy violations and explicit personalization cues. However, young people’s intuitive and experience-based insights into news personalization do not automatically enable young people to verbalize these, nor does having knowledge about algorithms necessarily stimulate users to intervene in algorithmic decisions

Blockchain architecture and its applications in a bank risk mitigation framework

This study proposes a simple two-period model to consider consumers’ borrowing behaviour in a decentralised consensus and information distribution platform. Based on this model, we develop a bank risk mitigation framework and find that decentralised digital identity and encryption technology are the most important factors for attaining market equilibrium between decentralised consensus and information distribution. Specifically, the greater the scope of digital identity construction and the more blockchain consensus records there are, the less likely the borrower will default. Our study provides meaningful practical implications for bankers and policy regulators to help them better understand consumers’ borrowing behaviour and decisions to default

Bitcoin, Virtual Currencies, and the Struggle of Law and Regulation to Keep Pace

At less than a decade old, Bitcoin and other virtual currencies have had a major societal impact, and proven to be a unique payment systems challenge for law enforcement, financial regulatory authorities worldwide, and the investment community. Rapid introduction and diffusion of technological changes throughout society, such as the blockchain that serves as Bitcoin’s crypto-foundation, continue to exceed the ability of law and regulation to keep pace. During 2017 alone, the market price of Bitcoin rose 1,735%, from about $970 to$14,292, causing an investor feeding frenzy. As of September 11, 2018, a total of 1,935 cryptocurrencies are reported, having an approximate market capitalization of $191.54 billion at that date. A brief history of the fast moving adoption of blockchain-based technology is provided, along with a look at the efforts of regulators to keep up with the staggering worldwide growth in the usage of virtual currencies. In the United States, enforcement actions for violations of law involving virtual currencies are brought primarily by: The Commodity Futures Trading Commission (CFTC); The Securities and Exchange Commission (SEC); and The Department of The Treasury through the Financial Crimes Enforcement Network (FinCEN). This Article contributes to the literature and our understanding of the constant struggle of law and regulation to keep pace with rapid technological developments

Essays on the New Blockchain-Based Digital Financial Market : Risks and Opportunities

This doctoral thesis consists of five original essays on the risks and opportunities of the new blockchain-based digital financial market. The purpose of this dissertation is to analyze, identify, and, if possible, predict some of the major risks in the market for blockchain-based digital assets. It analyzes how crypto-specific characteristics are associated with solvency risk, sustainability risk, seclusion risk, and sentiment risk. On top of that, it also sheds light on the opportunity side of this financial innovation. The first essay of this dissertation specifically focuses on cryptocurrency for solvency risks. To forecast potential cryptocurrency default at an early stage, this study focuses on variables that are part of the information set of the investor 1 month at most after the start of trading for a cryptocurrency. The results of this research show that bankruptcies among cryptocurrencies are predictable. The second essay explores energy risk as a fundamental market-driving force for the pricing of cryptocurrency. Cryptocurrencies using a high-energy-consumption consensus protocol are riskier than others because their mining costs are more exposed to changes in energy price. Surprisingly, the study finds that energy consumption does not seem to play a role in pricing cryptocurrency. The third essay hypothesizes that privacy coins form a distinct submarket in the cryptocurrency market, shedding light on seclusion risk. It shows that privacy coins and non-privacy coins are two distinct asset markets within the cryptocurrency market. The fourth essay is about news media sentiment risk. It explores whether news media sentiments have an impact on Bitcoin volatility. It also differentiates financial sentiment and psychological sentiment and finds that financially optimistic investors are driving the Bitcoin market. On the other hand, the fifth essay in this dissertation analyzes opportunities, especially the funding opportunity in the widely known category of new digital assets defined as crypto tokens. It analyzes the determinants of the success of initial coin offerings and finds that initial-coin-offering investors are largely guided by their emotions when making investment decisions. Surprisingly, regulatory framework has not yet become a priority among policymakers. Therefore, this doctoral dissertation not only facilitates future research, but also helps regulators in shaping the future of blockchain-based financial technologies.Tämä väitöskirja koostuu viidestä esseestä, jotka käsittelevät uuden lohkoketjupohjaisen digitaalisen rahoitusmarkkinan riskejä ja mahdollisuuksia. Väitöskirjan tarkoituksena on analysoida, tunnistaa ja mahdollisuuksien mukaan ennustaa joitakin lohkoketjupohjaisten digitaalisten varojen markkinoiden suurimpia riskejä. Siinä analysoidaan, miten kryptovaluuttakohtaiset ominaisuudet liittyvät vakavaraisuusriskiin, kestävyysriskiin, eristäytymisriskiin ja sentimenttiriskiin. Tämän lisäksi se valottaa myös tämän rahoitusinnovaation mahdollisuuksia. Tämän väitöskirjan ensimmäisessä esseessä keskitytään erityisesti kryptovaluuttaan maksukyvyttömyysriskinä. Tässä tutkimuksessa keskitytään muuttujiin, jotka ovat sijoittajan saatavilla korkeintaan 1 kuukausi sen jälkeen, kun kaupankäynti kryptovaluutalla on alkanut. Tämän tutkimuksen tulokset osoittavat, että kryptovaluuttojen konkurssit ovat ennustettavissa. Toisessa esseessä tutkitaan energiariskiä markkinoita ohjaavana voimana kryptovaluutan hinnoittelussa. Kryptovaluutat, jotka käyttävät paljon energiaa kuluttavaa konsensusprotokollaa, ovat muita riskialttiimpia, koska niiden louhintakustannukset ovat alttiimpia energian hinnan muutoksille. Yllättäen tutkimuksessa todetaan, että energiankulutuksella ei näytä olevan merkitystä kryptovaluuttojen hinnoittelussa. Kolmannessa esseessä hypoteesina on, että yksityisyyskolikot muodostavat erillisen alamarkkinan kryptovaluuttamarkkinoilla, ja tutkimus tarkastelee näiden eristäytymisriskiä. Siinä osoitetaan, että yksityisyyskolikot ja ei-yksityisyyskolikot ovat kaksi erillistä omaisuuserämarkkinaa kryptovaluuttamarkkinoilla. Neljäs essee käsittelee uutismedian sentimenttiriskiä. Siinä tutkitaan, vaikuttaako uutismedian sentimentti Bitcoinin volatiliteettiin. Siinä myös erotetaan toisistaan taloudellinen sentimentti ja psykologinen sentimentti ja todetaan, että taloudellisesti optimistiset sijoittajat ohjaavat Bitcoin-markkinoita. Väitöskirjan viidennessä esseessä analysoidaan mahdollisuuksia, erityisesti rahoitusmahdollisuuksi, liittyen laajalti tunnettuihin digitaalisiin tokeneihin. Siinä havaitaan, että näihin omaisuuseriin sijoittavat sijoittajat toimivat pitkälti tunteidensa ohjaamina sijoituspäätöksiä tehdessään. Yllättävää kyllä, sääntelykehyksestä ei ole vielä tullut poliittisten päättäjien prioriteettia. Siksi tämä väitöskirja ei ainoastaan tue tulevaa tutkimusta, vaan auttaa myös viranomaisia lohkoketjupohjaisten rahoitusteknologioiden tulevaisuuden määrittelyssä.fi=vertaisarvioitu|en=peerReviewed

Explosive behaviour in cryptocurrency prices

Bitcoin and other cryptocurrencies have enjoyed several well-documented episodes of price exuberance since they emerged on the scene. However, in May 2022, an almost perfect storm shook their world, causing their prices to plummet to unexpected lows. This dissertation aims to contribute to the quest for episodes of exuberance, suggestive of bubbles, in cryptocurrency prices by conducting a comprehensive empirical analysis, which employs state-of-the-art tests for explosive behaviour: the Generalized Supremum Augmented Dickey Fuller (GSADF) test developed by Phillips, Shi and Yu (2015) and, as an original contribution, its panel version proposed by Pavlidis et al (2016). These tests are complemented by an array of other econometric techniques, such as unit root and cointegration tests, and Granger causality tests, to conduct a comprehensive formal investigation of periods of explosiveness in cryptocurrency prices. The formal analysis is complemented and enhanced by a narrative analysis, which aims to shed light on the factors that triggered the episodes of explosiveness. A thorough examinations of the causes of these episodes is timely, as the recent dramatic crash in the prices of cryptocurrencies is evocative of past bubble episodes. To this end, I use data for (at least) the last 5 years (2017-2022) for 7 major cryptocurrencies, which feature in the top 20 cryptocurrencies by market capitalization (Coinmarketcap.com, 18 July 2022). Moreover, after detecting the periods of exuberance in cryptocurrency prices and analysing their causes, I examine whether there have been synchronized periods of explosiveness across the selected cryptocurrencies. Furthermore, I analyse if explosiveness in one or more individual cryptocurrencies led to global explosiveness episodes across all major cryptocurrencies by using a Logit model. Additionally, using Granger causality tests, I establish connectedness across cryptocurrencies, by unveiling some causal links between them. To set the scene for the empirical investigation, I provide a brief introduction about bubbles, types of bubbles, and detection of bubbles in asset markets. Subsequently, I provide a thorough review of the relevant literature on cryptocurrencies as a different asset class, and the existing evidence of bubbles in the cryptocurrency markets. In the empirical analysis, using the GSADF test I detected several explosiveness (bubble) periods in the individual cryptocurrencies and in the panel made of selected currencies. The results reveal that Bitcoin experienced most of the long-lived bubbles. Most of the detected explosivity periods coincide with the ones documented by previous studies. However, given that the data span of my analysis includes the more recent two years, I pay special attention to the period 2020-2021. I unveil evidence of synchronicity among cryptocurrencies, especially during periods of financial turbulence. This dissertation contributes to the ongoing debate about speculative bubbles in cryptocurrency markets, by using an extended the dataset and by conducting not only GSADF tests for individual cryptocurrencies, but also the Panel GSADF in order to detect possible multiple bubbles in the cryptocurrency markets, as well as by demonstrating the existence of synchronised periods of explosivity among cryptocurrencies, that ultimately can help investors in terms of portfolio management.A Bitcoin e outras criptomoedas passaram por vários episódios bem documentados de exuberância de preços desde que surgiram. No entanto, em Maio de 2022, uma tempestade quase perfeita abalou o mundo das criptomoedas, fazendo com que os respetivos preços caíssem para mínimos inesperados. Este Trabalho Final de Mestrado pretende contribuir para a procura de episódios de exuberância, que sugiram bolhas, nos preços das criptomoedas, através da realização de uma análise empírica abrangente que recorre a testes de última geração para comportamento explosivo: o teste Generalized Supremum Augmented Dickey Fuller (GSADF) desenvolvido por Phillips, Shi e Yu (2015) e, como contributo original, a sua versão em painel proposta por Pavlidis et al. (2016). Estes testes são complementados por um conjunto de outras técnicas econométricas, tais como testes de raiz unitária e cointegração e testes de causalidade de Granger, com vista a realizar uma investigação formal abrangente de períodos de explosão nos preços das criptomoedas. A análise formal é complementada e aperfeiçoada por uma análise narrativa com vista a esclarecer os fatores que desencadearam os episódios de explosão. Uma análise minuciosa das causas destes episódios é oportuna, pois a recente queda drástica nos preços das criptomoedas evoca episódios anteriores de bolhas. Para tal, utilizo dados para (pelo menos) os últimos 5 anos (2017-2022) para 7 principais criptomoedas, que figuram entre as 20 principais criptomoedas por capitalização de mercado (Coinmarketcap.com, 18 de julho de 2022). Além disso, após detetar os períodos de exuberância nos preços das criptomoedas e analisar as suas causas, estudo se ocorreram períodos sincronizados de explosão nas criptomoedas selecionadas. Analiso igualmente se a explosão numa ou mais criptomoedas individuais conduziu a episódios globais de explosão em todas as principais criptomoedas ix utilizando um modelo Logit. Além do mais, recorrendo a testes de causalidade de Granger, estabeleço a conectividade entre criptomoedas, revelando algumas ligações causais entre as mesmas. Para definir o cenário para a investigação empírica, apresento uma breve introdução sobre bolhas, tipos de bolhas e deteção de bolhas nos mercados de ativos. Posteriormente, apresento uma revisão completa da literatura relevante sobre criptomoedas como uma classe de ativos diferente e as evidências existentes de bolhas nos mercados de criptomoedas. Na análise empírica, recorrendo ao teste GSADF, detetei vários períodos de explosão (bolha) nas criptomoedas individuais e no painel composto pelas criptomoedas selecionadas. Os resultados revelam que a Bitcoin experienciou a maioria das bolhas de longa duração. A maioria dos períodos de explosão detetados coincidem com os documentados por estudos anteriores. No entanto, dado que o período de dados da minha análise inclui os dois anos mais recentes, presto especial atenção ao período 2020-2021. Revelo evidências de sincronicidade entre criptomoedas, especialmente durante períodos de turbulência financeira. Este Trabalho Final de Mestrado contribui para o debate em curso sobre bolhas especulativas nos mercados de criptomoedas, recorrendo a um conjunto de dados alargado e realizando não apenas testes GSADF para criptomoedas individuais, mas também o GSADF Painel para detetar possíveis múltiplas bolhas nos mercados de criptomoedas, assim como demonstrando a existência de períodos sincronizados de explosão das criptomoedas que, em última análise, podem ajudar os investidores em termos de gestão de portfolios

The disruption of blockchain in auditing – a systematic literature review and an agenda for future research

PURPOSE : This paper presents a systematic literature review, including content and bibliometric analyses, of the impact of blockchain technology (BT) in auditing, to identify trends, research areas and construct an agenda for future research. DESIGN/METHODOLOGY/APPROACH : The authors include studies from 2010 to 2020 in their structured literature review (SLR), using accounting journals on the Scopus database, which yielded 40 articles with blockchain and auditing at its core. FINDINGS : One of the contributions of the authors’ analyses is to group the prior research, and therefore also the agenda for future research, into three main research areas: (1) Blockchain as a tool for auditing professionals to improve business information systems to save time and prevent fraud; (2) Smart contracts enabling Audit 4.0 efficiency, reporting, disclosure and transparency; (3) Cryptocurrency and initial coin offerings (ICOs) as a springboard for corporate governance and new venture financing. The authors’ findings have several important implications for practice and theory. PRACTICAL IMPLICATIONS : The results of this study emphasise that (1) the disruption of blockchain in auditing is in a nascent phase and there is a need for compelling empirical studies and potential for the involvement of practitioners; (2) there may be a need to reconsider audit procedures especially suited for digitalisation and BT adoption; (3) standards, guidelines and training are required to pivot towards and confront the challenge BT will represent for auditing; and (4) there are two sides to the BT coin for auditing, enthusiasm about the potential and risk upon implementation. These practical implications can also be seen as a template for future research in a quest to align theory and practice. ORIGINALITY/VALUE : The authors’ SLR facilitates the identification of research areas and implications, forming a useful baseline for practitioners, professionals and academics, as they draft the state of the art on the disruption of blockchain in auditing, highlighting how BT is changing auditing activities and traditions.https://www.emerald.com/insight/publication/issn/0951-3574hj2021Accountin

Computational Methods for Medical and Cyber Security

Over the past decade, computational methods, including machine learning (ML) and deep learning (DL), have been exponentially growing in their development of solutions in various domains, especially medicine, cybersecurity, finance, and education. While these applications of machine learning algorithms have been proven beneficial in various fields, many shortcomings have also been highlighted, such as the lack of benchmark datasets, the inability to learn from small datasets, the cost of architecture, adversarial attacks, and imbalanced datasets. On the other hand, new and emerging algorithms, such as deep learning, one-shot learning, continuous learning, and generative adversarial networks, have successfully solved various tasks in these fields. Therefore, applying these new methods to life-critical missions is crucial, as is measuring these less-traditional algorithms' success when used in these fields