SciTech News Volume 71, No. 1 (2017)

Columns and Reports From the Editor 3 Division News Science-Technology Division 5 Chemistry Division 8 Engineering Division Aerospace Section of the Engineering Division 9 Architecture, Building Engineering, Construction and Design Section of the Engineering Division 11 Reviews Sci-Tech Book News Reviews 12 Advertisements IEEE

Mapping domain characteristics influencing Analytics initiatives: The example of Supply Chain Analytics

Purpose: Analytics research is increasingly divided by the domains Analytics is applied to. Literature offers little understanding whether aspects such as success factors, barriers and management of Analytics must be investigated domain-specific, while the execution of Analytics initiatives is similar across domains and similar issues occur. This article investigates characteristics of the execution of Analytics initiatives that are distinct in domains and can guide future research collaboration and focus. The research was conducted on the example of Logistics and Supply Chain Management and the respective domain-specific Analytics subfield of Supply Chain Analytics. The field of Logistics and Supply Chain Management has been recognized as early adopter of Analytics but has retracted to a midfield position comparing different domains. Design/methodology/approach: This research uses Grounded Theory based on 12 semi-structured Interviews creating a map of domain characteristics based of the paradigm scheme of Strauss and Corbin. Findings: A total of 34 characteristics of Analytics initiatives that distinguish domains in the execution of initiatives were identified, which are mapped and explained. As a blueprint for further research, the domain-specifics of Logistics and Supply Chain Management are presented and discussed. Originality/value: The results of this research stimulates cross domain research on Analytics issues and prompt research on the identified characteristics with broader understanding of the impact on Analytics initiatives. The also describe the status-quo of Analytics. Further, results help managers control the environment of initiatives and design more successful initiatives.DFG, 414044773, Open Access Publizieren 2019 - 2020 / Technische Universität Berli

THE USE OF ICT FOR SECURITY AND THEFT PREVENTION IN TWO UNIVERSITY LIBRARIES IN NIGERIA

The study investigated the use of ICT for security and theft prevention in two university libraries in Nigeria. Population of the study is made up of 80 library staff of the two universities, 40 library staff from Umaru Musa Yar’adua University, Katsina and 40 library staff from Al-Qalam University Katsina. Five research questions guided the study. The data was analyzed with the use of simple statistical tools like frequencies and percentages. Findings of the study revealed that maximum security in the libraries will prevent theft mutilation. It also revealed that a video surveillance system working in conjunction with a barcode and magnetic book control system could help prevent book theft and monitor the move of books and other resources as it moves from one user to another. Lack of literate or skilled personnel: libraries and information Centre’s lack skilled personnel that can operate, teach and instruct the use of these telecommunication security systems even when they are made available in the library. Poor power supply: These telecommunication security systems or devices need electricity to power them and due to the poor power supply, these systems often time are not working and as such are incapable of performing their expected task of securing the library and its collection. Hardware and software failure: This is major threat to the use of telecommunication security system in the library. When there is software failure or hardware breakdown that may require the need for an engineer who may not be available to put them in place as at when due, then the library system and its collections is at risk. Based on the findings of the study, it was recommended that the librarian and information experts should take out time to educate their parent organization on the benefits associated with libraries and information Centre’s in the use of telecommunication security systems and devices so that the library parent body can release adequate fund which will be used for the purchase of telecommunication security devices in the librar

Synchronized measurement data conditioning and real-time applications

Phasor measurement units (PMU), measuring voltage and current phasor with synchronized timestamps, is the fundamental component in wide-area monitoring systems (WAMS) and reveals complex dynamic behaviors of large power systems. The synchronized measurements collected from power grid may degrade due to many factors and impacts of the distorted synchronized measurement data are significant to WAMS. This dissertation focus on developing and improving applications with distorted synchronized measurements from power grid. The contributions of this dissertation are summarized below. In Chapter 2, synchronized frequency measurements of 13 power grids over the world, including both mainland and island systems, are retrieved from Frequency Monitoring Network (FNET/GridEye) and the statistical analysis of the typical power grids are presented. The probability functions of the power grid frequency based on the measurements are calculated and categorized. Developments of generation trip/load shedding and line outage events detection and localization based on high-density PMU measurements are investigated in Chapters 3 and 4 respectively. Four different types of abnormal synchronized measurements are identified from the PMU measurements of a power grid. The impacts of the abnormal synchronized measurements on generation trip/load shedding events detection and localization are evaluated. A line outage localization method based on power flow measurements is proposed to improve the accuracy of line outage events location estimation. A deep learning model is developed to detect abnormal synchronized measurements in Chapter 5. The performance of the model is evaluated with abnormal synchronized measurements from a power grid under normal operation status. Some types of abnormal synchronized measurements in the testing cases are recently observed and reported. An extensive study of hyper-parameters in the model is conducted and evaluation metrics of the model performance are presented. A non-contact synchronized measurements study using electric field strength is investigated in Chapter 6. The theoretical foundation and equation derivations are presented. The calculation process for a single circuit AC transmission line and a double circuit AC transmission line are derived. The derived method is implemented with Matlab and tested in simulation cases

Benefits and challenges of using smart meters for advancing residential water demand modeling and management: a review

Over the last two decades, water smart metering programs have been launched in a number of medium to large cities worldwide to nearly continuously monitor water consumption at the single household level. The availability of data at such very high spatial and temporal resolution advanced the ability in characterizing, modeling, and, ultimately, designing user-oriented residential water demand management strategies. Research to date has been focusing on one or more of these aspects but with limited integration between the specialized methodologies developed so far. This manuscript is the first comprehensive review of the literature in this quickly evolving water research domain. The paper contributes a general framework for the classification of residential water demand modeling studies, which allows revising consolidated approaches, describing emerging trends, and identifying potential future developments. In particular, the future challenges posed by growing population demands, constrained sources of water supply and climate change impacts are expected to require more and more integrated procedures for effectively supporting residential water demand modeling and management in several countries across the world

Anomaly Detection in BACnet/IP managed Building Automation Systems

Building Automation Systems (BAS) are a collection of devices and software which manage the operation of building services. The BAS market is expected to be a $19.25 billion USD industry by 2023, as a core feature of both the Internet of Things and Smart City technologies. However, securing these systems from cyber security threats is an emerging research area. Since initial deployment, BAS have evolved from isolated standalone networks to heterogeneous, interconnected networks allowing external connectivity through the Internet. The most prominent BAS protocol is BACnet/IP, which is estimated to hold 54.6% of world market share. BACnet/IP security features are often not implemented in BAS deployments, leaving systems unprotected against known network threats. This research investigated methods of detecting anomalous network traffic in BACnet/IP managed BAS in an effort to combat threats posed to these systems. This research explored the threats facing BACnet/IP devices, through analysis of Internet accessible BACnet devices, vendor-defined device specifications, investigation of the BACnet specification, and known network attacks identified in the surrounding literature. The collected data were used to construct a threat matrix, which was applied to models of BACnet devices to evaluate potential exposure. Further, two potential unknown vulnerabilities were identified and explored using state modelling and device simulation. A simulation environment and attack framework were constructed to generate both normal and malicious network traffic to explore the application of machine learning algorithms to identify both known and unknown network anomalies. To identify network patterns between the generated normal and malicious network traffic, unsupervised clustering, graph analysis with an unsupervised community detection algorithm, and time series analysis were used. The explored methods identified distinguishable network patterns for frequency-based known network attacks when compared to normal network traffic. However, as stand-alone methods for anomaly detection, these methods were found insufficient. Subsequently, Artificial Neural Networks and Hidden Markov Models were explored and found capable of detecting known network attacks. Further, Hidden Markov Models were also capable of detecting unknown network attacks in the generated datasets. The classification accuracy of the Hidden Markov Models was evaluated using the Matthews Correlation Coefficient which accounts for imbalanced class sizes and assess both positive and negative classification ability for deriving its metric. The Hidden Markov Models were found capable of repeatedly detecting both known and unknown BACnet/IP attacks with True Positive Rates greater than 0.99 and Matthews Correlation Coefficients greater than 0.8 for five of six evaluated hosts. This research identified and evaluated a range of methods capable of identifying anomalies in simulated BACnet/IP network traffic. Further, this research found that Hidden Markov Models were accurate at classifying both known and unknown attacks in the evaluated BACnet/IP managed BAS network

Urban Agriculture, Sustainability, and Internet-of-Things : Applying UTAUT to Determine the Behavioural Intention to Use IoT

Thesis (MIT (Information Systems))--University of Pretoria, 2022.The Internet of Things (IoT) is approaching the maturity stage of the technology adoption lifecycle in Sub-Saharan Africa (SSA). By 2025, most of the world's population will be living in urban areas. In South Africa, 66.8% of the population currently resides in urban areas with nearly two-thirds of these households experiencing food insecurity. Urbanisation affects food security in South Africa as people and physical resources migrate from the rural areas where food production typically happens. As such, there is a need to localise Sustainable Development Goals (SDGs) to make them more relevant and context-specific to urban farmers to ensure participation in working towards achieving these goals. Along with localising SDGs, IoT advancements should be considered by urban farmers to not only increase efficiency but to assist in realising the goal of sustainability and sustainable development. The study aims to adapt the unified theory of unified technology acceptance and use of technology (UTAUT) by introducing sustainability as a construct to determine how it influences urban farmers in Johannesburg and their behavioural intention to adopt IoT. This will contribute to making the theory robust to the determinants that influence individuals’ use of IoT, which aligns with recommendations made by the originators of the theory that researchers should identify constructs that serve to edify the prediction of intention and behaviour beyond what has already been studied. The results produced in the study are based on a pragmatist mixed methods approach. The quantitative approach was an online 25-question survey based on the existing UTAUT questionnaire items with the addition of the sustainability construct. This questionnaire was distributed to Gauteng-based urban farmers with active agribusinesses. The qualitative approach was a case study in the form of a semi-structured interview with three urban farmers in Gauteng with businesses currently in operation. The findings show significant relationships between behavioural intention and effort expectancy, as well as social influence, indicating that urban farmers’ behavioural intention to use IoT will be influenced by its ease of use and whether people they deem important, or look to for leadership, believe they should adopt IoT to be more sustainable. There was a non-significant relationship between behavioural intention and sustainability despite urban farmers’ belief that sustainability is important, with most being willing to explore any available means of ensuring the sustainability of their farms, including IoT. Based on the interviews and other data, this is due to constraints such as funding, accessibility, the effects of the COVID-19 pandemic on their businesses, the state of the economy and load-shedding. The study focuses on urban farms operating in cities around the province of Gauteng. This sector can be considered a niche, and this limited our sample size.InformaticsMIT (Information Systems)Unrestricte

The Influence of Identifiable Personality Traits on Nurses’ Intention to Use Wireless Implantable Medical Devices

Technically-driven medical devices such as wireless implantable medical devices (WIMD) have become ubiquitous within healthcare. The use of these devices has changed the way nurses administer patient care. Consequently, the nursing workforce is large and diverse, and with it comes an expected disparity in personalities. Research involving human factors and technology acceptance in healthcare is not new. Yet due to the changing variables in the manner of which patient care is being administered, both in person and in the mechanism of treatment, recent research suggests that individual human factors such as personality traits may hold unknown implications involving more successful adoption of emerging technologies for patient care. The purpose of this research was to empirically investigate the influence of personality traits on a nurse’s intention to use WIMDs for patient care. One hundred and two nurses from a tertiary teaching hospital in Michigan were surveyed to determine if their identifiable personality traits statistically related to their intention to use a WIMD. A predictive model was developed by combining constructs from the unified theory of acceptance and use of technology (UTAUT) model and the Five Factor personality trait model (FFM). The model used moderated multiple regression (MMR) to statistically identify if the personality traits of openness, conscientiousness, extraversion, agreeableness, and neuroticism, moderated one or more statistically significant relationships between 1) performance expectancy (PE) and intention to use (IU), 2) effort expectancy (EE) and IU, 3) and social influence (SI) and IU. It was predicted that PE, EE, and SI would show statistical significance on a nurse’s IU of a WIMD when moderated by one or more of the five personality traits. Results showed statistical significance between PE and IU, and EE and IU, but not between SI and IU, when moderated by extraversion. Results showed no statistical significance between PE and IU, EE and IU, or SI and IU when moderated by openness, conscientiousness, agreeableness, or neuroticism. This research has contributed by conducting an investigation on individual human factors that may impact nurses’ intention to use emerging technologies; and by providing statistical evidence that may help to better predict the role personality traits have on a nurse’s adoption of WIMDs for patient care

The Role of Visual and Verbal Processes in False Memory Susceptibility on the Misinformation Effect

The goal of this dissertation is to investigate links between susceptibility to misinformation on the misinformation effect paradigm and individual differences in visual and verbal source monitoring ability. Results from four studies are reported. The first three studies assess links between individual differences in perceptual misinformation endorsement levels and visualization (Word-As-Picture) as well as verbalization (Picture-As-Word) errors on the memory test of a source monitoring task in which a set of objects were initially presented either as pictures or words during study. In Study 1, this picture-word source monitoring task and a misinformation effect paradigm, with a True/False test format, was administered to a sample of 87 participants. In Study 2, the same picture-word source monitoring task and the misinformation effect paradigm, this time with a two-alternative forced-choice test format, was administered to a sample of 177 participants. In Study 3, electroencephalographic (EEG) data was recorded during the testing phases of a picture-word source monitoring task and a misinformation effect paradigm administered to a sample of 19 participants. Across all three studies, verbalization (Picture-As-Word) errors was more strongly linked with misinformation susceptibility than visualization errors (Word-As-Picture). Building on these results, Study 4 assessed the misinformation susceptibility related predictive value of individual differences in visual and verbal processing during the event and narrative study stages of the misinformation effect paradigm. In Study 4, EEG data was recorded during the during the event and narrative study phases of a misinformation effect paradigm administered to a sample of 30 participants. The primary findings from Study 4 indicate that during the event and narrative encoding stages in the misinformation effect, activity in neural regions associated with semantic and verbal processing is more strongly related to misinformation susceptibility relative to activity areas related to visual processing and encoding. Collectively, these results indicate that verbalization based processes may play a stronger role in misinformation susceptibility relative to visualization related processing. Drawing on this observation, an integrative framework highlighting the role of modality related features in a source monitoring perspective of the misinformation effect is proposed. Advisor: Robert F. Bell