Showing papers by "University of Johannesburg published in 2020"

Which Academic Search Systems are Suitable for Systematic Reviews or Meta‐Analyses? Evaluating Retrieval Qualities of Google Scholar, PubMed and 26 other Resources

Abstract: Rigorous evidence identification is essential for systematic reviews and meta-analyses (evidence syntheses) because the sample selection of relevant studies determines a review's outcome, validity, and explanatory power. Yet, the search systems allowing access to this evidence provide varying levels of precision, recall, and reproducibility and also demand different levels of effort. To date, it remains unclear which search systems are most appropriate for evidence synthesis and why. Advice on which search engines and bibliographic databases to choose for systematic searches is limited and lacking systematic, empirical performance assessments. This study investigates and compares the systematic search qualities of 28 widely used academic search systems, including Google Scholar, PubMed, and Web of Science. A novel, query-based method tests how well users are able to interact and retrieve records with each system. The study is the first to show the extent to which search systems can effectively and efficiently perform (Boolean) searches with regards to precision, recall, and reproducibility. We found substantial differences in the performance of search systems, meaning that their usability in systematic searches varies. Indeed, only half of the search systems analyzed and only a few Open Access databases can be recommended for evidence syntheses without adding substantial caveats. Particularly, our findings demonstrate why Google Scholar is inappropriate as principal search system. We call for database owners to recognize the requirements of evidence synthesis and for academic journals to reassess quality requirements for systematic reviews. Our findings aim to support researchers in conducting better searches for better evidence synthesis.

XGBoost Model for Chronic Kidney Disease Diagnosis

Abstract: Chronic Kidney Disease (CKD) is a menace that is affecting 10 percent of the world population and 15 percent of the South African population. The early and cheap diagnosis of this disease with accuracy and reliability will save 20,000 lives in South Africa per year. Scientists are developing smart solutions with Artificial Intelligence (AI). In this paper, several typical and recent AI algorithms are studied in the context of CKD and the extreme gradient boosting (XGBoost) is chosen as our base model for its high performance. Then, the model is optimized and the optimal full model trained on all the features achieves a testing accuracy, sensitivity, and specificity of 1.000, 1.000, and 1.000, respectively. Note that, to cover the widest range of people, the time and monetary costs of CKD diagnosis have to be minimized with fewest patient tests. Thus, the reduced model using fewer features is desirable while it should still maintain high performance. To this end, the set-theory based rule is presented which combines a few feature selection methods with their collective strengths. The reduced model using about a half of the original full features performs better than the models based on individual feature selection methods and achieves accuracy, sensitivity and specificity, of 1.000, 1.000, and 1.000, respectively.

The Agency Theory

Abstract: In this chapter, we discuss the agency theory. The agency theory is a principle utilized in an attempt to explain the complicated relationship that exists between the owners (principal) and managers (agents) of the business. Based on this, we propose that the agency theory is an attempt to explain the complexity of human behaviour in the principal-agent relationship. We are of the view that when the desires or goals of the principal and agent conflict, it is difficult or expensive for the principal to verify what the agent is doing. Since the theory is an attempt to explain the complexity of human behaviour in the principal-agent relationship, we pose the question: what happens to the theory in the era dominated by intelligent machines? We conclude that with AI, there will be new frontiers. These new frontiers include, among other things, the certainty of sharing of intelligent information that would, in the context of the agency theory, be available to both the agent and the principal. Further, we observe that the advantages of intelligent systems are updateability and connectivity. Using these strengths, we think intelligent agents will be swift in picking up the information discrepancies. Intelligent systems have the capability of harvesting information from different sources. Once gathered, this information will be updated in the principal’s system, given that the systems will be integrated. Finally, we think it conceivable that perhaps the agent, knowing that intelligent agents are deployed widely and that these intelligent systems have the capability of harvesting data from different repositories, will moderate his or her behaviour to be closely aligned to that of the principal.

Reproducible molecular networking of untargeted mass spectrometry data using GNPS

Abstract: Global Natural Product Social Molecular Networking (GNPS) is an interactive online small molecule-focused tandem mass spectrometry (MS2) data curation and analysis infrastructure. It is intended to provide as much chemical insight as possible into an untargeted MS2 dataset and to connect this chemical insight to the user's underlying biological questions. This can be performed within one liquid chromatography (LC)-MS2 experiment or at the repository scale. GNPS-MassIVE is a public data repository for untargeted MS2 data with sample information (metadata) and annotated MS2 spectra. These publicly accessible data can be annotated and updated with the GNPS infrastructure keeping a continuous record of all changes. This knowledge is disseminated across all public data; it is a living dataset. Molecular networking-one of the main analysis tools used within the GNPS platform-creates a structured data table that reflects the molecular diversity captured in tandem mass spectrometry experiments by computing the relationships of the MS2 spectra as spectral similarity. This protocol provides step-by-step instructions for creating reproducible, high-quality molecular networks. For training purposes, the reader is led through a 90- to 120-min procedure that starts by recalling an example public dataset and its sample information and proceeds to creating and interpreting a molecular network. Each data analysis job can be shared or cloned to disseminate the knowledge gained, thus propagating information that can lead to the discovery of molecules, metabolic pathways, and ecosystem/community interactions.

Search for electroweak production of charginos and sleptons decaying into final states with two leptons and missing transverse momentum in √s = 13 TeV pp collisions using the ATLAS detector

Abstract: A search for the electroweak production of charginos and sleptons decaying into final states with two electrons or muons is presented. The analysis is based on 139 fb$^{-1}$ of proton–proton collisions recorded by the ATLAS detector at the Large Hadron Collider at $\sqrt{s}=13$ $\text {TeV}$. Three R-parity-conserving scenarios where the lightest neutralino is the lightest supersymmetric particle are considered: the production of chargino pairs with decays via either W bosons or sleptons, and the direct production of slepton pairs. The analysis is optimised for the first of these scenarios, but the results are also interpreted in the others. No significant deviations from the Standard Model expectations are observed and limits at 95% confidence level are set on the masses of relevant supersymmetric particles in each of the scenarios. For a massless lightest neutralino, masses up to 420 $\text {Ge}\text {V}$ are excluded for the production of the lightest-chargino pairs assuming W-boson-mediated decays and up to 1 $\text {TeV}$ for slepton-mediated decays, whereas for slepton-pair production masses up to 700 $\text {Ge}\text {V}$ are excluded assuming three generations of mass-degenerate sleptons.

Big data analytics as an operational excellence approach to enhance sustainable supply chain performance

Abstract: Operations management is a core organizational function involved in the management of activities to produce and deliver products and services. Appropriate operations decisions rely on assessing and using information; a task made more challenging in the Big Data era. Effective management of data (big data analytics; BDA), along with staff capabilities (the talent capability in the use of big data) support firms to leverage big data analytics and organizational learning in support of sustainable supply chain management outcomes. The current study uses dynamic capability theory as a foundation for evaluating the role of BDA capability as an operational excellence approach in improving sustainable supply chain performance. We surveyed mining executives in the emerging economy of South Africa and received 520 valid responses (47% response rate). We used Partial Least Squares Structural Equation Modelling (PLS-SEM) to analyze the data. The findings show that big data analytics management capabilities have a strong and significant effect on innovative green product development and sustainable supply chain outcomes. Big data analytics talent capabilities have a weaker but still significant effect on employee development and sustainable supply chain outcomes. Innovation and learning performance affect sustainable supply chain performance, and supply chain innovativeness has an important moderating role. A contribution of the study is identifying two pathways that managers can use to improve sustainable supply chain outcomes in the mining industry, based on big data analytics capabilities.

A new nickel-based co-crystal complex electrocatalyst amplified by NiO dope Pt nanostructure hybrid; a highly sensitive approach for determination of cysteamine in the presence of serotonin.

Abstract: A highly sensitive electrocatalytic sensor was designed and fabricated by the incorporation of NiO dope Pt nanostructure hybrid (NiO–Pt–H) as conductive mediator, bis (1,10 phenanthroline) (1,10-phenanthroline-5,6-dione) nickel(II) hexafluorophosphate (B,1,10,P,1,10, PDNiPF6), and electrocatalyst into carbon paste electrode (CPE) matrix for the determination of cysteamine. The NiO–Pt–H was synthesized by one-pot synthesis strategy and characterized by XRD, elemental mapping analysis (MAP), and FESEM methods. The characterization data, which confirmed good purity and spherical shape with a diameter of ⁓ 30.64 nm for the synthesized NiO–Pt–H. NiO–Pt–H/B,1,10, P,1,10, PDNiPF6/CPE, showed an excellent catalytic activity and was used as a powerful tool for the determination of cysteamine in the presence of serotonin. The NiO–Pt–H/B,1,10, P,1,10, PDNiPF6/CPE was able to solve the overlap problem of the two drug signals and was used for the determination of cysteamine and serotonin in concentration ranges of 0.003–200 µM and 0.5–260 µM with detection limits of 0.5 nM and 0.1 µM, using square wave voltammetric method, respectively. The NiO–Pt–H/B,1,10,P,1,10,PDNiPF6/CPE showed a high-performance ability for the determination of cysteamine and serotonin in the drug and pharmaceutical serum samples with the recovery data of 98.1–103.06%.

The Fourth Catalog of Active Galactic Nuclei Detected by the Fermi Large Area Telescope

Abstract: The fourth catalog of active galactic nuclei (AGNs) detected by the Fermi Gamma-ray Space Telescope Large Area Telescope (4LAC) between 2008 August 4 and 2016 August 2 contains 2863 objects located at high Galactic latitudes (|b|>10{\deg}). It includes 85% more sources than the previous 3LAC catalog based on 4 years of data. AGNs represent at least 79% of the high-latitude sources in the fourth Fermi-Large Area Telescope Source Catalog (4FGL), which covers the energy range from 50 MeV to 1 TeV. In addition, 344 gamma-ray AGNs are found at low Galactic latitudes. Most of the 4LAC AGNs are blazars (98%), while the remainder are other types of AGNs. The blazar population consists of 24% Flat Spectrum Radio Quasars (FSRQs), 38% BL Lac-type objects (BL Lacs), and 38% blazar candidates of unknown types (BCUs). On average, FSRQs display softer spectra and stronger variability in the gamma-ray band than BL Lacs do, confirming previous findings. All AGNs detected by ground-based atmospheric Cherenkov telescopes are also found in the 4LAC.

Facile synthesis of paper based graphene electrodes for point of care devices: A double stranded DNA (dsDNA) biosensor

Abstract: The demand for high-quality graphene for electronic applications is increasing due to its high carrier mobility and electrical conductivity. In this connection, printing technology is a reliable method towards the fabrication of conductive, disposable graphene-based electrode for low-cost sensor application. Herein, we aimed to report the synthesis of high-quality graphene nanosheets obtained by electrochemical exfoliation of biomass-derived from corn cob. The conductive ink was prepared from this exfoliated graphene and was utilized for the preparation of paper-based graphene electrode towards double stranded DNA (dsDNA) sensor application. This paper, based graphene electrode opens the possibility of direct electrochemical analysis of analyte without any sample preparation. In this study, two irreversible oxide peaks were obtained from paper-based printed graphene electrode, corresponds to oxidation of guanine (G) and adenine (A) of dsDNA in the linear range of 0.2 pg mL−1 to 5 pg mL−1 with the detection limit of 0.68 pg mL−1 and the sensitivity of 0.00656 mA pg−1 cm−2. Further, a small-scale printable circuit is fabricated using this graphene shows good conductivity of 1.145x103(S/m).

Procurement 4.0 and its implications on business process performance in a circular economy

Abstract: The purpose of this study is to identify how Procurement 4.0 and digital transformations are related and how digital transformation impacts the intention to optimize the procurement process in the circular economy. The moderating effect of information processing capability is also investigated. We survey South African manufacturers and analyze survey results using the Partial Least Squares Structural Equation Modelling (PLS-SEM) approach to test the research hypotheses and our theoretical framework. Finally, a sample business process is simulated to evaluate how Industry 4.0 automation can influence organizational procurement process optimization and circular economy performance. The findings of this empirical study indicate that Procurement 4.0 strategy positively influences buyers’ intention to optimize business processes. Second, Procurement 4.0 performance review positively influences buyers’ intention to optimize business processes. Third, information processing capability moderates the effect of Procurement 4.0 performance review on buyers’ intention to optimize business processes. Finally, buyers’ intention to optimize business processes plays a key role in enhancing circular economy performance. The simulation results demonstrate the potential benefits from industry 4.0 applications in the procurement function in a circular economy.

Impact of Fermentation on the Phenolic Compounds and Antioxidant Activity of Whole Cereal Grains: A Mini Review.

Abstract: Urbanization, emergence, and prominence of diseases and ailments have led to conscious and deliberate consumption of health beneficial foods. Whole grain (WG) cereals are one type of food with an array of nutritionally important and healthy constituents, including carotenoids, inulin, β-glucan, lignans, vitamin E-related compounds, tocols, phytosterols, and phenolic compounds, which are beneficial for human consumption. They not only provide nutrition, but also confer health promoting effects in food, such as anti-carcinogenic, anti-microbial, and antioxidant properties. Fermentation is a viable processing technique to transform whole grains in edible foods since it is an affordable, less complicated technique, which not only transforms whole grains but also increases nutrient bioavailability and positively alters the levels of health-promoting components (particularly antioxidants) in derived whole grain products. This review addresses the impact of fermentation on phenolic compounds and antioxidant activities with most available studies indicating an increase in these health beneficial constituents. Such increases are mostly due to breakdown of the cereal cell wall and subsequent activities of enzymes that lead to the liberation of bound phenolic compounds, which increase antioxidant activities. In addition to the improvement of these valuable constituents, increasing the consumption of fermented whole grain cereals would be vital for the world’s ever-growing population. Concerted efforts and adequate strategic synergy between concerned stakeholders (researchers, food industry, and government/policy makers) are still required in this regard to encourage consumption and dispel negative presumptions about whole grain foods.

Performance of electron and photon triggers in ATLAS during LHC Run 2

Abstract: Electron and photon triggers covering transverse energies from 5 GeV to several TeV are essential for the ATLAS experiment to record signals for a wide variety of physics: from Standard Model processes to searches for new phenomena in both proton–proton and heavy-ion collisions. To cope with a fourfold increase of peak LHC luminosity from 2015 to 2018 (Run 2), to 2.1×1034cm-2s-1, and a similar increase in the number of interactions per beam-crossing to about 60, trigger algorithms and selections were optimised to control the rates while retaining a high efficiency for physics analyses. For proton–proton collisions, the single-electron trigger efficiency relative to a single-electron offline selection is at least 75% for an offline electron of 31 GeV, and rises to 96% at 60 GeV; the trigger efficiency of a 25 GeV leg of the primary diphoton trigger relative to a tight offline photon selection is more than 96% for an offline photon of 30 GeV. For heavy-ion collisions, the primary electron and photon trigger efficiencies relative to the corresponding standard offline selections are at least 84% and 95%, respectively, at 5 GeV above the corresponding trigger threshold.

Review of supported metal nanoparticles: synthesis methodologies, advantages and application as catalysts

Abstract: Supported metal nanoparticles, M-NPs, are of great scientific and economic interest as they encompass application in chemical manufacturing, oil refining and environmental catalysis. Oxidation and hydrogenation reactions are among the major reactions catalyzed by supported M-NPs. Although supported M-NPs are preferable due to their easy recovery and reuse, there are still some practical issues regarding their catalytic activity and deactivation. This review highlights the general features of supported M-NPs as catalysts with particular attention to copper, gold, platinum, palladium, ruthenium, silver, cobalt and nickel and their catalytic evaluation in various reactions. The catalytic performance of noble M-NPs has been explored extensively in various selective oxidation and hydrogenation reactions. In general, noble metals are expensive and sensitive to poisons. Despite their significant merits and potential (easily available, comparatively inexpensive and less sensitive to poisons), catalysis by base M-NPs is relatively less explored. Therefore, activity of base M-NPs can be improved, and still, there is potential for such catalysts.

Search for Heavy Higgs Bosons Decaying into Two Tau Leptons with the ATLAS Detector Using pp Collisions at s =13 TeV

Abstract: A search for heavy neutral Higgs bosons is performed using the LHC Run 2 data, corresponding to an integrated luminosity of 139 fb^{-1} of proton-proton collisions at sqrt[s]=13 TeV recorded with the ATLAS detector. The search for heavy resonances is performed over the mass range 0.2-2.5 TeV for the τ^{+}τ^{-} decay with at least one τ-lepton decaying into final states with hadrons. The data are in good agreement with the background prediction of the standard model. In the M_{h}^{125} scenario of the minimal supersymmetric standard model, values of tanβ>8 and tanβ>21 are excluded at the 95% confidence level for neutral Higgs boson masses of 1.0 and 1.5 TeV, respectively, where tanβ is the ratio of the vacuum expectation values of the two Higgs doublets.

Toxic Metal Implications on Agricultural Soils, Plants, Animals, Aquatic life and Human Health

Abstract: The problem of environmental pollution is a global concern as it affects the entire ecosystem. There is a cyclic revolution of pollutants from industrial waste or anthropogenic sources into the environment, farmlands, plants, livestock and subsequently humans through the food chain. Most of the toxic metal cases in Africa and other developing nations are a result of industrialization coupled with poor effluent disposal and management. Due to widespread mining activities in South Africa, pollution is a common site with devastating consequences on the health of animals and humans likewise. In recent years, talks on toxic metal pollution had taken center stage in most scientific symposiums as a serious health concern. Very high levels of toxic metals have been reported in most parts of South African soils, plants, animals and water bodies due to pollution. Toxic metals such as Zinc (Zn), Lead (Pb), Aluminium (Al), Cadmium (Cd), Nickel (Ni), Iron (Fe), Manganese (Mn) and Arsenic (As) are major mining effluents from tailings which contaminate both the surface and underground water, soil and food, thus affecting biological function, endocrine systems and growth. Environmental toxicity in livestock is traceable to pesticides, agrochemicals and toxic metals. In this review, concerted efforts were made to condense the information contained in literature regarding toxic metal pollution and its implications in soil, water, plants, animals, marine life and human health.

Performance Analysis of Intrusion Detection Systems Using a Feature Selection Method on the UNSW-NB15 Dataset

Abstract: Computer networks intrusion detection systems (IDSs) and intrusion prevention systems (IPSs) are critical aspects that contribute to the success of an organization. Over the past years, IDSs and IPSs using different approaches have been developed and implemented to ensure that computer networks within enterprises are secure, reliable and available. In this paper, we focus on IDSs that are built using machine learning (ML) techniques. IDSs based on ML methods are effective and accurate in detecting networks attacks. However, the performance of these systems decreases for high dimensional data spaces. Therefore, it is crucial to implement an appropriate feature extraction method that can prune some of the features that do not possess a great impact in the classification process. Moreover, many of the ML based IDSs suffer from an increase in false positive rate and a low detection accuracy when the models are trained on highly imbalanced datasets. In this paper, we present an analysis the UNSW-NB15 intrusion detection dataset that will be used for training and testing our models. Moreover, we apply a filter-based feature reduction technique using the XGBoost algorithm. We then implement the following ML approaches using the reduced feature space: Support Vector Machine (SVM), k-Nearest-Neighbour (kNN), Logistic Regression (LR), Artificial Neural Network (ANN) and Decision Tree (DT). In our experiments, we considered both the binary and multiclass classification configurations. The results demonstrated that the XGBoost-based feature selection method allows for methods such as the DT to increase its test accuracy from 88.13 to 90.85% for the binary classification scheme.

Associations between high temperatures in pregnancy and risk of preterm birth, low birth weight, and stillbirths: systematic review and meta-analysis.

Abstract: Objective To assess whether exposure to high temperatures in pregnancy is associated with increased risk for preterm birth, low birth weight, and stillbirth. Design Systematic review and random effects meta-analysis. Data sources Medline and Web of Science searched up to September 2018, updated in August 2019. Eligibility criteria for selecting studies Clinical studies on associations between high environmental temperatures, and preterm birth, birth weight, and stillbirths. Results 14 880 records and 175 full text articles were screened. 70 studies were included, set in 27 countries, seven of which were countries with low or middle income. In 40 of 47 studies, preterm births were more common at higher than lower temperatures. Exposures were classified as heatwaves, 1°C increments, and temperature threshold cutoff points. In random effects meta-analysis, odds of a preterm birth rose 1.05-fold (95% confidence interval 1.03 to 1.07) per 1°C increase in temperature and 1.16-fold (1.10 to 1.23) during heatwaves. Higher temperature was associated with reduced birth weight in 18 of 28 studies, with considerable statistical heterogeneity. Eight studies on stillbirths all showed associations between temperature and stillbirth, with stillbirths increasing 1.05-fold (1.01 to 1.08) per 1°C rise in temperature. Associations between temperature and outcomes were largest among women in lower socioeconomic groups and at age extremes. The multiple temperature metrics and lag analyses limited comparison between studies and settings. Conclusions Although summary effect sizes are relatively small, heat exposures are common and the outcomes are important determinants of population health. Linkages between socioeconomic status and study outcomes suggest that risks might be largest in low and middle income countries. Temperature rises with global warming could have major implications for child health. Systematic review registration PROSPERO CRD 42019140136 and CRD 42018118113.

A Review of Machine Learning Approaches to Power System Security and Stability

Abstract: Increasing use of renewable energy sources, liberalized energy markets and most importantly, the integrations of various monitoring, measuring and communication infrastructures into modern power system network offer the opportunity to build a resilient and efficient grid However, it also brings about various threats of instabilities and security concerns in form of cyberattack, voltage instability, power quality (PQ) disturbance among others to the complex network The need for efficient methodologies for quicker identification and detection of these problems have always been a priority to energy stakeholders over the years In recent times, machine learning techniques (MLTs) have proven to be effective in numerous applications including power system studies In the literature, various MLTs such as artificial neural networks (ANN), Decision Tree (DT), support vector machines (SVM) have been proposed, resulting in effective decision making and control actions in the secured and stable operations of the power system Given this growing trend, this paper presents a comprehensive review on the most recent studies whereby MLTs were developed for power system security and stability especially in cyberattack detections, PQ disturbance studies and dynamic security assessment studies The aim is to highlight the methodologies, achievements and more importantly the limitations in the classifier(s) design, dataset and test systems employed in the reviewed publications A brief review of reinforcement learning (RL) and deep reinforcement learning (DRL) approaches to transient stability assessment is also presented Finally, we highlighted some challenges and directions for future studies

Photodynamic Therapy for Cancer: What's Past is Prologue.

Abstract: Thomas J Dougherty from Roswell Park Cancer Center played a major role in the progress of photodynamic therapy (PDT) from a laboratory science into a real-world clinical therapy to treat patients with cancer. Nevertheless over the succeeding 45 years, it is fair to say that the overall progress of clinical PDT for cancer has been somewhat disappointing. The goal of this perspective article is to summarize some of the clinical trials run by various companies using photosensitizers with different structures that have been conducted for different types of cancer. While some have been successful, others have failed, and several are now ongoing. I will attempt to touch on some factors, which have influenced this checkered history and look forward to the future of clinical PDT for cancer.

The urgent need for integrated science to fight COVID-19 pandemic and beyond.

Abstract: The COVID-19 pandemic has become the leading societal concern. The pandemic has shown that the public health concern is not only a medical problem, but also affects society as a whole; so, it has also become the leading scientific concern. We discuss in this treatise the importance of bringing the world's scientists together to find effective solutions for controlling the pandemic. By applying novel research frameworks, interdisciplinary collaboration promises to manage the pandemic's consequences and prevent recurrences of similar pandemics.