Case Report: HIV test misdiagnosis

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Semi-Supervised Anomaly Detection for the Determination of Vehicle Hijacking Tweets

In South Africa, there is an ever-growing issue of vehicle hijackings. This leads to travellers constantly being in fear of becoming a victim to such an incident. This work presents a new semi-supervised approach to using tweets to identify hijacking incidents by using unsupervised anomaly detection algorithms. Tweets consisting of the keyword "hijacking" are obtained, stored, and processed using the term frequency-inverse document frequency (TF-IDF) and further analyzed by using two anomaly detection algorithms: 1) K-Nearest Neighbour (KNN); 2) Cluster Based Outlier Factor (CBLOF). The comparative evaluation showed that the KNN method produced an accuracy of 89%, whereas the CBLOF produced an accuracy of 90%. The CBLOF method was also able to obtain a F1-Score of 0.8, whereas the KNN produced a 0.78. Therefore, there is a slight difference between the two approaches, in favour of CBLOF, which has been selected as a preferred unsupervised method for the determination of relevant hijacking tweets. In future, a comparison will be done between supervised learning methods and the unsupervised methods presented in this work on larger dataset. Optimisation mechanisms will also be employed in order to increase the overall performance

On the Efficacy of Particle Swarm Optimization for Gateway Placement in LoRaWAN Networks

The efficacy of the Particle Swarm Optimization (PSO) in determining the optimal locations for gateways in LoRaWAN networks is investigated. A modified PSO approach, which introduces gateway distancing measures during the initialization phase and flight time, is proposed. For the ease of comparisons and the understanding of the behavior of the algorithms under study, a square LoRaWAN area is used for simulations. Optimization results on a LoRaWAN script, implemented in NS-3, show that the modified PSO converges faster and achieves better results than the traditional PSO, as the number of gateways increases. Results further show that the modified PSO approach achieves similar performance to a deterministic approach, in which gateways are uniformly distributed in the network. This shows that for swarm intelligence techniques such as PSO to be used for gateway placement in LoRaWAN networks, gateway distancing mechanisms must be incorporated in the optimization process. These results further show that these techniques can be easily deployed in geometrically more complex LoRaWAN figures such as rectangular, triangular, circular and trapezoidal shapes. It is generally difficult to figure out a deterministic gateway placement mechanism for such shapes. As part of future work, more realistic LoRaWAN networks will be developed by using real geographical information of an area

Capacity Requirements of Traffic Handling Schemes in Multi-Service Networks

Cataloged from PDF version of article.This paper deals with the impact of traffic handling mechanisms on capacity for different network architectures. Three traffic handling models are considered: per-flow, class-based and best-effort (BE). These models can be used to meet service guarantees, the major differences being in their complexity of implementations and in the quantity of network resources that must be provided. In this study, the performance is fixed and the required capacity determined for various combinations of traffic handling architectures for edge-core networks. This study provides a comparison of different QoS architectures. One key result of this work is that on the basis of capacity requirements, there is no significant difference between semi-aggregate traffic handling and per-flow traffic handling. However, best-effort handling requires significantly more capacity as compared to the other methods. (C) 2004 Elsevier B.V. All rights reserve

Design and protocol for a cluster randomised trial of enhanced diagnostics for tuberculosis screening among people living with HIV in hospital in Malawi (CASTLE study)

BACKGROUND: People living with HIV (PLHIV) have a high risk of death if hospitalised in low-income countries. Tuberculosis has long been the leading cause of admission and death, in part due to suboptimal diagnostics. Two promising new diagnostic tools are digital chest Xray with computer-aided diagnosis (DCXR-CAD) and urine testing with Fujifilm SILVAMP LAM (FujiLAM). Neither test has been rigorously evaluated among inpatients. Test characteristics may be complementary, with FujiLAM especially sensitive for disseminated tuberculosis and DCXR-CAD especially sensitive for pulmonary tuberculosis, making combined interventions of interest. DESIGN AND METHODS: An exploratory unblinded, single site, two-arm cluster randomised controlled trial, with day of admission as the unit of randomisation. A third, smaller, integrated cohort arm (4:4:1 random allocation) contributes to understanding case-mix, but not trial outcomes. Participants are adults living with HIV not currently on TB treatment. The intervention (DCXR-CAD plus urine FujiLAM plus usual care) is compared to usual care alone. The primary outcome is proportion of participants started on tuberculosis treatment by day 56, with secondary outcomes of mortality (time to event) measured to to 56 days from enrolment, proportions with undiagnosed tuberculosis at death or hospital discharge and comparing proportions with enrolment-day tuberculosis treatment initiation. DISCUSSION: Both DCXR-CAD and FujiLAM have potential clinical utility and may have complementary diagnostic performance. To our knowledge, this is the first randomised trial to evaluate these tests among hospitalised PLHIV

A Comparison of deep learning architectures for optical galaxy morphology classification

The classification of galaxy morphology plays a crucial role in understanding galaxy formation and evolution. Traditionally, this process is done manually. The emergence of deep learning techniques has given room for the automation of this process. As such, this paper offers a comparison of deep learning architectures to determine which is best suited for optical galaxy morphology classification. Adapting the model training method proposed by Walmsley et al in 2021, the Zoobot Python library is used to train models to predict Galaxy Zoo DECaLS decision tree responses, made by volunteers, using EfficientNet B0, DenseNet121 and ResNet50 as core model architectures. The predicted results are then used to generate accuracy metrics per decision tree question to determine architecture performance. DenseNet121 was found to produce the best results, in terms of accuracy, with a reasonable training time. In future, further testing with more deep learning architectures could prove beneficial

Capacity requirements of traffic handling schemes in multi-service networks

This paper deals with the impact of traffic handling mechanisms on capacity for different network architectures. Three traffic handling models are considered: per-flow, class-based and best-effort (BE). These models can be used to meet service guarantees, the major differences being in their complexity of implementations and in the quantity of network resources that must be provided. In this study, the performance is fixed and the required capacity determined for various combinations of traffic handling architectures for edge-core networks. This study provides a comparison of different QoS architectures. One key result of this work is that on the basis of capacity requirements, there is no significant difference between semi-aggregate traffic handling and per-flow traffic handling. However, best-effort handling requires significantly more capacity as compared to the other methods. © 2004 Elsevier B.V. All rights reserved

Grammar Error Analysis of Narrative Compositions of Learners in Senior Secondary School Grades: A Case Study of Selected Public Secondary Schools in Chingola District, Zambia

The study analysed grammar errors in narrative compositions of learners in senior grades (grades 10-12) in four selected public secondary schools in Chingola District of the Copperbelt Province, Zambia. The study relied on the Universal Grammar Theory of Noam Chomsky of 1960. The informants of the study were 60 (26 males and 34 females): 1 (one) from the District Education Board Secretary’s office, 2 (two) Head teachers, 4 (four) Deputy Head teachers, 3 (three) Heads of Departments, 2 (two) Heads of Sections, 8 (eight Subject Teachers and 40 learners. Purposive sampling was used to select participants positioned to give the needed information. Questionnaires, interview schedules, focus group discussions, lesson observation and assessment of learners’ narrative composition scripts were the methods of data collection used. Both quantitative and qualitative approaches to data collection were employed. Quantitative data was analysed using the Statistical Package for Social Sciences (SPSS) version 16 and Excel while qualitative data was analysed in a descriptive manner. The study revealed variations in the types of grammatical errors learners committed in narrative compositions in the four selected schools. The numbers and types of grammatical errors varied from school to school. Respondents further suggested to the investigation possible causes of grammatical errors learners made in narrative compositions. It was established that despite the variations as regards the types of grammatical errors learners made, some errors, namely spellings, tenses, punctuations, word order and paragraphing were common in all the four selected schools. Keywords: Grammatical errors, narrative composition, syntax, sentence construction, English language DOI: 10.7176/JEP/13-21-14 Publication date:June 30th 202

Prevalence and risk factors for chronic kidney disease of unknown cause in Malawi: a cross-sectional analysis in a rural and urban population

Background: An epidemic of chronic kidney disease of unknown cause (CKDu) is occurring in rural communities in tropical regions of low-and middle-income countries in South America and India. Little information is available from Southern African countries which have similar climatic and occupational characteristics to CKDu-endemic countries. We investigated whether CKDu is prevalent in Malawi and identified its potential risk factors in this setting. / Methods: We conducted a cross-sectional study from January–August 2018 collecting bio samples and anthropometric data in two Malawian populations. The sample comprised adults > 18 years (n = 821) without diabetes, hypertension, and proteinuria. Estimates of glomerular filtration rate (eGFR) were calculated using the CKD-EPI equation. Linear and logistic regression models were applied with potential risk factors, to estimate risk of reduced eGFR. / Results: The mean eGFR was 117.1 ± 16.0 ml/min per 1.73m2 and the mean participant age was 33.5 ± 12.7 years. The prevalence of eGFR< 60 was 0.2% (95% confidence interval (95% CI) 0.1, 0.9); the prevalence of eGFR< 90 was 5% (95% CI =3.2, 6.3). We observed a higher prevalence in the rural population (5% (3.6, 7.8)), versus urban (3% (1.4, 6.7)). Age and BMI were associated with reduced eGFR< 90 [Odds ratio (OR) (95%CI) =3.59 (2.58, 5.21) per ten-year increment]; [OR (95%CI) =2.01 (1.27, 3.43) per 5 kg/m2 increment] respectively. No increased risk of eGFR < 90 was observed for rural participants [OR (95%CI) =1.75 (0.50, 6.30)]. / Conclusions: Reduced kidney function consistent with the definition of CKDu is not common in the areas of Malawi sampled, compared to that observed in other tropical or sub-tropical countries in Central America and South Asia. Reduced eGFR< 90 was related to age, BMI, and was more common in rural areas. These findings are important as they contradict some current hypothesis that CKDu is endemic across tropical and sub-tropical countries. This study has enabled standardized comparisons of impaired kidney function between and within tropical/subtropical regions of the world and will help form the basis for further etiological research, surveillance strategies, and the implementation and evaluation of interventions