Causality Management and Analysis in Requirement Manuscript for Software Designs

For software design tasks involving natural language, the results of a causal investigation provide valuable and robust semantic information, especially for identifying key variables during product (software) design and product optimization. As the interest in analytical data science shifts from correlations to a better understanding of causality, there is an equal task focused on the accuracy of extracting causality from textual artifacts to aid requirement engineering (RE) based decisions. This thesis focuses on identifying, extracting, and classifying causal phrases using word and sentence labeling based on the Bi-directional Encoder Representations from Transformers (BERT) deep learning language model and five machine learning models. The aim is to understand the form and degree of causality based on their impact and prevalence in RE practice. Methodologically, our analysis is centered around RE practice, and we considered 12,438 sentences extracted from 50 requirement engineering manuscripts (REM) for training our machine models. Our research reports that causal expressions constitute about 32% of sentences from REM. We applied four evaluation metrics, namely recall, accuracy, precision, and F1, to assess our machine models’ performance and accuracy to ensure the results’ conformity with our study goal. Further, we computed the highest model accuracy to be 85%, attributed to Naive Bayes. Finally, we noted that the applicability and relevance of our causal analytic framework is relevant to practitioners for different functionalities, such as generating test cases for requirement engineers and software developers and product performance auditing for management stakeholders

Natural radioactivity measurement of water and sediment from the historic Ikogosi warm and cold spring, Nigeria

Natural radioactivity measurement and the resultant risk assessment in the water and sediments from the historic Ikogosi warm and cold spring were carried out. A total of 8 samples (4 water and 4 sediment samples) were collected from the Ikogosi spring resort. A sample each was collected from the warm spring, cold spring, meeting point and a sample outside the resort. The samples were thoroughly prepared following the IAEA recommended procedures and analyzed for 40K, 238U, and 232Th using NaI(Tl) detector. The activity concentrations of 40K, 238U and 232Th in water samples range from 40.14 ± 17.83 to 67.59 ± 19.87 Bq L-1, 8.15 ± 2.84 to 11.14 ± 3.78 Bq L-1 and 5.71 ± 1.32 to 8.24 ± 2.61 Bq L-1 respectively. The activity concentration of sediment samples range from 136.31 ± 17.01 to 246.21 ± 34.93 Bq kg-1, 17.98 ± 7.64 to 28.32 ± 5.98 Bq kg-1 and 9.57 ± 3.15 to 16.12 ± 3.41 Bq kg-1 respectively. These values compared reasonably well with the worldwide average concentrations of 400 Bq kg–1, 40 Bq kg–1, 40 Bq kg–1 for 40K, 238U, and 232Th respectively. The mean absorbed dose rate in air obtained for sediment was 40.33 nGy h–1, while the annual outdoor effective dose equivalent was 49.46 μSv y-1, which is lower than the world average of 70 μSv y-1 specified by UNSCEAR for an outdoor effective dose. The total annual effective dose due to ingestion of radionuclides in the water for 3 age groups range from 13.414.87 to 18.856.43 mSv y-1, 2.751.03 to 5.722.13 mSv y-1 and 2.621.01 to 4.951.43 mSv y-1 for infants (0 – 1 y), children (7 – 12 y) and adult (>17 y) respectively. These values were higher than 1 mSv y-1 recommended by ICRP.Keywords: Natural Radioactivity, Activity concentrations, Cold spring, Ikogosi, Nigeri

Automatic lung segmentation using graph cut optimization.

Master of Science in Computer Science. University of KwaZulu-Natal, Durban 2015.Medical Imaging revolutionized the practice of diagnostic medicine by providing a means of visualizing the internal organs and structure of the body. Computer technologies have played an increasing role in the acquisition and handling, storage and transmission of these images. Due to further advances in computer technology, research efforts have turned towards adopting computers as assistants in detecting and diagnosing diseases, resulting in the incorporation of Computer-aided Detection (CAD) systems in medical practice. Computed Tomography (CT) images have been shown to improve accuracy of diagnosis in pulmonary imaging. Segmentation is an important preprocessing necessary for high performance of the CAD. Lung segmentation is used to isolate the lungs for further analysis and has the advantage of reducing the search space and computation time involved in disease detection. This dissertation presents an automatic lung segmentation method using Graph Cut optimization. Graph Cut produces globally optimal solutions by modeling the image data and spatial relationship among the pixels. Several objects in the thoracic CT image have similar pixel values to the lungs, and the global solutions of Graph Cut produce segmentation results where the lungs, and all other objects similar in intensity value to the lungs, are included. A distance prior encoding the euclidean distance of pixels from the set of pixels belonging to the object of interest is proposed to constrain the solution space of the Graph Cut algorithm. A segmentation method using the distance-constrained Graph Cut energy is also proposed to isolate the lungs in the image. The results indicate the suitability of the distance prior as a constraint for Graph Cut and shows the effectiveness of the proposed segmentation method in accurately segmenting the lungs from a CT image

Candidate generation and validation techniques for pedestrian detection in thermal (infrared) surveillance videos.

Doctoral Degree. University of KwaZulu- Natal, Durban.Video surveillance systems have become prevalent. Factors responsible for this prevalence include, but are not limited to, rapid advancements in technology, reduction in the cost of surveillance systems and changes in user demand. Research in video surveillance is majorly driven by rising global security needs which in turn increase the demand for proactive systems which monitor persistently. Persistent monitoring is a challenge for most video surveillance systems because they depend on visible light cameras. Visible light cameras depend on the presence of external light and can easily be undermined by over-, under, or non-uniform illumination. Thermal infrared cameras have been considered as alternatives to visible light cameras because they measure the intensity of infrared energy emitted from objects and so can function persistently. Many methods put forward make use of methods developed for visible footage, but these tend to underperform in infrared images due to different characteristics of thermal footage compared to visible footage. This thesis aims to increase the accuracy of pedestrian detection in thermal infrared surveillance footage by incorporating strategies into existing frameworks used in visible image processing techniques for IR pedestrian detection without the need to initially assume a model for the image distribution. Therefore, two novel techniques for candidate generation were formulated. The first is an Entropy-based histogram modication algorithm that incorporates a strategy for energy loss to iteratively modify the histogram of an image for background elimination and pedestrian retention. The second is a Background Subtraction method featuring a strategy for building a reliable background image without needing to use the whole video frame. Furthermore, pedestrian detection involves simultaneously solving several sub-tasks while adapting each task with IR-speci_c adaptations. Therefore, a novel semi-supervised single model for pedestrian detection was formulated that eliminates the need for separate modules of candidate generation and validation by integrating region and boundary properties of the image with motion patterns such that all the _ne-tuning and adjustment happens during energy minimization. Performance evaluations have been performed on four publicly available benchmark surveillance datasets consisting of footage taken under a wide variety of weather conditions and taken from different perspectives

Natural Radioactivity and Radiological Impact Assessment of Soil, Food and Water around Iron and Steel Smelting Area in Fashina Village, Ile-Ife, Osun State, Nigeria

This study assessed the natural radioactivity and radiological health impact of thirty-eight (38) samples of soil, food and water in Fashina village, Ile-Ife, Osun State, Nigeria using portable survey meter with a Global Positioning System (GPS) and well-calibrated NaI(Tl) detector system The mean exposure rates in the study area were 0.14 μSv hr-1 and 0.12 μSv hr-1 in soil/food and water samples respectively. The mean radioactivity content obtained for 238U, 232Th and 40K were 12.14 ± 4.17Bq kg-1, 23.23 ± 7.67 Bq kg-1 and 270.14 ± 61.79Bq kg-1 respectively in soil samples and 8.56 ± 2.80Bq kg-1, 13.17 ± 4.48Bq kg-1 and 89.41 ± 24.15Bq L-1 respectively for 238U, 232Th and 40K in water samples. The mean values of 30.91, 15.64 and 12.47 nGy h-1 were obtained for the absorbed dose rate in soil, food and water, respectively, while 37.90,  178.79 and 1085.23 μSv y-1 were obtained for the Annual Effective Doses (AED). Similarly, the Radium equivalent (Raeq) were 66.16 Bq kg-1, 34.28Bq kg-1 and 27.31BqL-1, in soil, food and water, respectively. The external and internal radiation hazard indices were 0.18 and 0.21, 0.09 and 0.12, 0.07 and 0.09, respectively for soil, food and water. The values obtained for the Excess Lifetime Cancer Risk (ELCR) in (x 10-3) were also 0.13, 0.63 and 3.80 for the soil, food and water samples, respectively. It was found that the values of some exposure rate, radioactivity contents and radiological impact parameters in the study area which were higher than those of the control area and the world average values poses a serious health risk to the environment and its inhabitants.Keywords: Radioactivity, Scrap Metal, Gamma Spectrometry, Radiological Impact Paramete

ASSESSMENT OF RADIOACTIVITY LEVELS AND TRANSFER FACTOR OF NATURAL RADIONUCLIDES AROUND IRON AND STEEL SMELTING COMPANY LOCATED IN FASHINA VILLAGE, ILE-IFE, OSUN STATE, NIGERIA

This study assessed the radioactivity levels and transfer factor of natural radionuclides around iron and steel smelting company located in Fashina village, Ile-Ife, Osun State, Nigeria. This was with a view to evaluate the exposure rate of the study area, determine the radionuclides present and activity concentration (238U and 232Th and 40K) in the samples and evaluate the soil-to-food transfer ratio of the radionuclides. A portable survey meter with a Global Positioning System (GPS) were used for in-situ investigation and a well-calibrated NaI(Tl) detector system was used for the radioactivity measurement of the samples. A total of 38 samples comprising soil, food and water were collected at the study area (Fashina) and control area (Opa) in Osun state, Nigeria for spectrometry analysis. The soil and food samples were oven dried, pulverized and sieved while water samples were acidified with 10 mL of 11 M HCl per litre to prevent adsorption of radionuclides with the wall of the container. All the samples were then sealed and kept for at least 28 days in radon impermeable cylindrical container so as to reach secular equilibrium. The mean exposure rates in the study area were 0.14 µSv hr-1 and 0.12 µSv hr-1 for the control environment. The mean radioactivity content obtained for 238U, 232Th and 40K were 12.14 ± 4.17 Bq kg-1, 23.23 ± 7.67 Bq kg-1 and 270.14 ± 61.79 Bq kg-1 respectively in soil samples and 8.56 ± 2.80 Bq kg-1, 13.17 ± 4.48 Bq kg-1 and 89.41 ± 24.15 Bq kg-1 respectively in food samples. Similarly it was 7.64 ± 2.95 Bq L-1, 10.04 ± 3.43 Bq L-1 and 69.04 ± 15.49 Bq L-1 respectively for 238U, 232Th and 40K in water samples. The transfer factor from soil to food for 238U, 232Th and 40K were calculated from the activity concentrations of soil and food, while the means were 0.73, 0.61, and 0.39 respectively in the study area

Exploring factors influencing patient mortality and loss to follow-up in two paediatric hospital wards in Zamfara, North-West Nigeria, 2016-2018

Introduction Child mortality has been linked to infectious diseases, malnutrition and lack of access to essential health services. We investigated possible predictors for death and patients lost to follow up (LTFU) for paediatric patients at the inpatient department (IPD) and inpatient therapeutic feeding centre (ITFC) of the Anka General Hospital (AGH), Zamfara State, Nigeria, to inform best practices at the hospital. Methods We conducted a retrospective cohort review study using routinely collected data of all patient admissions to the IPD and ITFC with known hospital exit status between 2016 and 2018. Unadjusted and adjusted rate ratios (aRR) and respective 95% confidence intervals (95% CI) were calculated using Poisson regression to estimate the association between the exposure variables and mortality as well as LTFU. Results The mortality rate in IPD was 22% lower in 2018 compared to 2016 (aRR 0.78; 95% CI 0.66–0.93) and 70% lower for patients coming from lead-affected villages compared to patients from other villages (aRR 0.30; 95% CI 0.19–0.48). The mortality rate for ITFC patients was 41% higher during rainy season (aRR 1.41; 95% CI 1.2–1.6). LTFU rates in ITFC increased in 2017 and 2018 when compared to 2016 (aRR 1.6; 95% CI 1.2–2.0 and aRR 1.4; 95% CI 1.1–1.8) and patients in ITFC had 2.5 times higher LTFU rates when coming from a lead-affected village. Conclusions Our data contributes clearer understanding of the situation in the paediatric wards in AGH in Nigeria, but identifying specific predictors for the multifaceted nature of mortality and LTFU is challenging. Mortality in paediatric patients in IPD of AGH improved during the study period, which is likely linked to better awareness of the hospital, but still remains high. Access to healthcare due to seasonal restrictions contributes to mortalities due to late presentation. Increased awareness of and easier access to healthcare, such as for patients living in lead-affected villages, which are still benefiting from an MSF lead poisoning intervention, decreases mortalities, but increases LTFU. We recommend targeted case audits and qualitative studies to better understand the role of health-seeking behaviour, and social and traditional factors in the use of formal healthcare in this part of Nigeria and potentially similar settings in other countries

Multi-drug resistance and high mortality associated with community-acquired bloodstream infections in children in conflict-affected northwest Nigeria

Pediatric community-acquired bloodstream infections (CA-BSIs) in sub Saharan African humanitarian contexts are rarely documented. Effective treatment of these infections is additionally complicated by increasing rates of antimicrobial resistance. We describe the findings from epidemiological and microbiological surveillance implemented in pediatric patients with suspected CA-BSIs presenting for care at a secondary hospital in the conflict affected area of Zamfara state, Nigeria. Any child (> 2 months of age) presenting to Anka General Hospital from November 2018 to August 2020 with clinical severe sepsis at admission had clinical and epidemiological information and a blood culture collected at admission. Bacterial isolates were tested for antibiotic susceptibility. We calculated frequencies of epidemiological, microbiological and clinical parameters. We explored risk factors for death amongst severe sepsis cases using univariable and multivariable Poisson regression, adjusting for time between admission and hospital exit. We included 234 severe sepsis patients with 195 blood culture results. There were 39 positive blood cultures. Of the bacterial isolates, 14 were Gram positive and 18 were Gram negative; 5 were resistant to empiric antibiotics: methicillin-resistant Staphylococcus aureus (MRSA; n = 2) and Extended Spectrum Beta-Lactamase positive enterobacterales (n = 3). We identified no significant association between sex, age-group, ward, CA-BSI, appropriate intravenous antibiotic, malaria positivity at admission, suspected focus of sepsis, clinical severity and death in the multivariable regression. There is an urgent need for access to good clinical microbiological services, including point of care methods, and awareness and practice around rational antibiotic in healthcare staff in humanitarian settings to reduce morbidity and mortality from sepsis in children

Automatic Dynamic Range Adjustment for Pedestrian Detection in Thermal (Infrared) Surveillance Videos

This paper presents a novel candidate generation algorithm for pedestrian detection in infrared surveillance videos. The proposed method uses a combination of histogram specification and iterative histogram partitioning to progressively adjust the dynamic range and efficiently suppress the background of each video frame. This pairing eliminates the general-purpose nature associated with histogram partitioning where chosen thresholds, although reasonable, are usually not suitable for specific purposes. Moreover, as the initial threshold value chosen by histogram partitioning is sensitive to the shape of the histogram, specifying a uniformly distributed histogram before initial partitioning provides a stable histogram shape. This ensures that pedestrians are present in the image at the convergence point of the algorithm. The performance of the method is tested using four publicly available thermal datasets. Experiments were performed with images from four publicly available databases. The results show the improvement of the proposed method over thresholding with minimum-cross entropy, the robustness across images acquired under different conditions, and the comparable results with other methods in the literature