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Low-cost ambient sensors have been identified as a promising technology for monitoring air pollution at a high spatio-temporal resolution. However, the pollutant data captured by these cost-effective sensors are less accurate than their conventional counterparts and require careful calibration to improve their accuracy and reliability. In this paper, we propose to leverage temporal information, such as the duration of time a sensor has been deployed and the time of day the reading was taken, in order to improve the calibration of low-cost sensors. This information is readily available and has so far not been utilized in the reported literature for the calibration of cost-effective ambient gas pollutant sensors. We make use of three data sets collected by research groups around the world, who gathered the data from field-deployed low-cost CO and NO2 sensors co-located with accurate reference sensors. Our investigation shows that using the temporal information as a co-variate can significantly improve the accuracy of common machine learning-based calibration techniques, such as Random Forest and Long Short-Term Memory.

Background Haemophilia A (HA) is a bleeding disorder, due to a deficiency in factor VIII (FVIII). These patients are unable to produce a stable fibrin clot in the propagation phase of coagulation as they do not generate sufficient thrombin. The primary treatment for HA in South Africa remains replacement therapy with standard half-life FVIII clotting factor concentrate, aimed at reducing bleeding episodes. Objectives To evaluate the effect of varying concentrations of FVIII on whole blood (WB) clot architecture and kinetics during clot formation in patients with severe HA. Design A cross-sectional study where blood from 20 patients with HA was exposed to FVIII ex vivo and compared to a control group of 20 healthy individuals. Methods Scanning electron microscopy (SEM) was used to study WB clot architecture and thromboelastography ® (TEG ® ) was used to quantify WB clot kinetics. Results Scanning electron microscopy studies revealed that patients with HA have sparse, disorganized fibrin networks with limited crosslinking and red blood cells (RBCs) stacked in rouleaux formation. Haemophilia A blood spiked to a 10 to 15 IU/dL FVIII concentration showed improvements in the organisation of the fibrin network with some altered RBCs. In addition, blood spiked to a 30 to 35 IU/dL FVIII concentration showed an increase in fibrin formation with normal RBCs. Thromboelastography ® showed that patients with HA had an increased clot initiation time and decreased clot strength. When spiked to a 10 to 15 IU/dL FVIII concentration the clot kinetic profile showed normalization. However, an increase in FVIII concentration higher than 30 IU/dL showed altered clot architecture and kinetics. Conclusion Based on the current study, FVIII levels at 10 to 15 IU/dL improved clot kinetics but did not normalize the architecture. It may be sufficient for prevention of haemorrhages. Factor VIII levels at 30 to 35 IU/dL resulted in rapid but weaker clot formation. However, at this concentration the clot architecture was normalised which is important for haemostasis. Here it was also evident that the findings of these two modalities (TEG ® and SEM) should not be separated but interpreted in conjunction with each other.

Deep learning-based plant disease detection has gained significant attention from the scientific community. However, various aspects of real horticultural conditions have not yet been explored. For example, the disease should be considered not only on leaves, but also on other parts of plants, including stems, canes, and fruits. Furthermore, the detection of multiple diseases in a single plant organ at a time has not been performed. Similarly, plant disease has not been identified in various crops in the complex horticultural environment with the same optimized/modified model. To address these research gaps, this research presents a dataset named NZDLPlantDisease-v1, consisting of diseases in five of the most important horticultural crops in New Zealand: kiwifruit, apple, pear, avocado, and grapevine. An optimized version of the best obtained deep learning (DL) model named region-based fully convolutional network (RFCN) has been proposed to detect plant disease using the newly generated dataset. After finding the most suitable DL model, the data augmentation techniques were successively evaluated. Subsequently, the effects of image resizers with interpolators, weight initializers, batch normalization, and DL optimizers were studied. Finally, performance was enhanced by empirical observation of position-sensitive score maps and anchor box specifications. Furthermore, the robustness/practicality of the proposed approach was demonstrated using a stratified k-fold cross-validation technique and testing on an external dataset. The final mean average precision of the RFCN model was found to be 93.80%, which was 19.33% better than the default settings. Therefore, this research could be a benchmark step for any follow-up research on automatic control of disease in several plant species.

Deep learning (DL) is an effective approach to identifying plant diseases. Among several DL-based techniques, transfer learning (TL) produces significant results in terms of improved accuracy. However, the usefulness of TL has not yet been explored using weights optimized from agricultural datasets. Furthermore, the detection of plant diseases in different organs of various vegetables has not yet been performed using a trained/optimized DL model. Moreover, the presence/detection of multiple diseases in vegetable organs has not yet been investigated. To address these research gaps, a new dataset named NZDLPlantDisease-v2 has been collected for New Zealand vegetables. The dataset includes 28 healthy and defective organs of beans, broccoli, cabbage, cauliflower, kumara, peas, potato, and tomato. This paper presents a transfer learning method that optimizes weights obtained through agricultural datasets for better outcomes in plant disease identification. First, several DL architectures are compared to obtain the best-suited model, and then, data augmentation techniques are applied. The Faster Region-based Convolutional Neural Network (RCNN) Inception ResNet-v2 attained the highest mean average precision (mAP) compared to the other DL models including different versions of Faster RCNN, Single-Shot Multibox Detector (SSD), Region-based Fully Convolutional Networks (RFCN), RetinaNet, and EfficientDet. Next, weight optimization is performed on datasets including PlantVillage, NZDLPlantDisease-v1, and DeepWeeds using image resizers, interpolators, initializers, batch normalization, and DL optimizers. Updated/optimized weights are then used to retrain the Faster RCNN Inception ResNet-v2 model on the proposed dataset. Finally, the results are compared with the model trained/optimized using a large dataset, such as Common Objects in Context (COCO). The final mAP improves by 9.25% and is found to be 91.33%. Moreover, the robustness of the methodology is demonstrated by testing the final model on an external dataset and using the stratified k-fold cross-validation method.

Background: Haemoglobinopathies are one of the most common inherited diseases worldwide. Quantification of haemoglobin A2 is necessary for the diagnosis of the beta thalassaemia trait. In this context, it is important to have a reliable reference interval for haemoglobin A2 and a local reference range for South Africa has not been established. Objective: This study aimed to establish reference intervals for haemoglobin A2 using stored patient laboratory data. Methods: This descriptive study used retrospective data to evaluate haemoglobin A2 levels determined using high-performance liquid chromatography at the National Health Laboratory Service haematology laboratory in Pretoria, South Africa. All tests performed from 01 October 2012 to 31 December 2020 were screened for inclusion; of these, 144 patients’ data met the selection criteria. The reference interval was calculated using descriptive statistics (mean and standard deviation) with a 95% confidence interval. Results: Analysed data from enrolled patients showed a normal distribution. The mean age of the patients was 40 years (range: 3–84 years). The reference interval for haemoglobin A2 calculated from this data was 2.3% – 3.6%. The minimum haemoglobin A2 was 2.3% and the maximum was 3.9% with a mean of 2.95% and a standard deviation of 0.357%. Conclusion: A normal reference interval has been established for the population served by the laboratory that will assist with accurate diagnosis of the beta thalassaemia trait. This reference interval may also be useful to other laboratories that employ the same technology, especially smaller laboratories where obtaining a sufficiently large number of normal controls may be challenging.

The accurate identification of weeds is an essential step for a site-specific weed management system. In recent years, deep learning (DL) has got rapid advancements to perform complex agricultural tasks. The previous studies emphasized the evaluation of advanced training techniques or modifying the well-known DL models to improve the overall accuracy. In contrast, this research attempted to improve the mean average precision (mAP) for the detection and classification of eight classes of weeds by proposing a novel DL-based methodology. First, a comprehensive analysis of single-stage and two-stage neural networks including Single-shot MultiBox Detector (SSD), You look only Once (YOLO-v4), EfficientDet, CenterNet, RetinaNet, Faster Region-based Convolutional Neural Network (RCNN), and Region-based Fully Convolutional Network (RFCN), has been performed. Next, the effects of image resizing techniques along with four image interpolation methods have been studied. It led to the final stage of the research through optimization of the weights of the best-acquired model by initialization techniques, batch normalization, and DL optimization algorithms. The effectiveness of the proposed work is proven due to a high mAP of 93.44% and validated by the stratified k-fold cross-validation technique. It was 5.8% improved as compared to the results obtained by the default settings of the best-suited DL architecture (Faster RCNN ResNet-101). The presented pipeline would be a baseline study for the research community to explore several tasks such as real-time detection and reducing the computation/training time. All the relevant data including the annotated dataset, configuration files, and inference graph of the final model are provided with this article. Furthermore, the selection of the DeepWeeds dataset shows the robustness/practicality of the study because it contains images collected in a real/complex agricultural environment. Therefore, this research would be a considerable step toward an efficient and automatic weed control system.

The advent of cost-effective sensors and the rise of the Internet of Things (IoT) presents the opportunity to monitor urban pollution at a high spatio-temporal resolution. However, these sensors suffer from poor accuracy that can be improved through calibration. In this paper, we propose to use One Dimensional Convolutional Neural Network (1DCNN) based calibration for low-cost carbon monoxide sensors and benchmark its performance against several Machine Learning (ML) based calibration techniques. We make use of three large data sets collected by research groups around the world from field-deployed low-cost sensors co-located with accurate reference sensors. Our investigation shows that 1DCNN performs consistently across all datasets. Gradient boosting regression, another ML technique that has not been widely explored for gas sensor calibration, also performs reasonably well. For all datasets, the introduction of temperature and relative humidity data improves the calibration accuracy. Cross-sensitivity to other pollutants can be exploited to improve the accuracy further. This suggests that low-cost sensors should be deployed as a suite or an array to measure covariate factors.

To apply weed control treatments effectively, the weeds must be accurately detected. Deep learning (DL) has been quite successful in performing the weed identification task. However, various aspects of the DL have not been explored in previous studies. This research aimed to achieve a high average precision (AP) of eight classes of weeds and a negative (non-weed) class, using the DeepWeeds dataset. In this regard, a DL-based two-step methodology has been proposed. This article is the second stage of the research, while the first stage has already been published. The former phase presented a weed detection pipeline and consisted of the evaluation of various neural networks, image resizers, and weight optimization techniques. Although a significant improvement in the mean average precision (mAP) was attained. However, the Chinee apple weed did not reach a high average precision. This result provided a solid ground for the next stage of the study. Hence, this paper presents an in-depth analysis of the Faster Region-based Convolutional Neural Network (RCNN) with ResNet-101, the best-obtained model in the past step. The architectural details of the Faster RCNN model have been thoroughly studied to investigate each class of weeds. It was empirically found that the generation of anchor boxes affects the training and testing performance of the Faster RCNN model. An enhancement to the anchor box scales and aspect ratios has been attempted by various combinations. The final results, with the addition of 64 × 64 scale size, and aspect ratio of 1:3 and 3:1, produced the best classification and localization of all classes of weeds and a negative class. An enhancement of 24.95% AP was obtained in Chinee apple weed. Furthermore, the mAP was improved by 2.58%. The robustness of the approach has been shown by the stratified k-fold cross-validation technique and testing on an external dataset.

Acrylonitrile butadiene styrene (ABS) is a renowned commodity polymer for additive manufacturing, particularly fused deposition modelling (FDM). The recent large-scale applications of 3D-printed ABS require stable mechanical properties than ever needed. However, thermochemical scission of butadiene bonds is one of the contemporary challenges affecting the overall ABS stability. In this regard, literature reports melt-blending of ABS with different polymers with high thermal resistance. However, the comparison for the effects of different polymers on tensile strength of 3D-printed ABS blends was not yet reported. Furthermore, the cumulative studies comprising both blended polymers and in-process thermal variables for FDM were not yet presented as well. This research, for the first time, presents the statistical comparison of tensile properties for the added polymers and in-process thermal variables (printing temperature and build surface temperature). The research presents Fourier transform infrared spectroscopy (FTIR) and thermogravimetric analysis (TGA) to explain the thermochemical reasons behind achieved mechanical properties. Overall, ABS blend with PP shows high tensile strength (≈31 MPa) at different combinations of in-process parameters. Furthermore, some commonalities among both blends are noted, i.e., the tensile strength improves with increase of surface (bed) and printing temperature.

Recently, plant disease classification has been done by various state-of-the-art deep learning (DL) architectures on the publicly available/author generated datasets. This research proposed the deep learning-based comparative evaluation for the classification of plant disease in two steps. Firstly, the best convolutional neural network (CNN) was obtained by conducting a comparative analysis among well-known CNN architectures along with modified and cascaded/hybrid versions of some of the DL models proposed in the recent researches. Secondly, the performance of the best-obtained model was attempted to improve by training through various deep learning optimizers. The comparison between various CNNs was based on performance metrics such as validation accuracy/loss, F1-score, and the required number of epochs. All the selected DL architectures were trained in the PlantVillage dataset which contains 26 different diseases belonging to 14 respective plant species. Keras with TensorFlow backend was used to train deep learning architectures. It is concluded that the Xception architecture trained with the Adam optimizer attained the highest validation accuracy and F1-score of 99.81% and 0.9978 respectively which is comparatively better than the previous approaches and it proves the novelty of the work. Therefore, the method proposed in this research can be applied to other agricultural applications for transparent detection and classification purposes.

The identification of plant disease is an imperative part of crop monitoring systems. Computer vision and deep learning (DL) techniques have been proven to be state-of-the-art to address various agricultural problems. This research performed the complex tasks of localization and classification of the disease in plant leaves. In this regard, three DL meta-architectures including the Single Shot MultiBox Detector (SSD), Faster Region-based Convolutional Neural Network (RCNN), and Region-based Fully Convolutional Networks (RFCN) were applied by using the TensorFlow object detection framework. All the DL models were trained/tested on a controlled environment dataset to recognize the disease in plant species. Moreover, an improvement in the mean average precision of the best-obtained deep learning architecture was attempted through different state-of-the-art deep learning optimizers. The SSD model trained with an Adam optimizer exhibited the highest mean average precision (mAP) of 73.07%. The successful identification of 26 different types of defected and 12 types of healthy leaves in a single framework proved the novelty of the work. In the future, the proposed detection methodology can also be adopted for other agricultural applications. Moreover, the generated weights can be reused for future real-time detection of plant disease in a controlled/uncontrolled environment.

Low cost and accurate 3D surface profiling can help in numerous industry applications including inspection tasks, cleaning, minimizing bumps in navigation of non-uniform terrain, aid navigation, and road/pavement condition analysis. However, most of the available systems are costly or inaccessible for widespread use. This research presents investigation into the capability of cheap and accessible sensors to capture the floor surface profile information. A differential drive robotic platform has been developed to perform testing and conduct the research. 2D localization methods are extrapolated into 3D for the floor capturing process. Two different types of sensors, a 2D laser scanner and an RGB-D camera, are used for comparison of the floor profile capture ability. The robotic system is able to successfully capture the floor surface profile of a number of different type floors such as carpet, asphalt, and a coated floor. A key finding is that the surface itself is a significant factor on the measured profile, i.e. dirt or differing materials can cause false height measurements. Overall the methodology has proved a successful real time solution for creating a point cloud of the floor surface.

Plant diseases affect the growth of their respective species, therefore their early identification is very important. Many Machine Learning (ML) models have been employed for the detection and classification of plant diseases but, after the advancements in a subset of ML, that is, Deep Learning (DL), this area of research appears to have great potential in terms of increased accuracy. Many developed/modified DL architectures are implemented along with several visualization techniques to detect and classify the symptoms of plant diseases. Moreover, several performance metrics are used for the evaluation of these architectures/techniques. This review provides a comprehensive explanation of DL models used to visualize various plant diseases. In addition, some research gaps are identified from which to obtain greater transparency for detecting diseases in plants, even before their symptoms appear clearly.

Family members as well as informal caregivers are increasingly bearing the responsibility of taking care of persons diagnosed with HIV/AIDS (UNAIDS, 2000:5). The intensity of the caregiving process and the variety of stressors caregivers are exposed to (Uys, 2002:101-102; De Figueiredo & Turato, 2001:637-640; Flaskerud, Carter & Lee, 2000:128), have been well researched and documented. In this study, the salutogenic perspective was used to determine the sense of coherence of a group of eight informal caregivers. This perspective serves as one of the theories within the movement of positive psychology, where the focus is on the origins of psychological well-being. The results of the Sense of Coherence Scale (SOC) and the analysis of semi- structured interviews revealed a number of general resistance resources that have a positive influence on the caregivers’ sense of coherence, as well as their psychological well-being. These results may lead to the development of a psycho-educational program which will assist caregivers in maintaining their psychological well-being in spite of the stressors associated with caregiving. Opsomming Versorging van persone gediagnoseer met MIV/VIGS word toenemend die verantwoordelikheid van die pasiënte se familielede en informele versorgers (UNAIDS, 2000:5). Die intensiteit van die versorgingstaak, en die verskeidenheid stressore waaraan versorgers blootgestel word, is reeds goed nagevors en gedokumenteer (Uys, 2002:101-102; De Figueiredo & Turato, 2001:637-640; Flaskerud, Carter & Lee, 2000:128). In hierdie studie is die salutogene perspektief aangewend om die koherensiesin van ‘n groep bestaande uit agt informele versorgers te ondersoek. Hierdie perspektief dien as een van die teorieë binne die beweging van die positiewe sielkunde waar gefokus word op die oorsprong van psigologiese welstand. Die resultate van die Sense of Coherence Scale (SOC) en die ontleding van semigestruktureerde onderhoude het ‘n aantal algemene weerstandshulpbronne aan die lig gebring wat ‘n positiewe invloed gehad het op die versorgers se koherensiesin, asook hul psigologiese welstand. Hierdie resultate mag lei tot die samestelling van ‘n program wat versorgers sal help met die handhawing van hul sielkundige welstand te midde van die stressore wat met die versorgingstaak gepaardgaan.

Orientation: Recent literature has emphasised the important role that self-regulation plays in the mental health of individuals. The lack of a validated psychometric instrument to accurately measure self-regulation amongst Black South Africans however limits its potential impact within the challenging South African context. Research purpose: The aim of this study was to investigate the psychometric properties of the Short Self-Regulation Questionnaire (SSRQ) when used in a South African context, specifically focusing on Black South Africans. Motivation for the study: By commenting on the ability of the SSRQ to accurately capture the construct of self-regulation amongst Black educators, the current article serves to address and alleviate the lacunae in our understanding of self-regulation as a potentially protective factor. Research design, approach and method: The study was of a quantitative nature, and made use of a cross-sectional design and purposively selected study sample. Participants were urbanised Black teachers (N = 200) who completed the SSRQ and other measures of self-regulation and psychological well-being. Main findings: The reliability of the SSRQ total scale score proved satisfactory. Factor analysis produced five robust sub-constructs that were theoretically interpretable. Significant correlations between the SSRQ and other measures of self-regulation and psychological well-being also indicated good criterion-related validity. Practical/managerial implications: The availability of a reliable and valid instrument for the measurement of self-regulation in the South African context will enable both researchers and practitioners to better understand and utilise self-regulation in the enhancement of individual well-being. Contribution/value-add: The SSRQ’s ability to accurately measure self-regulation will contribute to our understanding of its role as preventive strength in the South African context. Refinement of item content will serve to further strengthen the SSRQ’s factor structure, and improve its validity for use within the Black South African population.