Leveraging Temporal Information to Improve Machine Learning-Based Calibration Techniques for Low-Cost Air Quality Sensors.

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.

Low-Cost CO Sensor Calibration Using One Dimensional Convolutional Neural Network.

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.

A weight optimization-based transfer learning approach for plant disease detection of New Zealand vegetables.

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.

Establishment of haemoglobin A2 reference intervals in Pretoria, South Africa: A retrospective secondary data analysis.

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.

Weed Identification by Single-Stage and Two-Stage Neural Networks: A Study on the Impact of Image Resizers and Weights Optimization Algorithms.

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.

Peer Helpers' Experience of Participation in an Adventure-Based Experiential Learning Program: A Grit Perspective.

The study focused on the adventure-based experiential learning (ABEL) component of the North-West University peer helper training program. The aim of this study was to explore and describe a group of peer helpers' subjective experiences of their participation in an ABEL program, with a focus on how these experiences related to the concept of grit. A total of 26 students at the North-West University, both male and female, participated in the study. A qualitative research approach with a case study research design was used. The participants completed daily reflective diaries for the duration of the three-day ABEL program. After 3 months of performing their duties as peer helpers, the same individuals participated in focus group interviews. Themes were identified through inductive analysis and discussed regarding their relevance to the concept of grit. The main themes that emerged from both phases of data collection included intra-, inter-, and transpersonal/transcendent aspects, within which participants regularly referred to elements of grit. It was concluded that ABEL, due to its unique nature and demands, provides an ideal mechanism for the facilitation of personal growth on various levels. More specifically, through its clear association with the improvement and/or development of participants' grit, it could equip these students to be more effective in their role as peer helpers.

Flourishing Beyond Borders: Facilitating the Well-Being of Accompanying Expatriate Partners.

One of the leading causes for failing at expatriate assignments is the accompanying expatriate partners' (AEPs) unhappiness with life abroad or inability to adjust to the challenges of the host country. Strength-based therapeutic interventions have the potential to increase individuals' mental health and well-being. The current study formed part of a multimethod study consisting of three related but independent sub-studies. The first sub-study identified the strengths of Gratitude, Curiosity and Hope to be positively associated with AEPs' resilience and well-being. These results were used to construct a quantitative model that illustrates the interplay between these constructs. In the second sub-study, the proposed model was qualitatively reviewed by a smaller group of AEPs to inform and enrich our understanding of AEPs' personal experiences of these constructs. In the current study, a panel of practicing psychologists who provide counselling services for South African expatriates and AEPs were asked to qualitatively review a proposed quantitative model. A cross-sectional, interpretive descriptive research design, applying purposive sampling was used to identify and recruit participants. The objective for the current study was firstly to ascertain why participants thought strengths of Gratitude, Curiosity and Hope featured so prominently in the model. Secondly, the study aimed to determine how these participants would, from their experience in working with AEPs, enhance these strengths and AEPs' resilience in therapy, and ultimately facilitate greater well-being and successful adjustment abroad. Participants completed an online questionnaire consisting of two semi-structured, open-ended questions. The data were analyzed using primary and secondary cycle coding to ultimately develop themes. Results indicated that strengths of Curiosity, Gratitude and Hope featured prominently because these strengths include elements that form part of the process of expatriation. Participants were able to suggest practical strength-based therapeutic techniques which would assist in enhancement of strengths, resilience and ultimately well-being. It is proposed that the therapeutic techniques and approaches suggested in this study could contribute to the success rate of expatriate assignments.

Partial Biodegradable Blend for Fused Filament Fabrication: In-Process Thermal and Post-Printing Moisture Resistance.

Despite the extensive research, the moisture-based degradation of the 3D-printed polypropylene and polylactic acid blend is not yet reported. This research is a part of study reported on partial biodegradable blends proposed for large-scale additive manufacturing applications. However, the previous work does not provide information about the stability of the proposed blend system against moisture-based degradation. Therefore, this research presents a combination of excessive physical interlocking and minimum chemical grafting in a partial biodegradable blend to achieve stability against in-process thermal and moisture-based degradation. In this regard, a blend of polylactic acid and polypropylene compatibilized with polyethylene graft maleic anhydride is presented for fused filament fabrication. The research implements, for the first time, an ANOVA for combined thermal and moisture-based degradation. The results are explained using thermochemical and microscopic techniques. Scanning electron microscopy is used for analyzing the printed blend. Fourier transform infrared spectroscopy has allowed studying the intermolecular interactions due to the partial blending and degradation mechanism. Differential scanning calorimetry analyzes the blending (physical interlocking or chemical grafting) and thermochemical effects of the degradation mechanism. The thermogravimetric analysis further validates the physical interlocking and chemical grafting. The novel concept of partial blending with excessive interlocking reports high mechanical stability against moisture-based degradation.

Partial Biodegradable Blend with High Stability against Biodegradation for Fused Deposition Modeling.

This research presents a partial biodegradable polymeric blend aimed for large-scale fused deposition modeling (FDM). The literature reports partial biodegradable blends with high contents of fossil fuel-based polymers (>20%) that make them unfriendly to the ecosystem. Furthermore, the reported polymer systems neither present good mechanical strength nor have been investigated in vulnerable environments that results in biodegradation. This research, as a continuity of previous work, presents the stability against biodegradability of a partial biodegradable blend prepared with polylactic acid (PLA) and polypropylene (PP). The blend is designed with intended excess physical interlocking and sufficient chemical grafting, which has only been investigated for thermal and hydrolytic degradation before by the same authors. The research presents, for the first time, ANOVA analysis for the statistical evaluation of endurance against biodegradability. The statistical results are complemented with thermochemical and visual analysis. Fourier transform infrared spectroscopy (FTIR) determines the signs of intermolecular interactions that are further confirmed by differential scanning calorimetry (DSC). The thermochemical interactions observed in FTIR and DSC are validated with thermogravimetric analysis (TGA). Scanning electron microscopy (SEM) is also used as a visual technique to affirm the physical interlocking. It is concluded that the blend exhibits high stability against soil biodegradation in terms of high mechanical strength and high mass retention percentage.

Partial Polymer Blend for Fused Filament Fabrication with High Thermal Stability.

The materials for large scale fused filament fabrication (FFF) are not yet designed to resist thermal degradation. This research presents a novel polymer blend of polylactic acid with polypropylene for FFF, purposefully designed with minimum feasible chemical grafting and overwhelming physical interlocking to sustain thermal degradation. Multi-level general full factorial ANOVA is performed for the analysis of thermal effects. The statistical results are further investigated and validated using different thermo-chemical and visual techniques. For example, Fourier transform infrared spectroscopy (FTIR) analyzes the effects of blending and degradation on intermolecular interactions. Differential scanning calorimetry (DSC) investigates the nature of blending (grafting or interlocking) and effects of degradation on thermal properties. Thermogravimetric analysis (TGA) validates the extent of chemical grafting and physical interlocking detected in FTIR and DSC. Scanning electron microscopy (SEM) is used to analyze the morphology and phase separation. The novel approach of overwhelmed physical interlocking and minimum chemical grafting for manufacturing 3D printing blends results in high structural stability (mechanical and intermolecular) against thermal degradation as compared to neat PLA.

Developments for Collagen Hydrolysate in Biological, Biochemical, and Biomedical Domains: A Comprehensive Review.

The collagen hydrolysate, a proteinic biopeptide, is used for various key functionalities in humans and animals. Numerous reviews explained either individually or a few of following aspects: types, processes, properties, and applications. In the recent developments, various biological, biochemical, and biomedical functionalities are achieved in five aspects: process, type, species, disease, receptors. The receptors are rarely addressed in the past which are an essential stimulus to activate various biomedical and biological activities in the metabolic system of humans and animals. Furthermore, a systematic segregation of the recent developments regarding the five main aspects is not yet reported. This review presents various biological, biochemical, and biomedical functionalities achieved for each of the beforementioned five aspects using a systematic approach. The review proposes a novel three-level hierarchy that aims to associate a specific functionality to a particular aspect and its subcategory. The hierarchy also highlights various key research novelties in a categorical manner that will contribute to future research.

Image-Based Plant Disease Identification by Deep Learning Meta-Architectures.

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.

Plant Disease Classification: A Comparative Evaluation of Convolutional Neural Networks and Deep Learning Optimizers.

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.

Acrylonitrile Butadiene Styrene and Polypropylene Blend with Enhanced Thermal and Mechanical Properties for Fused Filament Fabrication.

Acrylonitrile butadiene styrene (ABS) is the oldest fused filament fabrication (FFF) material that shows low stability to thermal aging due to hydrogen abstraction of the butadiene monomer. A novel blend of ABS, polypropylene (PP), and polyethylene graft maleic anhydride (PE-g-MAH) is presented for FFF. ANOVA was used to analyze the effects of three variables (bed temperature, printing temperature, and aging interval) on tensile properties of the specimens made on a custom-built pellet printer. The compression and flexure properties were also investigated for the highest thermal combinations. The blend showed high thermal stability with enhanced strength despite three days of aging, as well as high bed and printing temperatures. Fourier-transform infrared spectroscopy (FTIR) provided significant chemical interactions. Differential scanning calorimetry (DSC) confirmed the thermal stability with enhanced enthalpy of glass transition and melting. Thermogravimetric analysis (TGA) also revealed high temperatures for onset and 50% mass degradation. Signs of chemical grafting and physical interlocking in scanning electron microscopy (SEM) also explained the thermo-mechanical stability of the blend.

Plant Disease Detection and Classification by Deep Learning.

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.

Effect of Material and Process Specific Factors on the Strength of Printed Parts in Fused Filament Fabrication: A Review of Recent Developments.

Additive manufacturing (AM) is rapidly evolving as the most comprehensive tool to manufacture products ranging from prototypes to various end-user applications. Fused filament fabrication (FFF) is the most widely used AM technique due to its ability to manufacture complex and relatively high strength parts from many low-cost materials. Generally, the high strength of the printed parts in FFF is attributed to the research in materials and respective process factors (process variables, physical setup, and ambient temperature). However, these factors have not been rigorously reviewed for analyzing their effects on the strength and ductility of different classes of materials. This review systematically elaborates the relationship between materials and the corresponding process factors. The main focus is on the strength and ductility. A hierarchical approach is used to analyze the materials, process parameters, and void control before identifying existing research gaps and future research directions.

Depression Symptoms Facilitated Fibrinolytic Dysregulation and Future Coronary Artery Disease Risk in a Black Male Cohort: The Sympathetic Activity and Ambulatory Blood Pressure in Africans Study.

BACKGROUND: Hypercoagulation is associated with coronary artery disease (CAD). Whether depression symptoms dysregulate inflammatory and hemostatic markers in an African cohort is not known; therefore, we assessed the relationship between depressive symptoms and inflammatory and hemostatic markers as potential CAD risk markers in an African sex cohort. MATERIAL AND METHODS: We included 181 black African urban-dwelling teachers (88 men, 93 women; aged 25-60 years) from the Sympathetic Activity and Ambulatory Blood Pressure in Africans Study. The Patient Health Questionnaire was used to assess depressive symptoms. Fasting plasma concentrations of C-reactive protein, fibrinogen, D-dimer, plasminogen activator inhibitor-1 (PAI-1) and 24-hour blood pressure measures were obtained. RESULTS: Moderately severe depression symptom status was similar in the black sex groups. Both sex groups showed a mean hypertensive state and low-grade inflammation (C-reactive protein > 3 mg/L). Levels of PAI-1 were higher in depressed men, whereas D-dimer levels were lower in depressed women when considering concomitant confounders. In black men only, depressive symptoms were associated with levels of PAI-1 (adj. R = 0.12; ß = .22 [95% confidence interval, .0-.44]; P = .04) and D-dimer (adj. R = 0.12; ß = .28 [95% confidence interval, .08-.48]; P = .01), independent of confounders. CONCLUSION: In black men, depression symptoms accompanied by a mean hypertensive status may up-regulate inflammatory and thrombotic processes. Depression symptoms in black men facilitated hypercoagulation or fibrinolytic dysregulation and potentially increased their CAD risk. Early screening of fibrinolytic markers and for the presence of depressive symptoms is recommended.

3D scanning and printing skeletal tissues for anatomy education.

Detailed anatomical models can be produced with consumer-level 3D scanning and printing systems. 3D replication techniques are significant advances for anatomical education as they allow practitioners to more easily introduce diverse or numerous specimens into classrooms. Here we present a methodology for producing anatomical models in-house, with the chondrocranium cartilage from a spiny dogfish (Squalus acanthias) and the skeleton of a cane toad (Rhinella marina) as case studies. 3D digital replicas were produced using two consumer-level scanners and specimens were 3D-printed with selective laser sintering. The fidelity of the two case study models was determined with respect to key anatomical features. Larger-scale features of the dogfish chondrocranium and frog skeleton were all well-resolved and distinct in the 3D digital models, and many finer-scale features were also well-resolved, but some more subtle features were absent from the digital models (e.g. endolymphatic foramina in chondrocranium). All characters identified in the digital chondrocranium could be identified in the subsequent 3D print; however, three characters in the 3D-printed frog skeleton could not be clearly delimited (palatines, parasphenoid and pubis). Characters that were absent in the digital models or 3D prints had low-relief in the original scanned specimen and represent a minor loss of fidelity. Our method description and case studies show that minimal equipment and training is needed to produce durable skeletal specimens. These technologies support the tailored production of models for specific classes or research aims.

Implementation and Operational Research: Implementation of Multiple Point-of-Care Testing in 2 HIV Antiretroviral Treatment Clinics in South Africa.

BACKGROUND: A plethora of point-of-care (POC) tests exist in the HIV and tuberculosis diagnostic pipeline which require rigorous evaluation to ensure performance in the field. The accuracy and feasibility of nurse-operated multidisciplinary-POC testing for HIV antiretroviral therapy (ART) initiation/monitoring was evaluated. METHODS: Random HIV-positive adult patients presenting at 2 treatment clinics in South Africa for ART initiation/monitoring were consented and enrolled. POCT was performed by a dedicated nurse on a venipuncture specimen; Pima (CD4), HemoCue (hemoglobin), Reflotron (alanine aminotransferase, creatinine), Accutrend (lactate) and compared with laboratory testing. External quality assessment, training, workflow, and errors were assessed. RESULTS: n = 324 enrolled at site1; n = 469 enrolled at site 2. Clinical data on n = 305 participants: 65% (n = 198) female with a mean age of 39.8 (21-61) years; mean age of males 43.2 (26-61) years; 70% of patients required 3 or more POC tests/visit. External quality assessment material was suitable for POCT. CD4, hemoglobin and alanine aminotransferase testing showed good agreement with predicate methodology; creatinine and lactate had increased variability. Pima CD4 misclassified up to 11.6% of patients at 500 cells per microliter and reported 4.3%-6% error rate. A dedicated nurse could perform POCT on 7 patients/day; inclusion of Pima CD4 increased time for testing from 6 to 110 minutes. Transcription error rate was 1%. CONCLUSIONS: Nurses can accurately perform multidisciplinary POCT for HIV ART initiation/monitoring. This will however, require a dedicated nurse as current duties will increase if POC is added to workflow. The use of Pima CD4 will increase patients initiated on ART. Connectivity will be central to ensure quality management of results, but overall impact will need to still be addressed.