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.

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.

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.

Establishment of haemoglobin A 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 A is necessary for the diagnosis of the beta thalassaemia trait. In this context, it is important to have a reliable reference interval for haemoglobin A and a local reference range for South Africa has not been established.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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).

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.

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.

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.

Depression, cardiometabolic function and left ventricular hypertrophy in African men and women: the SABPA study.

Depressive symptoms are associated with an increased risk for developing cardiovascular diseases, driven by its link to the metabolic syndrome (MetS). This phenomenon, however, still needs to be investigated in the African population. The aim of this study is to investigate the association between left ventricular hypertrophy (LVH) and MetS risk markers in a determined sample.

Defensive coping, urbanization, and neuroendocrine function in Black Africans: the THUSA study.

Dissociation between β-adrenergic behavioral and physiological defensive active coping (AC) responses was associated with cardiometabolic risk in urban but not rural African males. Whether this is partly driven by underlying neuroendocrine dysfunction is not certain. We aimed to assess the association between coping style, urbanization, and neuroendocrine function.

Evaluation of the mental health continuum-short form (MHC-SF) in setswana-speaking South Africans.

A continuous assessment and a categorical diagnosis of the presence of mental health, described as flourishing, and the absence of mental health, characterized as languishing, is applied to a random sample of 1050 Setswana-speaking adults in the Northwest province of South Africa. Factor analysis revealed that the mental health continuum-short form (MHC-SF) replicated the three-factor structure of emotional, psychological and social well-being found in US samples. The internal reliability of the overall MHC-SF Scale was 0.