Digital and circular technologies for climate-smart and sustainable agriculture: The case of Vietnamese coffee

\ua9 Published under licence by IOP Publishing Ltd.This comprehensive article addresses the pressing challenges confronting the global agriculture, primarily driven by climate change and resource constraints. With a focus on promoting climate-smart and sustainable agricultural practices, the study explores the transformative potential of emerging technologies, e.g., the innovative use of digital technologies like Internet of Things, Artificial Intelligence, and Blockchain, showcasing real-world examples of their benefits, and circular technologies, e.g., waste-to-value practices. The challenges of population growth, climate change, environmental impact, and the plight of smallholder farmers are elucidated. Climate-Smart Agriculture initiatives supported by the World Bank Group demonstrate practical efforts in addressing these challenges, aligning with sustainable development goals. Here, we introduce an innovative and smart agriculture (INNSA) platform for the creation and operation of sustainable coffee value chain in Vietnam as a case of study. Thought-provoking questions for future research conclude the review, encouraging interdisciplinary collaboration. In summary, this article provides a compelling case for adopting sustainable agricultural practices through digital and circular technologies, offering a roadmap for global agriculture\u27s transformation and resilience in the face of climate change

Environmentally Responsible Bioengineering for Spore Surface Expression of <em>Helicobacter pylori </em>Antigen

The development of genetic technologies and bioengineering are creating an increasing number of genetically engineered microorganisms with new traits for diverse industrial applications such as vaccines, drugs and pollutant degraders. However, the destiny of genetically engineered bacterial spores released into the environment as long-life organisms has remained a big environmental challenge. In this study, an environmentally responsible and sustainable gene technology solution based on the concept of thymine starvation is successfully applied for cloning and expression of a Helicobacter pylori antigen on Bacillus subtilis spore surface. As an example, a recombinant Bacillus subtilis strain A1.13 has been created from a gene fusion of the corresponding N-terminal fragment of spore coat protein CotB in B. subtilis and the entire urease subunit A (UreA) in H. pylori and the fusion showed a high stability of spore surface expression. The outcomes can open the door for developing highly safe spore vectored vaccines against this kind of pathogen and contributing to reduced potential risks of genetically engineered microorganisms released in the environment

Non-Laboratory-Based Risk Factors for Automated Heart Disease Detection

© 2018 IEEE. Developing a heart disease detection model using simple non-laboratory risk factors plays an important role in preventive care, especially for high risk subjects. The model allows physicians/epidemiologists to effectively diagnose a person as having heart disease. In this work, we aim to develop a non-invasive risk prediction model for automated heart disease detection that involves age, gender, rest blood pressure, maximum heart rate, and rest electrocardiography. We examine four public datasets from 1071 participants who were referred for a special X-ray of the heart's arteries (i.e., to see if they are narrowed or blocked). The subjects also undertook a physical examination and three non-invasive tests. To estimate the heart disease status, we apply a generalized linear model with regularization paths via coordinate descent. Even without laboratory-based data (e.g., serum cholesterol, fasting blood sugar), we observed a prediction accuracy as high as 72%, compared with 76% of other comprehensive models. This observation suggests that few non-invasive factors utilizing recent advances in data analytics can replace the current practices of heart disease risk assessment

Effect of Tris-(hydroxymethyl)-amino methane on microalgae biomass growth in a photobioreactor

© 2016 Elsevier Ltd. One of the buffers namely Tris (Tris-(hydroxymethyl)-amino methane) was used to increase the growth of microalgae by stabilizing the pH value in microalgae cultures. The objective of this research is to determine the growth rate and biomass productivity of Chlorella sp. with and without Tris addition. Both conditions function at various N:P ratios cultured in photobioreactors (carbon dioxide of 5% (v/v), light intensity of 3.3 Klux). Daily variations in nutrient removal (nitrogen and phosphorus), cell concentration, DO, temperature and pH were measured for data analysis. The results show that the largest yield of biomass was achieved at the N:P ratio of 15:1 with and without Tris. After cultivation lasting 92 h, the algae concentration at this ratio was 1250 mg L-1 and 3568 mg L-1 with and without Tris, respectively. This indicates that adding Tris to the photobioreactor greatly reduces algae biomass due to bacterial competition

Ecological factors associated with dengue fever in a central highlands Province, Vietnam

<p>Abstract</p> <p>Background</p> <p>Dengue is a leading cause of severe illness and hospitalization in Vietnam. This study sought to elucidate the linkage between climate factors, mosquito indices and dengue incidence.</p> <p>Methods</p> <p>Monthly data on dengue cases and mosquito larval indices were ascertained between 2004 and 2008 in the Dak Lak province (Vietnam). Temperature, sunshine, rainfall and humidity were also recorded as monthly averages. The association between these ecological factors and dengue was assessed by the Poisson regression model with adjustment for seasonality.</p> <p>Results</p> <p>During the study period, 3,502 cases of dengue fever were reported. Approximately 72% of cases were reported from July to October. After adjusting for seasonality, the incidence of dengue fever was significantly associated with the following factors: higher household index (risk ratio [RR]: 1.66; 95% confidence interval [CI]: 1.62-1.70 per 5% increase), higher container index (RR: 1.78; 95% CI: 1.73-1.83 per 5% increase), and higher Breteau index (RR: 1.57; 95% CI: 1.53-1.60 per 5 unit increase). The risk of dengue was also associated with elevated temperature (RR: 1.39; 95% CI: 1.25-1.55 per 2°C increase), higher humidity (RR: 1.59; 95% CI: 1.51-1.67 per 5% increase), and higher rainfall (RR: 1.13; 95% CI: 1.21-1.74 per 50 mm increase). The risk of dengue was inversely associated with duration of sunshine, the number of dengue cases being lower as the sunshine increases (RR: 0.76; 95% CI: 0.73-0.79 per 50 hours increase).</p> <p>Conclusions</p> <p>These data suggest that indices of mosquito and climate factors are main determinants of dengue fever in Vietnam. This finding suggests that the global climate change will likely increase the burden of dengue fever infection in Vietnam, and that intensified surveillance and control of mosquito during high temperature and rainfall seasons may be an important strategy for containing the burden of dengue fever.</p

The REDD+ policy arena in Vietnam: participation of policy actors

Reducing emissions from deforestation and degradation (REDD+) has gained increasing global attention because of its potential to reduce carbon emissions and improve forest governance. Reducing emissions from deforestation and degradation requires successful inclusive decision making and accountability. However, there have been limited empirical studies that examine the effectiveness of the current participatory mechanism used in REDD+. Our research analyzes the participation of policy actors in the development of the REDD+ instrument in Vietnam. We are interested in how the political context and the different interests of actors influence the degree of participation in national REDD+ policy decision making. We explored participation through the analysis of the mechanisms, e.g., how actors involve and participate in decision making, and dynamics of participation, e.g., highly centralized policy event vs. donor led event. The study aims to answer three research questions: (1) Who is involved in national REDD+ policy making and what are their interests in participating in core political events? (2) What level of participation do the different political actors have in core political events? and (3) To what extent do the outcomes, e.g., regulations and strategies, of REDD+ policy events incorporate different preferences of policy actors? Our findings highlighted the dominant role of government agencies in REDD+ policy making, which leaves limited political space for nonstate actors, e.g., NGOs and civil society organizations (CSOs), in Vietnam to exert an influence on the final policy outputs. Even in this highly centralized context, however, we found evidence to suggest that some political space in decision making is given to nonstate actors. Within this space, such actors are able to propose alternative policy options. Ensuring inclusive decision making and accountability in the Vietnam context requires a shift in current governance from traditional top-down approaches to a more participatory form of decision making

Feature Analysis for Discrimination of Motor Unit Action Potentials

© 2018 IEEE. In electrophysiological signal processing for intramuscular electromyography data (nEMG), single motor unit activity is of great interest. The changes of action potential (MUAP) morphology, motor unit (MU) activation, and recruitment provide the most informative part to study the nature causality in neuromuscular disorders. In practice, for a single nEMG recording, more than one motor unit activities (in the surrounding area of a needle electrode) are usually collected. Such a fact makes the MUAP discrimination that separates single unit activities a crucial task. Most neurology laboratories worldwide still recruit specialists who spend hours to manually or semi-automatically sort MUAPs. From a machine learning perspective, this task is analogous to the clustering-based classification problem in which the number of classes and other class information are unfortunately missing. In this paper, we present a feature analysis strategy to help better utilize unsupervised (i.e., totally automated) methods for MUAP discrimination. To that end, we extract a large pool of features from each MUAP. Then we select the top ranked candidates using clusterability scores as selection criteria. We found spectrograms of wavelet decomposition as a top-ranking feature, highly correlated to the motor unit reference and was more separable than existing features. Using a correlation-based clustering technique, we demonstrate the sorting performance with this feature set. Compared with the reference produced by human experts, our method obtained a comparable result (e.g., equivalent number of classes was found, identical MUAP morphology in each pair of corresponding MU class, and similar histograms of MUs). Taking the manual labels as references, our method got a much higher sensitivity and accuracy than the compared unsupervised sorting method. We obtained a similar result in MUAP classification to the reference

A profiling analysis of contributions of cigarette smoking, dietary calcium intakes, and physical activity to fragility fracture in the elderly

© 2018 The Author(s). Fragility fracture and bone mineral density (BMD) are influenced by common and modifiable lifestyle factors. In this study, we sought to define the contribution of lifestyle factors to fracture risk by using a profiling approach. The study involved 1683 women and 1010 men (50+ years old, followed up for up to 20 years). The incidence of new fractures was ascertained by X-ray reports. A "lifestyle risk score" (LRS) was derived as the weighted sum of effects of dietary calcium intake, physical activity index, and cigarette smoking. Each individual had a unique LRS, with higher scores being associated with a healthier lifestyle. Baseline values of lifestyle factors were assessed. In either men or women, individuals with a fracture had a significantly lower age-adjusted LRS than those without a fracture. In men, each unit lower in LRS was associated with a 66% increase in the risk of total fracture (non-adjusted hazard ratio [HR] 1.66; 95% CI, 1.26 to 2.20) and still significant after adjusting for age, weight or BMD. However, in women, the association was uncertain (HR 1.30; 95% CI, 1.11 to 1.53). These data suggest that unhealthy lifestyle habits are associated with an increased risk of fracture in men, but not in women, and that the association is mediated by BMD

White hard clam (Meretrix lyrata) shells media to improve phosphorus removal in lab-scale horizontal sub-surface flow constructed wetlands: Performance, removal pathways, and lifespan.

This work examined the phosphorus (P) removal from the synthetic pretreated swine wastewater using lab-scale horizontal sub-surface flow constructed wetlands (HSSF-CWs). White hard clam (Meretrix lyrata) shells (WHC) and Paspalum atratum were utilized as substrate and plant, respectively. The focus was placed on treatment performance, removal mechanisms and lifespan of the HSSF-CWs. Results indicated that WHC-based HSSF-CW with P. atratum exhibited a high P removal (89.9%). The mean P efluent concentration and P removal rate were 1.34 ± 0.95 mg/L and 0.32 ± 0.03 g/m2/d, respectively. The mass balance study showed that media sorption was the dominant P removal pathway (77.5%), followed by microbial assimilation (14.5%), plant uptake (5.4%), and other processes (2.6%). It was estimated the WHC-based bed could work effectively for approximately 2.84 years. This WHC-based HSSF-CWs technology will therefore pave the way for recycling Ca-rich waste materials as media in HSSF-CWs to enhance P-rich wastewater purification

Freezing of Gait Detection in Parkinson's Disease: A Subject-Independent Detector Using Anomaly Scores

© 2012 IEEE. Freezing of gait (FoG) is common in Parkinsonian gait and strongly relates to falls. Current clinical FoG assessments are patients' self-report diaries and experts' manual video analysis. Both are subjective and yield moderate reliability. Existing detection algorithms have been predominantly designed in subject-dependent settings. In this paper, we aim to develop an automated FoG detector for subject independent. After extracting highly relevant features, we apply anomaly detection techniques to detect FoG events. Specifically, feature selection is performed using correlation and clusterability metrics. From a list of 244 feature candidates, 36 candidates were selected using saliency and robustness criteria. We develop an anomaly score detector with adaptive thresholding to identify FoG events. Then, using accuracy metrics, we reduce the feature list to seven candidates. Our novel multichannel freezing index was the most selective across all window sizes, achieving sensitivity (specificity) of 96% (79%). On the other hand, freezing index from the vertical axis was the best choice for a single input, achieving sensitivity (specificity) of 94% (84%) for ankle and 89% (94%) for back sensors. Our subject-independent method is not only significantly more accurate than those previously reported, but also uses a much smaller window (e.g., 3 s versus 7.5 s) and/or lower tolerance (e.g., 0.4 s versus 2 s)