62 research outputs found

    A Big Data Smart Agricultural System: Recommending Optimum Fertilisers For Crops

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    Nutrients are important to promote plant growth and nutrient deficiency is the primary factor limiting crop production. However, excess fertilisers can also have a negative impact on crop quality and yield, cause an increase in pollution and decrease producer profit. Hence, determining the suitable quantities of fertiliser for every crop is very useful. Currently, the agricultural systems with internet of things make very large data volumes. Exploiting agricultural Big Data will help to extract valuable information. However, designing and implementing a large scale agricultural data warehouse are very challenging. The data warehouse is a key module to build a smart crop system to make proficient agronomy recommendations. In our paper, an electronic agricultural record (EAR) is proposed to integrate many separate datasets into a unified dataset. Then, to store and manage the agricultural Big Data, we built an agricultural data warehouse based on Hive and Elasticsearch. Finally, we applied some statistical methods based on our data warehouse to extract fertiliser information such as a case study. These statistical methods propose the recommended quantities of fertiliser components across a wide range of environmental and crop management conditions, such as nitrogen (N), phosphorus (P) and potassium (K) for the top ten most popular crops in EU

    The CIPAZ study protocol: an open label randomised controlled trial of azithromycin versus ciprofloxacin for the treatment of children hospitalised with dysentery in Ho Chi Minh City, Vietnam

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    Background: Diarrhoeal disease remains a common cause of illness and death in children <5 years of age. Faecal-oral infection by Shigella spp. causing bacillary dysentery is a leading cause of moderate-to-severe diarrhoea, particularly in low and middle-income countries. In Southeast Asia, S. sonnei predominates and infections are frequently resistant to first-line treatment with the fluoroquinolone, ciprofloxacin. While resistance to all antimicrobials is increasing, there may be theoretical and clinical benefits to prioritizing treatment of bacillary dysentery with the azalide, azithromycin. In this study we aim to measure the efficacy of treatment with azithromycin compared with ciprofloxacin, the current standard of care, for the treatment of children with bacillary dysentery. Methods and analysis: We will perform a multicentre, open-label, randomized controlled trial of two therapeutic options for the antimicrobial treatment of children hospitalised with dysentery. Children (6–60 months of age) presenting with symptoms and signs of dysentery at Children’s Hospital 2 in Ho Chi Minh City will be randomised (1:1) to treatment with either oral ciprofloxacin (15mg/kg/twice daily for 3 days, standard-of-care) or oral azithromycin (10mg/kg/daily for 3 days). The primary endpoint will be the proportion of treatment failure (defined by clinical and microbiological parameters) by day 28 (+3 days) and will be compared between study arms by logistic regression modelling using treatment allocation as the main variable. Ethics and dissemination: The study protocol (version 1.2 dated 27th December 2018) has been approved by the Oxford Tropical Research Ethics Committee (47–18) and the ethical review boards of Children's Hospital 2 (1341/NĐ2-CĐT). The study has also been approved by the Vietnamese Ministry of Health (5044/QĐ-BYT). Trial registration: Clinicaltrials.gov: NCT03854929 (February 26th 2019)

    Mapping for engagement: setting up a community based participatory research project to reach underserved communities at risk for Hepatitis C in Ho Chi Minh City, Vietnam

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    Background: Approximately 1. 07 million people in Vietnam are infected with hepatitis C virus (HCV). To address this epidemic, the South East Asian Research Collaborative in Hepatitis (SEARCH) launched a 600-patient cohort study and two clinical trials, both investigating shortened treatment strategies for chronic HCV infection with direct-acting antiviral drugs. We conducted ethnographic research with a subset of trial participants and found that the majority were aware of HCV infection and its implications and were motivated to seek treatment. However, people who inject drugs (PWID), and other groups at risk for HCV were under-represented, although injecting drug use is associated with high rates of HCV. Material and Methods: We designed a community-based participatory research (CBPR) study to engage in dialogues surrounding HCV and other community-prioritized health issues with underserved groups at risk for HCV in Ho Chi Minh City. The project consists of three phases: situation analysis, CBPR implementation, and dissemination. In this paper, we describe the results of the first phase (i.e., the situation analysis) in which we conducted desk research and organized stakeholder mapping meetings with representatives from local non-government and community-based organizations where we used participatory research methods to identify and analyze key stakeholders working with underserved populations. Results: Twenty six institutions or groups working with the key underserved populations were identified. Insights about the challenges and dynamics of underserved communities were also gathered. Two working groups made up of representatives from the NGO and CBO level were formed. Discussion: Using the information provided by local key stakeholders to shape the project has helped us to build solid relationships, give the groups a sense of ownership from the early stages, and made the project more context specific. These steps are not only important preliminary steps for participatory studies but also for other research that takes place within the communities

    Associations of Underlying Health Conditions With Anxiety and Depression Among Outpatients: Modification Effects of Suspected COVID-19 Symptoms, Health-Related and Preventive Behaviors

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    Objectives: We explored the association of underlying health conditions (UHC) with depression and anxiety, and examined the modification effects of suspected COVID-19 symptoms (S-COVID-19-S), health-related behaviors (HB), and preventive behaviors (PB).Methods: A cross-sectional study was conducted on 8,291 outpatients aged 18–85 years, in 18 hospitals and health centers across Vietnam from 14th February to May 31, 2020. We collected the data regarding participant's characteristics, UHC, HB, PB, depression, and anxiety.Results: People with UHC had higher odds of depression (OR = 2.11; p &lt; 0.001) and anxiety (OR = 2.86; p &lt; 0.001) than those without UHC. The odds of depression and anxiety were significantly higher for those with UHC and S-COVID-19-S (p &lt; 0.001); and were significantly lower for those had UHC and interacted with “unchanged/more” physical activity (p &lt; 0.001), or “unchanged/more” drinking (p &lt; 0.001 for only anxiety), or “unchanged/healthier” eating (p &lt; 0.001), and high PB score (p &lt; 0.001), as compared to those without UHC and without S-COVID-19-S, “never/stopped/less” physical activity, drinking, “less healthy” eating, and low PB score, respectively.Conclusion: S-COVID-19-S worsen psychological health in patients with UHC. Physical activity, drinking, healthier eating, and high PB score were protective factors

    The context of HIV risk behaviours among HIV-positive injection drug users in Viet Nam: Moving toward effective harm reduction

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    <p>Abstract</p> <p>Background</p> <p>Injection drug users represent the largest proportion of all HIV reported cases in Viet Nam. This study aimed to explore the perceptions of risk and risk behaviours among HIV-positive injection drug users, and their experiences related to safe injection and safe sex practices.</p> <p>Methods</p> <p>This study used multiple qualitative methods in data collection including in-depth interviews, focus group discussions and participant observation with HIV-positive injection drug users.</p> <p>Results</p> <p>The informants described a change in the sharing practices among injection drug users towards more precautions and what was considered 'low risk sharing', like sharing among seroconcordant partners and borrowing rather than lending. However risky practices like re-use of injection equipment and 'syringe pulling' i.e. the use of left-over drugs in particular, were frequently described and observed. Needle and syringe distribution programmes were in place but carrying needles and syringes and particularly drugs could result in being arrested and fined. Fear of rejection and of loss of intimacy made disclosure difficult and was perceived as a major obstacle for condom use among recently diagnosed HIV infected individuals.</p> <p>Conclusion</p> <p>HIV-positive injection drug users continue to practice HIV risk behaviours. The anti-drug law and the police crack-down policy appeared as critical factors hampering ongoing prevention efforts with needle and syringe distribution programmes in Viet Nam. Drastic policy measures are needed to reduce the very high HIV prevalence among injection drug users.</p

    Socializing One Health: an innovative strategy to investigate social and behavioral risks of emerging viral threats

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    In an effort to strengthen global capacity to prevent, detect, and control infectious diseases in animals and people, the United States Agency for International Development’s (USAID) Emerging Pandemic Threats (EPT) PREDICT project funded development of regional, national, and local One Health capacities for early disease detection, rapid response, disease control, and risk reduction. From the outset, the EPT approach was inclusive of social science research methods designed to understand the contexts and behaviors of communities living and working at human-animal-environment interfaces considered high-risk for virus emergence. Using qualitative and quantitative approaches, PREDICT behavioral research aimed to identify and assess a range of socio-cultural behaviors that could be influential in zoonotic disease emergence, amplification, and transmission. This broad approach to behavioral risk characterization enabled us to identify and characterize human activities that could be linked to the transmission dynamics of new and emerging viruses. This paper provides a discussion of implementation of a social science approach within a zoonotic surveillance framework. We conducted in-depth ethnographic interviews and focus groups to better understand the individual- and community-level knowledge, attitudes, and practices that potentially put participants at risk for zoonotic disease transmission from the animals they live and work with, across 6 interface domains. When we asked highly-exposed individuals (ie. bushmeat hunters, wildlife or guano farmers) about the risk they perceived in their occupational activities, most did not perceive it to be risky, whether because it was normalized by years (or generations) of doing such an activity, or due to lack of information about potential risks. Integrating the social sciences allows investigations of the specific human activities that are hypothesized to drive disease emergence, amplification, and transmission, in order to better substantiate behavioral disease drivers, along with the social dimensions of infection and transmission dynamics. Understanding these dynamics is critical to achieving health security--the protection from threats to health-- which requires investments in both collective and individual health security. Involving behavioral sciences into zoonotic disease surveillance allowed us to push toward fuller community integration and engagement and toward dialogue and implementation of recommendations for disease prevention and improved health security

    An improved method of AODV routing protocol using reinforcement learning for ensuring QoS in 5G-based mobile ad-hoc networks

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    5G-based MANET has received a lot of attention recently. Its fundamental feature is that nodes are constantly subjected to high traffic loads, while QoS requirements are extremely stringent. When applied to 5G-based MANETs, existing routing protocols have shown drawbacks. In this paper, we propose an enhanced AODV protocol solution for 5G-based MANETs. Using reinforcement learning, each node updates a state information database of intermediate nodes along routes to destinations. This database is used by the routing algorithm to find guaranteed QoS routes. Our solution is highly efficient in terms of throughput, end-to-end delay, and SNR, according to the simulation results
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