Digital In Situ Data Collection in Earth Observation, Monitoring and Agriculture—Progress towards Digital Agriculture

Digital solutions in agricultural management promote food security and support the sustainable use of resources. As a result, remote sensing (RS) can be seen as an innovation for the fast generation of reliable information for agricultural management. Near real-time processed RS data can be used as a tool for decision making on multiple scales, from subplot to the global level. This high potential is not yet fully applied, due to often limited access to ground truth information, which is crucial for the development of transferable applications and acceptance. In this study we present a digital workflow for the acquisition, processing and dissemination of agroecological information based on proprietary and open-source software tools with state-of-the-art web-mapping technologies. Data is processed in near real-time and thus can be used as ground truth information to enhance quality and performance of RS-based products. Data is disseminated by easy-to-understand visualizations and download functionalities for specific application levels to serve specific user needs. It thus can increase expert knowledge and can be used for decision support at the same time. The fully digital workflow underpins the great potential to facilitate quality enhancement of future RS products in the context of precision agriculture by safeguarding data quality. The generated FAIR (findable, accessible, interoperable, reusable) datasets can be used to strengthen the relationship between scientists, initiatives and stakeholders.

Assessing the Role of Uric Acid as a Predictor of Preeclampsia

We assessed the diagnostic utility of uric acid for the prediction of preeclampsia. An observational prospective approach was carried out during 2014. Preeclamptic women were classified into 4 groups accordingly to the onset of preeclampsia and the presence of intrauterine growth restriction (IUGR). Serum uric acid levels, urea, and creatinine were measured. Receiver operating curves (ROC) of the uric acid levels ratio (UAr) between a dosage before and after the 20th week of gestation were performed. One thousand two hundred and ninety-third pregnant women were enrolled in this study. Eight hundred ten had non-complicated pregnancies, 40 preeclampsia, 33 gestational hypertension, and 20 IUGR without preeclampsia. Uric acid significantly raised after 20 weeks of gestation in women who develop preeclampsia before 34 weeks (Group A) or in those who develop preeclampsia after 37 weeks associated with IUGR (Group C). In women who develop preeclampsia after 34 weeks without IUGR (Groups B and D), uric acid increased after the 30th week of gestation. In all groups, UAr was greater than 1.5. In gestational hypertension, UAr was superior to 1.5 toward the end of gestation, while in IUGR without preeclampsia, the behavior of serum uric acid was similar to non-complicated pregnancies. In all cases, urea and creatinine showed normal values, confirming that patients had no renal compromise. ROC area was 0.918 [95% confidence interval (CI): 0.858–0.979) for the preeclampsia group and 0.955 (95% CI: 0.908–1.000) for Group A. UAr at a cut-off point ≥1.5 had a very low positive predictive value, but a high negative predictive value of 99.5% for preeclampsia and it reached 100% for Group A. Thus, a UAr less than 1.5 may be a helpful parameter with a strong exclusion value and high sensitivity for those women who are not expected to develop preeclampsia. Additionally, this low-cost test would allow for better use of resources in developing countries.

Value-Based Healthcare From the Perspective of the Healthcare Professional: A Systematic Literature Review

Introduction: Healthcare systems increasingly move toward “value-based healthcare” (VBHC), aiming to further improve quality and performance of care as well as the sustainable use of resources. Evidence about healthcare professionals' contributions to VBHC, experienced job demands and resources as well as employee well-being in VBHC is scattered. This systematic review synthesizes this evidence by exploring how VBHC relates to the healthcare professional, and vice versa.Method: Seven databases were systematically searched for relevant studies. The search yielded 3,782 records, of which 45 were eligible for inclusion based on a two-step screening process using exclusion criteria performed by two authors independently. The quality of the included studies was appraised using the Mixed Methods Appraisal Tool (MMAT). Based on inductive thematic analysis, the Job Demands-Resources (JD-R) model was modified. Subsequently, this modified model was applied deductively for a second round of thematic analysis.Results: Ten behaviors of healthcare professionals to enhance value in care were identified. These behaviors and associated changes in professionals' work content and work environment impacted the experienced job demands and resources and, in turn, employee well-being and job strain. This review revealed 16 constructs as job demand and/or job resource. Examples of these include role strain, workload and meaning in work. Four constructs related to employee well-being, including engagement and job satisfaction, and five constructs related to job strain, including exhaustion and concerns, were identified. A distinction was made between job demands and resources that were a pure characteristic of VBHC, and job demands and resources that resulted from environmental factors such as how care organizations shaped VBHC.Conclusion and Discussion: This review shows that professionals experience substantial job demands and resources resulting from the move toward VBHC and their active role therein. Several job demands are triggered by an unsupportive organizational environment. Hence, increased organizational support may contribute to mitigating or avoiding adverse psychosocial factors and enhance positive psychosocial factors in a VBHC context. Further research to estimate the effects of VBHC on healthcare professionals is warranted.

Machine learning and deep learning techniques to support clinical diagnosis of arboviral diseases: A systematic review

Background Neglected tropical diseases (NTDs) primarily affect the poorest populations, often living in remote, rural areas, urban slums or conflict zones. Arboviruses are a significant NTD category spread by mosquitoes. Dengue, Chikungunya, and Zika are three arboviruses that affect a large proportion of the population in Latin and South America. The clinical diagnosis of these arboviral diseases is a difficult task due to the concurrent circulation of several arboviruses which present similar symptoms, inaccurate serologic tests resulting from cross-reaction and co-infection with other arboviruses. Objective The goal of this paper is to present evidence on the state of the art of studies investigating the automatic classification of arboviral diseases to support clinical diagnosis based on Machine Learning (ML) and Deep Learning (DL) models. Method We carried out a Systematic Literature Review (SLR) in which Google Scholar was searched to identify key papers on the topic. From an initial 963 records (956 from string-based search and seven from a single backward snowballing procedure), only 15 relevant papers were identified. Results Results show that current research is focused on the binary classification of Dengue, primarily using tree-based ML algorithms. Only one paper was identified using DL. Five papers presented solutions for multi-class problems, covering Dengue (and its variants) and Chikungunya. No papers were identified that investigated models to differentiate between Dengue, Chikungunya, and Zika. Conclusions The use of an efficient clinical decision support system for arboviral diseases can improve the quality of the entire clinical process, thus increasing the accuracy of the diagnosis and the associated treatment. It should help physicians in their decision-making process and, consequently, improve the use of resources and the patient’s quality of life.

Innovative Teaching and Learning Pathways for Responsible Use of Resources Focusing on the ESEE Region

In a world depicted by rapid growth and consumerism, where pressing societal issues such as, the critical climate crises, resources exploitation in developing countries and much more, it is essential to educate all citizens about raw materials, their uses and about responsible production and consumption. Developing life-long learning wider society programmes is an essential tool to educate the population starting with the youngest members of society (pre-school) to adulthood (life-long learning) with a special focus on the life-world orientation from a learners’ perspective. The wider society learning flagship project, ‘Raw Matters Ambassadors at Schools’ (RM@Schools), is an innovative programme funded by the European Institute of Innovation & Technology (EIT) which promotes science education and careers in the raw materials sector. Since the project’s commencement in 2016, it has gained stronger European representation with a current total of 32 partners from 18 countries. This includes the consortium members from a parallel funded programme, RM@SchoolsESEE. This project aims at extending best practices and diversifying the current portfolio to East and Southeast European countries. What makes this project so innovative is in the programme and individual learning pathways. Considering a multidisciplinary and cultural perspective, a framework was developed to assist in forming raw materials ambassadors through engaging programmes for school pupils from aged 10 up to 19 years old and through the development of educational hands-on toolkits that range from experiments to game-based tools and much more. In particular, and as a way to increase impact, the project aims to mentor teenagers from all educational disciplines between the ages of 14–19 years old to become ‘Young RM Ambassadors’. This approach utilises state-of-the-art teaching and learning methods resulting in peer-to-peer knowledge exchange and dissemination. Built into the programme is a continuous feedback loop that involves stakeholders from all sides of the knowledge triangle: educators, pupils, professionals and researchers.

Sustainable Intensification of Beef Production in the Tropics: The Role of Genetically Improving Sexual Precocity of Heifers

Increasing productivity through continued animal genetic improvement is a crucial part of implementing sustainable livestock intensification programs. In Zebu cattle, the lack of sexual precocity is one of the main obstacles to improving beef production efficiency. Puberty-related traits are complex, but large-scale data sets from different “omics” have provided information on specific genes and biological processes with major effects on the expression of such traits, which can greatly increase animal genetic evaluation. In addition, genetic parameter estimates and genomic predictions involving sexual precocity indicator traits and productive, reproductive, and feed-efficiency related traits highlighted the feasibility and importance of direct selection for anticipating heifer reproductive life. Indeed, the case study of selection for sexual precocity in Nellore breeding programs presented here show that, in 12 years of selection for female early precocity and improved management practices, the phenotypic means of age at first calving showed a strong decreasing trend, changing from nearly 34 to less than 28 months, with a genetic trend of almost −2 days/year. In this period, the percentage of early pregnancy in the herds changed from around 10% to more than 60%, showing that the genetic improvement of heifer’s sexual precocity allows optimizing the productive cycle by reducing the number of unproductive animals in the herd. It has a direct impact on sustainability by better use of resources. Genomic selection breeding programs accounting for genotype by environment interaction represent promising tools for accelerating genetic progress for sexual precocity in tropical beef cattle.

Refugees’ Agency: On Resistance, Resilience, and Resources

This study set out to answer the question ‘Which kinds of agency do refugees perform when dealing with mental health problems of themselves and their children?’. Aiming to gain more insight in why it seems harder for refugee parents and minors than for the native population to talk to health professionals about their mental health and wellbeing, we combined two theoretical notions of agency to investigate a broad spectrum of informants’ behaviour. We conducted 25 interviews with 30 refugees from 8 countries (Syria, Yemen, Iran, Afghanistan, Armenia, Eritrea, Turkish Kurdistan, Vietnam), whose Dutch residence permit varied from 26 years to less than one year. Data were analysed through open and axial coding, followed by pattern analyses. Although sometimes refugees seek (mental) healthcare, at other times they show agency by doing ‘nothing’ or by deliberately using distracting activities to deal with severe stress. Making use of resources available to them, oftentimes refugees show agency in ways that are less visible to healthcare professionals, by surviving, showing resilience, and suffering. In these cases, we think healthcare for refugees should intervene in a non-medical way, e.g., by supporting them to obtain resources that help refugees to (re)gain agency.

Coordinating and Standardizing Outdoor Recreation Supply and Demand Databases to Facilitate Management and Promote Conservation, Health, and Accessibility

Recreation opportunities exist as a system at multiple scales and are offered by a variety of recreation providers sometimes with different goals and objectives. Incremental and disparate planning across providers can lead to mismatched supply and demand and inefficient use of resources. Furthermore, traditional recreation supply and demand studies have not systematically considered compatible benefits from conserving recreation lands including demand for and provision of biodiversity and wildlife conservation, ecosystem services, human health, and environmental justice issues.Historically, the supply of outdoor recreation and conservation lands was assembled by different state and federal agencies, counties and municipalities, Native American tribes, non-governmental organizations, or private organizations for the lands they directly managed with little systematic coordination. A national level Protected Area Databased (PAD-US) developed by the United States Geological Survey (USGS) Gap Analysis Project is changing this. It is the official, publicly available, and comprehensive spatial inventory of every park and protected area in the US. The PAD-US program has developed a standardized data set with consistent protocols and methodologies for data collection for continuous updates.Demand assessments are currently conducted by various federal and state agencies, industry associations, and academics. These studies are independently conducted at various levels form recreation site, across land ownership, by activity, or state and national studies. Initiated in 1960, what became the National Survey of Recreation and the Environment (NSRE) collected recreation demand data for analysis at state and national levels. Many recreation planners used this data until it was discontinued in 2014. While there has been coordination and systemization and standardization of recreation supply data collection, no similar actions have occurred for demand.Following the PAD-US, we identify opportunities to coordinate, standardize, and systematize the collection of demand data across agencies, ownerships, and scales. We propose a parallel publicly available National Recreation Demand Database (NRDD) with consistent protocols and methodologies to be the comprehensive and authoritative inventory of recreation demand. We suggest that a new National Survey on People and the Environment (NSPE) be developed to replace the NSRE to collect improved data on outdoor recreation, other resource uses, and compatible benefit demand information.

Cost-Effectiveness of Lipid-Lowering Therapies for Cardiovascular Prevention in Germany

Purpose Novel pharmaceutical treatments reducing cardiovascular events in dyslipidaemia patients must demonstrate clinical efficacy and cost-effectiveness to promote long-term adoption by patients, physicians, and insurers. Objective To assess the cost-effectiveness of statin monotherapy compared to additive lipid-lowering therapies for primary and secondary cardiovascular prevention from the perspective of Germany’s healthcare system. Methods Transition probabilities and hazard ratios were derived from cardiovascular outcome trials for statin combinations with icosapent ethyl (REDUCE-IT), evolocumab (FOURIER), alirocumab (ODYSSEY), ezetimibe (IMPROVE-IT), and fibrate (ACCORD). Costs and utilities were retrieved from previous literature. The incidence of major adverse cardiovascular events was simulated with a Markov cohort model. The main outcomes were the incremental cost-effectiveness ratios (ICER) per quality adjusted life year (QALY) gained. Results For primary prevention, the addition of icosapent ethyl to statin generated 0.81 QALY and €14,732 costs (ICER: 18,133), whereas fibrates yielded 0.63 QALY and € − 10,516 costs (ICER: − 16,632). For secondary prevention, the addition of ezetimibe to statin provided 0.61 QALY at savings of € − 5,796 (ICER: − 9,555) and icosapent ethyl yielded 0.99 QALY and €14,333 costs (ICER: 14,485). PCSK9 inhibitors offered 0.55 and 0.87 QALY at costs of €62,722 and €87,002 for evolocumab (ICER: 114,639) and alirocumab (ICER: 100,532), respectively. A 95% probability of cost-effectiveness was surpassed at €20,000 for icosapent ethyl (primary and secondary prevention), €119,000 for alirocumab, and €149,000 for evolocumab. Conclusions For primary cardiovascular prevention, a combination therapy of icosapent ethyl plus statin is a cost-effective use of resources compared to statin monotherapy. For secondary prevention, icosapent ethyl, ezetimibe, evolocumab, and alirocumab increase patient benefit at different economic costs.

Design and Implementation of Intelligent Vehicle Control System Based on Internet of Things and Intelligent Transportation

With the improvement of urbanization and the continuous expansion of transportation scale, traffic problem has become an important problem in our life. How to ensure traffic safety has become the key issue for the government to implement social management. Nowadays, Internet of Things (IOT) technology is widely used in the industrial technology field. It will have a great impact on human production and life. Intelligent transportation system is a research field involving many high and new technologies. This paper proposes an intelligent transportation system based on Internet of Things technology. This paper presents the optimal design structure of intelligent transportation system based on Internet of Things technology. The experimental results show that the intelligent transportation system can effectively realize the information interaction between the vehicle and the control center and understand the road conditions in advance. At the same time, the intelligent transportation system can improve the driving speed of vehicles on the road, make effective use of resources, reduce economic losses during vehicle operation, and reduce air pollution caused by gasoline emission.