bluuebunny/crossref_metadata_2025 huggingface dataset:
The importance of conserving Mexico's tomato agrodiversity to research plant biochemistry under different climates
Authors: Sandoval‐Ceballos María Guadalupe , Kalungwana Ng' Andwe , Griffin Jonathan Henry Charles , Martínez‐Guerra Geovanni , Ramírez‐Ramírez Iván , Maldonado‐Peralta Ramiro , Marshall Lisa , Bosch Christine , Cruz‐Huerta Nicacio , Gonzalez‐Santos Rosalinda , León Patricia , Chávez‐Servia José Luis , González‐Hernández Víctor A. , Phelps Jacob , Toledo‐Ortiz Gabriela
Published: August 2021

Societal Impact Statement

Tomatoes are important to agriculture, human nutrition and cuisines globally. However, many commercial tomato varieties, including the saladette that dominates the North American market, are highly sensitive to environmental changes that impact yields and critical biochemical pathways including carotenoids and isoprenoids that influence nutritional content and flavour. We highlight the potential of tomato agrodiversity, notably its genetic diversity, as an undervalued research tool for understanding environmental regulation of plant biochemistry under different climates. Yet, tomato genetic diversity in Mexico, the major centre of tomato domestication, is not formally described or protected. We propose that transdisciplinary efforts are essential to identify, conserve and research these globally significant genetic resources.
Multiple imputation analysis of Miettinen‐Nurminen interval for difference in proportions
Authors: Lu Kaifeng , Guo Hua
Published: August 2021

The standard multiple imputation technique focuses on parameter estimation and assumes that the parameter estimator is asymptotically normally distributed with a Wald‐type confidence interval for the parameter of interest. On the other hand, the Miettinen‐Nurminen (MN) method for difference in proportions (Miettinen O, Nurminen M. Stat Med. 1985;4:213–226) constructs the confidence interval using an asymptotic score method and hence is not directly amenable to the standard multiple imputation technique. We propose a multiple imputation analysis that is applicable to the MN method for difference in proportions. We use simulation studies to compare the proposed method with that of Li, Mehrotra and Barnard (LMB), which is based on effective sample sizes (Li X, Mehrotra DV, Barnard J. Stat Med. 2006;25:2107–2124). We show that both methods produce confidence intervals with adequate coverage, while the proposed method produces slightly shorter confidence intervals than the LMB method. In addition, the proposed method is evaluable for all datasets, while the LMB method cannot be used when the imputed cell counts are zero across imputations for a treatment group.
Targeting interleukin‐β by plant‐derived natural products: Implications for the treatment of atherosclerotic cardiovascular disease
Authors: Vahdat‐Lasemi Fatemeh , Aghaee‐Bakhtiari Seyed Hamid , Tasbandi Aida , Jaafari Mahmoud Reza , Sahebkar Amirhossein
Published: August 2021

Inflammation is the main contributing factor to atheroma formation in atherosclerosis. Interleukin‐1 beta (IL‐1β) is an inflammatory mediator found in endothelial cells and resident leukocytes. Canakinumab is a selective monoclonal antibody against IL‐1β which attenuates inflammation and concurrently precipitates fatal infections and sepsis. Natural products derived from medicinal plants, herbal remedy and functional foods are widely used nowadays. Experimental and clinical trial evidence supports that some natural products such as curcumin, resveratrol, and quercetin have potential effects on IL‐1β suppression. In this review, we tried to document findings that used medicinal plants and plant‐based natural products for treating atherosclerosis and its related diseases through the suppression of IL‐1β.
The role of the Scandium element concentration in the YN matrix: Ab initio study of structural, electronic, mechanical and thermal properties
Authors: Cherchab Youcef , Mir Ali , González‐Hernández Rafael , Talbi Khedija , Bennadji Abderrahim
Published: August 2021

This study shows that the substitution of Scandium in the Yttrium Nitride matrix improves the mechanic properties quality and increase his melting point. In this framework, we use the DFT‐FP‐LAPW method to verify all the properties, structural, electronic and thermal of the Sc xY1‐xN alloy in a concentration range [x = 0–1] with a step of 0.25. The strong curvature of the lattice band structure parameter and the effective mass is due to the incorporation of the Sc atom into the Y‐N matrix. The energy of formation indicates that this material is formed successfully and energetically stable due to the relatively small size of the nitrogen atoms and its considerable electronegativity to Sc and Y.
Activity Promotion of Core and Shell in Multifunctional Core–Shell Co2P@NC Electrocatalyst by Secondary Metal Doping for Water Electrolysis and Zn‐Air Batteries
Authors: Lv Xian‐Wei , Xu Wei‐Shan , Tian Wen‐Wen , Wang Hao‐Yu , Yuan Zhong‐Yong
Published: August 2021

Developing cost‐efficient multifunctional electrocatalysts is highly critical for the integrated electrochemical energy‐conversion systems such as water electrolysis based on hydrogen/oxygen evolution reactions (HER/OER) and metal‐air batteries based on OER/oxygen reduction reactions (ORR). The core–shell structured materials with transition metal phosphide as the core and nitrogen‐doped carbon (NC) as the shell have been known as promising HER electrocatalysts. However, their oxygen‐related electrocatalytic activities still remain unsatisfactory, which severely limits their further applications. Herein an effective strategy to improve the core and shell performances of core–shell Co2P@NC electrocatalysts through secondary metal (e.g., Fe, Ni, Mo, Al, Mn) doping (termed M‐Co2P@M‐N‐C) is reported. The as‐synthesized M‐Co2P@M‐N‐C electrocatalysts show multifunctional HER/OER/ORR activities and good integrated capabilities for overall water splitting and Zn‐air batteries. Among the M‐Co2P@M‐N‐C catalysts, Fe‐Co2P@Fe‐N‐C electrocatalyst exhibits the best catalytic activities, which is closely related to the configuration of highly active species (Fe‐doping Co2P core and Fe‐N‐C shell) and their subtle synergy, and a stable carbon shell for outstanding durability. Combination of electrochemical‐based in situ Fourier transform infrared spectroscopy with extensive experimental investigation provides deep insights into the origin of the activity and the underlying electrocatalytic mechanisms at the molecular level.
Ultralow Ru Incorporated Amorphous Cobalt‐Based Oxides for High‐Current‐Density Overall Water Splitting in Alkaline and Seawater Media
Authors: Wu Dulan , Chen Ding , Zhu Jiawei , Mu Shichun
Published: August 2021

Realizing efficiency and stable hydrogen production by water electrolysis under high current densities is essential to the forthcoming hydrogen economy. However, its industrial breakthrough is seriously limited by bifunctional catalysts with slow hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) electrocatalytic processes. Herein, an ultralow Ru incorporated amorphous cobalt‐based oxide (Ru‐CoOx/NF), effectively driving the electrolysis of water at high current densities in alkaline water and seawater, is designed and constructed. In 1 m KOH, to reach the current density of 1000 mA cm−2 for HER and OER, it only needs 252 and 370 mV overpotentials, respectively, beyond commercial Pt/C and RuO2 catalysts. At the high current density, it also presents outstanding electrochemical stability. Then the electrolyzer apparatus assembled with Ru‐CoOx/NF, just requires the ultra‐low voltage of 2.2 and 2.62 V to support the current density of 1000 mA cm−2 in alkaline water and seawater electrolysis, respectively, for hydrogen production, better than that of the commercial Pt/C and RuO2 catalysts. This work demonstrates that Ru‐CoOx/NF is one of the most promising catalysts for industrial applications and provides a possibility for exploration of high‐current‐density water electrocatalysis by changing the crystallinity of the catalyst.
Delivering on the Promise of the Science of Reading for All Children
Authors: Terry Nicole P.
Published: August 2021

If the science of reading is the solution, then why are so many Black and Brown children not reading well in school? Reconciling the science with the lived experiences of children who are vulnerable to poor academic achievement in school may be requisite to unlocking the transformative innovation needed to ensure that every child can read and succeed in school.
Evaluating shared decision‐making between companion animal veterinarians and their clients using the Observer OPTION5 instrument
Authors: Janke Natasha , Coe Jason B. , Sutherland Katja A. K. , Bernardo Theresa M. , Dewey Cate E. , Stone Elizabeth A.
Published: August 2021

Background: Communication practices used by veterinarians can impact client's involvement in decision‐making. The objective of this study was to evaluate the level of client involvement in veterinary care decisions and factors impacting client involvement.

Methods: A random sample of practising companion animal veterinarians and a convenience sample of pet owners were recruited. Each consultation was audio‐video recorded and analyzed using the Observer OPTION5 instrument, adapted from human medicine, to assess veterinarians’ level of shared decision‐making. A mixed linear model was used to assess statistical associations between veterinarian, client and appointment‐level factors and OPTION5 scores.

Results: Sixty veterinarians and 909 of their clients were audio‐video recorded during a consultation, of which 717 interactions included a decision identified for analysis using the OPTION5 instrument. Elements consistent with shared decision‐making were uncommon, with a mean OPTION5 score of 22.6 (median = 20.0; sd = 13.05; range = 0–75) out of 100. OPTION5 scores were positively associated with the length of the appointment and negatively associated with the veterinarian's years in practice when controlling for statistical interaction between client income and appointment type.

Conclusion: Findings suggest an opportunity exists for veterinarians to incorporate more client involvement through increased use of shared decisionmaking behaviours during veterinary consultations.
Development of a novel real‐time PCR assay targeting p54 gene for rapid detection of African swine fever virus (ASFV) strains circulating in Vietnam
Authors: Trinh Thi Bich Ngoc , Truong Thang , Nguyen Van Tam , Vu Xuan Dang , Dao Le Anh , Nguyen Thi Lan , Ambagala Aruna , Babiuk Shawn , Oh Jinsik , Song Daesub , Le Van Phan
Published: August 2021

African swine fever (ASF) continues to cause outbreaks throughout regions of Africa, Europe and Asia. The disease can cause severe morbidity and mortality resulting in serious economic losses. Since there is no vaccine available to control ASF, early detection is critical to contain and control the disease. The aim of this study was to develop a novel real‐time PCR assay based on highly conserved ASFV gene E183L (p54). The limit of detection of the assay, VNUA‐p54 real‐time PCR, was 2.63 copies/reaction and 2 Log10 HAD50/ml. The VNUA‐p54 real‐time PCR was able to detect fifteen different ASFV reference strains representing p72 genotypes I, II and V. The assay was specific and did not amplify other swine viruses including CSFV, FMDV, PRRSV and PEDV. The diagnostic sensitivity of the real‐time PCR assay was evaluated using 200 field clinical specimens collected from swine farms located in different provinces in Vietnam. The VNUA‐p54 real‐time PCR assay is an additional tool for ASF diagnostics and can be used in combination with other p72 based ASFV real‐time PCR assays as a rapid confirmatory assay.
Influences of porous reduction graphene oxide/molybdenum disulfide as filler on dielectric properties, thermal stability, and mechanical properties of natural rubber
Authors: Pan Hongwei , Wang Zhaokun , Cui Yongyin , Cao Lan , Zong Chengzhong
Published: August 2021

A ternary composite system consisting of natural rubber (NR), porous reduced graphene oxide (rPGO), and molybdenum disulfide (MoS2) was introduced for applying in the dielectric field, of which rPGO and MoS2 hybrid conductive filler (rPGM) was prepared by an effective and environmentally friendly method‐microwave reduction. And the well‐dispersed NR composites (NGM) were made by the latex co‐precipitation method. Due to the large specific surface area of rPGM itself and the synergistic dispersion of rPGO and MoS2, it formed many stable interface structures with the NR matrix, which not only made the blend exhibit high elasticity and withstood large deformation as NR but also greatly improved the dielectric, mechanical and thermal stability of the NR matrix. Compared with neat NR, the dielectric constant of nanocomposite increased by 11 times in the presence of rPGM conductive filler, and the leakage current generated by direct contact of fillers was reduced due to the attachment of MoS2 to the surface of rPGO; when 2% rPGM was added, the NR exhibited the highest tensile strength (21.3 MPa), elongation at break (495%), and abrasion resistance (0.165 cm−3); in addition, the thermal stability of the nanocomposite was also improved. These phenomena indicate that rPGM had great potential in conductive fillers and provided a reliable way for NR applications in the field of dielectric elastomers.
Assessing learning in low carbon technologies: Toward a more comprehensive approach
Authors: Lewis Joanna I. , Nemet Gregory F.
Published: August 2021

With decades of experience developing and deploying low carbon technologies around the world, much has been learned. We assess six categories that represent the diversity of methodological approaches that have been used to study low carbon learning: (1) learning curves; (2) expert elicitations; (3) patent analysis; (4) engineering‐based decomposition; (5) policy intervention studies; and (6) case studies. Based on a review of low carbon learning studies in these six areas, we summarize what we know about low carbon learning, and what we have yet to fully understand, including the methodological strengths and limitations of key studies conducted to date. We find that a more comprehensive understanding of low carbon learning is necessary and timely given the massive scale and short time horizon of the low carbon transition, and that there are real benefits to employing a comprehensive approach using multiple methods. We find a need for better data sets, and for studies of a more diverse set of technologies, as well as of interactions among technologies. In addition, studies should be more explicit about local context, with a particular need for additional focus on emerging and developing countries. We identify key topics that warrant further research, including technology specific learning methods; spatial distinctions and the local and global linkages that influence learning; and an expanded study of the cultural, social, environmental, and political factors that influence learning. Finally, we recommend more nuance in the design of policies directed at accelerating low carbon learning.

This article is categorized under:

The Carbon Economy and Climate Mitigation > Future of Global Energy
Stakeholders' perceptions and preferences towards industrial water sensitive development in New Zealand
Authors: Wang Yuliang , van Roon Marjorie
Published: August 2021

Understanding stakeholders' perceptions and preferences is key to promoting Industrial Water Sensitive Design (IWSD). A questionnaire was distributed to stakeholders in a conventional New Zealand industrial area. The results showed that technological feasibility limitation, institutional inertia, and cognitive shortage were the main barriers to IWSD application, though stakeholders supported IWSD and preferred the vegetation‐based measures. IWSD should focus on achieving the enhancement of environmental quality, greening functions, community well‐being, traffic and driving convenience, and balancing the aesthetic demand and attractiveness of IWSD projects. Improving the threshold of IWSD measures' pollutant removal capacity and optimizing infiltration places and flow paths are also vital. Results suggest IWSD implementation requires the transformation of public awareness, the establishment of sound regulations, plus IWSD guidance, practitioner training guidance, and incentive mechanisms. The findings facilitate public acceptance, strategic planning, the formulation of regulations for future IWSD application, and achievement of sustainable industrial development. The study concludes by providing recommendations for future work.

Practitioner Points

Industrial Sustainable Stormwater Management is a research and practice gap.

The main barriers of IWSD are technological feasibility limitation, institutional inertia, and cognitive shortage.

Traffic and driving convenience, greening functions, aesthetic value balance, and health improvement are vital for improving social acceptance of IWSD.

IWSD requires sound policy, awareness transformation, and reasonable design for capacity of IWSD measures and stormwater infiltration and flow.
Exploration of Arrhenius activation energy on hybrid nanofluid flow over a curved stretchable surface
Authors: Varun Kumar R.S , Alhadhrami A. , Punith Gowda R.J. , Naveen Kumar R. , Prasannakumara B.C.
Published: August 2021

Scientists and engineers have experimentally noticed that the heat transfer is essential for preeminence of multi scale production. Thermal properties are naturally accomplished via continuous phase liquids. Its importance is nevertheless constrained by the low capacity for heat transfer. Consequently, the increased heat transference phenomenon must be employed to accomplish the expected results. In this regard, the nature of the Arrhenius activation energy in the flow of hybrid nanoliquid over a curved stretchable surface (CSS) in the presence of exponential heat generation is scrutinized. The fluid suspended with Manganese zinc ferrite and Nickel zinc ferrite (NiZnFe2O4) as nanoparticles along with Kerosene oil as a base liquid is accounted in this study. The described flow equations are transformed by using appropriate similarity variables and then they are tackled with Runge Kutta Fehlberg‐45 (RKF‐45) scheme by adopting shooting process. It can be concluded that, the increasing values of Biot number and heat source/sink parameter improves the thermal gradient. Further, hybrid nanofluid shows high heat transfer rate when compared to nanoliquid for improved values of heat source/sink parameter.
Data Analytics and Machine Learning
Authors: Järvinen Paula , Siltanen Pekka , Kirschenbaum Amit
Published: August 2021

In this chapter we give an introduction to data analytics and machine learning technologies, as well as some examples of technologies used in the DataBio project.  We start with a short intdroduction of basic concepts. We then describe how data analytics and machine learning markets have evolved. Next, we describe some basic technologies in the area. Finally, we describe how data analytics and machine learning were used in selected pilot cases of the DataBio project.
Real-Time Data Processing
Authors: Fournier Fabiana , Skarbovsky Inna
Published: August 2021

To remain competitive, organizations are increasingly taking advantage of the high volumes of data produced in real time for actionable insights and operational decision-making. In this chapter, we present basic concepts in real-time analytics, their importance in today’s organizations, and their applicability to the bioeconomy domains investigated in the DataBio project. We begin by introducing key terminology for event processing, and motivation for the growing use of event processing systems, followed by a market analysis synopsis. Thereafter, we provide a high-level overview of event processing system architectures, with its main characteristics and components, followed by a survey of some of the most prominent commercial and open source tools. We then describe how we applied this technology in two of the DataBio project domains: agriculture and fishery. The devised generic pipeline for IoT data real-time processing and decision-making was successfully applied to three pilots in the project from the agriculture and fishery domains. This event processing pipeline can be generalized to any use case in which data is collected from IoT sensors and analyzed in real-time to provide real-time alerts for operational decision-making.
Privacy-Preserving Analytics, Processing and Data Management
Authors: Keerup Kalmer , Bogdanov Dan , Kubo Baldur , Auran Per Gunnar
Published: August 2021

Typically, data cannot be shared among competing organizations due to confidentiality or regulatory restrictions. We present several technological alternatives to solve the problem: secure multi-party computation (MPC), trusted execution environments (TEE) and multi-key fully homomorphic encryption (MKFHE). We compare these privacy-enhancing technologies from deployment and performance point of view and explain how we selected technology and machine learning methods. We introduce a demonstrator built in the DataBio project for securely combining private and public data for planning of fisheries. The secure machine learning of best catch locations is a web solution utilizing Intel® Software Guard Extensions (Intel® SGX)-based TEE and built with the Sharemind HI (Hardware Isolation) development tools. Knowing where to go fishing is a competitive advantage that a fishery is not interested to share with competitors. Therefore, joint intelligence from public and private sector data while protecting secrets of each contributing organization is an important enabler. Finally, we discuss the wider business impact of secure machine learning in situations where data confidentiality is a concern.
Smart Farming for Sustainable Agricultural Production
Authors: Rogotis Savvas , Marianos Nikolaos
Published: August 2021

The chapter describes DataBio’s pilot applications, led by NEUROPUBLIC S.A., for sustainable agricultural production in Greece. Initially, it introduces the main aspects that drive and motivate the execution of the pilot. The pilot set-up consisted of four (4) different locations, four (4) different crop types and three (3) different types of offered services. The technology pipeline was based on the exploitation of heterogeneous data and their transformation into facts and actionable advice fostering sustainable agricultural growth. The results of the pilot activities effectively showcased how smart farming methodologies can lead to a positive impact from an economical, environmental and societal perspective and achieve the ambitious goal to “produce more with less”. The chapter concludes with “how-to” guidelines and the pilot’s key findings.
Yield Prediction in Sorghum (Sorghum bicolor (L.) Moench) and Cultivated Potato (Solanum tuberosum L.)
Authors: Habyarimana Ephrem , Bartelds Nicole
Published: August 2021

Sorghum and potato pilots were conducted in this work to provide a solution to current limitations (dependability, cost) in crop monitoring in Europe. These limations include yield forecasting based mainly on field surveys, sampling, censuses, and the use of coarser spatial resolution satellites. We used the indexes decribing the fraction of absorbed photosynthetically active radiation as well as the leaf areas derived from Sentinel-2 satellites to predict yields and provide farmers with actionable advice in sorghum biomass and, in combination with WOFOST crop growth model, in cultivated potatoes. Overall, the Bayesian additive regression trees method modelled best sorghum biomass yields. The best explanatory variables were days 150 and 165 of the year. In potato, the use of earth observation information allowed to improve the growth model, resulting in better yield prediction with a limited number of field trials. The online platform provided the potato farmers more insight through benchmarking among themselves across cropping seasons, and observing  in-field variability Site-specific management became easier based on the field production potential and its performance relative to surrounding fields. The extensive pilots run in this work showed that farming is a business with several variables which not all can be controlled by the farmer. The technologies developed herein are expected to inform about the farming operations, giving rise to well-informed farmers with the advantage to be able to adapt to the circumstances, mitigating production risks, and ultimately staying longer in the business.
Delineation of Management Zones Using Satellite Imageries
Authors: Charvát Karel , Lukas Vojtěch , Charvát Karel , Horáková Šárka
Published: August 2021

The chapter describes the development of a platform for mapping crop status and long-time trends by using EO data as a support tool for fertilizing and crop protection. The main focus of the pilot is to monitor cereal fields by high-resolution satellite imagery data (Landsat 8, Sentinel 2) and delineation of management zones within the fields for variable rate application of fertilizers. The first part of the paper is focused on analysis of strategies for recommendations derived from satellite data. The second  part is focused on development of a software application with the goal to offer farmers a GIS portal. Here, users can monitor their fields from EO data, based on the specified period and select cloudless scenesfor further analysis. The tool supports collaborative communication between farmers and advisors.
Farm Weather Insurance Assessment
Authors: Catucci Antonella , Tricomi Alessia , De Vendictis Laura , Rogotis Savvas , Marianos Nikolaos
Published: August 2021

The pilot aimed to develop services supporting both the risk and the damage assessment in the agro-insurance domain. It is based on the use of remotely sensed data, integrated with meteorological data, and adopts machine learning and artificial intelligence tools. Netherlands and Greece have been selected as pilot areas . In the Netherlands, the pilot was focused on potato crops for the identification of areas with higher risk, based on the historical analysis of heavy rains. In addition, it covered automated detection  of potato parcels with anomalous behaviours (damage assessment) from satellite data, meteorological parameters and soil characteristics. In Greece, the pilot worked with 7 annual crops of high economic interest to the national agricultural sector. The crops have been modelled exploiting the last 3-year NDVI measurements to identify their deviations from  the normal crop health behaviour for an early identification of affected parcels in case of adverse events. The models were successfully tested on a flooding event that occurred in 2019 in the Komotini region. Even though the proposed methodologies should be tested over larger areas and compared against a larger validation dataset, the results already now demonstrate  how to reduce the operating costs of damage assessors through a more precise and automatic risk assessment. Additionally, the identification of parameters that most affect the crop yield could transform the insurance industry through index-based solutions allowing to dramatically cut costs.
Standards and EO Data Platforms
Authors: Simonis Ingo , Charvát Karel
Published: August 2021

In the digital bio-economy like in many other sectors, standards play an important role. With “Standards”, we refer here to the protocols that describe how data and the data-exchange are defined to enable digital exchange of data between devices. This chapter evaluates how Big Data, cloud processing, and app stores together form a new market that allows exploiting the full potential of geospatial data. This chapter focuses on the essential cornerstones that help make Big Data processing a more seamless experience for bioeconomy data. The described approach is domain-independent, thus can be applied to agriculture, fisheries, and forestry as well as earth observation sciences, climate change research, or disaster management. This flexibility is essential when it comes to addressing real world complexities for any domain, as no single domain has sufficient data available within its own limits to tackle the major research challenges our world is facing.
Copernicus Data and CAP Subsidies Control
Authors: Copăcenaru Olimpia , Stoica Adrian , Catucci Antonella , De Vendictis Laura , Tricomi Alessia , Rogotis Savvas , Marianos Nikolaos
Published: August 2021

This chapter integrates the results of three pilots developed within the framework of the Horizon 2020 DataBio project. It aims to provide a broad picture of how products based on Earth Observation techniques can support the European Union’s Common Agricultural Policy requirements, whose fulfillments are supervised by National and Local Paying Agencies operating in Romania, Italy and Greece. The concept involves the use of the same data sources, mainly multitemporal series of Copernicus Sentinel-2 imagery, but through three different Big Data processing chains, tailored to each paying agency’s needs in terms of farm compliance assessment. Particularities of each workflow are presented together with examples of the results and their accuracy, calculated by validation against independent sources. Business value aspects for each use case are also discussed, emphasizing the way in which the automation of the CAP requests verification process through satellite technologies has increased the efficiency and reduced cost and time resources for the subsidy process. We end the chapter by highlighting the benefits of continuous satellite tracking as a substitute, but also complementary to the classical field control methods, and also the enormous potential of Earth Observation-based products for the agri-food market.
Introduction—State of the Art of Technology and Market Potential for Big Data in Forestry
Authors: Miettinen Jukka , Tergujeff Renne
Published: August 2021

Forest monitoring is undergoing rapid changes due to the growing data volumes, developing data processing technologies and increasing monitoring requirements. The DataBio forestry pilots set out to demonstrate how big data approaches can support the forestry sector to get full benefit of the evolving technologies and to meet the increasing monitoring requirements. In this introductory chapter, we describe underlying technical and market forces driving the forestry sector toward big data approaches, and give short overviews on the forestry pilots to be presented in the following chapters.
Finnish Forest Data-Based Metsään.fi-services
Authors: Stenman Virpi
Published: August 2021

This chapter introduces the Finnish forest data ecosystem and its role in DataBio project pilots. In these DataBio pilots, the main objective is to improve the use of the Finnish forest resource data. The Finnish forest data provides a foundation for the forest big data-based online and e-services. The technical solution elements for the introduced DataBio pilots are based on standardized XML data sets, X-Road data transfer protocols, open forest data application programming interfaces (APIs) and crowdsourcing applications. The Metsään.fi-services including the open forest data APIs and Wuudis-mobile application are the key components for the customer’s user interface. In the end of the chapter, the pilot-specific business benefits and key performance indicators are decribed showing clear positive impacts of the pilots. At the end of this chapter, visions for the future of public online services are discussed.
Forest Variable Estimation and Change Monitoring Solutions Based on Remote Sensing Big Data
Authors: Miettinen Jukka , Bonnet Stéphanie , Nielsen Allan A. , Huurinainen Seppo , Tergujeff Renne
Published: August 2021

In this pilot, we demonstrate the usability of online platforms to provide forest inventory systems for exploiting the benefits of big data. The pilot highlights the technical transferability of online platform based forest inventory services. All of the services tested in the piloting sites were technically implemented successfully. However, in new geographical areas, strong user involvement in service definition and field data provision will be needed to provide reliable and meaningful results for the users. Overall, the pilot demonstrated well the benefits of technology use in forest monitoring through a range of forest inventory applications utilizing online big data processing approaches and inter-platform connections.
Monitoring Forest Health: Big Data Applied to Diseases and Plagues Control
Authors: Navarro Adrian , Checa María Jose , Lario Francisco , Luquero Laura , Roldán Asunción , Estrada Jesús
Published: August 2021

In this chapter, we present the technological background needed for understanding the problem addressed by this DataBio pilot. Spain has to face plagues and diseases affecting forest species, like Quercus ilex, Quercus suber or Eucaliptus sp. Consequently, Spanish Public Administrations need updated information about the health status of forests. This chapter explains the methodology created based on remote sensing images (satellite + aerial + Remotely Piloted Aircraft Systems (RPAS)) and field data for monitoring the mentioned forest status. The work focused on acquiring data for establishing the relationships between RPAS generated data and field data, and on the creation of a correlation model to obtain a prospection and prediction algorithm based on spectral data for early detection and monitoring of decaying trees. Those data were used to establish the links between EO image-derived indexes and biophysical parameters from field data allowing a health status monitoring for big areas based on EO information. This solution is providing Public Administrations with valuable information to help decision making.
Tuna Fisheries Fuel Consumption Reduction and Safer Operations
Authors: Fernandes Jose A. , Uriondo Zigor , Granado Igor , Quincoces Iñaki
Published: August 2021

This chapter demonstrates the potential of tuna fishing fleets to reduce their fuel oil consumption. In the “Oceanic tuna fisheries, immediate operational choices” pilot, the data monitoring system on vessels  periodically upload data to the server for shore analysis. The data analytics employs fuel oil consumption equations and propulsion engine fault detection models. The fuel consumption equations are being used to develop immediate operational decision models. The fault detection models are used to plan  maintenance operations and to prevent unexpected engine malfunctions. The data-driven planning software allows probabilistic forecasting of tuna biomass distribution and analysing changes in fishing strategies leading to fuel consumption reduction. These changes in fishing strategies can be summarized as a transition from hunting to harvesting. Vessels do not  search for fish, but instread  take less risks and fish, where it is more likely that the fish can be found and is easier to capture. Buoy data are  increasingly used to improve stock assessments and have the potential to allow better monitoring and planning of fish quotas fulfilment.
Sustainable and Added Value Small Pelagics Fisheries Pilots
Authors: Reite Karl-Johan , Haugen J. , Michelsen F. A. , Aarsæther K. G.
Published: August 2021

This chapter describes four pilot cases covering the Norwegian pelagic fisheries for small fish species in the North Atlantic Ocean, such as mackerel, herring and blue whiting. The pilot cases aim to improve sustainability and value creation. Big data methods and tools have been used to demonstrate the potential impact on fuel consumption, fisheries planning and fish stock assessments. Specifically, the pilots have targeted immediate operational choices, short-term fisheries planning, fish stock assessments and longer-term market predictions.
Conclusions and Future Vision on Big Data in Pelagic Fisheries Sustainability
Authors: Fernandes Jose A. , Reite Karl-Johan
Published: August 2021

The digitalization of the fisheries sector has been limited. However, in this book, the potential for making the sector more competitive and resilient through higher digitalization has been demonstrated using pelagic fisheries as an example. COVID-19 has recently shown the resilience advantages of having a more digitalized industry that makes larger use of big data and artificial intelligence. Moreover, these technologies can help us to mitigate climate change due to lower emissions and to adapt to climate change-induced changes of species distribution. One of the challenges is the accessibilty of  enough cost-effective information. This can be achieved if fishing vessels becme also scientific data gathering platforms in a circular data economy.  Then the fishing vessels are both  users and providers of environmental data.
Summary of Potential and Exploitation of Big Data and AI in Bioeconomy
Authors: Södergård Caj
Published: August 2021

In this final chapter, we summarize the DataBio learnings about how to exploit big data and AI in bioeconomy. The development platform for the software used in the 27 pilots was a central tool. The Enterprise Architecture model Archimate laid a solid basis for the complex software in the pilots. Handling data from sensors and earth observation were shown in numerous pilots. Genomic data from crop species allows us to significantly speed up plant breeding by predicting plant properties in-silico. Data integration is crucial and we show how linked data enables searches over multiple datasets. Real-time processing of events provides insights for fast decision-making, for example about ship engine conditions. We show how sensitive bioeconomy data can be analysed in a privacy-preserving way. The agriculture pilots show with clear numbers the impact of big data and AI on precision agriculture, insurance and subsidies control. In forestry, DataBio developed several big data tools for forest monitoring. In fishery, we demonstrate how to reduce maintenance cost and time as well as fuel consumption in the operation of fishing vessels as well as how to accurately predict fish catches. The chapter ends with perspectives on earth observation, machine learning, data sharing and crowdsourcing.
Crowdsourced Data
Authors: Charvát Karel , Kepka Michal
Published: August 2021

Crowdsourcing together with Volunteered Geographic Information (VGI) are currently part of  a broader concept – Citizens Science. The methods provide information on existing geospatial data or is a part of data collection from geolocated devices. They enable opening parts of scientific work to the general public. DataBio Crowdsourcing Solution is a combination of the SensLog server platform and HSLayers web and mobile applications. SensLog is a server system for managing sensor data, volunteered geographic information and other geospatial data. Web and mobile applications are used to collect and visualize SensLog data. SensLog data model builds on the Observations & Measurements conceptual model from ISO 19156 and includes additional sections, e.g., for user authentication or volunteered geographic information (VGI) collection. It uses PostgreSQL database with PostGIS for data storage and several API endpoints.
Genomics Data
Authors: Habyarimana Ephrem , Michailidou Sofia
Published: August 2021

In silico prediction of plant performance is gaining increasing breeders’ attention. Several statistical, mathematical and machine learning methodologies for analysis of phenotypic, omics and environmental data typically use individual or a few data layers. Genomic selection is one of the applications, where heterogeneous data, such as those from omics technologies, are handled, accommodating several genetic models of inheritance. There are many new high throughput Next Generation  Sequencing (NGS) platforms on the market producing whole-genome data at a low cost. Hence, large-scale genomic data can be produced and analyzed enabling intercrosses and  fast-paced recurrent selection. The offspring properties can be predicted instead of manually evaluated in the field . Breeders have a short time window to make decisions by the time they receive data, which is one of the major challenges in commercial breeding. To implement genomic selection routinely as part of breeding programs, data management systems and analytics capacity have therefore to be in order. The traditional relational database management systems (RDBMS), which are designed to store, manage and analyze large-scale data, offer appealing characteristics, particularly when they are upgraded with capabilities for working with binary large objects. In addition, NoSQL systems were considered effective tools for managing high-dimensional genomic data. MongoDB system, a document-based NoSQL database, was effectively used to develop web-based tools for visualizing and exploring genotypic information. The Hierarchical Data Format (HDF5), a member of the high-performance distributed file systems family, demonstrated superior performance with high-dimensional and highly structured data such as genomic sequencing data.
Linked Data and Metadata
Authors: Zinke-Wehlmann Christian , Kirschenbaum Amit , Palma Raul , Brahma Soumya , Charvát Karel , Charvát Karel , Reznik Tomas
Published: August 2021

Data is the basis for creating information and knowledge. Having data in a structured and machine-readable format facilitates the processing and analysis of the data. Moreover, metadata—data about the data, can help discovering data based on features as, e.g., by whom they were created, when, or for which purpose. These associated features make the data more interpretable and assist in turning it into useful information. This chapter briefly introduces the concepts of metadata and Linked Data—highly structured and interlinked data, their standards and their usages, with some elaboration on the role of Linked Data in bioeconomy.
Data Pipelines: Modeling and Evaluation of Models
Authors: Chaabouni Kaïs , Bagnato Alessandra
Published: August 2021

This chapter outlines the utility of data pipelines modeling in the context of a data driven project and enumerates metrics for evaluating the quality of the data modeling regarding the readability and the comprehensibility of the models. We start with explaining the challenges surrounding the DataBio project that led to the adoption of data pipelines modeling using the Enterprise Architecture language ArchiMate. Then we present the data modeling process with examples from DataBio pilot studies starting with modeling software components provided by project stakeholders and ending up with integration of components into data pipelines that achieve the data analytics lifecycle intended by the pilot study. We end the chapter with the evaluation of the quality of DataBio data pipelines models with metrics collected by a monitoring tool for ArchiMate models.
Silhouetting the Cost-Time Front: Multi-objective Resource Optimization in Business Processes
Authors: López-Pintado Orlenys , Dumas Marlon , Yerokhin Maksym , Maggi Fabrizio Maria
Published: August 2021

The allocation of resources in a business process determines the trade-off between cycle time and resource cost. A higher resource utilization leads to lower cost and higher cycle time, while a lower resource utilization leads to higher cost and lower waiting time. In this setting, this paper presents a multi-objective optimization approach to compute a set of Pareto-optimal resource allocations for a given process concerning cost and cycle time. The approach heuristically searches through the space of possible resource allocations using a simulation model to evaluate each allocation. Given the high number of possible allocations, it is imperative to prune the search space. Accordingly, the approach incorporates a method that selectively perturbs a resource utilization to derive new candidates that are likely to Pareto-dominate the already explored ones. The perturbation method relies on two indicators: resource utilization and resource impact, the latter being the contribution of a resource to the cost or cycle time of the process. Additionally, the approach incorporates a ranking method to accelerate convergence by guiding the search towards the resource allocations closer to the current Pareto front. The perturbation and ranking methods are embedded into two search meta-heuristics, namely hill-climbing and tabu-search. Experiments show that the proposed approach explores fewer resource allocations to compute Pareto fronts comparable to those produced by a well-known genetic algorithm for multi-objective optimization, namely NSGA-II.
The timing and duration of depressive symptoms from adolescence to young adulthood and young adults’ NEET status: the role of educational attainment
Authors: Veldman Karin , Reijneveld Sijmen A. , Hviid Andersen Johan , Nøhr Winding Trine , Labriola Merete , Lund Thomas , Bültmann Ute
Published: August 2021

Purpose

Depressive symptoms are negatively associated with labour market outcomes but whether the timing and duration of depressive symptoms or educational attainment (EA) affect NEET (Neither in Employment, Education, nor Training) is unknown. Therefore, this study aims to examine the effects of timing and duration of depressive symptoms in adolescence and the moderating and mediating role of EA on NEET in young adulthood.

Methods

Data were used from 1512 participants in the Vestliv Study, a Danish prospective cohort study. Depressive symptoms were measured at age 14, 18 and 21. EA at age 21 and NEET at age 23 were derived from national registers. Logistic regression analyses and a 4-way decomposition approach were applied.

Results

Among boys, depressive symptoms at ages 14 and 21 increased the risk of NEET (OR 1.65, 95% CI 1.00–2.74 and OR 2.20, 95% CI 1.37–3.53). Among girls, this regarded depressive symptoms at ages 18 and 21 (OR 1.76, 95% CI 1.26–2.46 and OR 1.59, 95% CI 1.13–2.22). For the duration of depressive symptoms, among boys any depressive symptoms increased the risk of NEET. Among girls, only persistent depressive symptoms increased the risk of NEET. EA did not mediate or moderate the association between depressive symptoms and NEET.

Conclusion

The timing and duration of depressive symptoms in adolescence matter for the association with NEET in young adulthood, with a double burden for those with both depressive symptoms and low EA. The results emphasize the importance of support for those who experience depressive symptoms in the school-to-work transition.