Data assimilation (DA) holds considerable potential for improving hydrologic predictions as demonstrated in numerous research studies. However, advances in hydrologic DA research have not been adequately or timely implemented in operational forecast systems to improve the skill of forecasts for better informed real-world decision making. This is due in part to a lack of mechanisms to properly quantify the uncertainty in observations and forecast models in real-time forecasting situations and to conduct the merging of data and models in a way that is adequately efficient and transparent to operational forecasters. The need for effective DA of useful hydrologic data into the forecast process has become increasingly recognized in recent years. This motivated a hydrologic DA workshop in Delft, the Netherlands in November 2010, which focused on advancing DA in operational hydrologic forecasting and water resources management. As an outcome of the workshop, this paper reviews, in relevant detail, the current status of DA applications in both hydrologic research and operational practices, and discusses the existing or potential hurdles and challenges in transitioning hydrologic DA research into cost-effective operational forecasting tools, as well as the potential pathways and newly emerging opportunities for overcoming these challenges. Several related aspects are discussed, including (1) theoretical or mathematical aspects in DA algorithms, (2) the estimation of different types of uncertainty, (3) new observations and their objective use in hydrologic DA, (4) the use of DA for real-time control of water resources systems, and (5) the development of community-based, generic DA tools for hydrologic applications. It is recommended that cost-effective transition of hydrologic DA from research to operations should be helped by developing community-based, generic modeling and DA tools or frameworks, and through fostering collaborative efforts among hydrologic modellers, DA developers, and operational forecasters.

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Advancing data assimilation in operational hydrologic forecasting: progresses, challenges, and emerging opportunities

Aquaponics is rapidly developing as the need for sustainable food production increases and freshwater and phosphorous reserves shrink. Starting from small-scale operations, aquaponics is at the brink of commercialization, attracting investment. Arising from integrated freshwater aquaculture, a variety of methods and system designs has developed that focus either on fish or plant production. Public interest in aquaponics has increased dramatically in recent years, in line with the trend towards more integrated value chains, greater productivity and less harmful environmental impact compared to other production systems. New business models are opening up, with new customers and markets, and with this expansion comes the potential for confusion, misunderstanding and deception. New stakeholders require guidelines and detail concerning the different system designs and their potentials. We provide a definitive definition of aquaponics, where the majority (> 50%) of nutrients sustaining the optimal plant growth derives from waste originating from feeding aquatic organisms, classify the available integrated aquaculture and aquaponics (open, domestic, demonstration, commercial) systems and designs, distinguish four different scales of production (≤ 50, > 50–≤ 100 m2, > 100–≤ 500 m2, > 500 m2) and present a definite nomenclature for aquaponics and aquaponic farming allowing distinctions between the technologies that are in use. This enables authorities, customers, producers and all other stakeholders to distinguish between the various systems, to better understand their potentials and constraints and to set priorities for business and regulations in order to transition RAS or already integrated aquaculture into commercial aquaponic systems.

Towards commercial aquaponics: a review of systems, designs, scales and nomenclature

Biofloc technology as a promising tool to improve aquaculture production

The use of simulators in robotics research is widespread, underpinning the majority of recent advances in the field. There are now more options available to researchers than ever before, however navigating through the plethora of choices in search of the right simulator is often non-trivial. Depending on the field of research and the scenario to be simulated there will often be a range of suitable physics simulators from which it is difficult to ascertain the most relevant one. We have compiled a broad review of physics simulators for use within the major fields of robotics research. More specifically, we navigate through key sub-domains and discuss the features, benefits, applications and use-cases of the different simulators categorised by the respective research communities. Our review provides an extensive index of the leading physics simulators applicable to robotics researchers and aims to assist them in choosing the best simulator for their use case.

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A Review of Physics Simulators for Robotic Applications

With the arrival of new technologies in modern smart factories, automated predictive maintenance is also related to production robotisation. Intelligent sensors make it possible to obtain an ever-increasing amount of data, which must be analysed efficiently and effectively to support increasingly complex systems' decision-making and management. The paper aims to review the current literature concerning predictive maintenance and intelligent sensors in smart factories. We focused on contemporary trends to provide an overview of future research challenges and classification. The paper used burst analysis, systematic review methodology, co-occurrence analysis of keywords, and cluster analysis. The results show the increasing number of papers related to key researched concepts. The importance of predictive maintenance is growing over time in relation to Industry 4.0 technologies. We proposed Smart and Intelligent Predictive Maintenance (SIPM) based on the full-text analysis of relevant papers. The paper's main contribution is the summary and overview of current trends in intelligent sensors used for predictive maintenance in smart factories.

Predictive Maintenance and Intelligent Sensors in Smart Factory: Review.

This paper describes the Unmanned Underwater Vehicle (UUV) Simulator, an extension of the open-source robotics simulator Gazebo to underwater scenarios, that can simulate multiple underwater robots and intervention tasks using robotic manipulators. This is achieved mainly through a set of newly implemented plugins that model underwater hydrostatic and hydrodynamic effects, thrusters, sensors, and external disturbances. In contrast to existing solutions, it reuses and extends a general-purpose robotics simulation platform to underwater environments.

Thomas Rauschenbach

Papers

UUV Simulator: A Gazebo-based package for underwater intervention and multi-robot simulation

Dynamic modeling of the INAPRO aquaponic system

First testing of an AUV mission planning and guidance system for water quality monitoring and fish behavior observation in net cage fish farming

Enhancing aquaponics management with IoT-based Predictive Analytics for efficient information utilization

Integrated navigation for autonomous underwater vehicles in aquaculture: A review