• Rural Digital Europe
• 2018-2022close
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To explore the diurnal variations, radiometric and geometric accuracy of UAV-based data for precision agriculture, a comprehensive dataset was created in a one-day field campaign (21 June 2017). The multi-sensor data set covers wheat, barley & potato experimental fields, located in Wageningen University and Research (WUR) farm maintained by Unifarm. UAV-based images were collected with several sensors over the experimental area, starting from 7:25am and ending at 20:00pm local solar time. The dataset consists of images collected by 9 flights with senseFly MSP4C, 9 with Parrot Sequoia, 2 with Slant Range P3, 5 with DJI Zenmuse X3 NIR, 4 with the senseFly Thermo-map and 1 with the RGB Sony WX-220. Additionally, validation measurements at radiometric calibration plates and plant sample locations were taken with a Cropscan handheld spectrometer and a tec5 Handyspec spectrometer. The dataset consists of the validation measurements, the raw images and the processed orthomosaics (both with and without geometric correction).

Nematodes are abundant and diverse in nearly any soil, and directly and indirectly contribute to important soil functions such as nutrient cycling, decomposition, and pest and pathogen regulation. In addition, nematode communities have been shown to be sensitive to agricultural management such as tillage and organic matter additions. As such, soil nematode are promising indicators for soil quality. Morphological assessment of nematode communities and indices such as the maturity index (MI), enrichment index (EI), structure index (SI) and channel index (CI) have been used for soil quality evaluations. Molecular methods to study community composition and diversity offer advantages compared to traditional methods in terms of cost, time, resolution and throughput. Thus far, the use of molecular data to calculate indices has not received much attention. In the present study we used molecular methods to: i) assess the effects of soil management on nematode qPCR counts, alpha- and beta- diversity, and food web indices; ii) identify nematode taxa specific to certain soil management, and iii) investigate the relationship between nematode community parameters with soil chemical, physical and biological parameters. We assessed nematodes communities with metabarcoding in 10 European long-term field experiments to study the effect of tillage (conventional vs reduced) and organic matter addition (low vs high).

Smart mechatronic systems and applications with actively controlled moving elements face increasing demands on size, motion speed, precision, adaptability, self-diagnostic, connectivity, new cognitive features, etc. Fulfillment of these requirements is essential for building smart, safe and reliable production complexes. This, however, implies completely new demands on control curricula of master degree students. The aim of this paper is to identify main gaps in motion control education and industrial practice with specific focus on multi-disciplinarity, i.e., contribute to a STEM education ecosystem.

Baseline data and Impacts of Policy Cards for the for the System Dynamics Model of the European Case Study in Sim4Nexus.

Recent advances in the field of neural rehabilitation, facilitated through technological innovation and improved neurophysiological knowledge of impaired motor control, have opened up new research directions. Such advances increase the relevance of existing interventions, as well as allow novel methodologies and technological synergies. New approaches attempt to partially overcome long-term disability caused by spinal cord injury, using either invasive bridging technologies or noninvasive human–machine interfaces. Muscular dystrophies benefit from electromyography and novel sensors that shed light on underlying neuromotor mechanisms in people with Duchenne. Novel wearable robotics devices are being tailored to specific patient populations, such as traumatic brain injury, stroke, and amputated individuals. In addition, developments in robot-assisted rehabilitation may enhance motor learning and generate movement repetitions by decoding the brain activity of patients during therapy. This is further facilitated by artificial intelligence algorithms coupled with faster electronics. The practical impact of integrating such technologies with neural rehabilitation treatment can be substantial. They can potentially empower nontechnically trained individuals—namely, family members and professional carers—to alter the programming of neural rehabilitation robotic setups, to actively get involved and intervene promptly at the point of care. This narrative review considers existing and emerging neural rehabilitation technologies through the perspective of replacing or restoring functions, enhancing, or improving natural neural output, as well as promoting or recruiting dormant neuroplasticity. Upon conclusion, we discuss the future directions for neural rehabilitation research, diagnosis, and treatment based on the discussed technologies and their major roadblocks. This future may eventually become possible through technological evolution and convergence of mutually beneficial technologies to create hybrid solutions

High-precision motion industrial systems must satisfy tight performance requirements. Both positioning accuracy and throughput demands are typically achieved through improvements in hardware, thereby raising the bill of materials. A cost saving alternative could be to strive for a reduction in the hardware components needed, in combination with advanced motion control, to still meet the desired specifications. Particularly, in this paper, the possibility is analyzed to allow for resource sharing among several actuators. This results in a switched system, for which we develop a real-time MPC algorithm for optimization of both the input and the switching signals. This implementation applies to a fairly general class of nonlinear systems and uses a novel offset-free formulation in velocity form for LTV prediction models, to realize good tracking performance under the resource sharing constraints. We provide a proof of concept for this MPC solution on a high fidelity model of an industrial SCARA robot, where it is proposed to use a single amplifier to serve two actuators. The MPC solution is compared to heuristically switched LTI controllers, and the potential of the proposed approach is shown in simulations.

Industry 4.0 refers to the digital transformation in the manufacturing domain through new technology. Currently, it expands well beyond manufacturing, affecting many areas of life and posing implications for all types of business. This paper focuses on the relationships between Industry 4.0 and Healthcare which transitions to increased interconnectivity, automation and smart decision making. The integration context of Industry 4.0 into Healthcare is only partly understood. Little was done until now to consolidate what is known on the integration benefits and the challenges. This article reports results of a systematic mapping study that analysed 69 papers to extract knowledge about the concepts of Industry 4.0 and the emerging Healthcare 4.0., and the relationships between them. We found 10 different perspectives of Healthcare 4.0, ranging from strategic to tactical and operational levels. Next, our results show: (i) nine applications of Industry 4.0 in the Healthcare domain: Augmented Reality and Simulation, Autonomous Robotics, Cybersecurity, Big Data Analytics, Internet of Things, Cloud Computing, Additive Manufacturing and Systems Integration; and (ii) 10 benefits and nine challenges in Healthcare 4.0. The most frequently mentioned benefits are patients' diagnosis, monitoring, treatment, and financial benefits. The most researched challenges are data fragmentation, heterogeneity, complexity, and privacy.

Identify and understand the technological, organisational and behavioural drivers and barriers for the uptake of the novel microbial technologies by arable farmers

Agriculture is the economy’s backbone for most developing countries. Most of these countries suffer from insufficient agricultural production. The availability of real-time, reliable and farm-specific information may significantly contribute to more sufficient and sustained production. Typically, such information is usually fragmented and often does fit one-on-one with the farm or farm plot. Automated, precise and affordable data collection and dissemination tools are vital to bring such information to these levels. The tools must address details of spatial and temporal variability. The Internet of Things (IoT) and wireless sensor networks (WSNs) are useful technology in this respect. This paper investigates the usability of IoT and WSN for smallholder agriculture applications. An in-depth qualitative and quantitative analysis of relevant work over the past decade was conducted. We explore the type and purpose of agricultural parameters, study and describe available resources, needed skills and technological requirements that allow sustained deployment of IoT and WSN technology. Our findings reveal significant gaps in utilization of the technology in the context of smallholder farm practices caused by social, economic, infrastructural and technological barriers. We also identify a significant future opportunity to design and implement affordable and reliable data acquisition tools and frameworks, with a possible integration of citizen science.