Anicut systems in Sri Lanka: The case of the Upper Walawe River Basin

Water resources / Tanks / Water use / River basins / Hydrology / Irrigation systems / Water shortage / Crops / Doemstic water / Fuelwood / Cultivation / Deforestation / Water supply / Economic development / Water management / Institutions / Land reform

Assessment of clogging effects on lateral hydraulics: proposing a monitoring and detection protocol

International audienceAgeing of drip irrigation systems due to clogging of emitters is considered the largest maintenance problem in microirrigation and this problem is enhanced in subsurface irrigation systems. Whatever the source of the clogging problem, a methodology for early detection of clogging in the field can be useful in decision-making about deploying cleaning processes (flushing or injection of chemicals) and avoiding replacement of laterals. This work presents a methodology for simulating clogging conditions able to reproduce the effects of clogging on pressure profiles, head loss, and emitters flow rate distribution along a single levelled lateral with constant inlet pressure. This methodology was validated by several experiments conducted under controlled conditions of clogging induced by changes in the flow rate of emitters. The effects of clogging intensity and position on hydraulic parameters of a single lateral were analysed in detail and aspects relating to pressure, head loss, and flow rate measurements were discussed. For a given lateral set-up, it is possible to draw a chart relating flow rate and head loss for various levels and positions of clogging. Assuming that measurements of head loss and flow rate are available, this diagram enables immediate estimation of the level and location of clogging

Selective engagement of FcγRIV by a M2e-specific single domain antibody construct protects against influenza A virus infection

Lower respiratory tract infections, such as infections caused by influenza A viruses, are a constant threat for public health. Antivirals are indispensable to control disease caused by epidemic as well as pandemic influenza A. We developed a novel anti-influenza A virus approach based on an engineered single-domain antibody (VHH) construct that can selectively recruit innate immune cells to the sites of virus replication. This protective construct comprises two VHHs. One VHH binds with nanomolar affinity to the conserved influenza A matrix protein 2 (M2) ectodomain (M2e). Co-crystal structure analysis revealed that the complementarity determining regions 2 and 3 of this VHH embrace M2e. The second selected VHH specifically binds to the mouse Fc gamma Receptor IV (Fc gamma RIV) and was genetically fused to the M2e-specific VHH, which resulted in a bi-specific VHH-based construct that could be efficiently expressed in Pichia pastoris. In the presence of M2 expressing or influenza A virus-infected target cells, this single domain antibody construct selectively activated the mouse Fc gamma RIV. Moreover, intranasal delivery of this bispecific Fc gamma RIV-engaging VHH construct protected wild type but not Fc gamma RIV-/- mice against challenge with an H3N2 influenza virus. These results provide proof of concept that VHHs directed against a surface exposed viral antigen can be readily armed with effector functions that trigger protective antiviral activity beyond direct virus neutralization

Learning to Grasp from a single demonstration

Learning-based approaches for robotic grasping using visual sensors typically require collecting a large size dataset, either manually labeled or by many trial and errors of a robotic manipulator in the real or simulated world. We propose a simpler learning-from-demonstration approach that is able to detect the object to grasp from merely a single demonstration using a convolutional neural network we call GraspNet. In order to increase robustness and decrease the training time even further, we leverage data from previous demonstrations to quickly fine-tune a GrapNet for each new demonstration. We present some preliminary results on a grasping experiment with the Franka Panda cobot for which we can train a GraspNet with only hundreds of train iterations.Comment: 10 pages, 5 figures, IAS-15 2018 workshop on Learning Applications for Intelligent Autonomous Robot

Cortico-muscular coupling to control a hybrid brain-computer interface for upper limb motor rehabilitation: A pseudo-online study on stroke patients

Brain-Computer Interface (BCI) systems for motor rehabilitation after stroke have proven their efficacy to enhance upper limb motor recovery by reinforcing motor related brain activity. Hybrid BCIs (h-BCIs) exploit both central and peripheral activation and are frequently used in assistive BCIs to improve classification performances. However, in a rehabilitative context, brain and muscular features should be extracted to promote a favorable motor outcome, reinforcing not only the volitional control in the central motor system, but also the effective projection of motor commands to target muscles, i.e., central-to-peripheral communication. For this reason, we considered cortico-muscular coupling (CMC) as a feature for a h-BCI devoted to post-stroke upper limb motor rehabilitation. In this study, we performed a pseudo-online analysis on 13 healthy participants (CTRL) and 12 stroke patients (EXP) during executed (CTRL, EXP unaffected arm) and attempted (EXP affected arm) hand grasping and extension to optimize the translation of CMC computation and CMC-based movement detection from offline to online. Results showed that updating the CMC computation every 125 ms (shift of the sliding window) and accumulating two predictions before a final classification decision were the best trade-off between accuracy and speed in movement classification, independently from the movement type. The pseudo-online analysis on stroke participants revealed that both attempted and executed grasping/extension can be classified through a CMC-based movement detection with high performances in terms of classification speed (mean delay between movement detection and EMG onset around 580 ms) and accuracy (hit rate around 85%). The results obtained by means of this analysis will ground the design of a novel non-invasive h-BCI in which the control feature is derived from a combined EEG and EMG connectivity pattern estimated during upper limb movement attempts

Multi-fidelity deep neural networks for adaptive inference in the internet of multimedia things

Internet of Things (IoT) infrastructures are more and more relying on multimedia sensors to provide information about the environment. Deep neural networks (DNNs) could extract knowledge from this audiovisual data but they typically require large amounts of resources (processing power, memory and energy). If all limitations of the execution environment are known beforehand, we can design neural networks under these constraints. An IoT setting however is a very heterogeneous environment where the constraints can change rapidly. We propose a technique allowing us to deploy a variety of different networks at runtime, each with a specific complexity-accuracy trade-off but without having to store each network independently. We train a sequence of networks of increasing size and constrain each network to contain the parameters of all smaller networks in the sequence. We only need to store the largest network to be able to deploy each of the smaller networks. We experimentally validate our approach on different benchmark datasets for image recognition and conclude that we can build networks that support multiple trade-offs between accuracy and computational cost. (C) 2019 Elsevier B.V. All rights reserved