Novel Mode of Trisiloxane Application Reduces Spider Mite and Aphid Infestation of Fruiting Shrub and Tree Crops

Application of pesticides leads to contamination of the natural environment, which entails the necessity to seek solutions that use substances which do not pose ecological hazards. The presented investigations tested the efficacy of a preparation containing organomodified trisiloxane and a cross-linking agent (Siltac EC) to limit the number of two-spotted spider mite (Tetranychus urticae) on the leaves of raspberry (Rubus idaeus) and blackcurrant (Ribes nigrum), as well as the numbers of green apple aphid (Aphis pomi) on apple trees (Malus domestica). The high effectiveness (more than 90%) of Siltac against spider mite on raspberry and blackcurrant leaves was rapid and persisted at least by two- three weeks after spraying. There was observed an inhibition of pest developing (i.e. significant decrease of eggs and larvae). Similar effect occurred per an apple tree shoot and the number of living apple aphids was reduced by more than 93% in comparison to untreated trees. In all experiments, the effectiveness of Siltac was similar and usually longer lasting than control pesticides. Moreover, no phytotoxicity of the tested preparation was observed during the investigations. In conclusion, on the basis of the presented results it was found that Siltac EC could be a good alternative to the currently used plant protection chemicals

Learning Behavior of Memristor-Based Neuromorphic Circuits in the Presence of Radiation

In this paper, a feed-forward spiking neural network with memristive synapses is designed to learn a spatio-temporal pattern representing the 25-pixel character ‘B’ by separating correlated and uncorrelated afferents. The network uses spike-timing-dependent plasticity (STDP) learning behavior, which is implemented using biphasic neuron spikes. A TiO2 memristor non-linear drift model is used to simulate synaptic behavior in the neuromorphic circuit. The network uses a many-to-one topology with 25 pre-synaptic neurons (afferent) each connected to a memristive synapse and one post-synaptic neuron. The memristor model is modified to include the experimentally observed effect of state-altering radiation. During the learning process, irradiation of the memristors alters their conductance state, and the effect on circuit learning behavior is determined. Radiation is observed to generally increase the synaptic weight of the memristive devices, making the network connections more conductive and less stable. However, the network appears to relearn the pattern when radiation ceases but does take longer to resolve the correlation and pattern. Network recovery time is proportional to flux, intensity, and duration of the radiation. Further, at lower but continuous radiation exposure, (flux 1x1010 cm−2 s−1 and below), the circuit resolves the pattern successfully for up to 100 s

Magnetic field distribution in a WPT system for electric vehicle charging

The objective of this paper is to discuss major factors that affect the magnetic field distribution of a wireless power transfer (WPT) system for electric vehicle (EV) charging. Both analytical and simulation approaches with a 3D finite element method (FEM) are employed to analyze the flux distribution in the system and its surroundings. The purpose of the work is to provide design guidelines for an efficient WPT system that conforms to international standards on safety and radiated EMI. To verify the obtained results, a full-scale prototype is built and tested up to 20kW power level. On the sending side, the system contains a PFC rectifier followed by a multiphase series resonant inverter connected to a transmitting coil. The receiving side is comprised of a magnetically coupled receiving coil tuned with a series-connected capacitor and a rectifier with a resistive load to emulate a battery. The coils are of a rectangular shape with 70 cm outer dimension, wound with 7 turns of litz wire, shielded with a layer of ferrite, and supported with aluminium plates. The receiving coil is attached to a steel plate that emulates a car chassis. The operating frequency of the system is 85 kHz. The calculations, simulations, and measurements are performed at various power levels and variable gap between coils (from 100mm to 300 mm). Furthermore, the effect of the coil misalignment on the magnetic field is analyzed and discussed based on two different misalignment situations. The theoretical and simulation results of this paper are in a good agreement with experimental measurements which validates the presented methodology

Design and synthesis of new quinazolin-4-one derivatives with negative mGlu7mGlu_7 receptor modulation activity and antipsychotic-like properties

Following the glutamatergic theory of schizophrenia and based on our previous study regarding the antipsychotic-like activity of mGlu7 NAMs, we synthesized a new compound library containing 103 members, which were examined for NAM mGlu7 activity in the T-REx 293 cell line expressing a recombinant human mGlu7 receptor. Out of the twenty-two scaffolds examined, active compounds were found only within the quinazolinone chemotype. 2-(2-Chlorophenyl)-6-(2,3-dimethoxyphenyl)-3-methylquinazolin-4(3H)-one (A9-7, ALX-171, mGlu7 IC50 = 6.14 µM) was selective over other group III mGlu receptors (mGlu4 and mGlu8), exhibited satisfactory drug-like properties in preliminary DMPK profiling, and was further tested in animal models of antipsychotic-like activity, assessing the positive, negative, and cognitive symptoms. ALX-171 reversed DOI-induced head twitches and MK-801-induced disruptions of social interactions or cognition in the novel object recognition test and spatial delayed alternation test. On the other hand, the efficacy of the compound was not observed in the MK-801-induced hyperactivity test or prepulse inhibition. In summary, the observed antipsychotic activity profile of ALX-171 justifies the further development of the group of quinazolin-4-one derivatives in the search for a new drug candidate for schizophrenia treatment

Towards interpreting recurrent neural networks through probabilistic abstraction

National Research Foundation (NRF) Singapore under its AI Singapore Programm

Structured adaptive and random spinners for fast machine learning computations

arXiv admin note: substantial text overlap with arXiv:1605.09046International audienceWe consider an efficient computational framework for speeding up several machine learning algorithms with almost no loss of accuracy. The proposed framework relies on projections via structured matrices that we call Structured Spinners, which are formed as products of three structured matrix-blocks that incorporate rotations. The approach is highly generic, i.e. i) structured matrices under consideration can either be fully-randomized or learned, ii) our structured family contains as special cases all previously considered structured schemes, iii) the setting extends to the non-linear case where the projections are followed by non-linear functions, and iv) the method finds numerous applications including kernel approximations via random feature maps, dimensionality reduction algorithms, new fast cross-polytope LSH techniques, deep learning, convex optimization algorithms via Newton sketches, quantization with random projection trees, and more. The proposed framework comes with theoretical guarantees characterizing the capacity of the structured model in reference to its unstructured counterpart and is based on a general theoretical principle that we describe in the paper. As a consequence of our theoretical analysis, we provide the first theoretical guarantees for one of the most efficient existing LSH algorithms based on the HD3HD2HD1 structured matrix [Andoni et al., 2015]. The exhaustive experimental evaluation confirms the accuracy and efficiency of structured spinners for a variety of different applications