Residues and dissipation kinetics of two imidacloprid nanoformulations on bean (Phaseolus vulgaris L.) under field conditions

The current study investigates the dissipation kinetics of two imidacloprid (IMI) nanoformulations (entitled: Nano-IMI and Nano-IMI/TiO2) on common bean (Phaseolus vulgaris) seeds under field conditions and compares them with 35% Suspension Concentrate (SC) commercial formulation. To do so, it sprays P. vulgaris plants at 30 and 60 g/ha within green bean stage, sampling them during the 14-day period after the treatment. Following extraction and quantification of IMI residues, dissipation data have been fitted to simple-first order kinetic model (SFOK) and to first-order double-exponential decay (FODED) models, with 50% and 90% dissipation times (DT50 and DT90, respectively) assessed along the pre-harvest interval (PHI). With the exception of Nano-IMI at 60 g/ha, other decline curves are best fitted to the FODED model. In general, dissipation is faster for Nano-IMI (at 30 g/ha: DT50 = 1.09 days, DT90 = 4.30 days, PHI = 1.23 days; at 60 g/ha: DT50 = 1.29 days, DT90 = 4.29 days, PHI = 2.95 days) and Nano-IMI/TiO2 (at 30 g/ha: DT50 = 1.15 days, DT90 = 4.40 days, PHI = 1.08 days; at 60 g/ha: DT50 = 0.86 days, DT90 = 4.92 days, PHI = 3.02 days), compared to 35% SC (at 30 g/ha: DT50 = 1.58, DT90 = 6.45, PHI = 1.93; at 60 g/ha: DT50 = 1.58 days, DT90 = 14.50 days, PHI = 5.37 days). These results suggest the suitability of Nano-IMI and Nano-IMI/TiO2 application at both rates in terms of their residues on P. vulgaris seeds

Online Optimal Neuro-Fuzzy Flux Controller for DTC Based Induction Motor Drives

In this paper a fast flux search controller based on the Neuro-fuzzy systems is proposed to achieve the best efficiency of a direct torque controlled induction motor at light load. In this method the reference flux value is determined through a minimization algorithm with stator current as objective function. This paper discusses and demonstrates the application of Neurofuzzy filtering to stator current estimation. Simulation and experimental results are presented to show the fast response of proposed controller

Prediction of copper recovery from geometallurgical data using D-vine copulas

Published: April 2019The accurate modelling of geometalhirgical data can significantly improve decision-making and help optimize mining operations. This case study compares models for predicting copper recovery from three indirect test measurements that are typically available, to avoid the cost of direct measurement of recovery. Geometallurgical data from 930 drill core samples, with an average length of 19 m, from an orebody in South America have been analysed. The data includes copper recovery and the results of three other tests: Bond mill index test; resistance to abrasion and breakage index; and semi-autogenous grinding power index test. A genetic algorithm is used to impute missing data at some locations so as to make use of all 930 samples. The distribution of the variables is modelled with D-vine copula and predictions of copper recovery are compared with those from regressions fitted by ordinary least squares and generalized least squares. The D-vine copula model had the least mean absolute error.E. Addo Jr, A.V. Metcalfe, E.K. Chanda, E. Sepulveda, W. Assibey-Bonsu, and A. Adel

Graphene Oxide‐Cyclic R10 Peptide Nuclear Translocation Nanoplatforms for the Surmounting of Multiple‐Drug Resistance

Multidrug resistance resulting from a variety of defensive pathways in cancer has become a global concern with a considerable impact on the mortality associated with the failure of traditional chemotherapy. Therefore, further research and new therapies are required to overcome this challenge. In this work, a cyclic R10 peptide (cR10) is conjugated to polyglycerol-covered nanographene oxide to engineer a nanoplatform for the surmounting of multidrug resistance. The nuclear translocation of the nanoplatform, facilitated by cR10 peptide, and subsequently, a laser-triggered release of the loaded doxorubicin result in efficient anticancer activity confirmed by both in vitro and in vivo experiments. The synthesized nanoplatform with a combination of different features, including active nucleus-targeting, high-loading capacity, controlled release of cargo, and photothermal property, provides a new strategy for circumventing multidrug resistant cancers.National Natural Science Foundation of China http://dx.doi.org/10.13039/501100001809Natural Science Foundation of Jiangsu Province http://dx.doi.org/10.13039/501100004608Fundamental Research Funds for the Central Universities http://dx.doi.org/10.13039/501100012226Iran Science Elites Federation and China Scholarship CouncilPeer Reviewe

Graphene Oxide‐Cyclic R10 Peptide Nuclear Translocation Nanoplatforms for the Surmounting of Multiple‐Drug Resistance

Multidrug resistance resulting from a variety of defensive pathways in cancer has become a global concern with a considerable impact on the mortality associated with the failure of traditional chemotherapy. Therefore, further research and new therapies are required to overcome this challenge. In this work, a cyclic R10 peptide (cR10) is conjugated to polyglycerol‐covered nanographene oxide to engineer a nanoplatform for the surmounting of multidrug resistance. The nuclear translocation of the nanoplatform, facilitated by cR10 peptide, and subsequently, a laser‐triggered release of the loaded doxorubicin result in efficient anticancer activity confirmed by both in vitro and in vivo experiments. The synthesized nanoplatform with a combination of different features, including active nucleus‐targeting, high‐loading capacity, controlled release of cargo, and photothermal property, provides a new strategy for circumventing multidrug resistant cancers

Plausible Petri nets as self-adaptive expert systems: A tool for infrastructure asset monitoring

This article provides a computational framework to model self-adaptive expert systems using the Petri net (PN) formalism. Self-adaptive expert systems are understood here as expert systems with the ability to autonomously learn from external inputs, like monitoring data. To this end, the Bayesian learning principles are investigated and also combined with the Plausible PNs (PPNs) methodology. PPNs are a variant within the PN paradigm, which are efficient to jointly consider the dynamics of discrete events, like maintenance actions, together with multiple sources of uncertain information about a state variable. The manuscript shows the mathematical conditions and computational procedure where the Bayesian updating becomes a particular case of a more general basic operation within the PPN execution semantics, which enables the uncertain knowledge being updated from monitoring data. The approach is general, but here it is demonstrated in a novel computational model acting as expert system for railway track inspection management taken as a case study using published data from a laboratory simulation of train loading on ballast. The results reveal selfadaptability and uncertainty management as key enabling aspects to optimize inspection actions in railway track, only being adaptively and autonomously triggered based on the actual learnt state of track and other contextual issues, like resource availability, as opposed to scheduled periodic maintenance activities.Lloyd'sRegister Foundation, Grant/Award Number: RB4539; Engineering and Physical SciencesResearch Council, Grant/Award Number:EP/M023028/

Infant Cognitive Scores Prediction With Multi-stream Attention-based Temporal Path Signature Features

There is stunning rapid development of human brains in the first year of life. Some studies have revealed the tight connection between cognition skills and cortical morphology in this period. Nonetheless, it is still a great challenge to predict cognitive scores using brain morphological features, given issues like small sample size and missing data in longitudinal studies. In this work, for the first time, we introduce the path signature method to explore hidden analytical and geometric properties of longitudinal cortical morphology features. A novel BrainPSNet is proposed with a differentiable temporal path signature layer to produce informative representations of different time points and various temporal granules. Further, a two-stream neural network is included to combine groups of raw features and path signature features for predicting the cognitive score. More importantly, considering different influences of each brain region on the cognitive function, we design a learning-based attention mask generator to automatically weight regions correspondingly. Experiments are conducted on an in-house longitudinal dataset. By comparing with several recent algorithms, the proposed method achieves the state-of-the-art performance. The relationship between morphological features and cognitive abilities is also analyzed

Multiview classification and dimensionality reduction of scalp and intracranial EEG data through tensor factorisation

Electroencephalography (EEG) signals arise as a mixture of various neural processes that occur in different spatial, frequency and temporal locations. In classification paradigms, algorithms are developed that can distinguish between these processes. In this work, we apply tensor factorisation to a set of EEG data from a group of epileptic patients and factorise the data into three modes; space, time and frequency with each mode containing a number of components or signatures. We train separate classifiers on various feature sets corresponding to complementary combinations of those modes and components and test the classification accuracy of each set. The relative influence on the classification accuracy of the respective spatial, temporal or frequency signatures can then be analysed and useful interpretations can be made. Additionaly, we show that through tensor factorisation we can perform dimensionality reduction by evaluating the classification performance with regards to the number mode components and by rejecting components with insignificant contribution to the classification accuracy