Impact of enterprise digitalization on green innovation performance under the perspective of production and operation

Introduction: How enterprises should practice digitalization transformation to effectively improve green innovation performance is related to the sustainable development of enterprises and the economy, which is an important issue that needs to be clarified. Methods: This research uses the perspective of production and operation to deconstruct the digitalization of industrial listed enterprises from 2016 to 2020 into six features. A variety of machine learning methods are used, including DBSCAN, CART and other algorithms, to specifically explore the complex impact of enterprise digitalization feature configuration on green innovation performance. Conclusions: (1) The more advanced digitalization transformation the enterprises have, the more possibly the high green innovation performance can be achieved. (2) Digitalization innovation is the digitalization element with the strongest influence ability on green innovation performance. (3) As the advancement of digitalization transformation, enterprises should also focus on digitalization innovation input and digitalization operation output, otherwise they should pay attention to digitalization management and digitalization operation output. Discussion: The conclusions of this research will help enterprises understand their digitalization competitiveness and how to practice digitalization transformation to enhance green innovation performance, and also help the government to formulate policies to promote the development of green innovation in the digital economy era

Image encryption for Offshore wind power based on 2D-LCLM and Zhou Yi Eight Trigrams

Offshore wind power is an important part of the new power system, due to the complex and changing situation at ocean, its normal operation and maintenance cannot be done without information such as images, therefore, it is especially important to transmit the correct image in the process of information transmission. In this paper, we propose a new encryption algorithm for offshore wind power based on two-dimensional lagged complex logistic mapping (2D-LCLM) and Zhou Yi Eight Trigrams. Firstly, the initial value of the 2D-LCLM is constructed by the Sha-256 to associate the 2D-LCLM with the plaintext. Secondly, a new encryption rule is proposed from the Zhou Yi Eight Trigrams to obfuscate the pixel values and generate the round key. Then, 2D-LCLM is combined with the Zigzag to form an S-box. Finally, the simulation experiment of the algorithm is accomplished. The experimental results demonstrate that the algorithm can resistant common attacks and has prefect encryption performance.Comment: accepted by Int. J. of Bio-Inspired Computatio

Wall enhancement predictive of abnormal hemodynamics and ischemia in vertebrobasilar non-saccular aneurysms: a pilot study

ObjectiveTo analyze how wall enhancement affects hemodynamics and cerebral ischemic risk factors in vertebrobasilar non-saccular intracranial aneurysms (VBNIAs).Materials and methodsTen consecutive non-saccular aneurysms were collected, including three transitional vertebrobasilar dolichoectasia (TVBD). A wall enhancement model was quantitatively constructed to analyze how wall enhancement interacts with hemodynamics and cerebral ischemic factors.ResultsEnhanced area revealed low wall shear stress (WSS) and wall shear stress gradient (WSSG), with high oscillatory shear index (OSI), relative residence time (RRT), and gradient oscillatory number (GON) while the vortex and slow flow region in fusiform aneurysms are similar to TVBD fusiform aneurysms. With low OSI, high RRT and similar GON in the dilated segment, the enhanced area still manifests low WSS and WSSG in the slow flow area with no vortex. In fusiform aneurysms, wall enhancement was negatively correlated with WSS (except for case 71, all p values  < 0.05, r = −0.52 ~ −0.95), while wall enhancement was positively correlated with OSI (except for case 5, all p values < 0.05, r = 0.50 ~ 0.83). For the 10 fusiform aneurysms, wall enhancement is significantly positively correlated with OSI (p = 0.0002, r = 0.75) and slightly negatively correlated with WSS (p = 0.196, r = −0.30) throughout the dataset. Aneurysm length, width, low wall shear stress area (LSA), high OSI, low flow volume (LFV), RRT, and high aneurysm-to-pituitary stalk contrast ratio (CRstalk) area plus proportion may be predictive of cerebral ischemia.ConclusionA wall enhancement quantitative model was established for vertebrobasilar non-saccular aneurysms. Low WSS was negatively correlated with wall enhancement, while high OSI was positively correlated with wall enhancement. Fusiform aneurysm hemodynamics in TVBD are similar to simple fusiform aneurysms. Cerebral ischemia risk appears to be correlated with large size, high OSI, LSA, and RRT, LFV, and wall enhancement

Crowdsourced mapping of unexplored target space of kinase inhibitors

Despite decades of intensive search for compounds that modulate the activity of particular protein targets, a large proportion of the human kinome remains as yet undrugged. Effective approaches are therefore required to map the massive space of unexplored compound-kinase interactions for novel and potent activities. Here, we carry out a crowdsourced benchmarking of predictive algorithms for kinase inhibitor potencies across multiple kinase families tested on unpublished bioactivity data. We find the top-performing predictions are based on various models, including kernel learning, gradient boosting and deep learning, and their ensemble leads to a predictive accuracy exceeding that of single-dose kinase activity assays. We design experiments based on the model predictions and identify unexpected activities even for under-studied kinases, thereby accelerating experimental mapping efforts. The open-source prediction algorithms together with the bioactivities between 95 compounds and 295 kinases provide a resource for benchmarking prediction algorithms and for extending the druggable kinome. The IDG-DREAM Challenge carried out crowdsourced benchmarking of predictive algorithms for kinase inhibitor activities on unpublished data. This study provides a resource to compare emerging algorithms and prioritize new kinase activities to accelerate drug discovery and repurposing efforts

25th annual computational neuroscience meeting: CNS-2016

The same neuron may play different functional roles in the neural circuits to which it belongs. For example, neurons in the Tritonia pedal ganglia may participate in variable phases of the swim motor rhythms [1]. While such neuronal functional variability is likely to play a major role the delivery of the functionality of neural systems, it is difficult to study it in most nervous systems. We work on the pyloric rhythm network of the crustacean stomatogastric ganglion (STG) [2]. Typically network models of the STG treat neurons of the same functional type as a single model neuron (e.g. PD neurons), assuming the same conductance parameters for these neurons and implying their synchronous firing [3, 4]. However, simultaneous recording of PD neurons shows differences between the timings of spikes of these neurons. This may indicate functional variability of these neurons. Here we modelled separately the two PD neurons of the STG in a multi-neuron model of the pyloric network. Our neuron models comply with known correlations between conductance parameters of ionic currents. Our results reproduce the experimental finding of increasing spike time distance between spikes originating from the two model PD neurons during their synchronised burst phase. The PD neuron with the larger calcium conductance generates its spikes before the other PD neuron. Larger potassium conductance values in the follower neuron imply longer delays between spikes, see Fig. 17.Neuromodulators change the conductance parameters of neurons and maintain the ratios of these parameters [5]. Our results show that such changes may shift the individual contribution of two PD neurons to the PD-phase of the pyloric rhythm altering their functionality within this rhythm. Our work paves the way towards an accessible experimental and computational framework for the analysis of the mechanisms and impact of functional variability of neurons within the neural circuits to which they belong

An Improved Active Damping Method Based on Single-Loop Inverter Current Control for LCL Resonance in Grid-Connected Inverters

This paper investigates active damping of LCL filter resonance in grid-connected inverters with only inverter current feedback control, since it only needs to sample one current to realize both current control and inverter protection. The traditional single-loop inverter current control (SLICC) can damp the LCL filter resonance actively. However, if the control delay is considered in digital control, the system stability will depend on the ratio of the LCL resonance frequency fres to the sampling frequency fs, and the valid damping region is only up to fs/6. Considering that the design region of the LCL resonance frequency fres is up to fs/2 , the system can easily become unstable due to the LCL resonance frequency shifting. Thus, this paper proposes an improved active damping method based on SLICC, including the asymmetric regular sampling method and delay compensation method. The improved sampling method minimizes the control delay without introducing a switching ripple, and the delay compensation method further compensates for the delay effect. With a proper parameter design, the upper limit of the valid damping region is extended up to fs/2, which can cover all the possible resonance frequencies, and it has inherent robustness against grid-impedance variation. Finally, a few simulations in MATLAB/SIMULINK and experiments based on a 6 kW prototype are performed to verify the theoretical analysis

Potentially Related Commodity Discovery Based on Link Prediction

The traditional method of related commodity discovery mainly focuses on direct co-occurrence association of commodities and ignores their indirect connection. Link prediction can estimate the likelihood of links between nodes and predict the existent yet unknown future links. This paper proposes a potentially related commodities discovery method based on link prediction (PRCD) to predict the undiscovered association. The method first builds a network with the discovered binary association rules among items and uses link prediction approaches to assess possible future links in the network. The experimental results show that the accuracy of the proposed method is better than traditional methods. In addition, it outperforms the link prediction based on graph neural network in some datasets