Tailoring Classical Conditioning Behavior in TiO_(2) Nanowires:ZnO QDs-Based Optoelectronic Memristors for Neuromorphic Hardware

Neuromorphic hardware equipped with associative learn-ing capabilities presents fascinating applications in the next generation of artificial intelligence.However,research into synaptic devices exhibiting complex associative learning behaviors is still nascent.Here,an optoelec-tronic memristor based on Ag/TiO_(2) Nanowires:ZnO Quantum dots/FTO was proposed and constructed to emulate the biological associative learning behaviors.Effective implementation of synaptic behaviors,including long and short-term plasticity,and learning-forgetting-relearning behaviors,were achieved in the device through the application of light and electrical stimuli.Leveraging the optoelectronic co-modulated characteristics,a simulation of neuromorphic computing was conducted,resulting in a handwriting digit recognition accuracy of 88.9%.Furthermore,a 3×7 memristor array was constructed,confirming its application in artificial visual memory.Most importantly,complex biological associative learning behaviors were emulated by mapping the light and electrical stimuli into conditioned and unconditioned stimuli,respectively.After training through associative pairs,reflexes could be triggered solely using light stimuli.Comprehen-sively,under specific optoelectronic signal applications,the four features of classical conditioning,namely acquisition,extinction,recovery,and generalization,were elegantly emulated.This work provides an optoelectronic memristor with associative behavior capabilities,offering a pathway for advancing brain-machine interfaces,autonomous robots,and machine self-learning in the future.

Advances in Research of Mechanism of Herb-Drug Interactions

With the concurrent consumption of herbal medicines and conventional drugs,herb-drug interactions(HDIs)have become the most important clinical consequence of this practice.A general overview and the significance of pharmacokinetic and pharmacodynamic HDIs are provided,detailing basic mechanism,especially the metabolic enzymes and drug transporters,such as CYP450 and P-gp.

A Pharmacological Study on Action Mechanism of Zhizi Ganjiang Decoction from Treatise on Cold Damage Diseases in Treating Sleep Disorders

[Objectives]To explore the molecular mechanism of Zhizi Ganjiang Decoction(ZZGJD)regulating sleep disorders based on the network pharmacology.[Methods]The BATMAN-TCM server was used to predict the potential targets of ZZGJD and constructed a compound-disease-target network map,and the GeneCards database was used to search for insomnia-related targets;with the aid of Cytoscape 3.5.1 software,the compound-insomnia target interaction network and protein-protein interaction(PPI)network were constructed,and gene ontology(GO)enrichment,Reactome pathway enrichment,and biological pathway enrichment analysis based on KEGG(Kyoto Encyclopedia of Genes and Enomes)was performed.[Results]The constructed PPI network of ZZGJD involves 204 nodes and 645 interaction relationships.Key nodes involve G protein-coupled receptors,rhodopsin-like adrenaline receptor families,zinc finger proteins,nuclear hormone receptor superfamilies,ligand-binding domains of hormone receptors,voltage-gated calcium(Ca^(2+))channel IQ domains,and neuropituitary hormones.The related entries of GO enrichment analysis pathway mainly involve G protein-coupled receptor activity,neurotransmitter receptor activity,adrenergic receptor activity,ammonium ion binding,catecholamine binding,G protein-coupled serotonin receptor activity,serotonin receptor activity,and steroid hormone receptors(SHRs)activity.Reactome pathway mainly involves amine ligand binding receptors,rhodopsin-like receptors,G protein-coupled receptor ligand binding,adrenergic receptors,neuronal systems and signal transduction,etc.KEGG channel analysis mainly involves neural activity ligand-receptor interaction,calcium ion messenger pathway,cAMP signaling pathway,serotonergic synapse,dopaminergic synapse,cGMP-PKG signaling pathway,and cholinergic synapse pathway,etc.[Conclusions]The potential targets of ZZGJD in the treatment of insomnia mainly involve G protein-coupled receptors,and regulate various neural receptor pathways such as calcium ion channels,serotonin,dopamine,and adrenergic receptors.INS,IGF-1,CTNNB1,ESR1,HIF-1A,etc.may be the key targets of ZZGJD in regulating sleep disorders,reflecting the multi-target and overall function characteristics of Chinese herbal compounds.ZZGJD is of great significance in the treatment of sleep disorders caused by blood sugar abnormality in patients with diabetes and perimenopausal hormones in women.This article is expected to It provide new ideas for in-depth study of the molecular mechanism of ZZGJD.

Simulation and Experimental Study on Spray Characteristics of Gas-Assisted Urea Spray Gun

In this paper, the internal flow field and external spray characteristics of the spray gun were simulated and analyzed by establishing a coupling model of the gas-liquid two-phase flow of the spray gun. The spray particle size and cone angle under different gas path pressures were mainly studied. The calculation results showed that the spray particle size distribution had a large span, but the overall spray particle size was small. The liquid flow and the air pressure had a little influence on the spray cone angle. The spray SMD was tested by a three-dimensional particle dynamic analyzer (PDA), and the spray cone angle was photographed with a high-speed camera. The test data was basically consistent with the simulation results. The experimental results showed that the model can accurately simulate the internal flow field of the spray gun and the atomization process of urea. It can be used to analyze the characteristics of urea spray and provide a theoretical basis for the optimal design of urea spray gun.

A Prediction Method of Trend-Type Capacity Index Based on Recurrent Neural Network

Due to the increase in the types of business and equipment in telecommunications companies,the performance index data collected in the operation and maintenance process varies greatly.The diversity of index data makes it very difficult to perform high-precision capacity prediction.In order to improve the forecasting efficiency of related indexes,this paper designs a classification method of capacity index data,which divides the capacity index data into trend type,periodic type and irregular type.Then for the prediction of trend data,it proposes a capacity index prediction model based on Recurrent Neural Network(RNN),denoted as RNN-LSTM-LSTM.This model includes a basic RNN,two Long Short-Term Memory(LSTM)networks and two Fully Connected layers.The experimental results show that,compared with the traditional Holt-Winters,Autoregressive Integrated Moving Average(ARIMA)and Back Propagation(BP)neural network prediction model,the mean square error(MSE)of the proposed RNN-LSTM-LSTM model are reduced by 11.82%and 20.34%on the order storage and data migration,which has greatly improved the efficiency of trend-type capacity index prediction.

N:ZnO/MoS_(2)-heterostructured flexible synaptic devices enabling optoelectronic co-modulation for robust artificial visual systems

With the merits of non-contact,highly efficient,and parallel computing,optoelectronic synaptic devices combining sensing and memory in a single unit are promising for constructing neuromorphic computing and artificial visual chip.Based on this,a N:ZnO/MoS_(2)-heterostructured flexible optoelectronic synaptic device is developed in this work,and its capability in mimicking the synaptic behaviors is systemically investigated under the electrical and light signals.Versatile synaptic functions,including synaptic plasticity,long-term/short-term memory,and learning-forgetting-relearning property,have been achieved in this synaptic device.Further,an artificial visual memory system integrating sense and memory is emulated with the device array,and the visual memory behavior can be regulated by varying the light parameters.Moreover,the optoelectronic co-modulation behavior is verified by applying mixed electric and light signals to the array.In detail,a transient recovery property is discovered when the electric signals are applied in synergy during the decay of the light response,of which property facilitates the development of robust artificial visual systems.Furthermore,by superimposing electrical signals during the light response process,a differentiated response of the array is achieved,which can be used as a proof of concept for the color perception of the artificial visual system.

Advances in flexible sensors for intelligent perception system enhanced by artificial intelligence

Intelligent perception means that with the assistance of artificial intelligence(AI)-motivated brain,flexible sensors achieve the ability of memory,learning,judgment,and reasoning about external information like the human brain.Due to the superiority of machine learning(ML)algorithms in data processing and intelligent recognition,intelligent perception systems possess the ability to match or even surpass human perception systems.However,the built-in flexible sensors in these systems need to work on dynamic and irregular surfaces,inevitably affecting the precision and fidelity of the acquired data.In recent years,the strategy of introducing the developed functional materials and innovative structures into flexible sensors has made some progress toward the above challenges,and with the blessing of ML algorithms,accurate perception and reasoning in various scenarios have been achieved.Here,the most representative functional materials and innovative structures for constructing flexible sensors are comprehensively reviewed,the research progress of intelligent perception systems based on flexible sensors and ML algorithms is further summarized,and the intersection of the two is expected to unlock new opportunities for next-stage AI development.