Discrimination of neutrons and gamma rays in plastic scintillator based on pulse-coupled neural network

Neutron and gamma ray pulse signal discrimination technology is an essential part of many modern scientific fields,such as biology,geology,radiation imaging,and nuclear medicine.Neutrons are always accompanied by gamma rays due to their unique penetration characteristic;thus,the development of n-γdiscrimination methods is especially crucial.In the present study,a novel n-γdiscrimination method is proposed that implements a pulse-coupled neural network for n-γdiscrimination.In addition,experiments were conducted on the pulse signals detected by an EJ299-33 plastic scintillator,which is especially suitable for n-γdiscrimination.The proposed method was compared to three other discrimination methods,including the back-propagation neural network(BPNN),the fractal spectrum method,and the charge comparison method,with respect to two aspects:(i)the figure of merit(FoM)and(ii)discrimination time.The experimental results showed that the pulse-coupled neural network(PCNN)has a 26.49%improvement in FoM-value compared to the charge comparison method,a72.80%improvement compared to the BPNN,a 66.24%improvement compared to the fractal spectrum method,and the second-fastest discrimination time of 2.22 s.In conclusion,the PCNN treats the input signal as a whole for analysis and processing,imparting it with an excellent antinoise effect and the ability to process the dynamic information contained in a pulse signal.

Linear Pulse-Coupled Oscillators Model¬—A New Approach for Time Synchronization in Wireless Sensor Networks

Mutual synchronization is a ubiquitous phenomenon that exists in various natural systems. The individual participants in this process can be modeled as oscillators, which interact by discrete pulses. In this paper, we analyze the synchronization condition of two- and multi-oscillators system, and propose a linear pulse-coupled oscillators model. We prove that the proposed model can achieve synchronization for almost all conditions. Numerical simulations are also included to investigate how different model parameters affect the synchronization. We also discuss the implementation of the model as a new approach for time synchronization in wireless sensor networks.

Multi-Stage-Based Siamese Neural Network for Seal Image Recognition

Seal authentication is an important task for verifying the authenticity of stamped seals used in various domains to protect legal documents from tampering and counterfeiting.Stamped seal inspection is commonly audited manually to ensure document authenticity.However,manual assessment of seal images is tedious and laborintensive due to human errors,inconsistent placement,and completeness of the seal.Traditional image recognition systems are inadequate enough to identify seal types accurately,necessitating a neural network-based method for seal image recognition.However,neural network-based classification algorithms,such as Residual Networks(ResNet)andVisualGeometryGroup with 16 layers(VGG16)yield suboptimal recognition rates on stamp datasets.Additionally,the fixed training data categories make handling new categories to be a challenging task.This paper proposes amulti-stage seal recognition algorithmbased on Siamese network to overcome these limitations.Firstly,the seal image is pre-processed by applying an image rotation correction module based on Histogram of Oriented Gradients(HOG).Secondly,the similarity between input seal image pairs is measured by utilizing a similarity comparison module based on the Siamese network.Finally,we compare the results with the pre-stored standard seal template images in the database to obtain the seal type.To evaluate the performance of the proposed method,we further create a new seal image dataset that contains two subsets with 210,000 valid labeled pairs in total.The proposed work has a practical significance in industries where automatic seal authentication is essential as in legal,financial,and governmental sectors,where automatic seal recognition can enhance document security and streamline validation processes.Furthermore,the experimental results show that the proposed multi-stage method for seal image recognition outperforms state-of-the-art methods on the two established datasets.

Dynamic Multi-Graph Spatio-Temporal Graph Traffic Flow Prediction in Bangkok:An Application of a Continuous Convolutional Neural Network

The ability to accurately predict urban traffic flows is crucial for optimising city operations.Consequently,various methods for forecasting urban traffic have been developed,focusing on analysing historical data to understand complex mobility patterns.Deep learning techniques,such as graph neural networks(GNNs),are popular for their ability to capture spatio-temporal dependencies.However,these models often become overly complex due to the large number of hyper-parameters involved.In this study,we introduce Dynamic Multi-Graph Spatial-Temporal Graph Neural Ordinary Differential Equation Networks(DMST-GNODE),a framework based on ordinary differential equations(ODEs)that autonomously discovers effective spatial-temporal graph neural network(STGNN)architectures for traffic prediction tasks.The comparative analysis of DMST-GNODE and baseline models indicates that DMST-GNODE model demonstrates superior performance across multiple datasets,consistently achieving the lowest Root Mean Square Error(RMSE)and Mean Absolute Error(MAE)values,alongside the highest accuracy.On the BKK(Bangkok)dataset,it outperformed other models with an RMSE of 3.3165 and an accuracy of 0.9367 for a 20-min interval,maintaining this trend across 40 and 60 min.Similarly,on the PeMS08 dataset,DMST-GNODE achieved the best performance with an RMSE of 19.4863 and an accuracy of 0.9377 at 20 min,demonstrating its effectiveness over longer periods.The Los_Loop dataset results further emphasise this model’s advantage,with an RMSE of 3.3422 and an accuracy of 0.7643 at 20 min,consistently maintaining superiority across all time intervals.These numerical highlights indicate that DMST-GNODE not only outperforms baseline models but also achieves higher accuracy and lower errors across different time intervals and datasets.

Application of virtual reality technology improves the functionality of brain networks in individuals experiencing pain

Medical procedures are inherently invasive and carry the risk of inducing pain to the mind and body.Recently,efforts have been made to alleviate the discomfort associated with invasive medical procedures through the use of virtual reality(VR)technology.VR has been demonstrated to be an effective treatment for pain associated with medical procedures,as well as for chronic pain conditions for which no effective treatment has been established.The precise mechanism by which the diversion from reality facilitated by VR contributes to the diminution of pain and anxiety has yet to be elucidated.However,the provision of positive images through VR-based visual stimulation may enhance the functionality of brain networks.The salience network is diminished,while the default mode network is enhanced.Additionally,the medial prefrontal cortex may establish a stronger connection with the default mode network,which could result in a reduction of pain and anxiety.Further research into the potential of VR technology to alleviate pain could lead to a reduction in the number of individuals who overdose on painkillers and contribute to positive change in the medical field.

Network pharmacology:Changes the treatment mode of"one disease-one target"in cancer treatment

The article concluded that network pharmacology provides new ideas and insights into the molecular mechanism of traditional Chinese medicine(TCM)treatment of cancer.TCM is a new choice and hot spot in the field of cancer treatment.We have also previously published studies on TCM and network pharmacology.In this letter,we summarize the new paradigm of network pharmacology in cancer treatment mechanisms.

3D tomographic analysis of equatorial plasma bubble using GNSS-TEC data from Indonesian GNSS Network

Equatorial Plasma Bubbles(EPBs)are ionospheric irregularities that take place near the magnetic equator.EPBs most commonly occur after sunset during the equinox months,although they can also be observed during other seasons.The phenomenon significantly disrupts radio wave signals essential to communication and navigation systems.The national network of Global Navigation Satellite System(GNSS)receivers in Indonesia(>30°longitudinal range)provides an opportunity for detailed EPB studies.To explore this,we conducted preliminary 3D tomography of total electron content(TEC)data captured by GNSS receivers following a geomagnetic storm on December 3,2023,when at least four EPB clusters occurred in the Southeast Asian sector.TEC and extracted TEC depletion with a 120-minute running average were then used as inputs for a 3D tomography program.Their 2D spatial distribution consistently captured the four EPB clusters over time.These tomography results were validated through a classical checkerboard test and comparisons with other ionospheric data sources,such as the Global Ionospheric Map(GIM)and International Reference Ionosphere(IRI)profile.Validation of the results demonstrates the capability of the Indonesian GNSS network to measure peak ionospheric density.These findings highlight the potential for future three-dimensional research of plasma bubbles in low-latitude regions using existing GNSS networks,with extensive longitudinal coverage.

Global Piecewise Analysis of HIV Model with Bi-Infectious Categories under Ordinary Derivative and Non-Singular Operator with Neural Network Approach

This study directs the discussion of HIV disease with a novel kind of complex dynamical generalized and piecewise operator in the sense of classical and Atangana Baleanu(AB)derivatives having arbitrary order.The HIV infection model has a susceptible class,a recovered class,along with a case of infection divided into three sub-different levels or categories and the recovered class.The total time interval is converted into two,which are further investigated for ordinary and fractional order operators of the AB derivative,respectively.The proposed model is tested separately for unique solutions and existence on bi intervals.The numerical solution of the proposed model is treated by the piece-wise numerical iterative scheme of Newtons Polynomial.The proposed method is established for piece-wise derivatives under natural order and non-singular Mittag-Leffler Law.The cross-over or bending characteristics in the dynamical system of HIV are easily examined by the aspect of this research having a memory effect for controlling the said disease.This study uses the neural network(NN)technique to obtain a better set of weights with low residual errors,and the epochs number is considered 1000.The obtained figures represent the approximate solution and absolute error which are tested with NN to train the data accurately.

Modeling and Comprehensive Review of Signaling Storms in 3GPP-Based Mobile Broadband Networks:Causes,Solutions,and Countermeasures

Control signaling is mandatory for the operation and management of all types of communication networks,including the Third Generation Partnership Project(3GPP)mobile broadband networks.However,they consume important and scarce network resources such as bandwidth and processing power.There have been several reports of these control signaling turning into signaling storms halting network operations and causing the respective Telecom companies big financial losses.This paper draws its motivation from such real network disaster incidents attributed to signaling storms.In this paper,we present a thorough survey of the causes,of the signaling storm problems in 3GPP-based mobile broadband networks and discuss in detail their possible solutions and countermeasures.We provide relevant analytical models to help quantify the effect of the potential causes and benefits of their corresponding solutions.Another important contribution of this paper is the comparison of the possible causes and solutions/countermeasures,concerning their effect on several important network aspects such as architecture,additional signaling,fidelity,etc.,in the form of a table.This paper presents an update and an extension of our earlier conference publication.To our knowledge,no similar survey study exists on the subject.

Unlocking the future:Mitochondrial genes and neural networks in predicting ovarian cancer prognosis and immunotherapy response

BACKGROUND Mitochondrial genes are involved in tumor metabolism in ovarian cancer(OC)and affect immune cell infiltration and treatment responses.AIM To predict prognosis and immunotherapy response in patients diagnosed with OC using mitochondrial genes and neural networks.METHODS Prognosis,immunotherapy efficacy,and next-generation sequencing data of patients with OC were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus.Mitochondrial genes were sourced from the MitoCarta3.0 database.The discovery cohort for model construction was created from 70% of the patients,whereas the remaining 30% constituted the validation cohort.Using the expression of mitochondrial genes as the predictor variable and based on neural network algorithm,the overall survival time and immunotherapy efficacy(complete or partial response)of patients were predicted.RESULTS In total,375 patients with OC were included to construct the prognostic model,and 26 patients were included to construct the immune efficacy model.The average area under the receiver operating characteristic curve of the prognostic model was 0.7268[95% confidence interval(CI):0.7258-0.7278]in the discovery cohort and 0.6475(95%CI:0.6466-0.6484)in the validation cohort.The average area under the receiver operating characteristic curve of the immunotherapy efficacy model was 0.9444(95%CI:0.8333-1.0000)in the discovery cohort and 0.9167(95%CI:0.6667-1.0000)in the validation cohort.CONCLUSION The application of mitochondrial genes and neural networks has the potential to predict prognosis and immunotherapy response in patients with OC,providing valuable insights into personalized treatment strategies.

Unraveling the mechanism of action of Shangxia Liangji formula for treating insomnia:a metabolomics and network pharmacology approach

Background:Insomnia is a prevalent clinical condition and Shangxia Liangji formula(SXLJF)is a well-established method of treatment.Nevertheless,the specific mechanism of action of SXLJF remains unclear.Methods:The mouse model of insomnia was established by intraperitoneal injection of para-chlorophenylalanine.Forty-two mice were randomly divided into a negative control group,model group,SXLJF group(18.72 g/kg/day),and positive control group(diazepam,2 mg/kg)and treated with the corresponding drugs for 7 consecutive days.The open field test and pentobarbital-induced sleeping test were conducted.LC-MS-based untargeted metabolomics and network pharmacology were applied to explore the potential targets of SXLJF for treating insomnia.Finally,key targets were validated using RT-qPCR.Results:Behavioral tests demonstrated that SXLJF reduced the total distance,average velocity,central distance,and sleep latency,and prolonged sleep duration.Metabolomics and network pharmacology revealed potential targets,signaling pathways,metabolic pathways,and metabolites associated with the anti-insomnia effects of SXLJF.Specifically,tyrosine hydroxylase(TH)and tyrosine metabolism emerged as crucial metabolic pathways and targets,respectively.RT-qPCR results supported the role of TH in the mechanism of SXLJF in treating insomnia.Conclusion:In conclusion,TH and tyrosine metabolism may represent significant targets and pathways for SXLJF in treating insomnia.

Advancing treatment strategies:Insights from network meta-analysis of hepatic arterial infusion chemotherapy for advanced hepatocellular carcinoma

This study examines the pivotal findings of the network meta-analysis of Zhou et al,which evaluated the efficacy of hepatic arterial infusion chemotherapy and combination therapies for advanced hepatocellular carcinoma(HCC).This meta-analysis suggests that therapeutic combinations have greater efficacy than do standard treatments.The article highlights the key insights that have the potential to shift current clinical practice and enhance outcomes for patients with advanced HCC.Additionally,this article discusses further research that can be conducted to optimize these treatments and achieve personalized care for patients with HCC.

Effects of Aging and Self-organized Criticality in a Pulse-Coupled Integrate-and-Fire Neuron Model Based on Small World Networks

Effects of aging and self-organized criticality in a pulse-coupled integrate-and-fire neuron model based on small world networks have been studied. We give the degree distribution of aging network, average shortest path length, the diameter of our network, and the clustering coefficient, and find that our neuron model displays the power-law behavior, and with the number of added links increasing, the effects of aging become smaller and smaller. This shows that if the brain works at the self-organized criticality state, it can relieve some effects caused by aging.

Self-organized Criticality and Synchronization in a Pulse-coupled Integrate-and-Fire Neuron Model Based on Small World Networks＊

A lattice model for a set of pulse-coupled integrate-and-fire neurons with small world structure is introduced.We find that our model displays the power-law behavior accompanied with the large-scale synchronized activities among the units. And the different connectivity topologies lead to different behaviors in models of integrate-and-fire neurons.

A Novel Self-Supervised Learning Network for Binocular Disparity Estimation

Two-dimensional endoscopic images are susceptible to interferences such as specular reflections and monotonous texture illumination,hindering accurate three-dimensional lesion reconstruction by surgical robots.This study proposes a novel end-to-end disparity estimation model to address these challenges.Our approach combines a Pseudo-Siamese neural network architecture with pyramid dilated convolutions,integrating multi-scale image information to enhance robustness against lighting interferences.This study introduces a Pseudo-Siamese structure-based disparity regression model that simplifies left-right image comparison,improving accuracy and efficiency.The model was evaluated using a dataset of stereo endoscopic videos captured by the Da Vinci surgical robot,comprising simulated silicone heart sequences and real heart video data.Experimental results demonstrate significant improvement in the network’s resistance to lighting interference without substantially increasing parameters.Moreover,the model exhibited faster convergence during training,contributing to overall performance enhancement.This study advances endoscopic image processing accuracy and has potential implications for surgical robot applications in complex environments.

DEEP NEURAL NETWORKS COMBINING MULTI-TASK LEARNING FOR SOLVING DELAY INTEGRO-DIFFERENTIAL EQUATIONS

Deep neural networks(DNNs)are effective in solving both forward and inverse problems for nonlinear partial differential equations(PDEs).However,conventional DNNs are not effective in handling problems such as delay differential equations(DDEs)and delay integrodifferential equations(DIDEs)with constant delays,primarily due to their low regularity at delayinduced breaking points.In this paper,a DNN method that combines multi-task learning(MTL)which is proposed to solve both the forward and inverse problems of DIDEs.The core idea of this approach is to divide the original equation into multiple tasks based on the delay,using auxiliary outputs to represent the integral terms,followed by the use of MTL to seamlessly incorporate the properties at the breaking points into the loss function.Furthermore,given the increased training dificulty associated with multiple tasks and outputs,we employ a sequential training scheme to reduce training complexity and provide reference solutions for subsequent tasks.This approach significantly enhances the approximation accuracy of solving DIDEs with DNNs,as demonstrated by comparisons with traditional DNN methods.We validate the effectiveness of this method through several numerical experiments,test various parameter sharing structures in MTL and compare the testing results of these structures.Finally,this method is implemented to solve the inverse problem of nonlinear DIDE and the results show that the unknown parameters of DIDE can be discovered with sparse or noisy data.

Resting-state brain network remodeling after different nerve reconstruction surgeries:a functional magnetic resonance imaging study in brachial plexus injury rats

Distinct brain remodeling has been found after different nerve reconstruction strategies,including motor representation of the affected limb.However,differences among reconstruction strategies at the brain network level have not been elucidated.This study aimed to explore intranetwork changes related to altered peripheral neural pathways after different nerve reconstruction surgeries,including nerve repair,endto-end nerve transfer,and end-to-side nerve transfer.Sprague–Dawley rats underwent complete left brachial plexus transection and were divided into four equal groups of eight:no nerve repair,grafted nerve repair,phrenic nerve end-to-end transfer,and end-to-side transfer with a graft sutured to the anterior upper trunk.Resting-state brain functional magnetic resonance imaging was obtained 7 months after surgery.The independent component analysis algorithm was utilized to identify group-level network components of interest and extract resting-state functional connectivity values of each voxel within the component.Alterations in intra-network resting-state functional connectivity were compared among the groups.Target muscle reinnervation was assessed by behavioral observation(elbow flexion)and electromyography.The results showed that alterations in the sensorimotor and interoception networks were mostly related to changes in the peripheral neural pathway.Nerve repair was related to enhanced connectivity within the sensorimotor network,while end-to-side nerve transfer might be more beneficial for restoring control over the affected limb by the original motor representation.The thalamic-cortical pathway was enhanced within the interoception network after nerve repair and end-to-end nerve transfer.Brain areas related to cognition and emotion were enhanced after end-to-side nerve transfer.Our study revealed important brain networks related to different nerve reconstructions.These networks may be potential targets for enhancing motor recovery.

Leveraging ROTI map derived from Indonesian GNSS receiver network for advancing study of Equatorial Plasma Bubble in Southeast/East Asia

This paper highlights the crucial role of Indonesia’s GNSS receiver network in advancing Equatorial Plasma Bubble(EPB)studies in Southeast and East Asia,as ionospheric irregularities within EPB can disrupt GNSS signals and degrade positioning accuracy.Managed by the Indonesian Geospatial Information Agency(BIG),the Indonesia Continuously Operating Reference Station(Ina-CORS)network comprises over 300 GNSS receivers spanning equatorial to southern low-latitude regions.Ina-CORS is uniquely situated to monitor EPB generation,zonal drift,and dissipation across Southeast Asia.We provide a practical tool for EPB research,by sharing two-dimensional rate of Total Electron Content(TEC)change index(ROTI)derived from this network.We generate ROTI maps with a 10-minute resolution,and samples from May 2024 are publicly available for further scientific research.Two preliminary findings from the ROTI maps of Ina-CORS are noteworthy.First,the Ina-CORS ROTI maps reveal that the irregularities within a broader EPB structure persist longer,increasing the potential for these irregularities to migrate farther eastward.Second,we demonstrate that combined ROTI maps from Ina-CORS and GNSS receivers in East Asia and Australia can be used to monitor the development of ionospheric irregularities in Southeast and East Asia.We have demonstrated the combined ROTI maps to capture the development of ionospheric irregularities in the Southeast/East Asian sector during the G5 Geomagnetic Storm on May 11,2024.We observed simultaneous ionospheric irregularities in Japan and Australia,respectively propagating northwestward and southwestward,before midnight,whereas Southeast Asia’s equatorial and low-latitude regions exhibited irregularities post-midnight.By sharing ROTI maps from Indonesia and integrating them with regional GNSS networks,researchers can conduct comprehensive EPB studies,enhancing the understanding of EPB behavior across Southeast and East Asia and contributing significantly to ionospheric research.

Pluggable multitask diffractive neural networks based on cascaded metasurfaces

Optical neural networks have significant advantages in terms of power consumption,parallelism,and high computing speed,which has intrigued extensive attention in both academic and engineering communities.It has been considered as one of the powerful tools in promoting the fields of imaging processing and object recognition.However,the existing optical system architecture cannot be reconstructed to the realization of multi-functional artificial intelligence systems simultaneously.To push the development of this issue,we propose the pluggable diffractive neural networks(P-DNN),a general paradigm resorting to the cascaded metasurfaces,which can be applied to recognize various tasks by switching internal plug-ins.As the proof-of-principle,the recognition functions of six types of handwritten digits and six types of fashions are numerical simulated and experimental demonstrated at near-infrared regimes.Encouragingly,the proposed paradigm not only improves the flexibility of the optical neural networks but paves the new route for achieving high-speed,low-power and versatile artificial intelligence systems.

Attractors with controllable basin sizes from cooperation of contracting and expanding dynamics in pulse-coupled oscillators

Unstable attractors are a novel type of attractor with local unstable dynamics, but with positive measures of basins.Here, we introduce local contracting dynamics by slightly modifying the function which mediates the interactions among the oscillators. Thus, the property of unstable attractors can be controlled through the cooperation of expanding and contracting dynamics. We demonstrate that one certain type of unstable attractor is successfully controlled through this simple modification. Specifically, the staying time for unstable attractors can be prolonged, and we can even turn the unstable attractors into stable attractors with predictable basin sizes. As an application, we demonstrate how to realize the switching dynamics that is only sensitive to the finite size perturbations.