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.

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.

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.

A Novel Optical Control Plane for Switching an Electro-Optical Hybrid Node in Translucent WDM Optical Networks

In a translucent network scenario, development of an optical control plane (OCP) that is aware of the location and number of available regenerators and all-optical wavelength converters (AOWCs) is of paramount importance. However, current generalized multiprotocol label switching (GMPLS) protocol suite does not consider the distribution of regenerator and AOWC availability information to all the network nodes. In this paper, we propose a novel optical control plane (OCP) architecture that 1) disseminates information about network components (i.e. regenerators and AOWCs) to all the network nodes, and 2) evaluates candidate routes which use fewest amounts of network components. Performance of the proposed OCP is compared with a recently proposed hybrid OCP approach in terms of blocking performance, number of deployed components and lightpath establishment setup times. The obtained simulation results show that the proposed OCP approach demonstrates low connection blocking and establishes lightpaths by 1) minimizing the overall network cost owing to the deployment of minimum total number of network components, and 2) demonstrating acceptable lightpath establishment setup times at all traffic loads. Further, the proposed OCP methodology is compatible and suitable for controlling the operations of a novel electro-optical hybrid translucent node which is a latency efficient technology capable of delivering a cost effective implementation suitable for large scale deployment.

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.

Electro-Optical Effect Measurement of Thin-Film Material Using PM Fiber Mach-Zehnder Interferometer

A polarization-maintaining （PM） fiber Mach-Zehnder （MZ） interferometer has been established to measure the EO effect of very thin film materials with optical anisotropy. Unlike a common MZ interferometer, all the components are connected via polarization-maintaining fibers. At the same time, a polarized DFB laser with a maximum power output of 10mW is adopted as the light source to induce a large extinction ratio. Here,we take it to determine the electro-optical coefficients of a very thin superlattice structure with GaAs, KTP, and GaN as comparative samples. The measured EO coefficients show good comparability with the others.

Electro-optical properties cholesteric liquid of polymer stabilized crystal film＊

Liquid crystals （LCs） and polymers are extensively used in various electro-optical applications. In this paper, normal mode polymer stabilized cholesteric LC film is prepared and studied. The effects of chiral dopant and monomer concentrations on the electro-optical properties, such as contrast ratio, driving voltage, hysteresis width and response time, are investigated. The reasons of electro-optical properties influenced by the concentrations of the materials are discussed. Through the proper material recipe, the electro-optical properties of polymer stabilized cholesteric LC film can be optimized.

Electro-optical dual modulation on resistive switching behavior in BaTiO3/BiFeO3/TiO2 heterojunction

The novel BaTiO3/BiFeO3/TiO2 multilayer heterojunction is prepared on a fluorine-doped tinoxide(FTO) substrate by the sol–gel method. The results indicate that the Pt/Ba TiO3/BiFeO3/TiO2/FTO heterojunction exhibits stable bipolar resistive switching characteristic, good retention performance, and reversal characteristic. Under different pulse voltages and light fields, four stable resistance states can also be realized. The analysis shows that the main conduction mechanism of the resistive switching characteristic of the heterojunction is space charge limited current(SCLC) effect. After the comprehensive analysis of the band diagram and the P–E ferroelectric property of the multilayer heterojunction, we can deduce that the SCLC is formed by the effect of the oxygen vacancy which is controlled by ferroelectric polarizationmodulated change of interfacial barrier. And the effective photo-generated carrier also plays a regulatory role in resistance state(RS), which is formed by the double ferroelectric layer Ba TiO3/BiFeO3 under different light fields. This research is of potential application values for developing the multi-state non-volatile resistance random access memory(RRAM) devices based on ferroelectric materials.

Experimental study of electro-optical-switched pulsed Nd:YAG laser

We report the specification of a compact and stable side diode-pumped Q-switched pulsed Nd：YAG laser. We ex- perimentally study and compare the performance of the pulsed Nd：YAG laser in the free-running and Q-switched modes at different pulse repetition rates from 1 Hz to 100 Hz. The laser output energy is stabilized by using a special configu- ration of the optical resonator. In this laser, an unsymmetrical concave-concave resonator is used and this structure helps the mode volume to be nearly fixed when the pulse repetition rate is increased. According to the experimental results in the Q-switched operation, the laser output energy is nearly constant around 70 mJ with an FWHM pulse width of 7 ns at 100 Hz. The optical-to-optical conversion efficiency in the Q-switched regime is 17.5%.

Electro-Optical Properties of Carbon Nanotubes Obtained by High Density Plasma Chemical Vapor Deposition

In this work, we studied the electro-optical properties of high-aligned carbon nanotubes deposited at room temperature. For this, we used the High Density Plasma Chemical Vapor Deposition system. This system uses a new concept of plasma generation: a planar coil is coupled to an RF system for plasma generation. This was used together with an electrostatic shield, for plasma densification, thereby obtaining high-density plasmas. The carbon nanotubes were deposited using pure methane plasmas. Three methods were used for the surface modification of the sample: reference substrate (silicon wafer only submitted to a chemical cleaning), silicon wafer with surface roughness generated by plasma etching, silicon wafer with a thin iron film and silicon wafer with diamond nano powder used as precursor materials. For each kind of silicon wafer surface, the carbon nanotubes were deposited with two different deposition times (two and three hours). The carbon nanotubes structural characteristics were analyzed by Atomic Force Microscope and Scanning Electronic Microscope. The carbon nanotubes electrical characteristics were observed by Raman Spectroscopy and the carbon nanotubes electro-optical properties were analyzed by current vs voltage electrical measurements and photo-luminescence spectroscopy measurements. The photoelectric effect in the carbon nanotubes were determined by photo-induced current measurements. In this work, we obtained carbon nanotubes with semiconductor properties and carbon nanotubes with metallic properties. The electro-optical effects depend strongly on the substrate preparation and the deposition parameters of the carbon nanotubes. The carbon nanotubes are high aligned and show singular properties that can be used for many applications.

Electro-optical properties of high birefringence liquid crystal compounds with isothiocyanate and naphthyl group

Liquid crystal（LC） compound with isothiocyanate and naphthyl group is an attractive high birefringence LC material,and can be used in optical devices. In this paper, the electro-optical properties of a series of this type of LC compounds were investigated. The melting points and enthalpy values of these LC compounds were higher than those of corresponding compounds with the phenyl group. These compounds exhibited high birefringence with a maximum value of 0.66. Fluorine substitution in the molecular almost does not affect the birefringence value. When these LC compounds with the naphthyl group were dissolved in a commercial LC mixture, the electro-optical properties depending on temperature were investigated. In the low-temperature region, LC mixtures with the naphthyl-group LC compounds exhibited higher viscosity than pure commercial LCs. In the high-temperature region, viscosity values very closely approached each other. When response performance was investigated, figure-of-merit（FoM） values were measured. The Fo M values of LC mixtures containing LC compounds with naphthyl group were lower than those of reference benzene LCs in the low-temperature region. However, in the high-temperature region, the results were reversed. These isothiocyanate LC compounds with naphthyl group can be applied in special fast-response LC device, particularly the ones used under high-temperature conditions.

Preparation and Microstructural,Structural,Optical and Electro-Optical Properties of La Doped Pmn-Pt Transparent Ceramics

Transparent relaxor ferroelectric ceramics of the system lanthanum modified lead magnesium niobate have been investigated for a variety of electro-optic properties could make these materials alternatives to (Pb,La)(Zr,Ti)O3. However, a study that relates the properties in function stoichiometric formula, it has not been analyzed heretofore. Therefore, in this work the effect of A-site substitution of La+3 in the characterization microstructural, structural, optical and electro-optical on (1-x)[Pb(1-3/2y)Lay(Mg1/3Nb2/3)O3]-xPbTiO3 and (1-z)[(1-x)Pb(Mg1/3Nb2/3)O3+xPbTiO3]+zLa2O3 has been performed. It was observed that the properties according to the stoichiometric formula and the PT had a maximum whose behavior was related to the addition of lanthanum in each stoichiometries.

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.