Enhancing Relational Triple Extraction in Specific Domains:Semantic Enhancement and Synergy of Large Language Models and Small Pre-Trained Language Models

In the process of constructing domain-specific knowledge graphs,the task of relational triple extraction plays a critical role in transforming unstructured text into structured information.Existing relational triple extraction models facemultiple challenges when processing domain-specific data,including insufficient utilization of semantic interaction information between entities and relations,difficulties in handling challenging samples,and the scarcity of domain-specific datasets.To address these issues,our study introduces three innovative components:Relation semantic enhancement,data augmentation,and a voting strategy,all designed to significantly improve the model’s performance in tackling domain-specific relational triple extraction tasks.We first propose an innovative attention interaction module.This method significantly enhances the semantic interaction capabilities between entities and relations by integrating semantic information fromrelation labels.Second,we propose a voting strategy that effectively combines the strengths of large languagemodels(LLMs)and fine-tuned small pre-trained language models(SLMs)to reevaluate challenging samples,thereby improving the model’s adaptability in specific domains.Additionally,we explore the use of LLMs for data augmentation,aiming to generate domain-specific datasets to alleviate the scarcity of domain data.Experiments conducted on three domain-specific datasets demonstrate that our model outperforms existing comparative models in several aspects,with F1 scores exceeding the State of the Art models by 2%,1.6%,and 0.6%,respectively,validating the effectiveness and generalizability of our approach.

The study of intelligent algorithm in particle identification of heavy-ion collisions at low and intermediate energies

Traditional particle identification methods face timeconsuming,experience-dependent,and poor repeatability challenges in heavy-ion collisions at low and intermediate energies.Researchers urgently need solutions to the dilemma of traditional particle identification methods.This study explores the possibility of applying intelligent learning algorithms to the particle identification of heavy-ion collisions at low and intermediate energies.Multiple intelligent algorithms,including XgBoost and TabNet,were selected to test datasets from the neutron ion multi-detector for reaction-oriented dynamics(NIMROD-ISiS)and Geant4 simulation.Tree-based machine learning algorithms and deep learning algorithms e.g.TabNet show excellent performance and generalization ability.Adding additional data features besides energy deposition can improve the algorithm’s performance when the data distribution is nonuniform.Intelligent learning algorithms can be applied to solve the particle identification problem in heavy-ion collisions at low and intermediate energies.

Research and application of composite stochastic resonance in enhancement detection

Aiming at the problem that the intermediate potential part of the traditional bistable stochastic resonance model cannot be adjusted independently, a new composite stochastic resonance(NCSR) model is proposed by combining the Woods–Saxon(WS) model and the improved piecewise bistable model. The model retains the characteristics of the independent parameters of WS model and the improved piecewise model has no output saturation, all the parameters in the new model have no coupling characteristics. Under α stable noise environment, the new model is used to detect periodic signal and aperiodic signal, the detection results indicate that the new model has higher noise utilization and better detection effect.Finally, the new model is applied to image denoising, the results showed that under the same conditions, the output peak signal-to-noise ratio(PSNR) and the correlation number of NCSR method is higher than that of other commonly used linear denoising methods and improved piecewise SR methods, the effectiveness of the new model is verified.

Femtosecond laser-induced nanoparticle implantation into flexible substrate for sensitive and reusable microfluidics SERS detection

Surface-enhanced Raman spectroscopy(SERS)microfluidic system,which enables rapid detection of chemical and biological analytes,offers an effective platform to monitor various food contaminants and disease diagnoses.The efficacy of SERS microfluidic systems is greatly dependent on the sensitivity and reusability of SERS detection substrates to ensure repeated use for prolonged periods.This study proposed a novel process of femtosecond laser nanoparticle array(NPA)implantation to achieve homogeneous forward transfer of gold NPA on a flexible polymer film and accurately integrated it within microfluidic chips for SERS detection.The implanted Au-NPA strips show a remarkable electromagnetic field enhancement with the factor of 9×108 during SERS detection of malachite green(MG)solution,achieving a detection limit lower than 10 ppt,far better than most laser-prepared SERS substrates.Furthermore,Au-NPA strips show excellent reusability after several physical and chemical cleaning,because of the robust embedment of laser-implanted NPA in flexible substrates.To demonstrate the performance of Au-NPA,a SERS microfluidic system is built to monitor the online oxidation reaction between MG/NaClO reactants,which helps infer the reaction path.The proposed method of nanoparticle implantation is more effective than the direct laser structuring technique.It provides better performance for SERS detection,robustness of detection,and substrate flexibility and has a wider range of applications for microfluidic systems without any negative impact.

Exploration of the Existence Forms and Patterns of Dissolved Oxygen Molecules in Water

The structure of liquid water is primarily composed of three-dimensional networks of water clusters formed by hydrogen bonds,and dis-solved oxygen is one of the most important indicators for assessing water qual-ity.In this work,distilled water with different concentration of dissolved oxygen were prepared,and a clear negative correlation between the size of water clus-ters and dissolved oxygen concentration was observed.Besides,a phenomenon of rapid absorption and release of oxygen at the water interfaces was unveiled,suggesting that oxygen molecules predominantly exist at the interfaces of water clusters.Oxygen molecules can move rapidly through the interfaces among water clusters,allowing dissolved oxygen to quickly reach a saturation level at certain partial pressure of oxygen and temperature.Further exploration into the mechanism by molecular dynamics simulations of oxygen and water clusters found that oxygen molecules can only exist stably at the interfaces among water clusters.A semi-empirical formula relating the average number of water molecules in a cluster(n)to ^(17)O NMR half-peak width(W)was summarized:n=0.1 W+0.85.These findings provide a foundation for exploring the structure and properties of water.

Investigating the elliptic anisotropy of identified particles in p-Pb collisions with a multi-phase transport model

The elliptic azimuthal anisotropy coefficient(v_(2))of the identified particles at midrapidity(|η|<0.8)was investigated in p-Pb collisions at√s_(NN)=5.02 TeV using a multi-phase transport model(AMPT).The calculations of differential v_(2)based on the advanced flow extraction method of light flavor hadrons(pions,kaons,protons,andΛ)in small collision systems were extended to a wider transverse momentum(p_(T))range of up to 8 GeV/c for the first time.The string-melting version of the AMPT model provides a good description of the measured p_(T)-differential v_(2)of the mesons but exhibits a slight deviation from the baryon v_(2).In addition,we observed the features of mass ordering at low p_(T)and the approximate number-of-constituentquark(NCQ)scaling at intermediate p_(T).Moreover,we demonstrate that hadronic rescattering does not have a significant impact on v_(2)in p-Pb collisions for different centrality selections,whereas partonic scattering dominates in generating the elliptic anisotropy of the final particles.This study provides further insight into the origin of collective-like behavior in small collision systems and has referential value for future measurements of azimuthal anisotropy.

Energy and Latency Optimization in Edge-Fog-Cloud Computing for the Internet of Medical Things

In this paper,the Internet ofMedical Things(IoMT)is identified as a promising solution,which integrates with the cloud computing environment to provide remote health monitoring solutions and improve the quality of service(QoS)in the healthcare sector.However,problems with the present architectural models such as those related to energy consumption,service latency,execution cost,and resource usage,remain a major concern for adopting IoMT applications.To address these problems,this work presents a four-tier IoMT-edge-fog-cloud architecture along with an optimization model formulated using Mixed Integer Linear Programming(MILP),with the objective of efficiently processing and placing IoMT applications in the edge-fog-cloud computing environment,while maintaining certain quality standards(e.g.,energy consumption,service latency,network utilization).A modeling environment is used to assess and validate the proposed model by considering different traffic loads and processing requirements.In comparison to the other existing models,the performance analysis of the proposed approach shows a maximum saving of 38%in energy consumption and a 73%reduction in service latency.The results also highlight that offloading the IoMT application to the edge and fog nodes compared to the cloud is highly dependent on the tradeoff between the network journey time saved vs.the extra power consumed by edge or fog resources.

Revealing the role of calcium ion intercalation of hydrated vanadium oxides for aqueous zinc-ion batteries

Exploring suitable high-capacity V_(2)O_(5)-based cathode materials is essential for the rapid advancement of aqueous zinc ion batteries(ZIBs).However,the typical problem of slow Zn^(2+)diffusion kinetics has severely limited the feasibility of such materials.In this work,unique hydrated vanadates(CaVO,BaVO)were obtained by intercalation of Ca^(2+)or Ba^(2+)into hydrated vanadium pentoxide.In the CaVO//Zn and BaVO//Zn batteries systems,the former delivered up to a 489.8 mAh g^(-1)discharge specific capacity at 0.1 A g^(-1).Moreover,the remarkable energy density of 370.07 Wh kg^(-1)and favorable cycling stability yard outperform BaVO,pure V_(2)O_(5),and many reported cathodes of similar ionic intercalation compounds.In addition,pseudocapacitance analysis,galvanostatic intermittent titration(GITT)tests,and Trasatti analysis revealed the high capacitance contribution and Zn^(2+)diffusion coefficient of CaVO,while an in-depth investigation based on EIS elucidated the reasons for the better electrochemical performance of CaVO.Notably,ex-situ XRD,XPS,and TEM tests further demonstrated the Zn^(2+)insertion/extraction and Zn-storage mechanism that occurred during the cycle in the CaVO//Zn battery system.This work provides new insights into the intercalation of similar divalent cations in vanadium oxides and offers new solutions for designing cathodes for high-capacity aqueous ZIBs.

Novel cyber-physical collaborative detection and localization method against dynamic load altering attacks in smart energy grids

Owing to the integration of energy digitization and artificial intelligence technology,smart energy grids can realize the stable,efficient and clean operation of power systems.However,the emergence of cyber-physical attacks,such as dynamic load-altering attacks(DLAAs)has introduced great challenges to the security of smart energy grids.Thus,this study developed a novel cyber-physical collaborative security framework for DLAAs in smart energy grids.The proposed framework integrates attack prediction in the cyber layer with the detection and localization of attacks in the physical layer.First,a data-driven method was proposed to predict the DLAA sequence in the cyber layer.By designing a double radial basis function network,the influence of disturbances on attack prediction can be eliminated.Based on the prediction results,an unknown input observer-based detection and localization method was further developed for the physical layer.In addition,an adaptive threshold was designed to replace the traditional precomputed threshold and improve the detection performance of the DLAAs.Consequently,through the collaborative work of the cyber-physics layer,injected DLAAs were effectively detected and located.Compared with existing methodologies,the simulation results on IEEE 14-bus and 118-bus power systems verified the superiority of the proposed cyber-physical collaborative detection and localization against DLAAs.

Flexible polydimethylsiloxane pressure sensor with micro-pyramid structures and embedded silver nanowires:A novel application in urinary flow measurement

Flexible pressure sensors are lightweight and highly sensitive,making them suitable for use in small portable devices to achieve precise measurements of tiny forces.This article introduces a low-cost and easy-fabrication strategy for piezoresistive flexible pressure sensors.By embedding silver nanowires into a polydimethylsiloxane layer with micro-pyramids on its surface,a flexible pressure sensor is created that can detect low pressure (17.3 Pa) with fast response (<20 ms) and high sensitivity (69.6 mA kPa-1).Furthermore,the pressure sensor exhibits a sensitive and stable response to a small amount of water flowing on its surface.On this basis,the flexible pressure sensor is innovatively combined with a micro-rotor to fabricate a novel urinary flow-rate meter (uroflowmeter),and results from a simulated human urination experiment show that the uroflowmeter accurately captured all the essential shape characteristics that were present in the pump-simulated urination curves.Looking ahead,this research provides a new reference for using flexible pressure sensors in urinary flow-rate monitoring.

Knowledge-reused transfer learning for molecular and materials science

Leveraging big data analytics and advanced algorithms to accelerate and optimize the process of molecular and materials design, synthesis, and application has revolutionized the field of molecular and materials science, allowing researchers to gain a deeper understanding of material properties and behaviors,leading to the development of new materials that are more efficient and reliable. However, the difficulty in constructing large-scale datasets of new molecules/materials due to the high cost of data acquisition and annotation limits the development of conventional machine learning(ML) approaches. Knowledgereused transfer learning(TL) methods are expected to break this dilemma. The application of TL lowers the data requirements for model training, which makes TL stand out in researches addressing data quality issues. In this review, we summarize recent progress in TL related to molecular and materials. We focus on the application of TL methods for the discovery of advanced molecules/materials, particularly, the construction of TL frameworks for different systems, and how TL can enhance the performance of models. In addition, the challenges of TL are also discussed.

Highly Sensitive Ammonia Gas Sensors at Room Temperature Based on the Catalytic Mechanism of N,C Coordinated Ni Single-Atom Active Center

Significant challenges are posed by the limitations of gas sensing mechanisms for trace-level detection of ammonia(NH3).In this study,we propose to exploit single-atom catalytic activation and targeted adsorption properties to achieve highly sensitive and selective NH3 gas detection.Specifically,Ni singleatom active sites based on N,C coordination(Ni-N-C)were interfacially confined on the surface of two-dimensional(2D)MXene nanosheets(Ni-N-C/Ti_(3)C_(2)Tx),and a fully flexible gas sensor(MNPE-Ni-N-C/Ti_(3)C_(2)Tx)was integrated.The sensor demonstrates a remarkable response value to 5 ppm NH3(27.3%),excellent selectivity for NH3,and a low theoretical detection limit of 12.1 ppb.Simulation analysis by density functional calculation reveals that the Ni single-atom center with N,C coordination exhibits specific targeted adsorption properties for NH3.Additionally,its catalytic activation effect effectively reduces the Gibbs free energy of the sensing elemental reaction,while its electronic structure promotes the spill-over effect of reactive oxygen species at the gas-solid interface.The sensor has a dual-channel sensing mechanism of both chemical and electronic sensitization,which facilitates efficient electron transfer to the 2D MXene conductive network,resulting in the formation of the NH3 gas molecule sensing signal.Furthermore,the passivation of MXene edge defects by a conjugated hydrogen bond network enhances the long-term stability of MXene-based electrodes under high humidity conditions.This work achieves highly sensitive room-temperature NH3 gas detection based on the catalytic mechanism of Ni single-atom active center with N,C coordination,which provides a novel gas sensing mechanism for room-temperature trace gas detection research.

Set Stabilization of Large-Scale Stochastic Boolean Networks:A Distributed Control Strategy

Dear Editor,This letter deals with the set stabilization of stochastic Boolean control networks(SBCNs)by the pinning control strategy,which is to realize the full control for systems by imposing control inputs on a fraction of agents.

Role of short-chain fatty acids in host physiology

Short-chain fatty acids(SCFAs)are major metabolites produced by the gut microbiota through the fermentation of dietary fiber,and they have garnered significant attention due to their close association with host health.As important mediators between the gut microbiota and the host,SCFAs serve as energy substrates for intestinal epithelial cells and maintain homeostasis in host immune and energy metabolism by influencing host epigenetics,activating G protein-coupled receptors,and inhibiting pathogenic microbial infections.This review provides a comprehensive summary of SCFAs synthesis and metabolism and offering an overview of the latest research progress on their roles in protecting gut health,enhancing energy metabolism,mitigating diseases such as cancer,obesity,and diabetes,modulating the gut-brain axis and gut-l ung axis,and promoting bone health.

Method for secondary spring load equalization of railway vehicles with two-stage spring suspension: Modeling and optimal regulation

To ensure running safety,the secondary spring loads of railway vehicles must be well equalized.Due to the coupling interactive effects of these hyper static suspended structures,the equalization adjustment through shimming procedure is quite complex.Therefore,an effective and reliable method in application is developed in this paper.Firstly,the best regulation of spring load is solved based on a mechanical model of the secondary suspension system,providing a target for actual adjustment.To reveal the relationship between secondary spring load distribution and shim quantity sequence,a forecasting model is constructed and then modified experimentally with consideration of car body’s elastic deformation.Further,a gradient-based algorithm with a momentum operation is proposed for the load optimization.Effectiveness of the whole method has been verified on a test rig.It is experimentally confirmed that this research provides an important basis for achieving an optimal regulation of spring load distribution for multiple types of railway vehicles.

Hypothesis testing analysis and unknown parameter estimation of GPS signal detection

Hypothesis testing analysis and unknown parameter estimation of both the intermediate frequency(IF) and baseband GPS signal detection are given by using the generalized likelihood ratio test(GLRT) approach,applying the model of GPS signal in white Gaussian noise,It is proved that the test statistic follows central or noncentral F distribution,It is also pointed out that the test statistic is nearly identical to central or noncentral chi-squared distribution because the processing samples are large enough to be considered as infinite in GPS acquisition problem.It is also proved that the probability of false alarm,the probability of detection and the threshold are affected largely when the hypothesis testing refers to the full pseudorandom noise(PRN) code phase and Doppler frequency search space cells instead of each individual cell.The performance of the test statistic is also given with combining the noncoherent integration.

Advances in isolation and detection of circulating tumor cells based on microfluidics

Circulating tumor cells(CTCs) are the cancer cells that circulate in the peripheral blood after escaping from the original or metastatic tumors. CTCs could be used as non-invasive source of clinical information in early diagnosis of cancer and evaluation of cancer development. In recent years, CTC research has become a hotspot field wherein many novel CTC detection technologies based on microfluidics have been developed. Great advances have been made that exhibit obvious technical advantages, but cannot yet satisfy the current clinical requirements. In this study, we review the main advances in isolation and detection methods of CTC based on microfluidics research over several years, propose five technical indicators for evaluating these methods, and explore the application prospects. We also discuss the concepts, issues, approaches, advantages, limitations, and challenges with an aim of stimulating a broader interest in developing microfluidics-based CTC detection technology.

Bioinspired MXene-Based User-Interactive Electronic Skin for Digital and Visual Dual-Channel Sensing

User-interactive electronic skin(e-skin) that could convert mechanical stimuli into distinguishable outputs displays tremendous potential for wearable devices and health care applications. However, the existing devices have the disadvantages such as complex integration procedure and lack of the intuitive signal display function. Here, we present a bioinspired user-interactive e-skin, which is simple in structure and can synchronously achieve digital electrical response and optical visualization upon external mechanical stimulus. The e-skin comprises a conductive layer with a carbon nanotubes/cellulose nanofibers/MXene nanohybrid network featuring remarkable electromechanical behaviors, and a stretchable elastomer layer, which is composed of silicone rubber and thermochromic pigments. Furthermore, the conductive nanohybrid network with outstanding Joule heating performance can generate controllable thermal energy under voltage input and then achieve the dynamic coloration of silicone-based elastomer. Especially, such an innovative fusion strategy of digital data and visual images enables the e-skin to monitor human activities with evermore intuition and accuracy. The simple design philosophy and reliable operation of the demonstrated e-skin are expected to provide an ideal platform for next-generation flexible electronics.

Human natural killer cells for targeting delivery of gold nanostars and bimodal imaging directed photothermal/photodynamic therapy and immunotherapy

Objective:To construct a novel nanoplatform GNS@CaCO3/Ce6-NK by loading the CaCO3-coated gold nanostars(GNSs)with Chlorin e6 molecules(Ce6)into human peripheral blood mononuclear cells(PBMCs)-derived NK cells for tumor targeted therapy.Methods:GNS@CaCO3/Ce6 nanoparticles were prepared and characterized by TEM and UV-vis.The cell surface markers and cytokines secretion of NK cells before and after loading the GNS@CaCO3/Ce6 nanoparticles were detected by Flow Cytometry(FCM)and ELISA.Effects of the GNS@CaCO3/Ce6-NK cells on A549 cancer cells was determined by FCM and CCK-8.Intracellular fluorescent signals of GNS@CaCO3/Ce6-NK cells were detected via Confocal laser scanning microscopic(CLSM)and FCM at different time points.Intracellular ROS generation of GNS@CaCO3/Ce6-NK cells under laser irradiation were examined by FCM.The distribution of GNS@CaCO3/Ce6-NK in A549 tumor-bearing mice were observed by fluorescence imaging and PA imaging.The combination therapy of GNS@CaCO3/Ce6-NK under laser irradiation were investigated on tumor-bearing mice.Results:The coated CaC03 shell on the surface of GNSs exhibited prominent delivery and protection effect of Ce6 during the cellular uptake process.The as-prepared multifunctional GNS@CaCO3/Ce6-NK cells possessed bimodal functions of fluorescence imaging and photoacoustic imaging.The as-prepared multifunctional GNS@CaCO3/Ce6-NK cells could actively target tumor tissues with the enhanced photothermal/photodynamic therapy and immunotherapy.Conclusions:The GNS@CaCO3/Ce6-NK shows effective tumor-targeting ability and prominent therapeutic efficacy toward lung cancer A549 tumor-bearing mice.Through fully utilizing the features of GNSs and NK cells,this new nanoplatform provides a new synergistic strategy for enhanced photothermal/photodynamic therapy and immunotherapy in the field of anticancer development in the near future.

Composites of Graphene and LiFePO_4 as Cathode Materials for Lithium-Ion Battery:A Mini-review

This mini-review highlights selectively the recent research progress in the composites of Li Fe PO4 and graphene. In particularly, the different fabrication protocols, and the electrochemical performance of the composites are summarized in detail. The structural and morphology characters of graphene sheets that may affect the property of the composites are discussed briefly. The possible ongoing researches in area are speculated upon.