基于RoBERTa和T5的两阶段医学术语标准化

医学术语标准化作为消除实体歧义性的重要手段,被广泛应用于知识图谱的构建过程之中.针对医学领域涉及大量的专业术语和复杂的表述方式,传统匹配模型往往难以达到较高的准确率的问题,提出语义召回加精准排序的两阶段模型来提升医学术语标准化效果.首先在语义召回阶段基于改进的有监督对比学习和RoBERTa-wwm提出语义表征模型CL-BERT,通过CL-BERT生成实体的语义表征向量,根据向量之间的余弦相似度进行召回并得到标准词候选集,其次在精准排序阶段使用T5结合prompt tuning构建语义精准匹配模型,并将FGM对抗训练应用到模型训练中,然后使用精准匹配模型对原词和标准词候选集分别进行精准排序得到最终标准词.采用ccks2019公开数据集进行实验,F1值达到了0.9206,实验结果表明所提出的两阶段模型具有较高的性能,为实现医学术语标准化提供了新思路.

A highly sensitive LITES sensor based on a multi-pass cell with dense spot pattern and a novel quartz tuning fork with low frequency

A highly sensitive light-induced thermoelectric spectroscopy(LITES)sensor based on a multi-pass cell(MPC)with dense spot pattern and a novel quartz tuning fork(QTF)with low resonance frequency is reported in this manuscript.An erbi-um-doped fiber amplifier(EDFA)was employed to amplify the output optical power so that the signal level was further enhanced.The optical path length(OPL)and the ratio of optical path length to volume(RLV)of the MPC is 37.7 m and 13.8 cm^(-2),respectively.A commercial QTF and a self-designed trapezoidal-tip QTF with low frequency of 9461.83 Hz were used as the detectors of the sensor,respectively.The target gas selected to test the performance of the system was acetylene(C2H2).When the optical power was constant at 1000 mW,the minimum detection limit(MDL)of the C2H2-LITES sensor can be achieved 48.3 ppb when using the commercial QTF and 24.6 ppb when using the trapezoid-al-tip QTF.An improvement of the detection performance by a factor of 1.96 was achieved after replacing the commer-cial QTF with the trapezoidal-tip QTF.

Morphological disruption and visual tuning alterations in the primary visual cortex in glaucoma(DBA/2J)mice

Glaucoma is a leading cause of irreve rsible blindness wo rldwide,and previous studies have shown that,in addition to affecting the eyes,it also causes abnormalities in the brain.However,it is not yet clear how the primary visual cortex(V1)is altered in glaucoma.This study used DBA/2J mice as a model for spontaneous secondary glaucoma.The aim of the study was to compare the electrophysiological and histomorphological chara cteristics of neurons in the V1between 9-month-old DBA/2J mice and age-matched C57BL/6J mice.We conducted single-unit recordings in the V1 of light-anesthetized mice to measure the visually induced responses,including single-unit spiking and gamma band oscillations.The morphology of layerⅡ/Ⅲneurons was determined by neuronal nuclear antigen staining and Nissl staining of brain tissue sections.Eighty-seven neurons from eight DBA/2J mice and eighty-one neurons from eight C57BL/6J mice were examined.Compared with the C57BL/6J group,V1 neurons in the DBA/2J group exhibited weaker visual tuning and impaired spatial summation.Moreove r,fewer neuro ns were observed in the V1 of DBA/2J mice compared with C57BL/6J mice.These findings suggest that DBA/2J mice have fewer neurons in the VI compared with C57BL/6J mice,and that these neurons have impaired visual tuning.Our findings provide a better understanding of the pathological changes that occur in V1 neuron function and morphology in the DBA/2J mouse model.This study might offer some innovative perspectives regarding the treatment of glaucoma.

Common Cuckoo(Cuculus canorus)nestlings adapt their begging behavior to the host signal system

Common Cuckoos(Cuculus canorus)dependent on parental care for post-hatching demonstrate an intriguing ability to modify their begging vocalizations to ensure maximum care and resources from their interspecific foster parents.Here,we compared begging calls of the Common Cuckoo nestlings fed by four host species,the Grey Bushchat(Saxicola ferreus),Siberian Stonechat(Saxicola maurus),Daurian Redstart(Phoenicurus auroreus),and Oriental Magpie-robin(Copsychus saularis).Results showed that begging calls of the stonechat-,redstart-,and robin-cuckoo resemble those of host species'nestlings in various aspects like low frequency,high frequency,frequency bandwidth and peak frequency,while the bushchat-cuckoo chicks'begging calls were only comparable to their host species in terms of how long they lasted and their peak frequency.In addition,cuckoo nestlings raised in different host nests displayed significant variations in their begging calls in low and peak frequency.This study suggests that cuckoo nestlings do not mimic host species nestlings'begging calls throughout the nestling period,but may tune their begging calls according to host species,while begging calls vary with cuckoo and host species nestlings'ages.Future research should study the parents'reactions to these calls in different host species for a better understanding of the mechanisms underlying such adaptations.

Conceptual design of a 714-MHz RFQ for compact proton injectors and development of a new tuning algorithm on its aluminium prototype

Radio frequency quadrupoles(RFQs),which are crucial components of proton injectors,significantly affect the performance of proton accelerator facilities.An RFQ with a high frequency of 714 MHz dedicated to compact proton injectors for medi-cal applications is designed in this study.The RFQ is designed to accelerate proton beams from 50 keV to 4 MeV within a short length of 2 m and can be matched closely with the downstream drift tube linac to capture more particles through a preliminary optimization.To develop an advanced RFQ,challenging techniques,including fabrication and tuning method,must be evaluated and verified using a prototype.An aluminium prototype is derived from the conceptual design of the RFQ and then redesigned to confirm the radio frequency performance,fabrication procedure,and feasibility of the tuning algorithm.Eventually,a new tuning algorithm based on the response matrix and least-squares method is developed,which yields favorable results based on the prototype,i.e.,the errors of the dipole and quadrupole components reduced to a low level after several tuning iterations.Benefiting from the conceptual design and techniques obtained from the prototype,the formal mechanical design of the 2-m RFQ is ready for the next manufacturing step.

Manipulation of High-Fidelity Sidebands under Large Detuning by Floquet Technology:Application to Multi-Mode Cooling

The Floquet technology,a powerful way to manipulate quantum states,is employed to drive sidebands transition under large detuning.Our results demonstrate that high fidelities over 99%can be achieved through optimizing suitable modulation frequencies under large detuning.We observe high-fidelity transitions within a high bandwidth by utilizing a single modulation frequency and reveal that this capability is due to the emergence of a flat-band structure in the bandwidth range.The key finding of high-fidelity sideband manipulation under large detuning is experimentally confirmed in nuclear magnetic resonance platform.Finally,we propose a new parallel sideband cooling scheme that enables simultaneous cooling of multiple motional modes.This approach improves the cooling rate compared to conventional schemes with fixed laser frequency and power,and eliminates the need for mode-specific addressing.Our Floquet parallel scheme is applicable to any harmonic oscillator system and is not limited by bandwidth in theory.

Advances in magnetic-assisted triboelectric nanogenerators:structures,materials and self-sensing systems

Triboelectric nanogenerators(TENG),renowned for their remarkable capability to harness weak mechanical energy from the environment,have gained considerable attention owing to their cost-effectiveness,high output,and adaptability.This review provides a unique perspective by conducting a comprehensive and in-depth analysis of magnetically assisted TENGs that encompass structures,materials,and self-powered sensing systems.We systematically summarize the diverse functions of the magnetic assistance for TENGs,including system stiffness,components of the hybrid electromagnetic-triboelectric generator,transmission,and interaction forces.In the material domain,we review the incorporation of magnetic nano-composites materials,along with ferrofluid-based TENG and microstructure verification,which have also been summarized based on existing research.Furthermore,we delve into the research progress on physical quantity sensing and human-machine interface in magnetic-assisted TENGs.Our analysis highlights that magnetic assistance extends beyond the repulsive and suction forces under a magnetic field,thereby playing multifaceted roles in improving the output performance and environmental adaptability of the TENGs.Finally,we present the prevailing challenges and offer insights into the future trajectory of the magnetic-assisted TENGs development.

Real-Time Observation of Instantaneous ac Stark Shift of a Vacuum Using a Zeptosecond Laser Pulse

Based on the numerical solution of the time-dependent Dirac equation,we propose a method to observe in real time the ac Stark shift of a vacuum driven by an ultra-intense laser field.By overlapping the ultra-intense pump pulse with another zeptosecond probe pulse whose photon energy is smaller than 2mc^(2),electron–positron pair creation can be controlled by tuning the time delay between the pump and probe pulses.Since the pair creation rate depends sensitively on the instantaneous vacuum potential,one can reconstruct the ac Stark shift of the vacuum potential according to the time-delay-dependent pair creation rate.

Tunable Three-Wavelength Fiber Laser and Transient Switching between Three-Wavelength Soliton and Q-Switched Mode-Locked States

We report a passive mode-locked fiber laser that can realize single-wavelength tuning and multi-wavelength spacing tuning simultaneously.The tuning range is from 1528 nm–1560 nm,and up to three bands of soliton states can be output at the same time.These results are confirmed by a nonlinear Schrodinger equation model based on the split-step Fourier method.In addition,we reveal a way to transform the multi-wavelength soliton state into the Q-switched mode-locked state,which is period doubling.These results will promote the development of optical communication,optical sensing and multi-signal pulse emission.

Improved isochronous mass spectrometry with tune measurement

In conventional isochronous mass spectrometry(IMS)performed on a storage ring,the precision of mass measurements for short-lived nuclei depends on the accurate determination of the revolution times(T)of stored ions.However,the resolution of T inevitably deteriorates due to the magnetic rigidity spread of the ions,limiting the mass-resolving power.In this study,we used the betatron tunes Q(the number of betatron oscillations per revolution)of the ions and established a correlation between T and Q.From this correlation,T was transformed to correspond to a fixed Q with higher resolution.Using these transformed T values,the masses of ^(63)Ge,^(65)As,^(67)Se,and ^(71)Kr agreed well with the mass values measured using the newly developed IMS(Bρ-IMS).We also studied the systematics of Coulomb displacement energies(CDEs)and found that anomalous staggering in CDEs was eliminated using new mass values.This method of T transformation is highly effective for conventional IMS equipped with a single time-of-flight detector.

Optimizing PID controller parameters for robust automatic voltage regulator system through indirect design approach-2

Automatic voltage regulators(AVR)are designed to manipulate a synchronous generator’s voltage level automatically.Proportional integral derivative(PID)controllers are typically used in AVR systems to regulate voltage.Although advanced PID tuning methods have been proposed,the actual voltage response differs from the theoretical predictions due to modeling errors and system uncertainties.This requires continuous fine tuning of the PID parameters.However,manual adjustment of these parameters can compromise the stability and robustness of the AVR system.This study focuses on the online self-tuning of PID controllers called indirect design approach-2(IDA-2)in AVR systems while preserving robustness.In particular,we indirectly tune the PID controller by shifting the frequency response.The new PID parameters depend on the frequency-shifting constant and the previously optimized PID parameters.Adjusting the frequency-shifting constant modifies all the PID parameters simultaneously,thereby improving the control performance and robustness.We evaluate the robustness of the proposed online PID tuning method by comparing the gain margins(GMs)and phase margins(PMs)with previously optimized PID parameters during parameter uncertainties.The proposed method is further evaluated in terms of disturbance rejection,measurement noise,and frequency response analysis during parameter uncertainty calculations against existing methods.Simulations show that the proposed method significantly improves the robustness of the controller in the AVR system.In summary,online self-tuning enables automated PID parameter adjustment in an AVR system,while maintaining stability and robustness.

Refined Anam-Net:Lightweight Deep Learning Model for Improved Segmentation Performance of Optic Cup and Disc for Glaucoma Diagnosis

In this work,we aim to introduce some modifications to the Anam-Net deep neural network(DNN)model for segmenting optic cup(OC)and optic disc(OD)in retinal fundus images to estimate the cup-to-disc ratio(CDR).The CDR is a reliable measure for the early diagnosis of Glaucoma.In this study,we developed a lightweight DNN model for OC and OD segmentation in retinal fundus images.Our DNN model is based on modifications to Anam-Net,incorporating an anamorphic depth embedding block.To reduce computational complexity,we employ a fixed filter size for all convolution layers in the encoder and decoder stages as the network deepens.This modification significantly reduces the number of trainable parameters,making the model lightweight and suitable for resource-constrained applications.We evaluate the performance of the developed model using two publicly available retinal image databases,namely RIM-ONE and Drishti-GS.The results demonstrate promising OC segmentation performance across most standard evaluation metrics while achieving analogous results for OD segmentation.We used two retinal fundus image databases named RIM-ONE and Drishti-GS that contained 159 images and 101 retinal images,respectively.For OD segmentation using the RIM-ONE we obtain an f1-score(F1),Jaccard coefficient(JC),and overlapping error(OE)of 0.950,0.9219,and 0.0781,respectively.Similarly,for OC segmentation using the same databases,we achieve scores of 0.8481(F1),0.7428(JC),and 0.2572(OE).Based on these experimental results and the significantly lower number of trainable parameters,we conclude that the developed model is highly suitable for the early diagnosis of glaucoma by accurately estimating the CDR.

Vibration attenuation performance of wind turbine tower using a prestressed tuned mass damper under seismic excitation

With the rapid development of large megawatt wind turbines,the operation environment of wind turbine towers(WTTs)has become increasingly complex.In particular,seismic excitation can create a resonance response and cause excessive vibration of the WTT.To investigate the vibration attenuation performance of the WTT under seismic excitations,a novel passive vibration control device,called a prestressed tuned mass damper(PS-TMD),is presented in this study.First,a mathematical model is established based on structural dynamics under seismic excitation.Then,the mathematical analytical expression of the dynamic coefficient is deduced,and the parameter design method is obtained by system tuning optimization.Next,based on a theoretical analysis and parameter design,the numerical results showed that the PS-TMD was able to effectively mitigate the resonance under the harmonic basal acceleration.Finally,the time-history analysis method is used to verify the effectiveness of the traditional pendulum tuned mass damper(PTMD)and the novel PS-TMD device,and the results indicate that the vibration attenuation performance of the PS-TMD is better than the PTMD.In addition,the PS-TMD avoids the nonlinear effect due to the large oscillation angle,and has the potential to dissipate hysteretic energy under seismic excitation.

An electromagnetic semi-active dynamic vibration absorber for thin-walled workpiece vibration suppression in mirror milling

As critical components of aircraft skins and rocket fuel storage tank shells,large thin-walled workpieces are susceptible to vibration and deformation during machining due to their weak local stiffness.To address these challenges,we propose a novel tunable electromagnetic semi-active dynamic vibration absorber(ESADVA),which integrates with a magnetic suction follower to form a followed ESADVA(follow-ESADVA)for mirror milling.This system combines a tunable magnet oscillator with a follower,enabling real-time vibration absorption and condition feedback throughout the milling process.Additionally,the device supports self-sensing and frequency adjustment by providing feedback to a linear actuator,which alters the distance between magnets.This resolves the traditional issue of being unable to directly monitor vibration at the machining point due to space constraints and tool interference.The frequency shift characteristics and vibration absorption performance are comprehensively investigated.Theoretical and experimental results demonstrate that the prototyped follow-ESADVA achieves frequency synchronization with the milling tool,resulting in a vibration suppression rate of approximately 47.57%.Moreover,the roughness of the machined surface decreases by18.95%,significantly enhancing the surface quality.The results of this work pave the way for higher-quality machined surfaces and a more stable mirror milling process.

3D-Printed Monolith Metallic Ni-Mo Electrodes for Ultrahigh Current Hydrogen Evolution

In this work,we reported a series of monolithic 3D-printed Ni-Mo alloy electrodes for highly efficient water splitting at high current density(1500 mA cm^(-2))with excellent stability,which provides a solution to scale up Ni-Mo catalysts for HER to industry use.All possible Ni-Mo metal/alloy phases were achieved by tuning the atomic composition and heat treatment procedure,and they were investigated through both experiment and simulation,and the optimal NiMo phase shows the best performance.Density functional theory(DFT)calculations elucidate that the NiMo phase has the lowest H2O dissociation energy,which further explains the exceptional performance of NiMo.In addition,the microporosity was modulated via controlled thermal treatment,indicating that the 1100℃sintered sample has the best catalytic performance,which is attributed to the high electrochemically active surface area(ECSA).Finally,the four different macrostructures were achieved by 3D printing,and they further improved the catalytic performance.The gyroid structure exhibits the best catalytic performance of driving 500 mA cm^(-2)at a low overpotential of 228 mV and 1500 mA cm^(-2)at 325 mV,as it maximizes the efficient bubble removal from the electrode surface,which offers the great potential for high current density water splitting.

Stability and accuracy of central difference method for real-time dynamic substructure testing considering mass participation coefficient

For real-time dynamic substructure testing(RTDST),the influence of the inertia force of fluid specimens on the stability and accuracy of the integration algorithms has never been investigated.Therefore,this study proposes to investigate the stability and accuracy of the central difference method(CDM)for RTDST considering the specimen mass participation coefficient.First,the theory of the CDM for RTDST is presented.Next,the stability and accuracy of the CDM for RTDST considering the specimen mass participation coefficient are investigated.Finally,numerical simulations and experimental tests are conducted for verifying the effectiveness of the method.The study indicates that the stability of the algorithm is affected by the mass participation coefficient of the specimen,and the stability limit first increases and then decreases as the mass participation coefficient increases.In most cases,the mass participation coefficient will increase the stability limit of the algorithm,but in specific circumstances,the algorithm may lose its stability.The stability and accuracy of the CDM considering the mass participation coefficient are verified by numerical simulations and experimental tests on a three-story frame structure with a tuned liquid damper.

An Optimized Approach to Deep Learning for Botnet Detection and Classification for Cybersecurity in Internet of Things Environment

The recent development of the Internet of Things(IoTs)resulted in the growth of IoT-based DDoS attacks.The detection of Botnet in IoT systems implements advanced cybersecurity measures to detect and reduce malevolent botnets in interconnected devices.Anomaly detection models evaluate transmission patterns,network traffic,and device behaviour to detect deviations from usual activities.Machine learning(ML)techniques detect patterns signalling botnet activity,namely sudden traffic increase,unusual command and control patterns,or irregular device behaviour.In addition,intrusion detection systems(IDSs)and signature-based techniques are applied to recognize known malware signatures related to botnets.Various ML and deep learning(DL)techniques have been developed to detect botnet attacks in IoT systems.To overcome security issues in an IoT environment,this article designs a gorilla troops optimizer with DL-enabled botnet attack detection and classification(GTODL-BADC)technique.The GTODL-BADC technique follows feature selection(FS)with optimal DL-based classification for accomplishing security in an IoT environment.For data preprocessing,the min-max data normalization approach is primarily used.The GTODL-BADC technique uses the GTO algorithm to select features and elect optimal feature subsets.Moreover,the multi-head attention-based long short-term memory(MHA-LSTM)technique was applied for botnet detection.Finally,the tree seed algorithm(TSA)was used to select the optimum hyperparameter for the MHA-LSTM method.The experimental validation of the GTODL-BADC technique can be tested on a benchmark dataset.The simulation results highlighted that the GTODL-BADC technique demonstrates promising performance in the botnet detection process.

Multiscale and Auto-Tuned Semi-Supervised Deep Subspace Clustering and Its Application in Brain Tumor Clustering

In this paper,we introduce a novel Multi-scale and Auto-tuned Semi-supervised Deep Subspace Clustering(MAS-DSC)algorithm,aimed at addressing the challenges of deep subspace clustering in high-dimensional real-world data,particularly in the field of medical imaging.Traditional deep subspace clustering algorithms,which are mostly unsupervised,are limited in their ability to effectively utilize the inherent prior knowledge in medical images.Our MAS-DSC algorithm incorporates a semi-supervised learning framework that uses a small amount of labeled data to guide the clustering process,thereby enhancing the discriminative power of the feature representations.Additionally,the multi-scale feature extraction mechanism is designed to adapt to the complexity of medical imaging data,resulting in more accurate clustering performance.To address the difficulty of hyperparameter selection in deep subspace clustering,this paper employs a Bayesian optimization algorithm for adaptive tuning of hyperparameters related to subspace clustering,prior knowledge constraints,and model loss weights.Extensive experiments on standard clustering datasets,including ORL,Coil20,and Coil100,validate the effectiveness of the MAS-DSC algorithm.The results show that with its multi-scale network structure and Bayesian hyperparameter optimization,MAS-DSC achieves excellent clustering results on these datasets.Furthermore,tests on a brain tumor dataset demonstrate the robustness of the algorithm and its ability to leverage prior knowledge for efficient feature extraction and enhanced clustering performance within a semi-supervised learning framework.

Credit Card Fraud Detection Using Improved Deep Learning Models

Fraud of credit cards is a major issue for financial organizations and individuals.As fraudulent actions become more complex,a demand for better fraud detection systems is rising.Deep learning approaches have shown promise in several fields,including detecting credit card fraud.However,the efficacy of these models is heavily dependent on the careful selection of appropriate hyperparameters.This paper introduces models that integrate deep learning models with hyperparameter tuning techniques to learn the patterns and relationships within credit card transaction data,thereby improving fraud detection.Three deep learning models:AutoEncoder(AE),Convolution Neural Network(CNN),and Long Short-Term Memory(LSTM)are proposed to investigate how hyperparameter adjustment impacts the efficacy of deep learning models used to identify credit card fraud.The experiments conducted on a European credit card fraud dataset using different hyperparameters and three deep learning models demonstrate that the proposed models achieve a tradeoff between detection rate and precision,leading these models to be effective in accurately predicting credit card fraud.The results demonstrate that LSTM significantly outperformed AE and CNN in terms of accuracy(99.2%),detection rate(93.3%),and area under the curve(96.3%).These proposed models have surpassed those of existing studies and are expected to make a significant contribution to the field of credit card fraud detection.

Heterostructural NiFeW disulfide and hydroxide dual‐trimetallic core‐shell nanosheets for synergistically effective water oxidatio

A stable and highly active core‐shell heterostructure electrocatalyst is essential for catalyzing oxygen evolution reaction(OER).Here,a dual‐trimetallic core‐shell heterostructure OER electrocatalyst that consists of a NiFeWS_(2) inner core and an amorphous NiFeW(OH)_(z)outer shell is designed and synthesized using in situ electrochemical tuning.The electrochemical measurements of different as‐synthesized catalysts with a similar mass loading suggest that the core‐shell Ni_(0.66)Fe_(0.17)W_(0.17)S_(2)@amorphous NiFeW(OH)_(z) nanosheets exhibit the highest overall performance compared with that of other bimetallic reference catalysts for the OER.Additionally,the nanosheet arrays were in situ grown on hydrophilic‐treated carbon paper to fabricate an integrated three‐dimensional electrode that affords a current density of 10 mA cm^(−2) at a small overpotential of 182 mV and a low Tafel slope of 35 mV decade^(−1) in basic media.The Faradaic efficiency of core‐shell Ni_(0.66)Fe_(0.17)W_(0.17)S_(2)@amorphous NiFeW(OH)_(z) is as high as 99.5% for OER.The scanning electron microscope,transmission electron microscope,and X‐ray photoelectron spectroscopy analyses confirm that this electrode has excellent stability in morphology and elementary composition after long‐term electrochemical measurements.Importantly,density functional theory calculations further indicate that the core‐shell heterojunction increased the conductivity of the catalyst,optimized the adsorption energy of the OER intermediates,and improved the OER activity.This study provides a universal strategy for designing more active core‐shell structure electrocatalysts based on the rule of coordinated regulation between electronic transport and active sites.