A novel small-signal equivalent circuit model for GaN HEMTs incorporating a dual-field-plate

An accurate and novel small-signal equivalent circuit model for GaN high-electron-mobility transistors(HEMTs)is proposed,which considers a dual-field-plate(FP)made up of a gate-FP and a source-FP.The equivalent circuit of the overall model is composed of parasitic elements,intrinsic transistors,gate-FP,and source-FP networks.The equivalent circuit of the gate-FP is identical to that of the intrinsic transistor.In order to simplify the complexity of the model,a series combination of a resistor and a capacitor is employed to represent the source-FP.The analytical extraction procedure of the model parameters is presented based on the proposed equivalent circuit.The verification is carried out on a 4×250μm GaN HEMT device with a gate-FP and a source-FP in a 0.45μm technology.Compared with the classic model,the proposed novel small-signal model shows closer agreement with measured S-parameters in the range of 1.0 to 18.0 GHz.

A Quasi-steady-state Model for Numerical Simulation of Batch Extractive Distillation

Batch extractive distillation(BED)is a special method used in the distillation process by adding a solvent into the batch distillation column to alter the relative volatility of the components and improve the separation. A comprehensive design and simulation method is required due to the complexity of BED.In this study,a quasi-steady-state model for BED is proposed,the derivation and solution of the model are presented.This shortcut model can be used to simulate the composition and temperature of the reboiler,the top and other plates of the column in a batch extractive distillation operation.The calculated values are in good agreement with the experimental data.The results show that the quasi-steady-state model is a practical method because of some advantages such as high precision and fast calculation.

A Novel Equivalent Circuit Model of GaAs PIN Diodes

A novel equivalent circuit model for a GaAs PIN diode is presented based on physical analysis. The diode is divided into three parts： the p^＋ n^- junction, the i-layer, and the n^- n^＋ junction, which are modeled separately. The entire model is then formed by combining the three sub-models. In this way, the model＇s accuracy is greatly enhanced. Furthermore, the corresponding parameter extraction method is easy, requiring no rigorous experiment or measurement. To validate this newly proposed model,fifteen groups of diodes are fabricated. Measurement shows that the model exactly represents behavior of GaAs PIN diodes under both forward and reversely biased conditions.

Small-Signal Equivalent Circuit Modeling of a Photodetector Chip

A small-signal equivalent circuit model and the ted. The equivalent lumped circuit, which takes the main extraction techniques for photodetector chips are presen- factors that limit a photodetector＇s RF performance into consideration,is first determined based on the device＇s physical structure. The photodetector＇s S parameters are then on-wafer measured, and the measured raw data are processed with further calibration. A genetic algorithm is used to fit the measured data, thereby allowing us to calculate each parameter value of the model. Experimental resuits show that the modeled parameters are well matched to the measurements in a frequency range from 130MHz to 20GHz, and the proposed method is proved feasible. This model can give an exact description of the photodetector chip＇s high frequency performance,which enables an effective circuit-level prediction for photodetector and optoelectronic integrated circuits.

Parameter Extraction for 2-π Equivalent Circuit Model of RF CMOS Spiral Inductors

A novel parameter extraction method with rational functions is presented for the 2-πequivalent circuit model of RF CMOS spiral inductors. The final S-parameters simulated by the circuit model closely match experimental data. The extraction strategy is straightforward and can be easily implemented as a CAD tool to model spiral inductors. The resulting circuit models will be very useful for RF circuit designers.

Artificial intelligence-driven radiomics study in cancer:the role of feature engineering and modeling

Modern medicine is reliant on various medical imaging technologies for non-invasively observing patients’anatomy.However,the interpretation of medical images can be highly subjective and dependent on the expertise of clinicians.Moreover,some potentially useful quantitative information in medical images,especially that which is not visible to the naked eye,is often ignored during clinical practice.In contrast,radiomics performs high-throughput feature extraction from medical images,which enables quantitative analysis of medical images and prediction of various clinical endpoints.Studies have reported that radiomics exhibits promising performance in diagnosis and predicting treatment responses and prognosis,demonstrating its potential to be a non-invasive auxiliary tool for personalized medicine.However,radiomics remains in a developmental phase as numerous technical challenges have yet to be solved,especially in feature engineering and statistical modeling.In this review,we introduce the current utility of radiomics by summarizing research on its application in the diagnosis,prognosis,and prediction of treatment responses in patients with cancer.We focus on machine learning approaches,for feature extraction and selection during feature engineering and for imbalanced datasets and multi-modality fusion during statistical modeling.Furthermore,we introduce the stability,reproducibility,and interpretability of features,and the generalizability and interpretability of models.Finally,we offer possible solutions to current challenges in radiomics research.

Quantitative Damage Detection for Planetary Gear Sets Based on Physical Models

Planetary gear set is the critical component in helicopter transmission train, and an important problem in condition monitoring and health management of planetary gear set is quantitative damage detection. In order to resolve this problem, an approach based on physical models is presented to detect damage quantitatively in planetary gear set. A particular emphasis is put on a feature generation and selection method, which is used for sun gear tooth breakage damage detection quantitatively in planetary gear box of helicopter transmission system. In this feature generation procedure, the pure torsional dynamical models of 2K-H planetary gear set is established for healthy case and sun gear tooth-breakage case. Then, a feature based on the spectrum of simulation signals of the dynamical models is generated. Aiming at selecting the best feature suitable for quantitative damage detection, a two-sample Z-test procedure is used to analyze the performance of features on damage evolution tracing. A feature named SR, which had better performance in tracking damage, is proposed to detect damage in planetary gear set. Meanwhile, the sun gear tooth-chipped seeded experiments with different severity are designed to validate the method above, and then the test vibration signal is picked up and used for damage detection. With the results of several experiments for quantitative damage detection, the feasibility and the effect of this approach are verified. The proposed method can supply an effective tool for degradation state identification in condition monitoring and health management of helicopter transmission system.

Extractive distillation of the benzene and acetonitrile mixture using an ionic liquid as the entrainer

The benzene and acetonitrile azeotropic mixture was proposed to be separated by extractive distillation using an ionic liquid(IL)as the entrainer.The suitable IL was selected by the COSMO-RS model,and 1-ethyl-3-methylimidazolium tetrafluoroborate([EMIM][BF_(4)])was considered as the suitable entrainer mainly due to its high selectivity,low viscosity,and low price.The experimental vapor pressure data of the IL-containing systems(benzene+[EMIM][BF_(4)]and acetonitrile+[EMIM][BF_(4)])were measured in the full concentration range.The results show that acetonitrile has a stronger interaction with IL than benzene,and the low deviations between the experimental and UNIFAC predicted values show the reliability of the UNFIAC model.The UNIFAC predicted vapor-liquid equilibrium data of the benzene+acetonitrile+dimethyl sulfoxide(DMSO)/[EMIM][BF_(4)]system show that the relative volatility of benzene to acetonitrile is higher when the entrainer is[EMIM][BF_(4)].The process simulation results show that[EMIM][BF_(4)]can reduce the material and energy consumptions compared with DMSO.

Numerical research of a 2D axial symmetry hybrid model for the radio-frequency ion thruster

Since the high efficiency discharge is critical to the radio-frequency ion thruster(RIT), a 2D axial symmetry hybrid model has been developed to study the plasma evolution of RIT. The fluid method and the drift energy correction of the electron energy distribution function(EEDF) are applied to the analysis of the RIT discharge. In the meantime, the PIC-MCC method is used to investigate the ion beam current extraction character for the plasma plume region. The beam current simulation results, with the hybrid model, agree well with the experimental results, and the error is lower than 11%, which shows the validity of the model. The further study shows there is an optimal ratio for the radio-frequency(RF) power and the beam current extraction power under the fixed RIT configuration. And the beam extraction efficiency will decrease when the discharge efficiency beyond a certain threshold(about 87 W). As the input parameters of the hybrid model are all the design values, it can be directly used to the optimum design for other kinds of RITs and radio-frequency ion sources.

Keladi candik(Alocasia longiloba Miq.) petiole extracts promote wound healing in a full thickness excision wound model in rats

Objective: To investigate the wound-healing effect of Alocasia longiloba(A. longiloba) petiole extract on wounds in rats.Methods: Twenty-two male Sprague-dawley rats were randomly assigned to receive 10% solcoseryl gel, phosphate buffer saline, 50% ethanol, 95% ethanol and hexane extracts of A. longiloba at 1.5%, 3% and 6% doses, respectively. A full thicknesses wound(6 mm) was created on the dorsal of the rat; and all rats were applied with the extract solutions, 10% solcoseryl gel and phosphate buffer saline once a day topically until day 12. The wound was photographed on day 1, 6 and 12, and the percentage of wound contraction was calculated. On day 12, rats were sacrificed and histological examination of granulation tissue was carried out using haematoxylin & eosin and Masson's Trichrome stain to determine the wound healing effect.Results: In this study, 6% of 50% and 95% ethanol extracts of A. longiloba showed 82.50% and 82.32% wound contraction, respectively, and were comparable with 10% solcoseryl gel(82.30%). Meanwhile, phosphate buffer saline treated group showed the lowest wound contraction(69.86%). Histological assessment of wound treated with 6% of 95% ethanol extract of A. longiloba showed distinct epidermal and dermal layer, higher proliferation of fibroblast and more angiogenesis with collagen compared to other wound treated groups.Conclusions: A. longiloba petiole extracts have a wound healing potential and 6% of 95% ethanol extract of A. longiloba is more effective. Further studies are required to understand the wound healing mechanism of action of the extract.

基于并行SDAE-Seq2Seq模型的轴承寿命预测方法

基于数据驱动的轴承寿命预测方法大多需要人工提取退化特征,而且对于不同工况下的轴承需要进行针对性优化,也是依赖专家知识和经验进行特征提取。为此,提出一种并行堆叠降噪自动编码器算法(PSDAE)来提取轴承退化特征,并结合Seq2Seq模型预测轴承剩余寿命。通过PSDAE直接对原始振动信号进行降噪、降维,得到退化特征,通过神经网络的学习和训练自动获得不同工况下的轴承退化特征。其次,引入注意力机制,将提取的特征输入Seq2Seq模型进行训练,并在PHM2012数据集上验证模型的预测效果。实验结果表明:PSDAE通过并行集成方式降低了模型的训练参数和整体误差,提取的退化特征在单调性和可预测性方面优于堆叠降噪自动编码器(SDAE),使用该退化特征有效减少了Seq2Seq模型的预测误差,提高了预测得分,具有更好的预测效果和稳定性。

Insight into the experiment and extraction mechanism for separating carbazole from anthracene oil with quaternary ammonium-based deep eutectic solvents

Carbazole is an irreplaceable basic organic chemical raw material and intermediate in industry.The separation of carbazole from anthracene oil by environmental benign solvents is important but still a challenge in chemical engineering.Deep eutectic solvents (DESs) as a sustainable green separation solvent have been proposed for the separation of carbazole from model anthracene oil.In this research,three quaternary ammonium-based DESs were prepared using ethylene glycol (EG) as hydrogen bond donor and tetrabutylammonium chloride (TBAC),tetrabutylammonium bromide or choline chloride as hydrogen bond acceptors.To explore their extraction performance of carbazole,the conductor-like screening model for real solvents (COSMO-RS) model was used to predict the activity coefficient at infinite dilution (γ^(∞)) of carbazole in DESs,and the result indicated TBAC:EG (1:2) had the stronger extraction ability for carbazole due to the higher capacity at infinite dilution (C^(∞)) value.Then,the separation performance of these three DESs was evaluated by experiments,and the experimental results were in good agreement with the COSMO-RS prediction results.The TBAC:EG (1:2) was determined as the most promising solvent.Additionally,the extraction conditions of TBAC:EG (1:2) were optimized,and the extraction efficiency,distribution coefficient and selectivity of carbazole could reach up to 85.74%,30.18 and 66.10%,respectively.Moreover,the TBAC:EG (1:2) could be recycled by using environmentally friendly water as antisolvent.In addition,the separation performance of TBAC:EG (1:2) was also evaluated by real crude anthracene,the carbazole was obtained with purity and yield of 85.32%,60.27%,respectively.Lastly,the extraction mechanism was elucidated byσ-profiles and interaction energy analysis.Theoretical calculation results showed that the main driving force for the extraction process was the hydrogen bonding ((N–H...Cl) and van der Waals interactions (C–H...O and C–H...π),which corresponding to the blue and green isosurfaces in IGMH analysis.This work presented a novel method for separating carbazole from crude anthracene oil,and will provide an important reference for the separation of other high value-added products from coal tar.

Phase equilibrium data prediction and process optimizationin butadiene extraction process

In response to the lack of reliable physical parameters in the process simulation of the butadiene extraction,a large amount of phase equilibrium data were collected in the context of the actual process of butadiene production by acetonitrile.The accuracy of five prediction methods,UNIFAC(UNIQUAC Functional-group Activity Coefficients),UNIFAC-LL,UNIFAC-LBY,UNIFAC-DMD and COSMO-RS,applied to the butadiene extraction process was verified using partial phase equilibrium data.The results showed that the UNIFAC-DMD method had the highest accuracy in predicting phase equilibrium data for the missing system.COSMO-RS-predicted multiple systems showed good accuracy,and a large number of missing phase equilibrium data were estimated using the UNIFAC-DMD method and COSMO-RS method.The predicted phase equilibrium data were checked for consistency.The NRTL-RK(non-Random Two Liquid-Redlich-Kwong Equation of State)and UNIQUAC thermodynamic models were used to correlate the phase equilibrium data.Industrial device simulations were used to verify the accuracy of the thermodynamic model applied to the butadiene extraction process.The simulation results showed that the average deviations of the simulated results using the correlated thermodynamic model from the actual values were less than 2%compared to that using the commercial simulation software,Aspen Plus and its database.The average deviation was much smaller than that of the simulations using the Aspen Plus database(>10%),indicating that the obtained phase equilibrium data are highly accurate and reliable.The best phase equilibrium data and thermodynamic model parameters for butadiene extraction are provided.This improves the accuracy and reliability of the design,optimization and control of the process,and provides a basis and guarantee for developing a more environmentally friendly and economical butadiene extraction process.

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.

Analyzing COVID-19 Discourse on Twitter: Text Clustering and Classification Models for Public Health Surveillance

Social media has revolutionized the dissemination of real-life information,serving as a robust platform for sharing life events.Twitter,characterized by its brevity and continuous flow of posts,has emerged as a crucial source for public health surveillance,offering valuable insights into public reactions during the COVID-19 pandemic.This study aims to leverage a range of machine learning techniques to extract pivotal themes and facilitate text classification on a dataset of COVID-19 outbreak-related tweets.Diverse topic modeling approaches have been employed to extract pertinent themes and subsequently form a dataset for training text classification models.An assessment of coherence metrics revealed that the Gibbs Sampling Dirichlet Mixture Model(GSDMM),which utilizes trigram and bag-of-words(BOW)feature extraction,outperformed Non-negative Matrix Factorization(NMF),Latent Dirichlet Allocation(LDA),and a hybrid strategy involving Bidirectional Encoder Representations from Transformers(BERT)combined with LDA and K-means to pinpoint significant themes within the dataset.Among the models assessed for text clustering,the utilization of LDA,either as a clustering model or for feature extraction combined with BERT for K-means,resulted in higher coherence scores,consistent with human ratings,signifying their efficacy.In particular,LDA,notably in conjunction with trigram representation and BOW,demonstrated superior performance.This underscores the suitability of LDA for conducting topic modeling,given its proficiency in capturing intricate textual relationships.In the context of text classification,models such as Linear Support Vector Classification(LSVC),Long Short-Term Memory(LSTM),Bidirectional Long Short-Term Memory(BiLSTM),Convolutional Neural Network with BiLSTM(CNN-BiLSTM),and BERT have shown outstanding performance,achieving accuracy and weighted F1-Score scores exceeding 80%.These results significantly surpassed other models,such as Multinomial Naive Bayes(MNB),Linear Support Vector Machine(LSVM),and Logistic Regression(LR),which achieved scores in the range of 60 to 70 percent.

Measurement of Mass Transfer Coefficients and Their Modelling of Continuous Counter-Current Aqueous Two-Phase System in a Packed Extraction Column

Overall dispersed side volumetric mass transfer coefficients for protein and amino acids were measured in continuous countercurrent PEG4000/KHP aqueous two-phase systems in a 57mm I.D. packed extraction column. A model for overall dispersed side volumetric mass transfer coefficients was derived by describing the motion of the drops based upon Navier-Stokes equation combined with the relationship between mass transfer coefficients and the drop velocity. The model provides good predictions and can be successfully used in aqueous two-phase extraction. The average relative deviation between calculated values and experimental data ranges from 8% to 14%.

A review of road 3D modeling based on light detection and ranging point clouds

Increasing development of accurate and efficient road three-dimensional(3D)modeling presents great opportunities to improve the data exchange and integration of building information modeling(BIM)models.3D modeling of road scenes is crucial for reference in asset management,construction,and maintenance.Light detection and ranging(Li DAR)technology is increasingly employed to generate high-quality point clouds for road inventory.In this paper,we specifically investigate the use of Li DAR data for road 3D modeling.The purpose of this review is to provide references about the existing work on the road 3D modeling based on Li DAR point clouds,critically discuss them,and provide challenges for further study.Besides,we introduce modeling standards for roads and discuss the components,types,and distinctions of various Li DAR measurement systems.Then,we review state-of-the-art methods and provide a detailed examination of road segmentation and feature extraction.Furthermore,we systematically introduce point cloud-based 3D modeling methods,namely,parametric modeling and surface reconstruction.Parameters and rules are used to define model components based on geometric and non-geometric information,whereas surface modeling is conducted through individual faces within its geometry.Finally,we discuss and summarize future research directions in this field.This review can assist researchers in enhancing existing approaches and developing new techniques for road modeling based on Li DAR point clouds.

Equilibrium of liquid-liquid extraction of 2-phenylbutyric acid enantiomers: Experiment and model

The utilization of liquid–liquid extraction for the separation of 2-phenylbutyric acid（2-PBA） enantiomers was proposed. Factors affecting the extract process were investigated, including organic solvents, β-cyclodextrin derivatives, cyclodextrin concentration, p H and temperature. A model was proposed to describe the separation process based on the homogeneous phase reaction mechanism. Important parameters of this model were determined experimentally. The physical distribution coefficients for molecular and ionic 2-PBA were0.129 and 7.455, respectively. The equilibrium constants of the complexation reactions were 89.36 and36.78 L·mol^-1 for（＋）-and（-）-2-PBA, respectively. The model was verified by experiments and proved to be an excellent means to optimize the separation system. Through modeling prediction and experiment, the best conditions（e.g., pH value of 3.00, extractant concentration of 0.1 mol·L^-1, temperature of 5.0 ℃） were acquired. Under this condition, the maximum enantioselectivity（2.096） was obtained.

Analysis of characteristic functions for equivalent circuit model in monolithic transformer

A model of monolithic transformers is presented, which is analyzed with characteristic functions. A closed- form analytical approach to extract all the model parameters for the equivalent circuit of Si-based on-chip transformers is proposed. A novel de-coupling technique is first developed to reduce the complexity in the Y parameters for the transformer, and the model parameters can then be extracted analytically by a set of characteristic functions. Simulation based on the extracted parameters has been carried out for transformers with different structures, and good accuracy is obtained compared to a 3-demensional full-wave numerical electro- magnetic field solver. The presented approach will be very useful to provide a scalable and wide-band compact circuit model for Si-based RF transformers.

Selective ensemble modeling based on nonlinear frequency spectral feature extraction for predicting load parameter in ball mills

Strong mechanical vibration and acoustical signals of grinding process contain useful information related to load parameters in ball mills. It is a challenge to extract latent features and construct soft sensor model with high dimensional frequency spectra of these signals. This paper aims to develop a selective ensemble modeling approach based on nonlinear latent frequency spectral feature extraction for accurate measurement of material to ball volume ratio. Latent features are first extracted from different vibrations and acoustic spectral segments by kernel partial least squares. Algorithms of bootstrap and least squares support vector machines are employed to produce candidate sub-models using these latent features as inputs. Ensemble sub-models are selected based on genetic algorithm optimization toolbox. Partial least squares regression is used to combine these sub-models to eliminate collinearity among their prediction outputs. Results indicate that the proposed modeling approach has better prediction performance than previous ones.