The Mechanism of Heating Rate on the Secondary Recrystallization Evolution in Grain Oriented Silicon Steel

Grain-oriented silicon steels were prepared at different heating rates during high temperature annealing,in which the evolution of magnetic properties,grain orientations and precipitates were studied.To illustrate the Zener factor,the diameter and number density of precipitates of interrupted testing samples were statistically calculated.The effect of precipitate ripening on the Goss texture and magnetic property was investigated.Data indicated that the trend of Zener factor was similar under different heating rates,first increasing and then decreasing,and that the precipitate maturing was greatly inhibited as the heating rate increased.Secondary recrystallization was developed at the temperature of 1010℃when a heating rate of 5℃/h was used,resulting in Goss,Brass and{110}<227>oriented grains growing abnormally and a magnetic induction intensity of 1.90T.Furthermore,increasing the heating rate to 20℃/h would inhibit the development of undesirable oriented grains and obtain a sharp Goss texture.However,when the heating rate was extremely fast,such as 40℃/h,poor secondary recrystallization was developed with many island grains,corresponding to a decrease in magnetic induction intensity to 1.87 T.At a suitable heating rate of 20℃/h,the sharpest Goss texture and the highest magnetic induction of 1.94 T with an onset secondary recrystallization temperature of 1020℃were found among the experimental variables in this study.The heating rate affected the initial temperature of secondary recrystallization by controlling the maturation of precipitates,leading to the deviation and dispersion of Goss texture,thereby reducing the magnetic properties.

Glomerular filtration rate and comorbidity factors in elderly hospitalizations

BACKGROUND With an increase in the elderly population,the frequency of hospitalizations in recent years has also risen at a rapid pace.This,in turn,has resulted in poor outcomes and costly treatments.Hospitalization rates increase in elderly patients due to a decline in glomerular filtration rate(GFR).AIM To investigate the connection between GFR and comorbidity and reasons for hospitalization in elderly patients.METHODS We analyzed patients aged 75 years and over who were admitted to the internal medicine clinic of a tertiary hospital in Eskisehir.At admission,we calculated GFR values using the Modification of Diet in Renal Disease study formula and classified them into six categories:G1,G2,G3a,G3b,G4,and G5.We analyzed associations with hospitalization diagnoses and comorbidity factors.RESULTS The average age of the patients was 80.8 years(±4.5 years).GFR was 57.287±29.5 mL/kg/1.73 m2 in women and 61.3±31.5 mL/kg/1.73 m2 in men(P=0.106).Most patients were admitted to the hospital at G2 stage(32.8%).The main reasons for hospitalization were anemia(34.4%and 28.6%)and malnutrition(20.9%and 20.8%)in women and men,respectively(P=0.078).The most frequent comor-bidity leading to hospitalization was arterial hypertension(n=168,28%),fo-llowed by diabetes(n=166,27.7%)(P=0.001).CONCLUSION When evaluating geriatric patients,low GFR alone does not provide sufficient information.Patients’comorbid factors should also be taken into account.There is no association between low GFR during hospitalization and hospitalization-Hamarat H.Aging and GFR related diagnoses.Knowing the GFR value before hospitalization will be more informative in such studies.

Text-and-Timbre-Based Speech Semantic Coding for Ultra-Low-Bitrate Communications

To address the contradiction between the explosive growth of wireless data and the limited spectrum resources,semantic communication has been emerging as a promising communication paradigm.In this paper,we thus design a speech semantic coded communication system,referred to as Deep-STS(i.e.,Deep-learning based Speech To Speech),for the lowbandwidth speech communication.Specifically,we first deeply compress the speech data through extracting the textual information from the speech based on the conformer encoder and connectionist temporal classification decoder at the transmitter side of Deep-STS system.In order to facilitate the final speech timbre recovery,we also extract the short-term timbre feature of speech signals only for the starting 2s duration by the long short-term memory network.Then,the Reed-Solomon coding and hybrid automatic repeat request protocol are applied to improve the reliability of transmitting the extracted text and timbre feature over the wireless channel.Third,we reconstruct the speech signal by the mel spectrogram prediction network and vocoder,when the extracted text is received along with the timbre feature at the receiver of Deep-STS system.Finally,we develop the demo system based on the USRP and GNU radio for the performance evaluation of Deep-STS.Numerical results show that the ac-Received:Jan.17,2024 Revised:Jun.12,2024 Editor:Niu Kai curacy of text extraction approaches 95%,and the mel cepstral distortion between the recovered speech signal and the original one in the spectrum domain is less than 10.Furthermore,the experimental results show that the proposed Deep-STS system can reduce the total delay of speech communication by 85%on average compared to the G.723 coding at the transmission rate of 5.4 kbps.More importantly,the coding rate of the proposed Deep-STS system is extremely low,only 0.2 kbps for continuous speech communication.It is worth noting that the Deep-STS with lower coding rate can support the low-zero-power speech communication,unveiling a new era in ultra-efficient coded communications.

On load dependence of detachment rate of kinesin motor

Kinesin is an archetypal microtubule-based molecular motor that can generate force to transport cargo in cells. The load dependence of the detachment rate is an important factor of the kinesin motor, the determination of which is critically related to the chemomechanical coupling mechanism of the motor. Here, we use three models for the load dependence of the detachment rate of the kinesin motor to study theoretically and numerically the maximal force generated and microtubuleattachment duration of the motor. By comparing the theoretical and numerical results with the available experimental data,we show that only one model can explain well the available experimental data, indicating that only this model can be applicable to the kinesin motor.

Effect of comprehensive perioperative nursing on pain intensity,complication rates,and comfort levels in patients undergoing gallstone surgery

BACKGROUND Surgery is the gold standard for gallstone treatment.Nevertheless,the complications associated with the surgical procedure can exert diverse and adverse impacts on patients’health and quality of life to varying extents.Hence,it is essential to offer perioperative care to patients undergoing gallstone surgery.AIM To examine the impact of perioperative comprehensive nursing on pain intensity,complication rates,and patient comfort in individuals undergoing gallstone surgery.METHODS From February 2022 to February 2024,195 patients who underwent gallstone surgery at Sanmen People’s Hospital were selected and divided into two groups:A control group receiving routine nursing care(95 patients)and a research group receiving perioperative comprehensive nursing(100 patients).Key postoperative recovery indicators,including time to first postoperative anal exhaust,oral food intake,and ambulation,were observed,along with pain intensity(measured by the numeric rating scale),complication rate(bleeding,incision infection,recurrence),patient comfort(assessed using the visual analogue scale),and quality of life(measured by the World Health Organization Quality of Life-BREF).RESULTS The research group showed significantly shorter times to first postoperative anal exhaust,oral intake,and ambulation.Moreover,numeric rating scale pain scores in the research group were markedly lower post-nursing,and the total complication rate was significantly reduced compared to the control group.Furthermore,comfort levels improved considerably in the research group,and World Health Organization Quality of Life-BREF scores across the physical,psychological,social,and environmental domains were significantly higher compared to the control group following nursing care.CONCLUSION Perioperative comprehensive nursing effectively enhances postoperative recovery in patients undergoing gallstone surgery,reducing pain,lowering complications,and improving patient comfort and quality of life,which deserves clinical application.

Failure rate analysis and maintenance plan optimization method for civil aircraft parts based on data fusion

In the face of data scarcity in the optimization of maintenance strategies for civil aircraft,traditional failure data-driven methods are encountering challenges owing to the increasing reliability of aircraft design.This study addresses this issue by presenting a novel combined data fusion algorithm,which serves to enhance the accuracy and reliability of failure rate analysis for a specific aircraft model by integrating historical failure data from similar models as supplementary information.Through a comprehensive analysis of two different maintenance projects,this study illustrates the application process of the algorithm.Building upon the analysis results,this paper introduces the innovative equal integral value method as a replacement for the conventional equal interval method in the context of maintenance schedule optimization.The Monte Carlo simulation example validates that the equivalent essential value method surpasses the traditional method by over 20%in terms of inspection efficiency ratio.This discovery indicates that the equal critical value method not only upholds maintenance efficiency but also substantially decreases workload and maintenance costs.The findings of this study open up novel perspectives for airlines grappling with data scarcity,offer fresh strategies for the optimization of aviation maintenance practices,and chart a new course toward achieving more efficient and cost-effective maintenance schedule optimization through refined data analysis.

Thermal effects on the strain rate-dependent behavior of highly compacted GMZ01 bentonite

Investigation of thermal effects on the strain rate-dependent properties of compacted bentonite is crucial for the long-term safety assessment of deep geological repository for disposal of high-level radioactive waste.In the present work,cylindrical GMZ01 bentonite specimens were compacted with suction-controlled by the vapor equilibrium technique.Then,a series of temperature-and suction-controlled stepwise constant rate of strain(CRS)tests was performed and the rate-dependent compressibility behavior of the highly compacted GMZ01 bentonite was investigated.The plastic compressibility parameterλ,the elastic compressibility parameterκ,the yield stress p0,as well as the viscous parameterαwere determined.Results indicate thatλ,κandαdecrease and p0 increases as suction increases.Upon heating,parametersλ,αand p0 decrease.It is also found that p0 increases linearly with increasing CRS in a double-logarithm coordinate.Based on the experimental results,a viscosity parameterα(s,T)was fitted to capture the effects of suction s and temperature T on the relationship between yield stress and strain rate.Then,an elastic-thermo-viscoplastic model for unsaturated soils was developed to describe the thermal effects on the rate-dependent behavior of highly compacted GMZ01 bentonite.Validation showed that the calculated results agreed well to the measured ones.

Study on discharge characteristics of low-temperature sub-atmospheric pressure under steep change rate voltage

Aiming at the gas discharge problem in electric aircraft,this work studies the gas discharge characteristics at low-temperature sub-atmospheric pressure.A gas discharge shooting platform was built,and the discharge process was photographed by intensified charge-coupled device(ICCD).A two-dimensional axisymmetric model of needle-plate electrode gas discharge was established,and three sets of Helmholtz equations were used to solve the photoionization.The results show that under the same voltage,the electric field intensity in the discharge process increases first,then decreases and finally increases again.The discharge speed increases with the increase of altitude,and the electron density in the streamer decreases with the increase of altitude.The development speed of the streamer in the middle stage is higher than that in the early stage,and the speed increases more obviously with the increase of altitude.The development speed of the streamer in the later stage is lower than that in the middle stage,but with the increase of altitude,the development speed of the streamer in the later stage is higher than that in the middle stage.

Longitudinal assessment of measured and estimated glomerular filtration-rate in autosomal dominant polycystic kidney disease:Real practice experience

BACKGROUND Equations for estimation glomerular filtration rate(eGFR)have been associated with poor clinical performance and their clinical accuracy and reliability have been called into question.AIM To assess the longitudinal changes in measured glomerular filtration rate(mGFR)in patients with autosomal dominant polycystic kidney disease(ADPKD).METHODS Analysis of an ambispective data base conducted on consecutive patients diagnosed with ADPKD.The mGFR was assessed by iohexol clearance;while eGFR was calculated by three different formulas:(1)The chronic kidney disease epidemiology collaboration(CKD-EPI);(2)Modification of diet in renal disease(MDRD);and(3)The 24-hour urine creatinine clearance(CrCl).The primary end-points were the mean change in mGFR between the baseline and final visit,as well as the comparison of the mean change in mGFR with the change estimated by the different formulas.RESULTS Thirty-seven patients were included in the study.As compared to baseline,month-6 mGFR was significantly decrease by-4.4 mL/minute±10.3 mL/minute(P=0.0132).However,the CKD-EPI,MDRD,and CrCl formulas underestimated this change by 48.3%,89.0%,and 45.8%respectively,though none of these differences reached statistical significance(P=0.3647;P=0.0505;and P=0.736,respectively).The discrepancies between measured and estimated glomerular filtration rate values,as evaluated by CKD-EPI(r=0.29,P=0.086);MDRD(r=0.19,P=0.272);and CrCl(r=0.09,P=0.683),were not correlated with baseline mGFR values.CONCLUSION This study indicated that eGFR inaccurately reflects the decline in mGFR and cannot reliably track changes over time.This poses significant challenges for clinical decision-making,particularly in treatment strategies.

Prediction model for corrosion rate of low-alloy steels under atmospheric conditions using machine learning algorithms

This work constructed a machine learning(ML)model to predict the atmospheric corrosion rate of low-alloy steels(LAS).The material properties of LAS,environmental factors,and exposure time were used as the input,while the corrosion rate as the output.6 dif-ferent ML algorithms were used to construct the proposed model.Through optimization and filtering,the eXtreme gradient boosting(XG-Boost)model exhibited good corrosion rate prediction accuracy.The features of material properties were then transformed into atomic and physical features using the proposed property transformation approach,and the dominant descriptors that affected the corrosion rate were filtered using the recursive feature elimination(RFE)as well as XGBoost methods.The established ML models exhibited better predic-tion performance and generalization ability via property transformation descriptors.In addition,the SHapley additive exPlanations(SHAP)method was applied to analyze the relationship between the descriptors and corrosion rate.The results showed that the property transformation model could effectively help with analyzing the corrosion behavior,thereby significantly improving the generalization ability of corrosion rate prediction models.

Pulse rate estimation based on facial videos:an evaluation and optimization of the classical methods using both self-constructed and public datasets

Pulse rate is one of the important characteristics of traditional Chinese medicine pulse diagnosis,and it is of great significance for determining the nature of cold and heat in diseases.The prediction of pulse rate based on facial video is an exciting research field for getting palpation information by observation diagnosis.However,most studies focus on optimizing the algorithm based on a small sample of participants without systematically investigating multiple influencing factors.A total of 209 participants and 2,435 facial videos,based on our self-constructed Multi-Scene Sign Dataset and the public datasets,were used to perform a multi-level and multi-factor comprehensive comparison.The effects of different datasets,blood volume pulse signal extraction algorithms,region of interests,time windows,color spaces,pulse rate calculation methods,and video recording scenes were analyzed.Furthermore,we proposed a blood volume pulse signal quality optimization strategy based on the inverse Fourier transform and an improvement strategy for pulse rate estimation based on signal-to-noise ratio threshold sliding.We found that the effects of video estimation of pulse rate in the Multi-Scene Sign Dataset and Pulse Rate Detection Dataset were better than in other datasets.Compared with Fast independent component analysis and Single Channel algorithms,chrominance-based method and plane-orthogonal-to-skin algorithms have a more vital anti-interference ability and higher robustness.The performances of the five-organs fusion area and the full-face area were better than that of single sub-regions,and the fewer motion artifacts and better lighting can improve the precision of pulse rate estimation.

Dynamic interactive bitwise meta-holography with ultra-high computational and display frame rates

Interactive holography offers unmatched levels of immersion and user engagement in the field of future display.Despite of the substantial progress has been made in dynamic meta-holography,the realization of real-time,highly smooth interactive holography remains a significant challenge due to the computational and display frame rate limitations.In this study,we introduced a dynamic interactive bitwise meta-holography with ultra-high computational and display frame rates.To our knowledge,this is the first reported practical dynamic interactive metasurface holographic system.We spa-tially divided the metasurface device into multiple distinct channels,each projecting a reconstructed sub-pattern.The switching states of these channels were mapped to bitwise operations on a set of bit values,which avoids complex holo-gram computations,enabling an ultra-high computational frame rate.Our approach achieves a computational frame rate of 800 kHz and a display frame rate of 23 kHz on a low-power Raspberry Pi computational platform.According to this methodology,we demonstrated an interactive dynamic holographic Tetris game system that allows interactive gameplay,color display,and on-the-fly hologram creation.Our technology presents an inspiration for advanced dynamic meta-holography,which is promising for a broad range of applications including advanced human-computer interaction,real-time 3D visualization,and next-generation virtual and augmented reality systems.

INTERFACE BEHAVIOR AND DECAY RATES OF COMPRESSIBLE NAVIER-STOKES SYSTEM WITH DENSITY-DEPENDENT VISCOSITY AND A VACUUM

In this paper,we study the one-dimensional motion of viscous gas near a vacuum,with the gas connecting to a vacuum state with a jump in density.The interface behavior,the pointwise decay rates of the density function and the expanding rates of the interface are obtained with the viscosity coefficientμ(ρ)=ρ^(α)for any 0<α<1;this includes the timeweighted boundedness from below and above.The smoothness of the solution is discussed.Moreover,we construct a class of self-similar classical solutions which exhibit some interesting properties,such as optimal estimates.The present paper extends the results in[Luo T,Xin Z P,Yang T.SIAM J Math Anal,2000,31(6):1175-1191]to the jump boundary conditions case with density-dependent viscosity.

The Probability Density Function Related to Shallow Cumulus Entrainment Rate and Its Influencing Factors in a Large-Eddy Simulation

The process of entrainment-mixing between cumulus clouds and the ambient air is important for the development of cumulus clouds.Accurately obtaining the entrainment rate(λ)is particularly important for its parameterization within the overall cumulus parameterization scheme.In this study,an improved bulk-plume method is proposed by solving the equations of two conserved variables simultaneously to calculateλof cumulus clouds in a large-eddy simulation.The results demonstrate that the improved bulk-plume method is more reliable than the traditional bulk-plume method,becauseλ,as calculated from the improved method,falls within the range ofλvalues obtained from the traditional method using different conserved variables.The probability density functions ofλfor all data,different times,and different heights can be well-fitted by a log-normal distribution,which supports the assumed stochastic entrainment process in previous studies.Further analysis demonstrate that the relationship betweenλand the vertical velocity is better than other thermodynamic/dynamical properties;thus,the vertical velocity is recommended as the primary influencing factor for the parameterization ofλin the future.The results of this study enhance the theoretical understanding ofλand its influencing factors and shed new light on the development ofλparameterization.

A new era of mutation rate analyses: Concepts and methods

The mutation rate is a pivotal biological characteristic,intricately governed by natural selection and historically garnering considerable attention.Recent advances in high-throughput sequencing and analytical methodologies have profoundly transformed our understanding in this domain,ushering in an unprecedented era of mutation rate research.This paper aims to provide a comprehensive overview of the key concepts and methodologies frequently employed in the study of mutation rates.It examines various types of mutations,explores the evolutionary dynamics and associated theories,and synthesizes both classical and contemporary hypotheses.Furthermore,this review comprehensively explores recent advances in understanding germline and somatic mutations in animals and offers an overview of experimental methodologies,mutational patterns,molecular mechanisms,and driving forces influencing variations in mutation rates across species and tissues.Finally,it proposes several potential research directions and pressing questions for future investigations.

Experimental study on the effect of unloading rate on gneiss rockburst

Rockburst are often encountered in tunnel construction due to the complex geological conditions.To study the influence of unloading rate on rockburst,gneiss rockburst experiments were conducted under three groups of unloading rates.A high-speed photography system and acoustic emission(AE)system were used to monitor the entire process of rockburst process in real-time.The results show that the intensity of gneiss rockburst decreases with decrease of unloading rate,which is manifested as the reduction of AE energy and fragments ejection velocity.The mechanisms are proposed to explain this effect:(i)The reduction of unloading rate changes the crack propagation mechanism in the process of rockburst.This makes the rockbursts change from the tensile failure mechanism at high unloading rate to the tension-shear mixed failure mechanism at low unloading rate,and more energy released in the form of shear crack propagation.Then,less strain energy is converted into kinetic energy of fragments ejection.(ii)Less plate cracking degree of gneiss has taken shape due to decrease of unloading rate,resulting in the destruction of rockburst incubation process.The enlightenments of reducing the unloading rate for the project are also described quantitatively.The rockburst magnitude is reduced from the medium magnitude at the unloading rate of 0.1 MPa/s to the slight magnitude at the unloading rate of 0.025 MPa/s,which was judged by the ejection velocity.

Present-day Upper-crustal Strain Rate Field in Southeastern Tibet and its Geodynamic Implications:Constraints from GPS Measurements with ABIC Method

The Earth’s surface kinematics and deformation are fundamental to understanding crustal evolution.An effective research approach is to estimate regional motion field and deformation fields based on modern geodetic networks.If the discrete observed velocity field is obtained,the velocity related fields,such as dilatation rate and maximum shear strain rate,can be estimated by applying varied mathematical approaches.This study applied Akaike's Bayesian Information Criterion(ABIC)method to calculate strain rate fields constrained by GPS observations in the southeast Tibetan Plateau.Comparison with results derived from other three methods revealed that our ABIC-derived strain rate fields were more precise.The maximum shear strain rate highlighted the Xianshuihe–Xiaojiang fault system as the main boundary for the outward migration of material in southeastern Tibet,indicating rotation of eastern Tibet material around the eastern Himalaya rather than whole extrusion along a fixed channel.Additionally,distinct dilatation rate patterns in the northeast and southwest regions of the fault system were observed.The northeast region,represented by the Longmenshan area,exhibited negative dilatational anomalies;while the southwest region,represented by the Jinsha River area north of 29°N,displayed positive dilatational anomalies.This indicates compression in the former and extension in the latter.Combined with deep geophysical observations,we believe that the upper and lower crusts of the Jinsha River area north of 29°N are in an entire expanding state,probably caused by the escape-drag effect of material.The presence of a large,low-viscosity region south of 29°N may not enable the entire escape of the crust,but instead result in a differential escape of the lower crust faster than the upper crust.

Federated Learning Model for Auto Insurance Rate Setting Based on Tweedie Distribution

In the assessment of car insurance claims,the claim rate for car insurance presents a highly skewed probability distribution,which is typically modeled using Tweedie distribution.The traditional approach to obtaining the Tweedie regression model involves training on a centralized dataset,when the data is provided by multiple parties,training a privacy-preserving Tweedie regression model without exchanging raw data becomes a challenge.To address this issue,this study introduces a novel vertical federated learning-based Tweedie regression algorithm for multi-party auto insurance rate setting in data silos.The algorithm can keep sensitive data locally and uses privacy-preserving techniques to achieve intersection operations between the two parties holding the data.After determining which entities are shared,the participants train the model locally using the shared entity data to obtain the local generalized linear model intermediate parameters.The homomorphic encryption algorithms are introduced to interact with and update the model intermediate parameters to collaboratively complete the joint training of the car insurance rate-setting model.Performance tests on two publicly available datasets show that the proposed federated Tweedie regression algorithm can effectively generate Tweedie regression models that leverage the value of data fromboth partieswithout exchanging data.The assessment results of the scheme approach those of the Tweedie regressionmodel learned fromcentralized data,and outperformthe Tweedie regressionmodel learned independently by a single party.

Development of a high-repetition-rate lumped-inductance kicker magnet prototype for the beam switchyard of SHINE

The Shanghai high-repetition-rate X-ray free-electron laser and extreme light facility(SHINE)operates at a maximum repetition rate of 1 MHz.Kicker magnets are key components that distribute electron bunches into three different undulator lines in a bunch-by-bunch mode.The kicker field width must be less than the time interval between bunches.A lumpedinductance kicker prototype was developed using a vacuum chamber with a single-turn coil.The full magnetic field strength was 0.005 T.This paper presents the requirements,design considerations,design parameters,magnetic field calculations,and measurements of the kicker magnets.The relevant experimental results are also presented.The pulse width of the magnetic field was approximately 600 ns,and the maximum operation repetition rate was 1 MHz.The developed kicker satisfies the requirements for the SHINE project.Finally,numerous recommendations for the future optimization of kicker magnets are provided.

A performance-based hybrid deep learning model for predicting TBM advance rate using Attention-ResNet-LSTM

The technology of tunnel boring machine(TBM)has been widely applied for underground construction worldwide;however,how to ensure the TBM tunneling process safe and efficient remains a major concern.Advance rate is a key parameter of TBM operation and reflects the TBM-ground interaction,for which a reliable prediction helps optimize the TBM performance.Here,we develop a hybrid neural network model,called Attention-ResNet-LSTM,for accurate prediction of the TBM advance rate.A database including geological properties and TBM operational parameters from the Yangtze River Natural Gas Pipeline Project is used to train and test this deep learning model.The evolutionary polynomial regression method is adopted to aid the selection of input parameters.The results of numerical exper-iments show that our Attention-ResNet-LSTM model outperforms other commonly-used intelligent models with a lower root mean square error and a lower mean absolute percentage error.Further,parametric analyses are conducted to explore the effects of the sequence length of historical data and the model architecture on the prediction accuracy.A correlation analysis between the input and output parameters is also implemented to provide guidance for adjusting relevant TBM operational parameters.The performance of our hybrid intelligent model is demonstrated in a case study of TBM tunneling through a complex ground with variable strata.Finally,data collected from the Baimang River Tunnel Project in Shenzhen of China are used to further test the generalization of our model.The results indicate that,compared to the conventional ResNet-LSTM model,our model has a better predictive capability for scenarios with unknown datasets due to its self-adaptive characteristic.