Interaction between systemic iron parameters and left ventricular structure and function in the preserved ejection fraction population:a two-sample bidirectional Mendelian randomization study

BACKGROUND Left ventricular(LV)remodeling and diastolic function in people with heart failure(HF)are correlated with iron status;however,the causality is uncertain.This Mendelian randomization(MR)study investigated the bidirectional causal relationship between systemic iron parameters and LV structure and function in a preserved ejection fraction population.METHODS Transferrin saturation(TSAT),total iron binding capacity(TIBC),and serum iron and ferritin levels were extracted as instrumental variables for iron parameters from meta-analyses of public genome-wide association studies.Individuals without myocardial infarction history,HF,or LV ejection fraction(LVEF)<50%(n=16,923)in the UK Biobank Cardiovascular Magnetic Resonance Imaging Study constituted the outcome dataset.The dataset included LV end-diastolic volume,LV endsystolic volume,LV mass(LVM),and LVM-to-end-diastolic volume ratio(LVMVR).We used a two-sample bidirectional MR study with inverse variance weighting(IVW)as the primary analysis method and estimation methods using different algorithms to improve the robustness of the results.RESULTS In the IVW analysis,one standard deviation(SD)increased in TSAT significantly correlated with decreased LVMVR(β=-0.1365;95%confidence interval[CI]:-0.2092 to-0.0638;P=0.0002)after Bonferroni adjustment.Conversely,no significant relationships were observed between other iron and LV parameters.After Bonferroni correction,reverse MR analysis showed that one SD increase in LVEF significantly correlated with decreased TSAT(β=-0.0699;95%CI:-0.1087 to-0.0311;P=0.0004).No heterogeneity or pleiotropic effects evidence was observed in the analysis.CONCLUSIONS We demonstrated a causal relationship between TSAT and LV remodeling and function in a preserved ejection fraction population.

Performance of physical-informed neural network (PINN) for the key parameter inference in Langmuir turbulence parameterization scheme

The Stokes production coefficient(E_(6))constitutes a critical parameter within the Mellor-Yamada type(MY-type)Langmuir turbulence(LT)parameterization schemes,significantly affecting the simulation of turbulent kinetic energy,turbulent length scale,and vertical diffusivity coefficient for turbulent kinetic energy in the upper ocean.However,the accurate determination of its value remains a pressing scientific challenge.This study adopted an innovative approach by leveraging deep learning technology to address this challenge of inferring the E_(6).Through the integration of the information of the turbulent length scale equation into a physical-informed neural network(PINN),we achieved an accurate and physically meaningful inference of E_(6).Multiple cases were examined to assess the feasibility of PINN in this task,revealing that under optimal settings,the average mean squared error of the E_(6) inference was only 0.01,attesting to the effectiveness of PINN.The optimal hyperparameter combination was identified using the Tanh activation function,along with a spatiotemporal sampling interval of 1 s and 0.1 m.This resulted in a substantial reduction in the average bias of the E_(6) inference,ranging from O(10^(1))to O(10^(2))times compared with other combinations.This study underscores the potential application of PINN in intricate marine environments,offering a novel and efficient method for optimizing MY-type LT parameterization schemes.

Construction of an Arrhenius constitutive model for Mg-Y-Nd-Zr-Gd rare earth magnesium alloy based on the Zener-Hollomon parameter and objective evaluation of its accuracy in the twinning-rich intervals

According to a high-temperature compression test of rare earth magnesium alloy(WE43),a strain-compensated constitutive model of the Arrhenius equation based on Zener-Hollomon parameters was established,and the rheological behaviors were predicted.The model exhibited relatively serious prediction distortion in the low-temperature and high-strain rate parameter interval,and its accuracy was still unsatisfactory even after modification by a correction operator considering the coupling of temperature and strain rate.The microstructure characterization and statistical analysis showed that a large number of twinning occurred in the parameter intervals with prediction deviation.The occurrence of twinning complicated the local internal stress distribution by drastically changing the crystal orientation and led to significant fluctuations in the macroscopic strain-stress and hardening curves relative to the rheological processes dominated by the dislocation and softening mechanisms,making the logarithm of the strain rate and stress deviate from the linear relationship.This twinning phenomenon was greatly influenced by the temperature and strain rate.Herein,the influence mechanism on twinning behavior was analyzed from the perspective of the interaction of dislocation and twinning.

Effects of processing parameters on fabrication defects,microstructure and mechanical properties of additive manufactured Mg–Nd–Zn–Zr alloy by selective laser melting process

Mg–3Nd–0.2Zn–0.4Zr(NZ30K,wt.%)alloy is a new kind of high-performance metallic biomaterial.The combination of the NZ30K Magnesium(Mg)alloy and selective laser melting(SLM)process seems to be an ideal solution to produce porous Mg degradable implants.However,the microstructure evolution and mechanical properties of the SLMed NZ30K Mg alloy were not yet studied systematically.Therefore,the fabrication defects,microstructure,and mechanical properties of the SLMed NZ30K alloy under different processing parameters were investigated.The results show that there are two types of fabrication defects in the SLMed NZ30K alloy,gas pores and unfused defects.With the increase of the laser energy density,the porosity sharply decreases to the minimum first and then slightly increases.The minimum porosity is 0.49±0.18%.While the microstructure varies from the large grains with lamellar structure inside under low laser energy density,to the large grains with lamellar structure inside&the equiaxed grains&the columnar grains under middle laser energy density,and further to the fine equiaxed grains&the columnar grains under high laser energy density.The lamellar structure in the large grain is a newly observed microstructure for the NZ30K Mg alloy.Higher laser energy density leads to finer grains,which enhance all the yield strength(YS),ultimate tensile strength(UTS)and elongation,and the best comprehensive mechanical properties obtained are YS of 266±2.1 MPa,UTS of 296±5.2 MPa,with an elongation of 4.9±0.68%.The SLMed NZ30K Mg alloy with a bimodal-grained structure consisting of fine equiaxed grains and coarser columnar grains has better elongation and a yield drop phenomenon.

Optimization of chemistry and process parameters for control of intermetallic formation in Mg sludges

Intermetallic formation in sludge during magnesium(Mg)melting,holding and high pressure die casting practices is a very important issue.But,very often it is overlooked by academia,original equipment manufacturers(OEM),metal ingot producers and even die casters.The aim of this study was to minimize the intermetallic formation in Mg sludge via the optimization of the chemistry and process parameters.The Al8Mn5 intermetallic particles were identified by the microstructure analysis based on the Al and Mn ratio.The design of experiment(DOE)technique,Taguchi method,was employed to minimize the intermetallic formation in the sludge of Mg alloys with various chemical compositions of Al,Mn,Fe,and different process parameters,holding temperature and holding time.The sludge yield(SY)and intermetallic size(IS)was selected as two responses.The optimum combination of the levels in terms of minimizing the intermetallic formation were 9 wt.%Al,0.15 wt.%Mn,0.001 wt.%(10 ppm)Fe,690℃ for the holding temperature and holding at 30 mins for the holding time,respectively.The best combination for smallest intermetallic size were 9 wt.%Al,0.15 wt.%Mn,0.001 wt.%(10 ppm)Fe,630℃ for the holding temperature and holding at 60 mins for the holding time,respectively.Three groups of sludge factors,Chemical Sludge(CSF),Physical Sludge(PSF)and Comprehensive Sludge Factors(and CPSF)were established for prediction of sludge yields and intermetallic sizes in Al-containing Mg alloys.The CPSF with five independent variables including both chemical elements and process parameters gave high accuracy in prediction,as the prediction of the PSF with only the two processing parameters of the melt holding temperature and time showed a relatively large deviation from the experimental data.The Chemical Sludge Factor was primarily designed for small ingot producers and die casters with a limited melting and holding capacity,of which process parameters could be fixed easily.The Physical Sludge Factor could be used for mass production with a single type of Mg alloy,in which the chemistry fluctuation might be negligible.In large Mg casting suppliers with multiple melting and holding furnaces and a number of Mg alloys in production,the Comprehensive Sludge Factor should be implemented to diminish the sludge formation.

Optimizing near-carbon-free nuclear energy systems:advances in reactor operation digital twin through hybrid machine learning algorithms for parameter identification and state estimation

Accurate and efficient online parameter identification and state estimation are crucial for leveraging digital twin simulations to optimize the operation of near-carbon-free nuclear energy systems.In previous studies,we developed a reactor operation digital twin(RODT).However,non-differentiabilities and discontinuities arise when employing machine learning-based surrogate forward models,challenging traditional gradient-based inverse methods and their variants.This study investigated deterministic and metaheuristic algorithms and developed hybrid algorithms to address these issues.An efficient modular RODT software framework that incorporates these methods into its post-evaluation module is presented for comprehensive comparison.The methods were rigorously assessed based on convergence profiles,stability with respect to noise,and computational performance.The numerical results show that the hybrid KNNLHS algorithm excels in real-time online applications,balancing accuracy and efficiency with a prediction error rate of only 1%and processing times of less than 0.1 s.Contrastingly,algorithms such as FSA,DE,and ADE,although slightly slower(approximately 1 s),demonstrated higher accuracy with a 0.3%relative L_2 error,which advances RODT methodologies to harness machine learning and system modeling for improved reactor monitoring,systematic diagnosis of off-normal events,and lifetime management strategies.The developed modular software and novel optimization methods presented offer pathways to realize the full potential of RODT for transforming energy engineering practices.

A Data-Oriented Method to Optimize Hydraulic Fracturing Parameters of Tight Sandstone Reservoirs

Based on the actual data collected from the tight sandstone development zone, correlation analysis using theSpearman method was conducted to determine the main factors influencing the gas production rate of tightsandstone fracturing. An integrated model combining geological engineering and numerical simulation of fracturepropagation and production was completed. Based on data analysis, the hydraulic fracture parameters wereoptimized to develop a differentiated fracturing treatment adjustment plan. The results indicate that the influenceof geological and engineering factors in the X1 and X2 development zones in the study area differs significantly.Therefore, it is challenging to adopt a uniform development strategy to achieve rapid production increase. Thedata analysis reveals that the variation in gas production rate is primarily affected by the reservoir thickness andpermeability parameters as geological factors. On the other hand, the amount of treatment fluid and proppantaddition significantly impact the gas production rate as engineering factors. Among these factors, the influence ofgeological factors is more pronounced in block X1. Therefore, the main focus should be on further optimizing thefracturing interval and adjusting the geological development well location. Given the existing well location, thereis limited potential for further optimizing fracture parameters to increase production. For block X2, the fracturingparameters should be optimized. Data screening was conducted to identify outliers in the entire dataset, and adata-driven fracturing parameter optimization method was employed to determine the basic adjustment directionfor reservoir stimulation in the target block. This approach provides insights into the influence of geological,stimulation, and completion parameters on gas production rate. Consequently, the subsequent fracturing parameteroptimization design can significantly reduce the modeling and simulation workload and guide field operations toimprove and optimize hydraulic fracturing efficiency.

Process Parameters Optimization of Laser Cladding for HT200 with 316L Coating Based on Response Surface Method

In order to improve the sealing surface performance of gray cast iron gas gate valves and achieve precise molding control of the cladding layer,as well as to reveal the influence of laser cladding process parameters on the morphology and structure of the cladding layer,we prepared the 316L coating on HT 200 by using Design-Expert software central composite design(CCD)based on response surface analysis.We built a regression prediction model and analyzed the ANOVA with the inspection results.With a target cladding layer width of 3.5 mm and height of 1.3 mm,the process parameters were optimized to obtain the best combination of process parameters.The microstructure,phases,and hardness variations of the cladding layer from experiments with optimal parameters were analyzed by the metallographic microscope,confocal microscope,and microhardness instrument.The experimental results indicate that laser power has a significant impact on the cladding layer width,followed by powder feed rate;scan speed has a significant impact on the cladding layer height,followed by powder feed rate.The HT200 substrate and 316L can metallurgically bond well,and the cladding layer structure consists of dendritic crystals,columnar crystals,and equiaxed crystals in sequence.The optimal process parameter combination satisfying the morphology requirements is laser power(A)of 1993 W,scan speed(B)of 8.949 mm/s,powder feed rate(C)of 1.408 r/min,with a maximum hardness of 1564.3 HV0.5,significantly higher than the hardness of the HT200 substrate.

Improved Particle Swarm Optimization for Parameter Identification of Permanent Magnet Synchronous Motor

In the process of identifying parameters for a permanent magnet synchronous motor,the particle swarm optimization method is prone to being stuck in local optima in the later stages of iteration,resulting in low parameter accuracy.This work proposes a fuzzy particle swarm optimization approach based on the transformation function and the filled function.This approach addresses the topic of particle swarmoptimization in parameter identification from two perspectives.Firstly,the algorithm uses a transformation function to change the form of the fitness function without changing the position of the extreme point of the fitness function,making the extreme point of the fitness function more prominent and improving the algorithm’s search ability while reducing the algorithm’s computational burden.Secondly,on the basis of themulti-loop fuzzy control systembased onmultiplemembership functions,it is merged with the filled function to improve the algorithm’s capacity to skip out of the local optimal solution.This approach can be used to identify the parameters of permanent magnet synchronous motors by sampling only the stator current,voltage,and speed data.The simulation results show that the method can effectively identify the electrical parameters of a permanent magnet synchronous motor,and it has superior global convergence performance and robustness.

The effects of deformation parameters and cooling rates on the aging behavior of AZ80+0.4%Ce

The extruded AZ80+0.4%Ce magnesium alloy was twisted in the temperature range of 300-380℃by using a Gleeble 3500 thermal simulation test machine with a torsion unit.The deformed cylindrical specimens were cooled at a cooling rate of 10℃/s or 0.1℃/s,respectively,and aged at 170℃.The microstructure analysis results showed that the grain size decreased with increasing specimen radial position from center(SRPC),and that the strong initial basal texture of the extruded magnesium alloy was weakened.Both continuous and discontinuous dynamic recrystallization mechanisms were involved in contributing to the grain refinement for all specimens investigated.And a novel extension twinning induced dynamic recrystallization mechanism was proposed for specimen deformed at 300℃.For the specimens deformed at 300℃and 340℃followed by a slow cooling rate(0.1℃/s),precipitates of various shapes(β-Mg_(17)Al_(12)),with the dominant precipitates being on the grains boundaries,appeared on the surface section.For specimen deformed at 380℃,lamellar precipitates(LPS)in the interiors of the grains were predominant.After aging,the LPS still dominated for specimens twisted at 380℃;however,the LPS gradually decreased with decreasing deformation temperatures from 380℃to 300℃.Dynamically precipitatedβ,especially those decorating the grain boundaries,changed the competition pictures for the LPS and precipitates of other shapes after aging.Interestingly,LPS dominated the areas for the center section of the specimens after aging regardless of deformation temperatures.Low temperature deformation with high SRPC followed by rapid cooling rate increased the micro hardness of the alloy after aging due to refined grain,reduced precipitates size,decreased lamellar spacing as well as strain hardening.

Study on thermal decomposition kinetics of azobenzene-4,4′-dicarboxylic acid by using compensation parameter method and nonlinear fitting evaluation

Recently,azobenzene-4,4'-dicarboxylic acid(ADCA)has been produced gradually for use as an organic synthesis or pharmaceutical intermediate due to its eminent performance.With large quantities put into application in the future,the thermal stability of this substance during storage,transportation,and use will become quite important.Thus,in this work,the thermal decomposition behavior,thermal decomposition kinetics,and thermal hazard of ADCA were investigated.Experiments were conducted by using a SENSYS evo DSC device.A combination of differential iso-conversion method,compensation parameter method,and nonlinear fitting evaluation were also used to analyze thermal kinetics and mechanism of ADCA decomposition.The results show that when conversion rate α increases,the activation energies of ADCA's first and main decomposition peaks fall.The amount of heat released during decomposition varies between 182.46 and 231.16 J·g^(-1).The proposed kinetic equation is based on the Avrami-Erofeev model,which is consistent with the decomposition progress.Applying the Frank-Kamenetskii model,a calculated self-accelerating decomposition temperature of 287.0℃is obtained.

Artificial Intelligence Based Meteorological Parameter Forecasting for Optimizing Response of Nuclear Emergency Decision Support System

This paper presents a novel artificial intelligence (AI) based approach to predict crucial meteorological parameters such as temperature,pressure,and wind speed,typically calculated from computationally intensive weather research and forecasting (WRF) model.Accurate meteorological data is indispensable for simulating the release of radioactive effluents,especially in dispersion modeling for nuclear emergency decision support systems.Simulation of meteorological conditions during nuclear emergencies using the conventional WRF model is very complex and time-consuming.Therefore,a new artificial neural network (ANN) based technique was proposed as a viable alternative for meteorological prediction.A multi-input multi-output neural network was trained using historical site-specific meteorological data to forecast the meteorological parameters.Comprehensive evaluation of this technique was conducted to test its performance in forecasting various parameters including atmospheric pressure,temperature,and wind speed components in both East-West and North-South directions.The performance of developed network was evaluated on an unknown dataset,and acquired results are within the acceptable range for all meteorological parameters.Results show that ANNs possess the capability to forecast meteorological parameters,such as temperature and pressure,at multiple spatial locations within a grid with high accuracy,utilizing input data from a single station.However,accuracy is slightly compromised when predicting wind speed components.Root mean square error (RMSE) was utilized to report the accuracy of predicted results,with values of 1.453℃for temperature,77 Pa for predicted pressure,1.058 m/s for the wind speed of U-component and 0.959 m/s for the wind speed of V-component.In conclusion,this approach offers a precise,efficient,and wellinformed method for administrative decision-making during nuclear emergencies.

Optimization Study of Active-Passive Heating System Parameters in Village Houses in the Southern Xinjiang Province

Aiming at the problems of large energy consumption and serious pollution of winter heating existing in the rural buildings in Southern Xinjiang,a combined active-passive heating system was proposed,and the simulation software was used to optimize the parameters of the system,according to the parameters obtained from the optimization,a test platform was built and winter heating test was carried out.The simulation results showed that the thickness of the air layer of 75 mm,the total area of the vent holes of 0.24 m^(2),and the thickness of the insulation layer of 120 mm were the optimal construction for the passive part;solar collector area of 28 m^(2),hot water storage tank volume of 1.4 m^(3),mass flow rate of 800 kg/h on the collector side,mass flow rate of 400 kg/h on the heat exchanger side,and output power of auxiliary heat source of 5∼9 kWwere the optimal constructions for active heating system.Test results showed that during the heating period,the system could provide sufficient heat to the room under different heating modes,and the indoor temperature reached over 18°C,which met the heating demand.The economic and environmental benefits of the system were analyzed,and the economic benefits of the systemwere better than coal-fired heating,and the CO_(2) emissionswere reduced by 3,292.25 kg compared with coalfiredheating.The results of the study showed that the combinedactive-passiveheating systemcouldeffectively solve the heating problems existing in rural buildings in Southern Xinjiang,and it also laid the theoretical foundation for the popularization of the combined heating systems.

Auxiliary Software for Defining the Parameters of the Structural Organization of a Complex System

The developed auxiliary software serves to simplify, standardize and facilitate the software loading of the structural organization of a complex technological system, as well as its further manipulation within the process of solving the considered technological system. Its help can be especially useful in the case of a complex structural organization of a technological system with a large number of different functional elements grouped into several technological subsystems. This paper presents the results of its application for a special complex technological system related to the reference steam block for the combined production of heat and electricity.

A frequency domain estimation and compensation method for system synchronization parameters of distributed-HFSWR

To analyze the influence of time synchronization error,phase synchronization error,frequency synchronization error,internal delay of the transceiver system,and range error and angle error between the unit radars on the target detection performance,firstly,a spatial detection model of distributed high-frequency surface wave radar(distributed-HFSWR)is established in this paper.In this model,a method for accurate extraction of direct wave spectrum based on curve fitting is proposed to obtain accurate system internal delay and frequency synchronization error under complex electromagnetic environment background and low signal to noise ratio(SNR),and to compensate for the shift of range and Doppler frequency caused by time-frequency synchronization error.The direct wave component is extracted from the spectrum,the range estimation error and Doppler estimation error are reduced by the method of curve fitting,and the fitting accuracy of the parameters is improved.Then,the influence of frequency synchronization error on target range and radial Doppler velocity is quantitatively analyzed.The relationship between frequency synchronization error and radial Doppler velocity shift and range shift is given.Finally,the system synchronization parameters of the trial distributed-HFSWR are obtained by the proposed spectrum extraction method based on curve fitting,the experimental data is compensated to correct the shift of the target,and finally the correct target parameter information is obtained.Simulations and experimental results demonstrate the superiority and correctness of the proposed method,theoretical derivation and detection model proposed in this paper.

How to Predict the Temperature of the CMB Directly Using the Hubble Parameter and the Planck Scale Using the Stefan-Boltzmann Law

Based on recent progress in quantum gravity and quantum cosmology, we are also presenting a way to estimate the temperature in the cosmos, the Hubble sphere, from a relation between the Planck temperature and the Hubble scale. Our analysis predicts the Hubble sphere temperature of 2.72 K with the one standard deviation confidence interval between 2.65 K and 2.80 K, which corresponds well with the measured temperature observed from the cosmic microwave background (CMB) of about 2.72 K. This adds evidence that there is a close connection between the Planck scale, gravity, and the cosmological scales as anticipated by Eddington already in 1918.1.

Analysis of the Quality of Physical and Chemical Parameters of Groundwater in Three Localities in the Sub-Prefecture of Zanzra (Ivory Coast)

The availability of groundwater of acceptable quality has become a difficult challenge to meet in many countries, particularly Côte d’Ivoire. The State of Côte d’Ivoire and rural populations are building hydraulic infrastructure for this purpose. However, the quality of water coming from these infrastructures remains to be determined. This study therefore aims to analyze certain physicochemical parameters of the groundwater of Gloazra, Kouezra and Trahonfla. These are temperature, pH, electrical conductivity, TDS, salinity level, hardness, alkalinity, chloride content and bicarbonate. To determine the pH, electrical conductivity, temperature, TDS, and salinity rate, an electrochemical analysis was carried out. The titrimetric analysis made it possible to determine the chloride and bicarbonate content, alkalinity, and hardness. The results indicate that the studied groundwater is soft and acidic with good mineralization. This water does not present a health hazard to consumers but can affect hydraulic equipment and household containers.

Phase-Based Optical Flow Method with Optimized Parameter Settings for Enhancing Displacement Measurement Adaptability

To enhance the applicability and measurement accuracy of phase-based optical flow method using complex steerable pyramids in structural displacement measurement engineering applications, an improved method of optimizing parameter settings is proposed. The optimized parameters include the best measurement points of the Region of Interest (ROI) and the levels of pyramid filters. Additionally, to address the issue of updating reference frames in practical applications due to the difficulty in estimating the maximum effective measurement value, a mechanism for dynamically updating reference frames is introduced. Experimental results demonstrate that compared to representative image gradient-based displacement measurement methods, the proposed method exhibits higher measurement accuracy in engineering applications. This provides reliable data support for structural damage identification research based on vibration signals and is expected to broaden the engineering application prospects for structural health monitoring.

Drilling-based measuring method for the c-φ parameter of rock and its field application

The technology of drilling tests makes it possible to obtain the strength parameter of rock accurately in situ. In this paper, a new rock cutting analysis model that considers the influence of the rock crushing zone(RCZ) is built. The formula for an ultimate cutting force is established based on the limit equilibrium principle. The relationship between digital drilling parameters(DDP) and the c-φ parameter(DDP-cφ formula, where c refers to the cohesion and φ refers to the internal friction angle) is derived, and the response of drilling parameters and cutting ratio to the strength parameters is analyzed. The drillingbased measuring method for the c-φ parameter of rock is constructed. The laboratory verification test is then completed, and the difference in results between the drilling test and the compression test is less than 6%. On this basis, in-situ rock drilling tests in a traffic tunnel and a coal mine roadway are carried out, and the strength parameters of the surrounding rock are effectively tested. The average difference ratio of the results is less than 11%, which verifies the effectiveness of the proposed method for obtaining the strength parameters based on digital drilling. This study provides methodological support for field testing of rock strength parameters.

Two-dimensional Parameter Relationships for W UMa-type Systems Revisited

Reviewing the empirical and theoretical parameter relationships between various parameters is a good way to understand more about contact binary systems.In this investigation,two-dimensional(2D)relationships for P–MV(system),P–L1,2,M1,2–L1,2,and q–Lratiowere revisited.The sample used is related to 118 contact binary systems with an orbital period shorter than 0.6 days whose absolute parameters were estimated based on the Gaia Data Release 3 parallax.We reviewed previous studies on 2D relationships and updated six parameter relationships.Therefore,Markov chain Monte Carlo and Machine Learning methods were used,and the outcomes were compared.We selected 22 contact binary systems from eight previous studies for comparison,which had light curve solutions using spectroscopic data.The results show that the systems are in good agreement with the results of this study.