In this paper,we propose a neural network approach to learn the parameters of a class of stochastic Lotka-Volterra systems.Approximations of the mean and covariance matrix of the observational variables are obtained f...In this paper,we propose a neural network approach to learn the parameters of a class of stochastic Lotka-Volterra systems.Approximations of the mean and covariance matrix of the observational variables are obtained from the Euler-Maruyama discretization of the underlying stochastic differential equations(SDEs),based on which the loss function is built.The stochastic gradient descent method is applied in the neural network training.Numerical experiments demonstrate the effectiveness of our method.展开更多
The widespread use of disinfectants and various medications in response to the COVID-19 pandemic has raised concerns about their potential impact on the characteristics of natural waters. To assess the effect of the C...The widespread use of disinfectants and various medications in response to the COVID-19 pandemic has raised concerns about their potential impact on the characteristics of natural waters. To assess the effect of the COVID-19 response on surface waters in Yaoundé, various physicochemical parameters of three rivers (Mfoundi, Tongolo, and Mingoa) were examined over 8 months. The selection of these rivers was based on their proximity to hospitals involved in COVID-19 patient management. Physico-chemical parameters were measured following standard protocols, and their spatiotemporal variations and the influence of various factors, were examined. The results revealed that, during the study period, the values for temperature (23˚C to 30˚C), dissolved oxygen (14% to 90%), pH (6.2 to 9.5), electrical conductivity (100 to 662 µS/cm), oxidability (0.19 to 42.19 mg/l), and suspended solids (1 to 725 mg/l) exhibited variations, except for total dissolved solids (30 to 470 mg/l), whose levels remained within the recommended limit (s = 0.812, P = 0.014) with oxidability levels in the Tongolo river. The COVID-19 response measures had a limited negative effect on the surface waters of Yaoundé during the study period. This could be attributed to the disproportionate application of hygiene measures among the city’s populations. Additionally, the lack of flow observed in certain rivers requires particular attention from authorities and the populations to safeguard the city’s ecosystems.展开更多
The study of the morphometric parameters of the three most abundant species in the lower course of the Kouilou River (Chrysichthys auratus, Liza falcipinnis and Pellonula vorax) was carried out. The standard length of...The study of the morphometric parameters of the three most abundant species in the lower course of the Kouilou River (Chrysichthys auratus, Liza falcipinnis and Pellonula vorax) was carried out. The standard length of Chrysichthys auratus varies between 43.57 and 210 mm, for an average of 96.70 ± 28.63 mm;the weight varies between 2.92 and 140.83 mg, an average of 73.03 ± 21.62 mg. The condition coefficient is equal to 4.42 ± 1.52. Liza falcipinnis has a standard length which varies between 59.9 mm and 158.08 mm for an average of 88.15 ± 29.74 mm;its weight varies between 4.77 and 76.21 mg, an average of 18.61 ± 11.82 mg. The condition coefficient is equal to 2.47 ± 1.57. Pellonula vorax has a standard length which varies between 60.33 mm and 117.72 mm;for an average of 80.48 ± 17.75 mm;the weight varies between 3.61 and 25.17 mg, an average of 9.03 ± 3.61 mg. The condition coefficient is equal to 2.17 ± 0.57. These three species have a minor allometric growth.展开更多
The Rydberg atom-based receiver, as a novel type of antenna, demonstrates broad application prospects in the field of microwave communications. However, since Rydberg atomic receivers are nonlinear systems, mismatches...The Rydberg atom-based receiver, as a novel type of antenna, demonstrates broad application prospects in the field of microwave communications. However, since Rydberg atomic receivers are nonlinear systems, mismatches between the parameters of the received amplitude modulation(AM) signals and the system's linear workspace and demodulation operating points can cause severe distortion in the demodulated signals. To address this, the article proposes a method for determining the operational parameters based on the mean square error(MSE) and total harmonic distortion(THD) assessments and presents strategies for optimizing the system's operational parameters focusing on linear response characteristics(LRC) and linear dynamic range(LDR). Specifically, we employ a method that minimizes the MSE to define the system's linear workspace, thereby ensuring the system has a good LRC while maximizing the LDR. To ensure that the signal always operates within the linear workspace, an appropriate carrier amplitude is set as the demodulation operating point. By calculating the THD at different operating points, the LRC performance within different regions of the linear workspace is evaluated, and corresponding optimization strategies based on the range of signal strengths are proposed. Moreover, to more accurately restore the baseband signal, we establish a mapping relationship between the carrier Rabi frequency and the transmitted power of the probe light, and optimize the slope of the linear demodulation function to reduce the MSE to less than 0.8×10^(-4). Finally, based on these methods for determining the operational parameters, we explore the effects of different laser Rabi frequencies on the system performance, and provide optimization recommendations. This research provides robust support for the design of high-performance Rydberg atom-based AM receivers.展开更多
Objective:The objective of this study was to compare the effect of nurse and beloved family member’s recording voice on consciousness and physical parameters in patients with coma state.Materials and Methods:A random...Objective:The objective of this study was to compare the effect of nurse and beloved family member’s recording voice on consciousness and physical parameters in patients with coma state.Materials and Methods:A randomized control trial parallel group design was conducted among 45 comatose patients divided into two intervention groups,i.e.nurse voice stimulus group,receiving nurses voice with standard care,family members voice stimulus group receiving their beloved family member voice with standard care and one control group receiving only standard care in medicine intensive care unit.The intervention was provided three times a day,each lasting 5 min for 7 days in addition to standard care.Repeated measure analysis of variance and independent t-test were used to compare within and between groups,respectively.Results:The study found significant differences in Glasgow coma scale(GCS)scores within both the nurse(F=2.78,P=0.042)and family member(F=10.27,P=0.0001)voice stimulus groups over 7 days.Comparing GCS scores between intervention groups showed significant variations before(P=0.028),during(P=0.047),and after(P=0.036)the intervention on day 7.Comparing GCS scores between the family members’voice stimulus group and the control group,significant changes were observed on days 5 and 7(P=0.043,0.030,0.030,and 0.014,0.012,0.012)before,during,and after the intervention.Conclusions:The use of beloved family members’voices proved more effective in elevating the patients’level of consciousness compared to both the nurse voice stimulus group and the control group.展开更多
The Bei Dou satellite system(BDS)has progressed with the full operationalization of the secondgeneration regional system(BDS-2)and the third-generation global system(BDS-3).This technology plays a crucial role in dete...The Bei Dou satellite system(BDS)has progressed with the full operationalization of the secondgeneration regional system(BDS-2)and the third-generation global system(BDS-3).This technology plays a crucial role in determining Earth Rotation Parameters(ERPs).In this study,we determine the ERPs based on the observations of BDS-2,BDS-3 and BDS-2+BDS-3,with the time spanning from August18,2022,to August 18,2023.The IERS EOP 20C04 series is used as a reference to evaluate the accuracy of the ERP estimates.We analyze the impact of different numbers of reference stations,polyhedron volumes,observation arc lengths,satellite types,and satellite systems on solving ERPs using BDS-2 and BDS-3 observation data provided by the International GNSS Service(IGS)stations.When selecting a specific satellite type,it is necessary to choose an appropriate observation arc length based on different numbers of reference stations while maximizing the volume of the formed polyhedron to achieve optimal efficiency and accuracy in parameter estimation.When both the number of reference stations and observation arc length are fixed,higher precision of the ERPs can be achieved using observations from MEO than MEO+IGSO and MEO+IGSO+GEO.Moreover,when considering only IGSO and MEO satellites as options for analysis purposes,BDS-3 provides higher accuracy compared to BDS-2.In summary,when using BDS for ERP estimation and MEO satellite observations with the same observation arc length,selecting stations from reference stations with larger polyhedral volumes can significantly improve the efficiency and accuracy of parameter estimation.展开更多
The suddenness, uncertainty, and randomness of rockbursts directly affect the safety of tunnel construction. The prediction of rockbursts is a fundamental aspect of mitigating or even eliminating rockburst hazards. To...The suddenness, uncertainty, and randomness of rockbursts directly affect the safety of tunnel construction. The prediction of rockbursts is a fundamental aspect of mitigating or even eliminating rockburst hazards. To address the shortcomings of the current rockburst prediction models, which have a limited number of samples and rely on manual test results as the majority of their input features, this paper proposes rockburst prediction models based on multi-featured drilling parameters of rock drilling jumbo. Firstly, four original drilling parameters, namely hammer pressure (Ph), feed pressure (Pf), rotation pressure (Pr), and feed speed (VP), together with the rockburst grades, were collected from 1093 rockburst cases. Then, a feature expansion investigation was performed based on the four original drilling parameters to establish a drilling parameter feature system and a rockburst prediction database containing 42 features. Furthermore, rockburst prediction models based on multi-featured drilling parameters were developed using the extreme tree (ET) algorithm and Bayesian optimization. The models take drilling parameters as input parameters and rockburst grades as output parameters. The effects of Bayesian optimization and the number of drilling parameter features on the model performance were analyzed using the accuracy, precision, recall and F1 value of the prediction set as the model performance evaluation indices. The results show that the Bayesian optimized model with 42 drilling parameter features as inputs performs best, with an accuracy of 91.89%. Finally, the reliability of the models was validated through field tests.展开更多
To guarantee safe and efficient tunneling of a tunnel boring machine(TBM),rapid and accurate judgment of the rock mass condition is essential.Based on fuzzy C-means clustering,this paper proposes a grouped machine lea...To guarantee safe and efficient tunneling of a tunnel boring machine(TBM),rapid and accurate judgment of the rock mass condition is essential.Based on fuzzy C-means clustering,this paper proposes a grouped machine learning method for predicting rock mass parameters.An elaborate data set on field rock mass is collected,which also matches field TBM tunneling.Meanwhile,target stratum samples are divided into several clusters by fuzzy C-means clustering,and multiple submodels are trained by samples in different clusters with the input of pretreated TBM tunneling data and the output of rock mass parameter data.Each testing sample or newly encountered tunneling condition can be predicted by multiple submodels with the weight of the membership degree of the sample to each cluster.The proposed method has been realized by 100 training samples and verified by 30 testing samples collected from the C1 part of the Pearl Delta water resources allocation project.The average percentage error of uniaxial compressive strength and joint frequency(Jf)of the 30 testing samples predicted by the pure back propagation(BP)neural network is 13.62%and 12.38%,while that predicted by the BP neural network combined with fuzzy C-means is 7.66%and6.40%,respectively.In addition,by combining fuzzy C-means clustering,the prediction accuracies of support vector regression and random forest are also improved to different degrees,which demonstrates that fuzzy C-means clustering is helpful for improving the prediction accuracy of machine learning and thus has good applicability.Accordingly,the proposed method is valuable for predicting rock mass parameters during TBM tunneling.展开更多
Promoting the high penetration of renewable energies like photovoltaic(PV)systems has become an urgent issue for expanding modern power grids and has accomplished several challenges compared to existing distribution g...Promoting the high penetration of renewable energies like photovoltaic(PV)systems has become an urgent issue for expanding modern power grids and has accomplished several challenges compared to existing distribution grids.This study measures the effectiveness of the Puma optimizer(PO)algorithm in parameter estimation of PSC(perovskite solar cells)dynamic models with hysteresis consideration considering the electric field effects on operation.The models used in this study will incorporate hysteresis effects to capture the time-dependent behavior of PSCs accurately.The PO optimizes the proposed modified triple diode model(TDM)with a variable voltage capacitor and resistances(VVCARs)considering the hysteresis behavior.The suggested PO algorithm contrasts with other wellknown optimizers from the literature to demonstrate its superiority.The results emphasize that the PO realizes a lower RMSE(Root mean square errors),which proves its capability and efficacy in parameter extraction for the models.The statistical results emphasize the efficiency and supremacy of the proposed PO compared to the other well-known competing optimizers.The convergence rates show good,fast,and stable convergence rates with lower RMSE via PO compared to the other five competitive optimizers.Moreover,the lowermean realized via the PO optimizer is illustrated by the box plot for all optimizers.展开更多
Additive manufacturing(AM)has revolutionized modern manufacturing,but the application of magnesium(Mg)alloys in laser-based AM remains underexplored due to challenges such as oxidation,low boiling point,and thermal ex...Additive manufacturing(AM)has revolutionized modern manufacturing,but the application of magnesium(Mg)alloys in laser-based AM remains underexplored due to challenges such as oxidation,low boiling point,and thermal expansion,which lead to defects like porosity and cracking.This study provides a comprehensive analysis of microstructure changes in WE43 magnesium(Mg)alloy after laser surface melting(LSM),examining grain morphology,orientation,size,microsegregation,and defects under various combinations of laser power,scan speed,and spot size.Ourfindings reveal that variations in laser power and spot size exert a more significant influence on the depth and aspect ratio of the keyhole melt pool compared to laser scan speed.Critically,we demonstrate that laser energy density,while widely used as a quantitative metric to describe the combined effects of process parameters,exhibits significant limitations.Notable variations in melt pool depth,normalized width,and microstructure with laser energy density were observed,as reflected by low R²values.Additionally,we underscore the importance of assessing the temperature gradient across the width of the melt pool,which determines whether conduction or keyhole melting modes dominate.These modes exhibit distinct heatflow mechanisms and yield fundamentally different microstructural outcomes.Furthermore,we show that the microstructure and grain size in conduction mode exhibit a good correlation with the temperature gradient(G)and solidification rate(R).This research provides a framework for achieving localized microstructural control in LSM,providing insights to optimize process parameters for laser-based 3D printing of Mg alloys,and advancing the integration of Mg alloys into AM technologies.展开更多
The gears of new energy vehicles are required to withstand higher rotational speeds and greater loads,which puts forward higher precision essentials for gear manufacturing.However,machining process parameters can caus...The gears of new energy vehicles are required to withstand higher rotational speeds and greater loads,which puts forward higher precision essentials for gear manufacturing.However,machining process parameters can cause changes in cutting force/heat,resulting in affecting gear machining precision.Therefore,this paper studies the effect of different process parameters on gear machining precision.A multi-objective optimization model is established for the relationship between process parameters and tooth surface deviations,tooth profile deviations,and tooth lead deviations through the cutting speed,feed rate,and cutting depth of the worm wheel gear grinding machine.The response surface method(RSM)is used for experimental design,and the corresponding experimental results and optimal process parameters are obtained.Subsequently,gray relational analysis-principal component analysis(GRA-PCA),particle swarm optimization(PSO),and genetic algorithm-particle swarm optimization(GA-PSO)methods are used to analyze the experimental results and obtain different optimal process parameters.The results show that optimal process parameters obtained by the GRA-PCA,PSO,and GA-PSO methods improve the gear machining precision.Moreover,the gear machining precision obtained by GA-PSO is superior to other methods.展开更多
This research investigates the influence of indoor and outdoor factors on photovoltaic(PV)power generation at Utrecht University to accurately predict PV system performance by identifying critical impact factors and i...This research investigates the influence of indoor and outdoor factors on photovoltaic(PV)power generation at Utrecht University to accurately predict PV system performance by identifying critical impact factors and improving renewable energy efficiency.To predict plant efficiency,nineteen variables are analyzed,consisting of nine indoor photovoltaic panel characteristics(Open Circuit Voltage(Voc),Short Circuit Current(Isc),Maximum Power(Pmpp),Maximum Voltage(Umpp),Maximum Current(Impp),Filling Factor(FF),Parallel Resistance(Rp),Series Resistance(Rs),Module Temperature)and ten environmental factors(Air Temperature,Air Humidity,Dew Point,Air Pressure,Irradiation,Irradiation Propagation,Wind Speed,Wind Speed Propagation,Wind Direction,Wind Direction Propagation).This study provides a new perspective not previously addressed in the literature.In this study,different machine learning methods such as Multilayer Perceptron(MLP),Multivariate Adaptive Regression Spline(MARS),Multiple Linear Regression(MLR),and Random Forest(RF)models are used to predict power values using data from installed PVpanels.Panel values obtained under real field conditions were used to train the models,and the results were compared.The Multilayer Perceptron(MLP)model was achieved with the highest classification accuracy of 0.990%.The machine learning models used for solar energy forecasting show high performance and produce results close to actual values.Models like Multi-Layer Perceptron(MLP)and Random Forest(RF)can be used in diverse locations based on load demand.展开更多
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 th...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.展开更多
On-site and real-time non-destructive measurement of elastic constants for materials of a component in a in-service structure is a challenge due to structural complexities,such as ambiguous boundary,variable thickness...On-site and real-time non-destructive measurement of elastic constants for materials of a component in a in-service structure is a challenge due to structural complexities,such as ambiguous boundary,variable thickness,nonuniform material properties.This work develops for the first time a method that uses ultrasound echo groups and artificial neural network(ANN)for reliable on-site real-time identification of material parameters.The use of echo groups allows the use of lower frequencies,and hence more accommodative to structural complexity.To train the ANNs,a numerical model is established that is capable of computing the waveform of ultrasonic echo groups for any given set of material properties of a given structure.The waveform of an ultrasonic echo groups at an interest location on the surface the structure with material parameters varying in a predefined range are then computed using the numerical model.This results in a set of dataset for training the ANN model.Once the ANN is trained,the material parameters can be identified simultaneously using the actual measured echo waveform as input to the ANN.Intensive tests have been conducted both numerically and experimentally to evaluate the effectiveness and accuracy of the currently proposed method.The results show that the maximum identification error of numerical example is less than 2%,and the maximum identification error of experimental test is less than 7%.Compared with currently prevailing methods and equipment,the proposefy the density and thickness,in addition to the elastic constants.Moreover,the reliability and accuracy of inverse prediction is significantly improved.Thus,it has broad applications and enables real-time field measurements,which has not been fulfilled by any other available methods or equipment.展开更多
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 pr...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.展开更多
In order to overcome the problems of many pores,large deformation and unstable weld quality of traditional laser welded aluminumcopper alloy joints,a red-blue dual-beam laser source and a swinging laser were introduce...In order to overcome the problems of many pores,large deformation and unstable weld quality of traditional laser welded aluminumcopper alloy joints,a red-blue dual-beam laser source and a swinging laser were introduced for welding.T2 copper and 6063 aluminum thin plates were lap welded by coaxial dual-beam laser welding.The morphology of weld cross section was compared to explore the influence of process parameters on the formation of lap joints.The microstructure characteristics of the weld zone were observed and compared by optical microscope.The results show that the addition of laser beam swing can eliminate the internal pores of the weld.With the increase of the swing width,the weld depth decreases,and the weld width increases first and then decreases.The influence of welding speed on the weld cross section morphology is similar to that of swing width.With the increase of welding speed,the weld width increases first and then decreases,while the weld depth decreases all the time.This is because that the red laser is used as the main heat source to melt the base metals,with the increase of red laser power,the weld depth increases.As an auxiliary laser source,blue laser reduces the total energy consumption,consequently,the effective heat input increases and the spatter is restrained effectively.As a result,the increase of red laser power has an enhancement effect on the weld width and weld depth.When the swing width is 1.2 mm,the red laser power is 550 W,the blue laser power is 500 W,and the welding speed is 35 mm/s,the weld forming is the best.The lap joint of T2 copper and 6063 aluminum alloy thin plate can be connected stably with the hybrid of blue laser.The effect rules of laser beam swing on the weld formation were obtained,which improved the quality of the joints.展开更多
Exoskeletons generally require accurate dynamic models to design the model-based controller conveniently under the human-robot interaction condition.However,due to unknown model parameters such as the mass,moment of i...Exoskeletons generally require accurate dynamic models to design the model-based controller conveniently under the human-robot interaction condition.However,due to unknown model parameters such as the mass,moment of inertia and mechanical size,the dynamic model of exoskeletons is difficult to construct.Hence,an enhanced whale optimization algorithm(EWOA)is proposed to identify the exoskeleton model parameters.Meanwhile,the periodic excitation trajectories are designed by finite Fourier series to input the desired position demand of exoskeletons with mechanical physical constraints.Then a backstepping controller based on the identified model is adopted to improve the human-robot wearable comfortable performance under cooperative motion.Finally,the proposed Model parameters identification and control are verified by a two-DOF exoskeletons platform.The knee joint motion achieves a steady-state response after 0.5 s.Meanwhile,the position error of hip joint response is less than 0.03 rad after 0.9 s.In addition,the steady-state human-robot interaction torque of the two joints is constrained within 15 N·m.This research proposes a whale optimization algorithm to optimize the excitation trajectory and identify model parameters.Furthermore,an enhanced mutation strategy is adopted to avoid whale evolution’s unsatisfactory local optimal value.展开更多
Coal has a highly complex chemical structure,similar to polymers,coal is a macromolecular structure composed of a large number of“similar compounds”,which is called the basic structural unit.Understanding coal struc...Coal has a highly complex chemical structure,similar to polymers,coal is a macromolecular structure composed of a large number of“similar compounds”,which is called the basic structural unit.Understanding coal structure is the basis of its transformation and utilization.Shendong(SD)coal was analyzed by FTIR,XRD,XPS,and NMR.The results show that SD coal normalized structure formula is C_(100)H_(68.5)O_(35.7)N_(1.2)S_(0.2)and the average number of aromatic rings is 1.98.-CH_(2)-content accounts for about 82%in aliphatic CeH region,and the ratio of ether bond CeO,aromatic ether C-O and C=O is about 2:1:11 in oxygen-containing functional group region.The d_(002),L_(C),L_(a)and N_(C)of S_(D)coal microcrystalline structure parameters are 0.1832 nm,1.4688 nm,2.0852 nm and 9.017,respectively.Aromatic carbon and aliphatic carbon ratios of SD coal are 55.67%and 29.97%,aromatic cluster size and average methylene chain length are 0.224 and 1.817.Based on these structural parameters,molecular model of SD coal was constructed with^(13)C SSNMR experimental spectra as a reference.The model was constructed with an atom composition of C_(214)H_(214)O_(49)N_(2)S.展开更多
BACKGROUND Carbon ion radiotherapy(CIRT)is currently used to treat prostate cancer.Rectal bleeding is a major cause of toxicity even with CIRT.However,to date,a correlation between the dose and volume parameters of th...BACKGROUND Carbon ion radiotherapy(CIRT)is currently used to treat prostate cancer.Rectal bleeding is a major cause of toxicity even with CIRT.However,to date,a correlation between the dose and volume parameters of the 12 fractions of CIRT for prostate cancer and rectal bleeding has not been shown.Similarly,the clinical risk factors for rectal bleeding were absent after 12 fractions of CIRT.AIM To identify the risk factors for rectal bleeding in 12 fractions of CIRT for prostate cancer.METHODS Among 259 patients who received 51.6 Gy[relative biological effectiveness(RBE)],in 12 fractions of CIRT,15 had grade 1(5.8%)and nine had grade 2 rectal bleeding(3.5%).The dose-volume parameters included the volume(cc)of the rectum irradiated with at least x Gy(RBE)(Vx)and the minimum dose in the most irradiated x cc normal rectal volume(Dx).RESULTS The mean values of D6cc,D2cc,V10 Gy(RBE),V20 Gy(RBE),V30 Gy(RBE),and V40 Gy(RBE)were significantly higher in the patients with rectal bleeding than in those without.The cutoff values were D6cc=34.34 Gy(RBE),D2cc=46.46 Gy(RBE),V10 Gy(RBE)=9.85 cc,V20 Gy(RBE)=7.00 cc,V30 Gy(RBE)=6.91 cc,and V40 Gy(RBE)=4.26 cc.The D2cc,V10 Gy(RBE),and V20 Gy(RBE)cutoff values were significant predictors of grade 2 rectal bleeding.CONCLUSION The above dose-volume parameters may serve as guidelines for preventing rectal bleeding after 12 fractions of CIRT for prostate cancer.展开更多
BACKGROUND By comprehensively analyzing the blood flow parameters of the umbilical and middle cerebral arteries,doctors can more accurately identify fetal intrauterine distress,as well as assess its severity,so that t...BACKGROUND By comprehensively analyzing the blood flow parameters of the umbilical and middle cerebral arteries,doctors can more accurately identify fetal intrauterine distress,as well as assess its severity,so that timely interventions can be implemented to safeguard the health and safety of the fetus.AIM To identify the relationship between ultrasound parameters of the umbilical and middle cerebral arteries and intrauterine distress.METHODS Clinical data of pregnant women admitted between January 2021 and January 2023 were collected and divided into the observation and control groups(n=50 each),according to the presence or absence of intrauterine distress.The ultrasound hemodynamic parameters of the uterine artery(UtA),fetal middle cerebral artery(MCA),and umbilical artery(UmA)were compared with neonatal outcomes and occurrence of intrauterine distress in the two groups.RESULTS Comparison of ultrasonic hemodynamic parameters,resistance index(RI),pulsatility index(PI),and systolic maximal blood flow velocity of UmA compared to diastolic blood flow velocity(S/D),revealed higher values of fetal MCA,PI,and S/D of UmA in pregnant women with UtA compared to controls(P<0.05),while there was no difference between the two groups in terms of RI(P<0.05)The incidence of a neonatal Apgar score of 8-10 points was lower in the observation group(66.7%)than in the control group(90.0%),and neonatal weight(2675.5±27.6 g)was lower than in the control group(3117.5±31.2 g).Further,cesarean section rate was higher in the observation group(70.0%)than in the control group(11.7%),and preterm labor rate was higher in the observation group(40.0%)than in the control group(10.0%).The incidence of fetal distress,neonatal growth restriction and neonatal asphyxia were also higher in the observation group(all P<0.05).CONCLUSION Fetal MCA,UmA,and maternal UtA hemodynamic abnormalities all develop in pregnant women with intrauterine distress during late pregnancy,which suggests that clinical attention should be paid to them,and monitoring should be strengthened to provide guidance for clinical intervention.展开更多
基金Supported by the National Natural Science Foundation of China(11971458,11471310)。
文摘In this paper,we propose a neural network approach to learn the parameters of a class of stochastic Lotka-Volterra systems.Approximations of the mean and covariance matrix of the observational variables are obtained from the Euler-Maruyama discretization of the underlying stochastic differential equations(SDEs),based on which the loss function is built.The stochastic gradient descent method is applied in the neural network training.Numerical experiments demonstrate the effectiveness of our method.
文摘The widespread use of disinfectants and various medications in response to the COVID-19 pandemic has raised concerns about their potential impact on the characteristics of natural waters. To assess the effect of the COVID-19 response on surface waters in Yaoundé, various physicochemical parameters of three rivers (Mfoundi, Tongolo, and Mingoa) were examined over 8 months. The selection of these rivers was based on their proximity to hospitals involved in COVID-19 patient management. Physico-chemical parameters were measured following standard protocols, and their spatiotemporal variations and the influence of various factors, were examined. The results revealed that, during the study period, the values for temperature (23˚C to 30˚C), dissolved oxygen (14% to 90%), pH (6.2 to 9.5), electrical conductivity (100 to 662 µS/cm), oxidability (0.19 to 42.19 mg/l), and suspended solids (1 to 725 mg/l) exhibited variations, except for total dissolved solids (30 to 470 mg/l), whose levels remained within the recommended limit (s = 0.812, P = 0.014) with oxidability levels in the Tongolo river. The COVID-19 response measures had a limited negative effect on the surface waters of Yaoundé during the study period. This could be attributed to the disproportionate application of hygiene measures among the city’s populations. Additionally, the lack of flow observed in certain rivers requires particular attention from authorities and the populations to safeguard the city’s ecosystems.
文摘The study of the morphometric parameters of the three most abundant species in the lower course of the Kouilou River (Chrysichthys auratus, Liza falcipinnis and Pellonula vorax) was carried out. The standard length of Chrysichthys auratus varies between 43.57 and 210 mm, for an average of 96.70 ± 28.63 mm;the weight varies between 2.92 and 140.83 mg, an average of 73.03 ± 21.62 mg. The condition coefficient is equal to 4.42 ± 1.52. Liza falcipinnis has a standard length which varies between 59.9 mm and 158.08 mm for an average of 88.15 ± 29.74 mm;its weight varies between 4.77 and 76.21 mg, an average of 18.61 ± 11.82 mg. The condition coefficient is equal to 2.47 ± 1.57. Pellonula vorax has a standard length which varies between 60.33 mm and 117.72 mm;for an average of 80.48 ± 17.75 mm;the weight varies between 3.61 and 25.17 mg, an average of 9.03 ± 3.61 mg. The condition coefficient is equal to 2.17 ± 0.57. These three species have a minor allometric growth.
基金Project supported by the National Natural Science Foundation of China (Grant No. U22B2095)the Civil Aerospace Technology Research Project (Grant No. D010103)。
文摘The Rydberg atom-based receiver, as a novel type of antenna, demonstrates broad application prospects in the field of microwave communications. However, since Rydberg atomic receivers are nonlinear systems, mismatches between the parameters of the received amplitude modulation(AM) signals and the system's linear workspace and demodulation operating points can cause severe distortion in the demodulated signals. To address this, the article proposes a method for determining the operational parameters based on the mean square error(MSE) and total harmonic distortion(THD) assessments and presents strategies for optimizing the system's operational parameters focusing on linear response characteristics(LRC) and linear dynamic range(LDR). Specifically, we employ a method that minimizes the MSE to define the system's linear workspace, thereby ensuring the system has a good LRC while maximizing the LDR. To ensure that the signal always operates within the linear workspace, an appropriate carrier amplitude is set as the demodulation operating point. By calculating the THD at different operating points, the LRC performance within different regions of the linear workspace is evaluated, and corresponding optimization strategies based on the range of signal strengths are proposed. Moreover, to more accurately restore the baseband signal, we establish a mapping relationship between the carrier Rabi frequency and the transmitted power of the probe light, and optimize the slope of the linear demodulation function to reduce the MSE to less than 0.8×10^(-4). Finally, based on these methods for determining the operational parameters, we explore the effects of different laser Rabi frequencies on the system performance, and provide optimization recommendations. This research provides robust support for the design of high-performance Rydberg atom-based AM receivers.
文摘Objective:The objective of this study was to compare the effect of nurse and beloved family member’s recording voice on consciousness and physical parameters in patients with coma state.Materials and Methods:A randomized control trial parallel group design was conducted among 45 comatose patients divided into two intervention groups,i.e.nurse voice stimulus group,receiving nurses voice with standard care,family members voice stimulus group receiving their beloved family member voice with standard care and one control group receiving only standard care in medicine intensive care unit.The intervention was provided three times a day,each lasting 5 min for 7 days in addition to standard care.Repeated measure analysis of variance and independent t-test were used to compare within and between groups,respectively.Results:The study found significant differences in Glasgow coma scale(GCS)scores within both the nurse(F=2.78,P=0.042)and family member(F=10.27,P=0.0001)voice stimulus groups over 7 days.Comparing GCS scores between intervention groups showed significant variations before(P=0.028),during(P=0.047),and after(P=0.036)the intervention on day 7.Comparing GCS scores between the family members’voice stimulus group and the control group,significant changes were observed on days 5 and 7(P=0.043,0.030,0.030,and 0.014,0.012,0.012)before,during,and after the intervention.Conclusions:The use of beloved family members’voices proved more effective in elevating the patients’level of consciousness compared to both the nurse voice stimulus group and the control group.
基金received financial support from the National Natural Science Foundation of China(Grant No.42030105,No.42204006,No.42274011,No.42304095)Funded by State Key Laboratory of Geo-Information Engineering and Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of MNR,CASM(Grant No.2024-01-01)+2 种基金Open Fund of Hubei Luojia Laboratory(Grant No.230100020,230100019)the China Postdoctoral Science Foundation(Certificate Number:2023M743580)the Key Project of Natural Science Research in Universities of Anhui Province(No.2023AH051634)。
文摘The Bei Dou satellite system(BDS)has progressed with the full operationalization of the secondgeneration regional system(BDS-2)and the third-generation global system(BDS-3).This technology plays a crucial role in determining Earth Rotation Parameters(ERPs).In this study,we determine the ERPs based on the observations of BDS-2,BDS-3 and BDS-2+BDS-3,with the time spanning from August18,2022,to August 18,2023.The IERS EOP 20C04 series is used as a reference to evaluate the accuracy of the ERP estimates.We analyze the impact of different numbers of reference stations,polyhedron volumes,observation arc lengths,satellite types,and satellite systems on solving ERPs using BDS-2 and BDS-3 observation data provided by the International GNSS Service(IGS)stations.When selecting a specific satellite type,it is necessary to choose an appropriate observation arc length based on different numbers of reference stations while maximizing the volume of the formed polyhedron to achieve optimal efficiency and accuracy in parameter estimation.When both the number of reference stations and observation arc length are fixed,higher precision of the ERPs can be achieved using observations from MEO than MEO+IGSO and MEO+IGSO+GEO.Moreover,when considering only IGSO and MEO satellites as options for analysis purposes,BDS-3 provides higher accuracy compared to BDS-2.In summary,when using BDS for ERP estimation and MEO satellite observations with the same observation arc length,selecting stations from reference stations with larger polyhedral volumes can significantly improve the efficiency and accuracy of parameter estimation.
基金supported by the China Railway Corporation Science and Technology Research and Development Program(Grant Nos.K2020G035 and K2021G024)the National Natural Science Foundation of China(Grant No.52378411).
文摘The suddenness, uncertainty, and randomness of rockbursts directly affect the safety of tunnel construction. The prediction of rockbursts is a fundamental aspect of mitigating or even eliminating rockburst hazards. To address the shortcomings of the current rockburst prediction models, which have a limited number of samples and rely on manual test results as the majority of their input features, this paper proposes rockburst prediction models based on multi-featured drilling parameters of rock drilling jumbo. Firstly, four original drilling parameters, namely hammer pressure (Ph), feed pressure (Pf), rotation pressure (Pr), and feed speed (VP), together with the rockburst grades, were collected from 1093 rockburst cases. Then, a feature expansion investigation was performed based on the four original drilling parameters to establish a drilling parameter feature system and a rockburst prediction database containing 42 features. Furthermore, rockburst prediction models based on multi-featured drilling parameters were developed using the extreme tree (ET) algorithm and Bayesian optimization. The models take drilling parameters as input parameters and rockburst grades as output parameters. The effects of Bayesian optimization and the number of drilling parameter features on the model performance were analyzed using the accuracy, precision, recall and F1 value of the prediction set as the model performance evaluation indices. The results show that the Bayesian optimized model with 42 drilling parameter features as inputs performs best, with an accuracy of 91.89%. Finally, the reliability of the models was validated through field tests.
基金Natural Science Foundation of Shandong Province,Grant/Award Number:ZR202103010903Doctoral Fund of Shandong Jianzhu University,Grant/Award Number:X21101Z。
文摘To guarantee safe and efficient tunneling of a tunnel boring machine(TBM),rapid and accurate judgment of the rock mass condition is essential.Based on fuzzy C-means clustering,this paper proposes a grouped machine learning method for predicting rock mass parameters.An elaborate data set on field rock mass is collected,which also matches field TBM tunneling.Meanwhile,target stratum samples are divided into several clusters by fuzzy C-means clustering,and multiple submodels are trained by samples in different clusters with the input of pretreated TBM tunneling data and the output of rock mass parameter data.Each testing sample or newly encountered tunneling condition can be predicted by multiple submodels with the weight of the membership degree of the sample to each cluster.The proposed method has been realized by 100 training samples and verified by 30 testing samples collected from the C1 part of the Pearl Delta water resources allocation project.The average percentage error of uniaxial compressive strength and joint frequency(Jf)of the 30 testing samples predicted by the pure back propagation(BP)neural network is 13.62%and 12.38%,while that predicted by the BP neural network combined with fuzzy C-means is 7.66%and6.40%,respectively.In addition,by combining fuzzy C-means clustering,the prediction accuracies of support vector regression and random forest are also improved to different degrees,which demonstrates that fuzzy C-means clustering is helpful for improving the prediction accuracy of machine learning and thus has good applicability.Accordingly,the proposed method is valuable for predicting rock mass parameters during TBM tunneling.
基金supported via funding from Prince Sattam Bin Abdulaziz University project number(PSAU/2025/R/1446).
文摘Promoting the high penetration of renewable energies like photovoltaic(PV)systems has become an urgent issue for expanding modern power grids and has accomplished several challenges compared to existing distribution grids.This study measures the effectiveness of the Puma optimizer(PO)algorithm in parameter estimation of PSC(perovskite solar cells)dynamic models with hysteresis consideration considering the electric field effects on operation.The models used in this study will incorporate hysteresis effects to capture the time-dependent behavior of PSCs accurately.The PO optimizes the proposed modified triple diode model(TDM)with a variable voltage capacitor and resistances(VVCARs)considering the hysteresis behavior.The suggested PO algorithm contrasts with other wellknown optimizers from the literature to demonstrate its superiority.The results emphasize that the PO realizes a lower RMSE(Root mean square errors),which proves its capability and efficacy in parameter extraction for the models.The statistical results emphasize the efficiency and supremacy of the proposed PO compared to the other well-known competing optimizers.The convergence rates show good,fast,and stable convergence rates with lower RMSE via PO compared to the other five competitive optimizers.Moreover,the lowermean realized via the PO optimizer is illustrated by the box plot for all optimizers.
文摘Additive manufacturing(AM)has revolutionized modern manufacturing,but the application of magnesium(Mg)alloys in laser-based AM remains underexplored due to challenges such as oxidation,low boiling point,and thermal expansion,which lead to defects like porosity and cracking.This study provides a comprehensive analysis of microstructure changes in WE43 magnesium(Mg)alloy after laser surface melting(LSM),examining grain morphology,orientation,size,microsegregation,and defects under various combinations of laser power,scan speed,and spot size.Ourfindings reveal that variations in laser power and spot size exert a more significant influence on the depth and aspect ratio of the keyhole melt pool compared to laser scan speed.Critically,we demonstrate that laser energy density,while widely used as a quantitative metric to describe the combined effects of process parameters,exhibits significant limitations.Notable variations in melt pool depth,normalized width,and microstructure with laser energy density were observed,as reflected by low R²values.Additionally,we underscore the importance of assessing the temperature gradient across the width of the melt pool,which determines whether conduction or keyhole melting modes dominate.These modes exhibit distinct heatflow mechanisms and yield fundamentally different microstructural outcomes.Furthermore,we show that the microstructure and grain size in conduction mode exhibit a good correlation with the temperature gradient(G)and solidification rate(R).This research provides a framework for achieving localized microstructural control in LSM,providing insights to optimize process parameters for laser-based 3D printing of Mg alloys,and advancing the integration of Mg alloys into AM technologies.
基金Projects(U22B2084,52275483,52075142)supported by the National Natural Science Foundation of ChinaProject(2023ZY01050)supported by the Ministry of Industry and Information Technology High Quality Development,China。
文摘The gears of new energy vehicles are required to withstand higher rotational speeds and greater loads,which puts forward higher precision essentials for gear manufacturing.However,machining process parameters can cause changes in cutting force/heat,resulting in affecting gear machining precision.Therefore,this paper studies the effect of different process parameters on gear machining precision.A multi-objective optimization model is established for the relationship between process parameters and tooth surface deviations,tooth profile deviations,and tooth lead deviations through the cutting speed,feed rate,and cutting depth of the worm wheel gear grinding machine.The response surface method(RSM)is used for experimental design,and the corresponding experimental results and optimal process parameters are obtained.Subsequently,gray relational analysis-principal component analysis(GRA-PCA),particle swarm optimization(PSO),and genetic algorithm-particle swarm optimization(GA-PSO)methods are used to analyze the experimental results and obtain different optimal process parameters.The results show that optimal process parameters obtained by the GRA-PCA,PSO,and GA-PSO methods improve the gear machining precision.Moreover,the gear machining precision obtained by GA-PSO is superior to other methods.
文摘This research investigates the influence of indoor and outdoor factors on photovoltaic(PV)power generation at Utrecht University to accurately predict PV system performance by identifying critical impact factors and improving renewable energy efficiency.To predict plant efficiency,nineteen variables are analyzed,consisting of nine indoor photovoltaic panel characteristics(Open Circuit Voltage(Voc),Short Circuit Current(Isc),Maximum Power(Pmpp),Maximum Voltage(Umpp),Maximum Current(Impp),Filling Factor(FF),Parallel Resistance(Rp),Series Resistance(Rs),Module Temperature)and ten environmental factors(Air Temperature,Air Humidity,Dew Point,Air Pressure,Irradiation,Irradiation Propagation,Wind Speed,Wind Speed Propagation,Wind Direction,Wind Direction Propagation).This study provides a new perspective not previously addressed in the literature.In this study,different machine learning methods such as Multilayer Perceptron(MLP),Multivariate Adaptive Regression Spline(MARS),Multiple Linear Regression(MLR),and Random Forest(RF)models are used to predict power values using data from installed PVpanels.Panel values obtained under real field conditions were used to train the models,and the results were compared.The Multilayer Perceptron(MLP)model was achieved with the highest classification accuracy of 0.990%.The machine learning models used for solar energy forecasting show high performance and produce results close to actual values.Models like Multi-Layer Perceptron(MLP)and Random Forest(RF)can be used in diverse locations based on load demand.
基金funded by the Key Research and Development of the Gansu Province(No.20YF8FA 079)the Construction Project of the Gansu Clinical Medical Research Center(No.18JR2FA003).
文摘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.
基金Supported by National Natural Science Foundation of China(Grant No.51805141)Funds for Creative Research Groups of Hebei Province of China(Grant No.E2020202142)+2 种基金Tianjin Municipal Science and Technology Plan Project of China(Grant No.19ZXZNGX00100)Key R&D Program of Hebei Province of China(Grant No.19227208D)National Key Research and development Program of China(Grant No.2020YFB2009400).
文摘On-site and real-time non-destructive measurement of elastic constants for materials of a component in a in-service structure is a challenge due to structural complexities,such as ambiguous boundary,variable thickness,nonuniform material properties.This work develops for the first time a method that uses ultrasound echo groups and artificial neural network(ANN)for reliable on-site real-time identification of material parameters.The use of echo groups allows the use of lower frequencies,and hence more accommodative to structural complexity.To train the ANNs,a numerical model is established that is capable of computing the waveform of ultrasonic echo groups for any given set of material properties of a given structure.The waveform of an ultrasonic echo groups at an interest location on the surface the structure with material parameters varying in a predefined range are then computed using the numerical model.This results in a set of dataset for training the ANN model.Once the ANN is trained,the material parameters can be identified simultaneously using the actual measured echo waveform as input to the ANN.Intensive tests have been conducted both numerically and experimentally to evaluate the effectiveness and accuracy of the currently proposed method.The results show that the maximum identification error of numerical example is less than 2%,and the maximum identification error of experimental test is less than 7%.Compared with currently prevailing methods and equipment,the proposefy the density and thickness,in addition to the elastic constants.Moreover,the reliability and accuracy of inverse prediction is significantly improved.Thus,it has broad applications and enables real-time field measurements,which has not been fulfilled by any other available methods or equipment.
基金financial supports from the National Natural Science Foundation of China(52130104,51821001)High Technology and Key Development Project of Ningbo,China(2019B10102)。
文摘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.
基金supported by Guangdong Science and Technology Plan Project(Grant No.20170902,No.20180902)Yangjiang Science and Technology Plan Project(Grant No.SDZX2020063)+1 种基金Shenzhen Key Projects of Innovation and Entrepreneurship Plan Technology(JSGG20210420091802007)Yunfu 2023 Science and Technology Plan Project(S2023020201).
文摘In order to overcome the problems of many pores,large deformation and unstable weld quality of traditional laser welded aluminumcopper alloy joints,a red-blue dual-beam laser source and a swinging laser were introduced for welding.T2 copper and 6063 aluminum thin plates were lap welded by coaxial dual-beam laser welding.The morphology of weld cross section was compared to explore the influence of process parameters on the formation of lap joints.The microstructure characteristics of the weld zone were observed and compared by optical microscope.The results show that the addition of laser beam swing can eliminate the internal pores of the weld.With the increase of the swing width,the weld depth decreases,and the weld width increases first and then decreases.The influence of welding speed on the weld cross section morphology is similar to that of swing width.With the increase of welding speed,the weld width increases first and then decreases,while the weld depth decreases all the time.This is because that the red laser is used as the main heat source to melt the base metals,with the increase of red laser power,the weld depth increases.As an auxiliary laser source,blue laser reduces the total energy consumption,consequently,the effective heat input increases and the spatter is restrained effectively.As a result,the increase of red laser power has an enhancement effect on the weld width and weld depth.When the swing width is 1.2 mm,the red laser power is 550 W,the blue laser power is 500 W,and the welding speed is 35 mm/s,the weld forming is the best.The lap joint of T2 copper and 6063 aluminum alloy thin plate can be connected stably with the hybrid of blue laser.The effect rules of laser beam swing on the weld formation were obtained,which improved the quality of the joints.
基金Supported by National Key Research and Development Program of China(Grant No.2022YFF0708903)Ningbo Municipal Key Technology Research and Development Program of China(Grant No.2022Z006)Youth Fund of National Natural Science Foundation of China(Grant No.52205043)。
文摘Exoskeletons generally require accurate dynamic models to design the model-based controller conveniently under the human-robot interaction condition.However,due to unknown model parameters such as the mass,moment of inertia and mechanical size,the dynamic model of exoskeletons is difficult to construct.Hence,an enhanced whale optimization algorithm(EWOA)is proposed to identify the exoskeleton model parameters.Meanwhile,the periodic excitation trajectories are designed by finite Fourier series to input the desired position demand of exoskeletons with mechanical physical constraints.Then a backstepping controller based on the identified model is adopted to improve the human-robot wearable comfortable performance under cooperative motion.Finally,the proposed Model parameters identification and control are verified by a two-DOF exoskeletons platform.The knee joint motion achieves a steady-state response after 0.5 s.Meanwhile,the position error of hip joint response is less than 0.03 rad after 0.9 s.In addition,the steady-state human-robot interaction torque of the two joints is constrained within 15 N·m.This research proposes a whale optimization algorithm to optimize the excitation trajectory and identify model parameters.Furthermore,an enhanced mutation strategy is adopted to avoid whale evolution’s unsatisfactory local optimal value.
基金financed by the Department of education of Gansu Province:Young Doctor Fund Project(2022QB-029)the Fundamental Research Funds for the Central Universities(31920240125-06,31920240059)+1 种基金the Scientific Research Project of Introducing Talents of Northwest Minzu University(xbmuyjrc202215,xbmuyjrc202216)the National Natural Science Foundation of China(22178289).
文摘Coal has a highly complex chemical structure,similar to polymers,coal is a macromolecular structure composed of a large number of“similar compounds”,which is called the basic structural unit.Understanding coal structure is the basis of its transformation and utilization.Shendong(SD)coal was analyzed by FTIR,XRD,XPS,and NMR.The results show that SD coal normalized structure formula is C_(100)H_(68.5)O_(35.7)N_(1.2)S_(0.2)and the average number of aromatic rings is 1.98.-CH_(2)-content accounts for about 82%in aliphatic CeH region,and the ratio of ether bond CeO,aromatic ether C-O and C=O is about 2:1:11 in oxygen-containing functional group region.The d_(002),L_(C),L_(a)and N_(C)of S_(D)coal microcrystalline structure parameters are 0.1832 nm,1.4688 nm,2.0852 nm and 9.017,respectively.Aromatic carbon and aliphatic carbon ratios of SD coal are 55.67%and 29.97%,aromatic cluster size and average methylene chain length are 0.224 and 1.817.Based on these structural parameters,molecular model of SD coal was constructed with^(13)C SSNMR experimental spectra as a reference.The model was constructed with an atom composition of C_(214)H_(214)O_(49)N_(2)S.
文摘BACKGROUND Carbon ion radiotherapy(CIRT)is currently used to treat prostate cancer.Rectal bleeding is a major cause of toxicity even with CIRT.However,to date,a correlation between the dose and volume parameters of the 12 fractions of CIRT for prostate cancer and rectal bleeding has not been shown.Similarly,the clinical risk factors for rectal bleeding were absent after 12 fractions of CIRT.AIM To identify the risk factors for rectal bleeding in 12 fractions of CIRT for prostate cancer.METHODS Among 259 patients who received 51.6 Gy[relative biological effectiveness(RBE)],in 12 fractions of CIRT,15 had grade 1(5.8%)and nine had grade 2 rectal bleeding(3.5%).The dose-volume parameters included the volume(cc)of the rectum irradiated with at least x Gy(RBE)(Vx)and the minimum dose in the most irradiated x cc normal rectal volume(Dx).RESULTS The mean values of D6cc,D2cc,V10 Gy(RBE),V20 Gy(RBE),V30 Gy(RBE),and V40 Gy(RBE)were significantly higher in the patients with rectal bleeding than in those without.The cutoff values were D6cc=34.34 Gy(RBE),D2cc=46.46 Gy(RBE),V10 Gy(RBE)=9.85 cc,V20 Gy(RBE)=7.00 cc,V30 Gy(RBE)=6.91 cc,and V40 Gy(RBE)=4.26 cc.The D2cc,V10 Gy(RBE),and V20 Gy(RBE)cutoff values were significant predictors of grade 2 rectal bleeding.CONCLUSION The above dose-volume parameters may serve as guidelines for preventing rectal bleeding after 12 fractions of CIRT for prostate cancer.
文摘BACKGROUND By comprehensively analyzing the blood flow parameters of the umbilical and middle cerebral arteries,doctors can more accurately identify fetal intrauterine distress,as well as assess its severity,so that timely interventions can be implemented to safeguard the health and safety of the fetus.AIM To identify the relationship between ultrasound parameters of the umbilical and middle cerebral arteries and intrauterine distress.METHODS Clinical data of pregnant women admitted between January 2021 and January 2023 were collected and divided into the observation and control groups(n=50 each),according to the presence or absence of intrauterine distress.The ultrasound hemodynamic parameters of the uterine artery(UtA),fetal middle cerebral artery(MCA),and umbilical artery(UmA)were compared with neonatal outcomes and occurrence of intrauterine distress in the two groups.RESULTS Comparison of ultrasonic hemodynamic parameters,resistance index(RI),pulsatility index(PI),and systolic maximal blood flow velocity of UmA compared to diastolic blood flow velocity(S/D),revealed higher values of fetal MCA,PI,and S/D of UmA in pregnant women with UtA compared to controls(P<0.05),while there was no difference between the two groups in terms of RI(P<0.05)The incidence of a neonatal Apgar score of 8-10 points was lower in the observation group(66.7%)than in the control group(90.0%),and neonatal weight(2675.5±27.6 g)was lower than in the control group(3117.5±31.2 g).Further,cesarean section rate was higher in the observation group(70.0%)than in the control group(11.7%),and preterm labor rate was higher in the observation group(40.0%)than in the control group(10.0%).The incidence of fetal distress,neonatal growth restriction and neonatal asphyxia were also higher in the observation group(all P<0.05).CONCLUSION Fetal MCA,UmA,and maternal UtA hemodynamic abnormalities all develop in pregnant women with intrauterine distress during late pregnancy,which suggests that clinical attention should be paid to them,and monitoring should be strengthened to provide guidance for clinical intervention.
