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.

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.

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.

Transient elastography with controlled attenuation parameter for the diagnosis of colorectal polyps in patients with nonalcoholic fatty liver disease

BACKGROUND The severity of nonalcoholic fatty liver disease(NAFLD)and lipid metabolism are related to the occurrence of colorectal polyps.Liver-controlled attenuation parameters(liver-CAPs)have been established to predict the prognosis of hepatic steatosis patients.AIM To explore the risk factors associated with colorectal polyps in patients with NAFLD by analyzing liver-CAPs and establishing a diagnostic model.METHODS Patients who were diagnosed with colorectal polyps in the Department of Gastroenterology of our hospital between June 2021 and April 2022 composed the case group,and those with no important abnormalities composed the control group.The area under the receiver operating characteristic curve was used to predict the diagnostic efficiency.Differences were considered statistically significant when P<0.05.RESULTS The median triglyceride(TG)and liver-CAP in the case group were significantly greater than those in the control group(mmol/L,1.74 vs 1.05;dB/m,282 vs 254,P<0.05).TG and liver-CAP were found to be independent risk factors for colorectal polyps,with ORs of 2.338(95%CI:1.154–4.733)and 1.019(95%CI:1.006–1.033),respectively(P<0.05).And there was no difference in the diagnostic efficacy between liver-CAP and TG combined with liver-CAP(TG+CAP)(P>0.05).When the liver-CAP was greater than 291 dB/m,colorectal polyps were more likely to occur.CONCLUSION The levels of TG and liver-CAP in patients with colorectal polyps are significantly greater than those patients without polyps.Liver-CAP alone can be used to diagnose NAFLD with colorectal polyps.

Parameter-Free Shifted Laplacian Reconstruction for Multiple Kernel Clustering

Dear Editor,This letter proposes a parameter-free multiple kernel clustering(MKC)method by using shifted Laplacian reconstruction.Traditional MKC can effectively cluster nonlinear data,but it faces two main challenges:1)As an unsupervised method,it is up against parameter problems which makes the parameters intractable to tune and is unfeasible in real-life applications;2)Only considers the clustering information,but ignores the interference of noise within Laplacian.

Critical Opalescence and Its Impact on the Jet Quenching Parameter q

Jet quenching parameter q is essential for characterizing the interaction strength between jet partons and nuclear matter.Based on the quark-meson model,we develop a new framework for calculating q at finite chemical potentials,in which q is related to the spectral function of the chiral order parameter.

Chronic Hepatitis B Virus Infection: Biological Parameters in Patients Treated with Tenofovir Disoproxil Fumarate

Chronic hepatitis B causes a liver disease characterized by inflammation of the liver parenchyma. The aim of this study was to investigate the evolution of biological parameters in patients treated with Tenofovir for chronic B infection at the Commune V referral health center in Bamako. We obtained a prevalence of 14.15%. The most represented age group was 31 - 40 years, with 36.8%. The sex ratio was 1.44 in favour of men. Viral load was undetectable after 18 months of treatment in 25 patients (42.37%). Tenofovir, the 1st-line drug in Mali, is effective on the biological parameters monitored in patients.

Study on the clogging mechanism of punching screen in sand control by the punching structure parameters

As an independent sand control unit or a common protective shell of a high-quality screen,the punching screen is the outermost sand retaining unit of the sand control pipe which is used in geothermal well or oil and gas well.However,most screens only consider the influence of the internal sand retaining medium parameters in the sand control performance design while ignoring the influence of the plugging of the punching screen on the overall sand retaining performance of the screen.To explore the clogging mechanism of the punching screen,this paper established the clogging mechanism calculation model of a single punching screen sand control unit by using the computational fluid mechanics-discrete element method(CFD-DEM)combined method.According to the combined motion of particles and fluids,the influence of the internal flow state on particle motion and accumulation was analyzed.The results showed that(1)the clogging process of the punching sand control unit is divided into three stages:initial clogging,aggravation of clogging and stability of clogging.In the initial stage of blockage,coarse particles form a loose bridge structure,and blockage often occurs preferentially at the streamline gathering place below chamfering inside the sand control unit.In the stage of blockage intensification,the particle mass develops into a relatively complete sand bridge,which develops from both ends of the opening to the center of the opening.In the stable plugging stage,the sand deposits show a“fan shape”and form a“V-shaped”gully inside the punching slot element.(2)Under a certain reservoir particle-size distribution,The slit length and opening height have a large influence on the permeability and blockage rate,while the slit width size has little influence on the permeability and blockage rate.The microscopic clogging mechanism and its law of the punching screen prevention unit are proposed in this study,which has some field guidance significance for the design of punching screen and sand prevention selection.

Quaternion-Based Adaptive Trajectory Tracking Control of a Rotor-Missile with Unknown Parameters Identification

This paper investigates the adaptive trajectory tracking control problem and the unknown parameter identification problem of a class of rotor-missiles with parametric system uncertainties.First,considering the uncertainty of structural and aerodynamic parameters,the six-degree-of-freedom(6Do F) nonlinear equations describing the position and attitude dynamics of the rotor-missile are established,respectively,in the inertial and body-fixed reference frames.Next,a hierarchical adaptive trajectory tracking controller that can guarantee closed-loop stability is proposed according to the cascade characteristics of the 6Do F dynamics.Then,a memory-augmented update rule of unknown parameters is proposed by integrating all historical data of the regression matrix.As long as the finitely excited condition is satisfied,the precise identification of unknown parameters can be achieved.Finally,the validity of the proposed trajectory tracking controller and the parameter identification method is proved through Lyapunov stability theory and numerical simulations.

Dynamics Modeling and Parameter Identification for a Coupled-Drive Dual-Arm Nursing Robot

A dual-arm nursing robot can gently lift patients and transfer them between a bed and a wheelchair.With its lightweight design,high load-bearing capacity,and smooth surface,the coupled-drive joint is particularly well suited for these robots.However,the coupled nature of the joint disrupts the direct linear relationship between the input and output torques,posing challenges for dynamic modeling and practical applications.This study investigated the transmission mechanism of this joint and employed the Lagrangian method to construct a dynamic model of its internal dynamics.Building on this foundation,the Newton-Euler method was used to develop a dynamic model for the entire robotic arm.A continuously differentiable friction model was incorporated to reduce the vibrations caused by speed transitions to zero.An experimental method was designed to compensate for gravity,inertia,and modeling errors to identify the parameters of the friction model.This method establishes a mapping relationship between the friction force and motor current.In addition,a Fourier series-based excitation trajectory was developed to facilitate the identification of the dynamic model parameters of the robotic arm.Trajectory tracking experiments were conducted during the experimental validation phase,demonstrating the high accuracy of the dynamic model and the parameter identification method for the robotic arm.This study presents a dynamic modeling and parameter identification method for coupled-drive joint robotic arms,thereby establishing a foundation for motion control in humanoid nursing robots.

Observer-based dynamic event-triggered control for distributed parameter systems over mobile sensor-plus-actuator networks

We develop a policy of observer-based dynamic event-triggered state feedback control for distributed parameter systems over a mobile sensor-plus-actuator network.It is assumed that the mobile sensing devices that provide spatially averaged state measurements can be used to improve state estimation in the network.For the purpose of decreasing the update frequency of controller and unnecessary sampled data transmission, an efficient dynamic event-triggered control policy is constructed.In an event-triggered system, when an error signal exceeds a specified time-varying threshold, it indicates the occurrence of a typical event.The global asymptotic stability of the event-triggered closed-loop system and the boundedness of the minimum inter-event time can be guaranteed.Based on the linear quadratic optimal regulator, the actuator selects the optimal displacement only when an event occurs.A simulation example is finally used to verify that the effectiveness of such a control strategy can enhance the system performance.

Comparison of ocular biometric parameters between two swept-source optical coherence tomography devices and Scheimpflug tomography in patients with cataract

AIM:To assess and compare the variations and agreements across different ocular biometric parameters using swept-source optical coherence tomography(SS-OCT)and Scheimpflug tomography in patients diagnosed with cataract.METHODS:This prospective case series was conducted at Tianjin Medical University Eye Hospital.In total,212 eyes from 212 patients scheduled for phacoemulsification were included.Eyes were evaluated preoperatively using two SSOCT devices(IOLMaster700 and CASIA2)and Scheimpflug tomography(Pentacam).Central corneal thickness(CCT),anterior chamber depth(ACD),aqueous depth(AQD),white-to-white distance(WTW),flat simulated keratometry(Kf),steep simulated keratometry(Ks),mean keratometry(Km),and total corneal keratometry(TKm)were measured.Intraclass correlation coefficient(ICC),95%confidence intervals(CI)and limits of agreement(LoA)widths were conducted to assess differences and correlations between devices.RESULTS:All parameters,except for Ks,were significantly different.Pairwise comparison revealed no significant differences between keratometry obtained by IOLMaster 700 and Pentacam.LoA widths of all paired comparisons for Ks were>0.80 D.Except for WTW between IOLMaster 700 and CASIA2 and between CASIA2 and Pentacam,other Pearson’s coefficients between devices showed a strong correlation(all r>0.95).The ICC of WTW(ICC=0.438,95%CI 0.167-0.625)showed poor reliability.The reliability of CCT,ACD,and AQD was excellent(all ICC>0.95),whereas that of TKm was good(ICC=0.827,95%CI 0.221-0.939).A significant linear correlation was also observed among devices.CONCLUSION:The ocular parameters derived from the use of IOLMaster700,CASIA2,and Pentacam exhibit significant discrepancies;as such,measurements from these devices should not be deemed as interchangeable.

Parameter estimation in n-dimensional massless scalar field

Quantum Fisher information(QFI)associated with local metrology has been used to parameter estimation in open quantum systems.In this work,we calculated the QFI for a moving Unruh-DeWitt detector coupled with massless scalar fields in n-dimensional spacetime,and analyzed the behavior of QFI with various parameters,such as the dimension of spacetime,evolution time,and Unruh temperature.We discovered that the QFI of state parameter decreases monotonically from 1 to 0 over time.Additionally,we noted that the QFI for small evolution times is several orders of magnitude higher than the QFI for long evolution times.We also found that the value of QFI decreases at first and then stabilizes as the Unruh temperature increases.It was observed that the QFI depends on initial state parameterθ,and Fθis the maximum forθ=0 orθ=π,Fφis the maximum forθ=π/2.We also obtain that the maximum value of QFI for state parameters varies for different spacetime dimensions with the same evolution time.

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.

Digital measuring the ocular morphological parameters of guinea pig eye in vivo with Python

AIM:To quantitatively measure ocular morphological parameters of guinea pig with Python technology.METHODS:Thirty-six eyeballs of eighteen 3-weekold guinea pigs were measured with keratometer and photographed to obtain the horizontal,coronal,and sagittal planes respectively.The corresponding photo pixels-actual length ratio was acquired by a proportional scale.The edge coordinates were identified artificially by ginput function.Circle and conic curve fitting were applied to fit the contour of the eyeball in the sagittal,coronal and horizontal view.The curvature,curvature radius,eccentricity,tilt angle,corneal diameter,and binocular separation angle were calculated according to the geometric principles.Next,the eyeballs were removed,canny edge detection was applied to identify the contour of eyeball in vitro.The results were compared between in vivo and in vitro.RESULTS:Regarding the corneal curvature and curvature radius on the horizontal and sagittal planes,no significant differences were observed among results in vivo,in vitro,and the keratometer.The horizontal and vertical binocular separation angles were 130.6°±6.39°and 129.8°±9.58°respectively.For the corneal curvature radius and eccentricity in vivo,significant differences were observed between horizontal and vertical planes.CONCLUSION:The Graphical interface window of Python makes up the deficiency of edge detection,which requires too much definition in Matlab.There are significant differences between guinea pig and human beings,such as exotropic eye position,oblique oval eyeball,and obvious discrepancy of binoculus.This study helps evaluate objectively the ocular morphological parameters of small experimental animals in emmetropization research.

Analysis and comparison of retinal vascular parameters under different glucose metabolic status based on deep learning

AIM:To develop a deep learning-based model for automatic retinal vascular segmentation,analyzing and comparing parameters under diverse glucose metabolic status(normal,prediabetes,diabetes)and to assess the potential of artificial intelligence(AI)in image segmentation and retinal vascular parameters for predicting prediabetes and diabetes.METHODS:Retinal fundus photos from 200 normal individuals,200 prediabetic patients,and 200 diabetic patients(600 eyes in total)were used.The U-Net network served as the foundational architecture for retinal arteryvein segmentation.An automatic segmentation and evaluation system for retinal vascular parameters was trained,encompassing 26 parameters.RESULTS:Significant differences were found in retinal vascular parameters across normal,prediabetes,and diabetes groups,including artery diameter(P=0.008),fractal dimension(P=0.000),vein curvature(P=0.003),C-zone artery branching vessel count(P=0.049),C-zone vein branching vessel count(P=0.041),artery branching angle(P=0.005),vein branching angle(P=0.001),artery angle asymmetry degree(P=0.003),vessel length density(P=0.000),and vessel area density(P=0.000),totaling 10 parameters.CONCLUSION:The deep learning-based model facilitates retinal vascular parameter identification and quantification,revealing significant differences.These parameters exhibit potential as biomarkers for prediabetes and diabetes.

Effects of Fresh Cupressus sempervirens Leaves Infusion on Growth Performance, Intestinal Microbiota and Haemato-Biochemical Parameters in Broilers

The accumulation of growth-promoting antibiotic residues in animal products and the resistance developed by bacteria in poultry farms has led to a search for natural compounds derived from plants. This study was designed to promote the production performance of broiler chickens using fresh Cupressus sempervirens leaves infusion. Fresh Cupressus sempervirens leaves were harvested, washed, chopped and ground to a paste using a blender and fermented for three days in a closed container at a rate of 500 g/L of water. The solution obtained was filtered and added at the rate of 2, 4, 6, 8 and 10 ml/L of drinking water. The chickens fed on the graded level of the solution were compared to a control ration without an additive and positive control group supplemented with 1 g antibiotic/kg feed. At the finisher phase and throughout the study period, water intake increased significantly (P < 0.05) with increasing levels of infusion. Feed intake decreased significantly (P < 0.05) with 2 and 4 ml of infusion/L drinking water. Live weight and weight gain were significantly (P < 0.05) higher with 6 ml/L, while feed conversion significantly (P < 0.05) decreased with the same treatment compared with the control treatment without additives (T0). Carcass characteristics were not significantly (P > 0.05) affected by the inclusion of Cupressus sempervirens infusion. Haematological parameters significantly (P < 0.05) increase independently of the rate of incorporation of the infusion into the drinking water, with the exception of RBCs, MCHT and PCT. Serum content in total protein, globulins, LDL cholesterol and triglycerides were significantly (P < 0.05) high with 8 and 10 ml Cupressus sempervirens infusion/litre drinking water as compared to all other treatments. AST, ALT, urea, creatine, albumin, total cholesterol and HDL-cholesterol were not significantly affected. The lactic acid bacteria load increased significantly (P E. coli and salmonella counts decreased significantly (P < 0.05) with infusion compared to the control without additive. In conclusion, 6 ml of Cupressus sempervirens infusion can be used as an alternative to antibiotic feed additives to promote growth performance in broilers.

Time Parameter Based Low-Energy Data Encryption Method for Mobile Applications

Various mobile devices and applications are now used in daily life.These devices require high-speed data processing,low energy consumption,low communication latency,and secure data transmission,especially in 5G and 6G mobile networks.High-security cryptography guarantees that essential data can be transmitted securely;however,it increases energy consumption and reduces data processing speed.Therefore,this study proposes a low-energy data encryption(LEDE)algorithm based on the Advanced Encryption Standard(AES)for improving data transmission security and reducing the energy consumption of encryption in Internet-of-Things(IoT)devices.In the proposed LEDE algorithm,the system time parameter is employed to create a dynamic S-Box to replace the static S-Box of AES.Tests indicated that six-round LEDE encryption achieves the same security level as 10-round conventional AES encryption.This reduction in encryption time results in the LEDE algorithm having a 67.4%lower energy consumption and 43.9%shorter encryption time than conventional AES;thus,the proposed LEDE algorithm can improve the performance and the energy consumption of IoT edge devices.

Determination of uncertainties of geomechanical parameters of metamorphic rocks using petrographic analyses

Geomechanical parameters of intact metamorphic rocks determined from laboratory testing remain highly uncertain because of the great intrinsic variability associated with the degrees of metamorphism.The aim of this paper is to develop a proper methodology to analyze the uncertainties of geomechanical characteristics by focusing on three domains,i.e.data treatment process,schistosity angle,and mineralogy.First,the variabilities of the geomechanical laboratory data of Westwood Mine(Quebec,Canada)were examined statistically by applying different data treatment techniques,through which the most suitable outlier methods were selected for each parameter using multiple decision-making criteria and engineering judgment.Results indicated that some methods exhibited better performance in identifying the possible outliers,although several others were unsuccessful because of their limitation in large sample size.The well-known boxplot method might not be the best outlier method for most geomechanical parameters because its calculated confidence range was not acceptable according to engineering judgment.However,several approaches,including adjusted boxplot,2MADe,and 2SD,worked very well in the detection of true outliers.Also,the statistical tests indicate that the best-fitting probability distribution function for geomechanical intact parameters might not be the normal distribution,unlike what is assumed in most geomechanical studies.Moreover,the negative effects of schistosity angle on the uniaxial compressive strength(UCS)variabilities were reduced by excluding the samples within a specific angle range where the UCS data present the highest variation.Finally,a petrographic analysis was conducted to assess the associated uncertainties such that a logical link was found between the dispersion and the variabilities of hard and soft minerals.

A Novel On-Site-Real-Time Method for Identifying Characteristic Parameters Using Ultrasonic Echo Groups and Neural Network

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.