度:生态包装的重要标尺

发展生态包装,是我国包装工业的必然选择,是建设"两型社会"的必然要求。"量"度、"质"度和"法"度是生态包装的重要标尺。拒绝过量化媚俗包装,辩证看待绿色材质,熟悉与遵循"绿色"法规,是正确看待和发展生态包装应该把握的3个基本维度。

Advancing automated pupillometry:a practical deep learning model utilizing infrared pupil images

AIM:To establish pupil diameter measurement algorithms based on infrared images that can be used in real-world clinical settings.METHODS:A total of 188 patients from outpatient clinic at He Eye Specialist Shenyang Hospital from Spetember to December 2022 were included,and 13470 infrared pupil images were collected for the study.All infrared images for pupil segmentation were labeled using the Labelme software.The computation of pupil diameter is divided into four steps:image pre-processing,pupil identification and localization,pupil segmentation,and diameter calculation.Two major models are used in the computation process:the modified YoloV3 and Deeplabv 3+models,which must be trained beforehand.RESULTS:The test dataset included 1348 infrared pupil images.On the test dataset,the modified YoloV3 model had a detection rate of 99.98% and an average precision(AP)of 0.80 for pupils.The DeeplabV3+model achieved a background intersection over union(IOU)of 99.23%,a pupil IOU of 93.81%,and a mean IOU of 96.52%.The pupil diameters in the test dataset ranged from 20 to 56 pixels,with a mean of 36.06±6.85 pixels.The absolute error in pupil diameters between predicted and actual values ranged from 0 to 7 pixels,with a mean absolute error(MAE)of 1.06±0.96 pixels.CONCLUSION:This study successfully demonstrates a robust infrared image-based pupil diameter measurement algorithm,proven to be highly accurate and reliable for clinical application.

Improvement of High-Speed Detection Algorithm for Nonwoven Material Defects Based on Machine Vision

Defect detection is vital in the nonwoven material industry,ensuring surface quality before producing finished products.Recently,deep learning and computer vision advancements have revolutionized defect detection,making it a widely adopted approach in various industrial fields.This paper mainly studied the defect detection method for nonwoven materials based on the improved Nano Det-Plus model.Using the constructed samples of defects in nonwoven materials as the research objects,transfer learning experiments were conducted based on the Nano DetPlus object detection framework.Within this framework,the Backbone,path aggregation feature pyramid network(PAFPN)and Head network models were compared and trained through a process of freezing,with the ultimate aim of bolstering the model's feature extraction abilities and elevating detection accuracy.The half-precision quantization method was used to optimize the model after transfer learning experiments,reducing model weights and computational complexity to improve the detection speed.Performance comparisons were conducted between the improved model and the original Nano Det-Plus model,YOLO,SSD and other common industrial defect detection algorithms,validating that the improved methods based on transfer learning and semi-precision quantization enabled the model to meet the practical requirements of industrial production.

Research on Collaborative Filtering Recommendation Algorithm Based on Improved User Portraits

With the arrival of the big data era,the phenomenon of information overload is becoming increasingly severe.In response to the common issue of sparse user rating matrices in recommendation systems,a collaborative filtering recommendation algorithm was proposed based on improved user profiles in this study.Firstly,a profile labeling system was constructed based on user characteristics.This study proposed that user profile labels should be created using basic user information and basic item information,in order to construct multidimensional user profiles.TF-IDF algorithm was used to determine the weights of user-item feature labels.Secondly,user similarity was calculated by weighting both profile-based collaborative filtering and user-based collaborative filtering algorithms,and the final user similarity was obtained by harmonizing these weights.Finally,personalized recommendations were generated using Top-N method.Validation with the MovieLens-1M dataset revealed that this algorithm enhances both recommendation Precision and Recall compared to single-method approaches(recommendation algorithm based on user portrait and user-based collaborative filtering algorithm).

江西诗派的精神内核是宋明理学——二论宋明理学是"江西之学

从诗学思想这个角度去考察,宋代诗学的主流是江西诗派的诗学思想。因江西诗派的首领黄庭坚等人对诗歌的观点与理学家接近,为理学家所接受,而当时理学的影响正在日益扩大,故得相辅而行;不少江西诗派的主要成员同时又是理学家,其创作实践也渗透着理学观。以此观照,江西诗派的精神内核无疑应该就是宋明理学。

An Adaptive Spectral Conjugate Gradient Method with Restart Strategy

As a generalization of the two-term conjugate gradient method(CGM),the spectral CGM is one of the effective methods for solving unconstrained optimization.In this paper,we enhance the JJSL conjugate parameter,initially proposed by Jiang et al.(Computational and Applied Mathematics,2021,40:174),through the utilization of a convex combination technique.And this improvement allows for an adaptive search direction by integrating a newly constructed spectral gradient-type restart strategy.Then,we develop a new spectral CGM by employing an inexact line search to determine the step size.With the application of the weak Wolfe line search,we establish the sufficient descent property of the proposed search direction.Moreover,under general assumptions,including the employment of the strong Wolfe line search for step size calculation,we demonstrate the global convergence of our new algorithm.Finally,the given unconstrained optimization test results show that the new algorithm is effective.

论江西诗学形式与内容的内在矛盾

江西诗学对形式的过度关注历来被人诟病,然而,这只是江西诗学为解决初学诗者的难题而不得不采取的下策。江西诗人们自然知道诗歌情"意"内容的重要性,但对那些初学诗者而言,形式"法"度则是他们更关注的问题;如何克服这二者之间紧张关系是江西诗学面临的难题。江西诗学提出"悟"之主张,依然未能有效解决这一难题。

Shear mechanical properties and frictional sliding responses of rough joint surfaces under dynamic normal displacement conditions

A comprehensive understanding of the dynamic frictional characteristics in rock joints under high normal load and strong confinement is essential for ensuring the safety of deep engineering construction and mitigating geological disasters.This study conducted shear experiments on rough rock joints under displacement-controlled dynamic normal loads,investigating the shear behaviors of joints across varying initial normal loads,normal loading frequencies,and normal loading amplitudes.Experimental results showed that the peak/valley shear force values increased with initial normal loads and normal loading frequencies but showed an initial increase followed by a decrease with normal loading amplitudes.Dynamic normal loading can either increase or decrease shear strength,while this study demonstrates that higher frequencies lead to enhanced friction.Increased initial normal loading and normal loading frequency result in a gradual decrease in joint roughness coefficient(JRC)values of joint surfaces after shearing.Positive correlations existed between frictional energy dissipation and peak shear forces,while post-shear joint surface roughness exhibited a negative correlation with peak shear forces through linear regression analysis.This study contributes to a better understanding of the sliding responses and shear mechanical characteristics of rock joints under dynamic disturbances.

Enhancing reliability assessment of curved low-stiffness track-viaducts with an adaptive surrogate-based approach emphasizing track dynamic geometric state

Traditional track dynamic geometric state(TDGS)simulation incurs substantial computational burdens,posing challenges for developing reliability assessment approach that accounts for TDGS.To overcome these,firstly,a simulation-based TDGS model is established,and a surrogate-based model,grid search algorithm-particle swarm optimization-genetic algorithm-multi-output least squares support vector regression,is established.Among them,hyperparameter optimization algorithm’s effectiveness is confirmed through test functions.Subsequently,an adaptive surrogate-based probability density evolution method(PDEM)considering random track geometry irregularity(TGI)is developed.Finally,taking curved train-steel spring floating slab track-U beam as case study,the surrogate-based model trained on simulation datasets not only shows accuracy in both time and frequency domains,but also surpasses existing models.Additionally,the adaptive surrogate-based PDEM shows high accuracy and efficiency,outperforming Monte Carlo simulation and simulation-based PDEM.The reliability assessment shows that the TDGS part peak management indexes,left/right vertical dynamic irregularity,right alignment dynamic irregularity,and track twist,have reliability values of 0.9648,0.9918,0.9978,and 0.9901,respectively.The TDGS mean management index,i.e.,track quality index,has reliability value of 0.9950.These findings show that the proposed framework can accurately and efficiently assess the reliability of curved low-stiffness track-viaducts,providing a theoretical basis for the TGI maintenance.

Strength criterion for crystalline rocks considering grain size effect and tensile-compressive strength ratio

The macroscopic mechanical properties of rocks are significantly influenced by their microstructure.As a material bonded by mineral grains,the grain morphology of crystalline rock is the primary factor influencing the strength.However,most strength criteria neglect the strength variations caused by different grain characteristics in rocks.Furthermore,the traditional linear criteria tend to overestimate tensile strength and exhibit apex singularity.To address these shortcomings,a piecewise strength criterion that considers the grain size effect has been proposed.A part of an ellipse was employed to construct the envelope of the tensive-shear region on the meridian plane,to accurately reproduce the low tensile-compressive strength ratio.Based on the analysis of experimental data,both linear and exponential modification functions that account for grain size effects were integrated into the proposed criterion.The corresponding finite element algorithm has been implemented.The accuracy and applicability of the proposed criterion were validated by comparing with the experimental data.

Data-driven methods for predicting the representative temperature of bridge cable based on limited measured data

Cable-stayed bridges have been widely used in high-speed railway infrastructure.The accurate determination of cable’s representative temperatures is vital during the intricate processes of design,construction,and maintenance of cable-stayed bridges.However,the representative temperatures of stayed cables are not specified in the existing design codes.To address this issue,this study investigates the distribution of the cable temperature and determinates its representative temperature.First,an experimental investigation,spanning over a period of one year,was carried out near the bridge site to obtain the temperature data.According to the statistical analysis of the measured data,it reveals that the temperature distribution is generally uniform along the cable cross-section without significant temperature gradient.Then,based on the limited data,the Monte Carlo,the gradient boosted regression trees(GBRT),and univariate linear regression(ULR)methods are employed to predict the cable’s representative temperature throughout the service life.These methods effectively overcome the limitations of insufficient monitoring data and accurately predict the representative temperature of the cables.However,each method has its own advantages and limitations in terms of applicability and accuracy.A comprehensive evaluation of the performance of these methods is conducted,and practical recommendations are provided for their application.The proposed methods and representative temperatures provide a good basis for the operation and maintenance of in-service long-span cable-stayed bridges.

Harmonic Impedance Modeling and Oscillation Analysis of Modular Multilevel Converter

To facilitate rapid analysis of the oscillation stability mechanism in modular multilevel converter-based high voltage direct current(MMC-HVDC)systems and streamline the simulation process for determining MMC impedance characteristics,a simplified mathematical simulation model for MMC closed-loop impedance is developed using the harmonic state space method.This model considers various control strategies and includes both AC-side and DC-side impedance models.By applying a Nyquist criterion-based impedance analysis method,the stability mechanisms on the AC and DC sides of the MMC are examined.In addition,a data-driven oscillation stability analysis method is also proposed,leveraging a global sensitivity algorithm based on fast model results to identify key parameters influencing MMC oscillation stability.Based on sensitivity analysis results,a parameter adjustment strategy for oscillation suppression is proposed.The simulation results from the MATLAB/Simulinkbased MMC model validate the effectiveness of the proposed method.

A novel refined dynamic model of high-speed maglev train-bridge coupled system for random vibration and running safety assessment

Running safety assessment and tracking irregularity parametric sensitivity analysis of high-speed maglev train-bridge system are of great concern,especially need perfect refinement models in which all properties can be well characterized based on various stochastic excitations.A three-dimensional refined spatial random vibration analysis model of high-speed maglev train-bridge coupled system is established in this paper,in which multi-source uncertainty excitation can be considered simultaneously,and the probability density evolution method(PDEM)is adopted to reveal the system-specific uncertainty dynamic characteristic.The motion equation of the maglev vehicle model is composed of multi-rigid bodies with a total 210-degrees of freedom for each vehicle,and a refined electromagnetic force-air gap model is used to account for the interaction and coupling effect between the moving train and track beam bridges,which are directly established by using finite element method.The model is proven to be applicable by comparing with Monte Carlo simulation.By applying the proposed stochastic framework to the high maglev line,the random dynamic responses of maglev vehicles running on the bridges are studied for running safety and stability assessment.Moreover,the effects of track irregularity wavelength range under different amplitude and running speeds on the coupled system are investigated.The results show that the augmentation of train speed will move backward the sensitive wavelength interval,and track irregularity amplitude influences the response remarkably in the sensitive interval.

Microthermokinetic study of xanthate adsorption on impurity-doped galena

Six kinds of galena with different impurities were synthesized and the effects of impurities on the floatability of galena were investigated. The thermodynamic and kinetic parameters on the galena surface were measured using microcalorimetry, and the adsorption configuration and energy of butyl xanthate on the surfaces of galena with different impurities were simulated by density functional theory. Flotation experiments showed that Ag and Bi significantly promoted the recovery of galena, while Zn, Sb, Mn, and Cu reduced the recovery of the flotation. Microthermokinetic results indicated that the absolute value of xanthate adsorption heat was directly proportional to the flotation recovery of galena. Adsorption heat and reaction rate coefficients of xanthate on galena containing Ag or Bi were larger than those on pure galena, but smaller on galena containing Cu or Sb. Additionally, the relationship between the heat of unsaturated adsorption of xanthate and the adsorption energy of impurity atom on galena surface was investigated.

Application of the Sub-Model Method in the Engine Strength Analysis

On the basis of introducing the fundamental theory and the basic analysis steps of the sub model method, the strength of the new engine complex assembly structure was analyzed according to the properties of the engine structures, some of the key parts of the engine were analyzed with refined mesh by sub model method and the error of the FEM solution was estimated by the extrapolation method. The example showed that the sub model can not only analyze the comlex structures without the restriction of the software and hardware of the computers, but get the more precise analysis result also. This method is more suitable for the strength analysis of the complex assembly structure.

3D density inversion of gravity gradient data using the extrapolated Tikhonov regularization

We use the extrapolated Tikhonov regularization to deal with the ill-posed problem of 3D density inversion of gravity gradient data. The use of regularization parameters in the proposed method reduces the deviations between calculated and observed data. We also use the depth weighting function based on the eigenvector of gravity gradient tensor to eliminate undesired effects owing to the fast attenuation of the position function. Model data suggest that the extrapolated Tikhonov regularization in conjunction with the depth weighting function can effectively recover the 3D distribution of density anomalies. We conduct density inversion of gravity gradient data from the Australia Kauring test site and compare the inversion results with the published research results. The proposed inversion method can be used to obtain the 3D density distribution of underground anomalies.

Influence of superplasticizers on the early-age crack resistance of concrete

In order to improve the early-age cracking resistance of concrete, different types of superplasticizers are used. Two types of polycarboxlic salt/acid superplasticizers and one retarding naphthalene superplasticizer are selected to investigate the influence of superplasticizers on the early-age cracking resistance of the concrete by using the slab test and the temperature-stress test. The results show that the polycarboxlic salt/acid superplasticizer cannot always improve the cracking resistance capacity of the concrete compared with the naphthalene superplasticizer, which is related to the chemical structure of the polycarboxlic salt/acid superplasticizer. High plastic tensile strength and dynamic elastic modulus at the early stage are beneficial to avoid cracking, and low hydration heat is also helpful. The evolutions of the drying shrilakage stress and the hydration heat temperature stress varying with time can be comprehensively evaluated by means of the slab test and the temperature stress test.

Spectrophotometric Determination of Lodine in Soils by Chloramine T-Tetrabase System

Optimized the experimental conditions of determination of trace iodine in soil in chloramine T-Tetrabase system, and analysis the national standards material, the results showed that the measured values was to be identical with recommended values with a detection limit of 0.16 μg/g, and the relative standard deviation was less than 8%, the whole process was short in time and simple, so it was applicable to the determination of trace iodine in batches.

Modified MK model combined with ductile fracture criterion and its application in warm hydroforming

A modified MK model combined with ductile fracture criterion（DFC-MK model） is proposed to compute the forming limit diagrams（FLDs） of 5A06-O aluminum alloy sheet at different temperatures.The material constant（C） of ductile fracture criterion and initial thickness imperfection parameter（f0） at various temperatures are determined by using a new computing method based on wide sheet bending test.The FLDs at 20 and 200 °C are calculated through the DFC-MK model.The DFC-MK model,which includes the influence of through-thickness normal stress,is written into the subroutine VUMAT embedded in Abaqus/ Explicit.The cylindrical cup hydroforming tests are carried out to verify the model.The results show that compared with experimental observations,the predicted FLDs based on DFC-MK model are more accurate than the conventional MK model;the errors between the simulations and experiments in warm hydroforming are 8.23% at 20 °C and 9.24% at 200 °C,which verify the effectiveness of the proposed model.

Improved tilt-depth method for fast estimation of top and bottom depths of magnetic bodies

The tilt-depth method can be used to make fast estimation of the top depth of magnetic bodies. However, it is unable to estimate bottom depths and its every inversion point only has a single solution. In order to resolve such weaknesses, this paper presents an improved tilt-depth method based on the magnetic anomaly expression of vertical contact with a finite depth extent, which can simultaneously estimate top and bottom depths of magnetic bodies. In addition, multiple characteristic points are selected on the tilt angle map for joint computation to improve reliability of inversion solutions. Two- and three- dimensional model tests show that this improved tilt-depth method is effective in inverting buried depths of top and bottom bodies, and has a higher inversion precision for top depths than the conventional method. The improved method is then used to process aeromagnetic data over the Changling Fault Depression in the Songliao Basin, and inversion results of top depths are found to be more accurate for actual top depths of volcanic rocks in two nearby drilled wells than those using the conventional tilt-depth method.