Insight Into the Separation-of-Variable Methods for the Closed-Form Solutions of Free Vibration of Rectangular Thin Plates

The separation-of-variable(SOV)methods,such as the improved SOV method,the variational SOV method,and the extended SOV method,have been proposed by the present authors and coworkers to obtain the closed-form analytical solutions for free vibration and eigenbuckling of rectangular plates and circular cylindrical shells.By taking the free vibration of rectangular thin plates as an example,this work presents the theoretical framework of the SOV methods in an instructive way,and the bisection–based solution procedures for a group of nonlinear eigenvalue equations.Besides,the explicit equations of nodal lines of the SOV methods are presented,and the relations of nodal line patterns and frequency orders are investigated.It is concluded that the highly accurate SOV methods have the same accuracy for all frequencies,the mode shapes about repeated frequencies can also be precisely captured,and the SOV methods do not have the problem of missing roots as well.

Estimation of Chloride Diffusivity in Hydrated Tricalcium Silicate Using a Hydration-Diffusion Integrated Method

This study aims to develop a chloride diffusion simulation method that considers the hydration microstructure and pore solution properties during the hydration of tricalcium silicate(C3S).The method combines the hydration simulation,thermodynamic calculation,and finite element analysis to examine the effects of pore solution,including effect of electrochemical potential,effect of chemical activity,and effect of mechanical interactions between ions,on the chloride effective diffusion coefficient of hydrated C3S paste.The results indicate that the effect of electrochemical potential on chloride diffusion becomes stronger with increasing hydration age due to the increase in the content of hydrated calcium silicate;as the hydration age increases,the effect of chemical activity on chloride diffusion weakens when the number of diffusible elements decreases;the effect of mechanical interactions between ions on chloride diffusion decreases with the increase of hydration age.

Structural Modal Parameter Recognition and Related Damage Identification Methods under Environmental Excitations: A Review

Modal parameters can accurately characterize the structural dynamic properties and assess the physical state of the structure.Therefore,it is particularly significant to identify the structural modal parameters according to the monitoring data information in the structural health monitoring(SHM)system,so as to provide a scientific basis for structural damage identification and dynamic model modification.In view of this,this paper reviews methods for identifying structural modal parameters under environmental excitation and briefly describes how to identify structural damages based on the derived modal parameters.The paper primarily introduces data-driven modal parameter recognition methods(e.g.,time-domain,frequency-domain,and time-frequency-domain methods,etc.),briefly describes damage identification methods based on the variations of modal parameters(e.g.,natural frequency,modal shapes,and curvature modal shapes,etc.)and modal validation methods(e.g.,Stability Diagram and Modal Assurance Criterion,etc.).The current status of the application of artificial intelligence(AI)methods in the direction of modal parameter recognition and damage identification is further discussed.Based on the pre-vious analysis,the main development trends of structural modal parameter recognition and damage identification methods are given to provide scientific references for the optimized design and functional upgrading of SHM systems.

Effects of spatial heterogeneity on pseudo-static stability of coal mine overburden dump slope,using random limit equilibrium and random finite element methods:A comparative study

Sudden and unforeseen seismic failures of coal mine overburden(OB)dump slopes interrupt mining operations,cause loss of lives and delay the production of coal.Consideration of the spatial heterogeneity of OB dump materials is imperative for an adequate evaluation of the seismic stability of OB dump slopes.In this study,pseudo-static seismic stability analyses are carried out for an OB dump slope by considering the material parameters obtained from an insitu field investigation.Spatial heterogeneity is simulated through use of the random finite element method(RFEM)and the random limit equilibrium method(RLEM)and a comparative study is presented.Combinations of horizontal and vertical spatial correlation lengths were considered for simulating isotropic and anisotropic random fields within the OB dump slope.Seismic performances of the slope have been reported through the probability of failure and reliability index.It was observed that the RLEM approach overestimates failure probability(P_(f))by considering seismic stability with spatial heterogeneity.The P_(f)was observed to increase with an increase in the coefficient of variation of friction angle of the dump materials.Further,it was inferred that the RLEM approach may not be adequately applicable for assessing the seismic stability of an OB dump slope for a horizontal seismic coefficient that is more than or equal to 0.1.

Design of Voltage Equalization Circuit and Control Method for Lithium-ion Battery Packs

The active equalization of lithium-ion batteries involves transferring energy from high-voltage cells to low-voltage cells,ensuring consistent voltage levels across the battery pack and maintaining safety.This paper presents a voltage balancing circuit and control method.First,a single capacitor method is used to design the circuit topology for energy transfer.Next,real-time voltage detection and control are employed to balance energy between cells.Finally,simulation and experimental results demonstrate the effectiveness of the proposed method,achieving balanced voltages of 3.97 V from initial voltages of 4.10,3.97,and 3.90 V.The proposed circuit is simple,reliable,and effectively prevents overcharge and overdischarge.

An Improved Local RBF Collocation Method for 3D Excavation Deformation Based on Direct Method and Mapping Technique

Since the plasticity of soil and the irregular shape of the excavation,the efficiency and stability of the traditional local radial basis function(RBF)collocation method(LRBFCM)are inadequate for analyzing three-dimensional(3D)deformation of deep excavation.In this work,the technique known as the direct method,where the local influence nodes are collocated on a straight line,is introduced to optimize the LRBFCM.The direct method can improve the accuracy of the partial derivative,reduce the size effect caused by the large length-width ratio,and weaken the influence of the shape parameters on the LRBFCM.The mapping technique is adopted to transform the physical coordinates of a quadratic-type block to normalized coordinates,in which the deformation problem can easily be solved using the direct method.The stability of the LRBFCM is further modified by considering the irregular shape of 3D excavation,which is divided into several quadratic-type blocks.The soil’s plasticity is described by the Drucker-Prager(D-P)model.The improved LRBFCM is integrated with the incremental method to analyze the plasticity.Five different examples,including strip excavations and circular excavations,are presented to validate the proposed approach’s efficiency.

Assessment of leachate-contaminated clays using experimental and artificial methods

The investigation of leachate-contaminated clay(LCC)is essential for landfill engineering assessment and achievement of sustainable development goals.Several static and dynamic laboratory tests,including unconfined compressive strength(UCS),California bearing ratio(CBR),and cyclic simple shear,are conducted.Cyclic simple shear experiments on LCCs were performed to evaluate the damping and shear modulus.The investigated factors are vertical load(VL),leachate content(LC),frequency(F),and shear strain(ShS)for LCC.Forensic-based investigation optimization(FBIO)and equilibrium optimizer algorithm(EOA)were utilized in addition to multiple types of ensemble models,including adaptive boosting(ADB),gradient boosting regression tree(GBRT),extreme gradient boosting(XGB) and random forest(RF).The comparison of the methods showed that GBRT-FBIO and XGB-EOA models outperformed other models for shear modulus and damping of LCC.The p-value less than 0.0001 shows the significance of the used models in the response surface methodology(RSM)method.

Hollow cerium nanoparticles synthesized by one-step method for multienzyme activity to reduce colitis in mice

BACKGROUND Inflammatory bowel disease(IBD)is a common chronic intestinal inflammatory disease.High oxidative stress is a treatment target for IBD.Cerium oxide(CeO2)nanomaterials as nanozymes with antioxidant activity are potential drugs for the treatment of colitis.AIM To synthesize hollow cerium(H-CeO2)nanoparticles by one-step method and to validate the therapeutic efficacy of H-CeO2 in IBD.METHODS H-CeO2 was synthesized by one-step method and examined its characterization and nanoenzymatic activity.Subsequently,we constructed dextran sulfate so-dium(DSS)-induced colitis in mice to observe the effects of H-CeO2 on colonic inflammation.The effects of H-CeO2 on colon inflammation and reactive oxygen species(ROS)levels in IBD mice were detected by hematoxylin and eosin staining and dichlorofluorescein diacetate staining,respectively.Finally,the biological sa-fety of H-CeO2 on mice was evaluated by hematoxylin and eosin staining,blood routine,and blood biochemistry.RESULTS H-CeO2 nanoparticles prepared by the one-step method were uniform,monodi-sperse and hollow.H-CeO2 had a good ability to scavenge ROS,∙OH and∙OOH.H-CeO2 reduced DSS-induced decreases in body weight and colon length,colonic epithelial damage,inflammatory infiltration,and ROS accumulation.H-CeO2 administration reduced the disease activity index of DSS-induced animals from about 8 to 5.H-CeO2 had no significant effect on body weight,total platelet count,hemoglobin,white blood cell,and red blood cell counts in healthy mice.No significant damage to major organs was observed in healthy mice following H-CeO2 administration.CONCLUSION The one-step synthesis of H-CeO2 nanomaterials had good antioxidant activity,biosafety,and inhibited deve-lopment of DSS-induced IBD in mice by scavenging ROS.

Retrospective comparative study of different surgical methods for gastric ulcer perforation:Efficacy and postoperative complications

BACKGROUND Gastric ulcer perforation is a critical condition that can lead to significant morbidity and mortality if not promptly addressed.It is often the result of chronic peptic ulcer disease,which is characterized by a breach in the gastric wall due to ulceration.Surgical intervention is essential for managing this life-threatening complication.However,the optimal surgical technique remains debatable among clinicians.Various methods have been employed,including simple closure,omental patch repair,and partial gastrectomy,each with distinct advantages and disadvantages.Understanding the comparative efficacy and postoperative outcomes of these techniques is crucial for improving patient care and surgical decision-making.This study addresses the need for a comprehensive analysis in this area.AIM To compare the efficacy and postoperative complications of different surgical methods for the treatment of gastric ulcer perforation.METHODS A retrospective analysis was conducted on 120 patients who underwent surgery for gastric ulcer perforation between September 2020 and June 2023.The patients were divided into three groups based on the surgical method:Simple closure,omental patch repair,and partial gastrectomy.The primary outcomes were the operative success rate and incidence of postoperative complications.Secondary outcomes included the length of hospital stay,recovery time,and long-term quality of life.RESULTS The operative success rates for simple closure,omental patch repair,and partial gastrectomy were 92.5%,95%,and 97.5%,respectively.Postoperative complications occurred in 20%,15%,and 17.5%of patients in each group,respectively.The partial gastrectomy group showed a significantly longer operative time(P<0.001)but the lowest rate of ulcer recurrence(2.5%,P<0.05).The omental patch repair group demonstrated the shortest hospital stay(mean 7.2 days,P<0.05)and fastest recovery time.CONCLUSION While all three surgical methods showed high success rates,omental patch repair demonstrated the best overall outcomes,with a balance of high efficacy,low complication rates,and shorter recovery time.However,the choice of the surgical method should be tailored to individual patient factors and the surgeon’s expertise.

Towards a blank design method for manufacturing big-tapered profiled ring disk by spinning-rolling process

The big-tapered profiled ring disk is a key component of engines for rockets and missiles.A new forming technology,as called spinning-rolling process,has been proposed previously for the high performance,high efficiency and low-cost manufacturing of the component.Blank design is the key part of plastic forming process design.For spinning-rolling process,the shape and size of the blank play a crucial role in process stability,deformation behavior and dimensional accuracy.So this work proposes a blank design method to determine the geometry structure and sizes of the blank.The mathematical model for calculating the blank size has been deduced based on volume conservation and neutral layer length invariance principle.The FE simulation and corresponding trial production of an actual big-tapered profiled ring disk show that the proposed blank design method is applicative.In order to obtain a preferred blank,the influence rules of blank size determined by different deformation degrees(rolling ratio k)on the spinning-rolling process are revealed by comprehensive FE simulations.Overall considering the process stability,circularity of the deformed ring disk and forming forces,a reasonable range of deformation degree(rolling ratio k)is recommended for the blank design of the new spinning-rolling process.

Sensitivity Analysis of Structural Dynamic Behavior Based on the Sparse Polynomial Chaos Expansion and Material Point Method

This paper presents a framework for constructing surrogate models for sensitivity analysis of structural dynamics behavior.Physical models involving deformation,such as collisions,vibrations,and penetration,are devel-oped using the material point method.To reduce the computational cost of Monte Carlo simulations,response surface models are created as surrogate models for the material point system to approximate its dynamic behavior.An adaptive randomized greedy algorithm is employed to construct a sparse polynomial chaos expansion model with a fixed order,effectively balancing the accuracy and computational efficiency of the surrogate model.Based on the sparse polynomial chaos expansion,sensitivity analysis is conducted using the global finite difference and Sobol methods.Several examples of structural dynamics are provided to demonstrate the effectiveness of the proposed method in addressing structural dynamics problems.

Self-Learning and Its Application to Laminar Cooling Model of Hot Rolled Strip

The mathematical model for online controlling hot rolled steel cooling on run-out table （ROT for abbreviation） was analyzed, and water cooling is found to be the main cooling mode for hot rolled steel. The calculation of the drop in strip temperature by both water cooling and air cooling is summed up to obtain the change of heat transfer coefficient. It is found that the learning coefficient of heat transfer coefficient is the kernel coefficient of coiler temperature control （CTC） model tuning. To decrease the deviation between the calculated steel temperature and the measured one at coiler entrance, a laminar cooling control self-learning strategy is used. Using the data acquired in the field, the results of the self-learning model used in the field were analyzed. The analyzed results show that the self-learning function is effective.

Self-Learning of Multivariate Time Series Using Perceptually Important Points

In machine learning,positive-unlabelled(PU)learning is a special case within semi-supervised learning.In positiveunlabelled learning,the training set contains some positive examples and a set of unlabelled examples from both the positive and negative classes.Positive-unlabelled learning has gained attention in many domains,especially in time-series data,in which the obtainment of labelled data is challenging.Examples which originate from the negative class are especially difficult to acquire.Self-learning is a semi-supervised method capable of PU learning in time-series data.In the self-learning approach,observations are individually added from the unlabelled data into the positive class until a stopping criterion is reached.The model is retrained after each addition with the existent labels.The main problem in self-learning is to know when to stop the learning.There are multiple,different stopping criteria in the literature,but they tend to be inaccurate or challenging to apply.This publication proposes a novel stopping criterion,which is called Peak evaluation using perceptually important points,to address this problem for time-series data.Peak evaluation using perceptually important points is exceptional,as it does not have tunable hyperparameters,which makes it easily applicable to an unsupervised setting.Simultaneously,it is flexible as it does not make any assumptions on the balance of the dataset between the positive and the negative class.

Where Have Network-based Self-learning Classes Gone?——Reflections & Expectations on the Employment of Network-based Self-learning Classes

To respond to the further development of college English reforms,many universities employed network-based selflearning classes to aid the traditional classroom teaching,especially in teaching listening,but as time went by,some universities gradually gave them up.The paper intends to reflect on the employment of network-based self-learning listening classes,analyz ing the learning with and without its aid,and meanwhile introduce the need to re-employ it,and discuss how we can improve the network-based self-learning classes to help with students' listening.

Optimal Constrained Self-learning Battery Sequential Management in Microgrid Via Adaptive Dynamic Programming

This paper concerns a novel optimal self-learning battery sequential control scheme for smart home energy systems. The main idea is to use the adaptive dynamic programming U+0028 ADP U+0029 technique to obtain the optimal battery sequential control iteratively. First, the battery energy management system model is established, where the power efficiency of the battery is considered. Next, considering the power constraints of the battery, a new non-quadratic form performance index function is established, which guarantees that the value of the iterative control law cannot exceed the maximum charging/discharging power of the battery to extend the service life of the battery. Then, the convergence properties of the iterative ADP algorithm are analyzed, which guarantees that the iterative value function and the iterative control law both reach the optimums. Finally, simulation and comparison results are given to illustrate the performance of the presented method. © 2017 Chinese Association of Automation.

SELF-LEARNING FUZZY CONTROL RULES USING GENETIC ALGORITHMS

This papcr presents a new genetic algorithms(GAs)-based method for self-learniag fuzzy control rules. An improved GA is used to learn to optimally select the fuzzy membership functions of the linguistic labels in the condition portion of each rule, and to automatically generate fuzzy control actions under each condition. The dynamics of the controlled system is unknown to the GA. The only information for evaluating performance is a failure signal indicating that the controlled system is out of control. We compare its performance with that of other learning methods for the same problem. We also examine the ability of the algorithm to adapt to changing conditions. Simulation results show that such an approach for self-learning fuzzy control rules is both effective and robust.

A Review on Sources,Extractions and Analysis Methods of a Sustainable Biomaterial:Tannins

Condensed and hydrolysable tannins are non-toxic natural polyphenols that are a commercial commodity industrialized for tanning hides to obtain leather and for a growing number of other industrial applications mainly to substitute petroleum-based products.They are a definite class of sustainable materials of the forestry industry.They have been in operation for hundreds of years to manufacture leather and now for a growing number of applications in a variety of other industries,such as wood adhesives,metal coating,pharmaceutical/medical applications and several others.This review presents the main sources,either already or potentially commercial of this forestry by-materials,their industrial and laboratory extraction systems,their systems of analysis with their advantages and drawbacks,be these methods so simple to even appear primitive but nonetheless of proven effectiveness,or very modern and instrumental.It constitutes a basic but essential summary of what is necessary to know of these sustainable materials.In doing so,the review highlights some of the main challenges that remain to be addressed to deliver the quality and economics of tannin supply necessary to fulfill the industrial production requirements for some materials-based uses.

Mathematical model for cooling process and its self-learning applied in hot rolling mill

Control precision of coiling temperature is one of the key factors affecting the profile shape and surface quality during the cooling process of hot rolled steel strip.For this reason,the core of temperature control precision is to establish an effective cooling mathematical model with self-learning function.Starting from this point,a cooling mathematical model with nonlinear structural characteristics is established in this paper for the cooling process of hot rolled steel strip.By the analysis of self-learning ability,key parameters of the mathematical model could be constantly corrected so as to improve temperature control precision and adaptive capability of the model.The site actual application results proved the stable performance and high control precision of the proposed mathematical model,which would lay a solid foundation to improve the steel product qualities.

Study on intelligent digital welding machine with a self-learning function

A design idea was proposed that it was about intelligent digital welding machine with self-learning and self- regulation functions. The overall design scheme of software and hardware was provided. It was introduced that a parameter self-learning algorithm was based on large-step calibration and partial Newton interpolation. Furthermore, experimental verification was carried out with different welding technologies. The results show that weld bead is pegrect. Therefore, good welding quality and stability are obtained, and intelligent regulation is realized by parameters self-learning.

Self-learning Fuzzy Controllers Based On a Real-time Reinforcement Genetic Algorithm

This paper presents a novel method for constructing fuzzy controllers based on a real time reinforcement genetic algorithm. This methodology introduces the real-time learning capability of neural networks into globally searching process of genetic algorithm, aiming to enhance the convergence rate and real-time learning ability of genetic algorithm, which is then used to construct fuzzy controllers for complex dynamic systems without any knowledge about system dynamics and prior control experience. The cart-pole system is employed as a test bed to demonstrate the effectiveness of the proposed control scheme, and the robustness of the acquired fuzzy controller with comparable result.