Nonlinear constitutive models of rock structural plane and their applications

Structural planes play an important role in controlling the stability of rock engineering,and the influence of structural planes should be considered in the design and construction process of rock engineering.In this paper,mechanical properties,constitutive theory,and numerical application of structural plane are studied by a combination method of laboratory tests,theoretical derivation,and program development.The test results reveal the change laws of various mechanical parameters under different roughness and normal stress.At the pre-peak stage,a non-stationary model of shear stiffness is established,and threedimensional empirical prediction models for initial shear stiffness and residual stage roughness are proposed.The nonlinear constitutive models are established based on elasto-plastic mechanics,and the algorithms of the models are developed based on the return mapping algorithm.According to a large number of statistical analysis results,empirical prediction models are proposed for model parameters expressed by structural plane characteristic parameters.Finally,the discrete element method(DEM)is chosen to embed the constitutive models for practical application.The running programs of the constitutive models have been compiled into the discrete element model library.The comparison results between the proposed model and the Mohr-Coulomb slip model show that the proposed model can better describe nonlinear changes at different stages,and the predicted shear strength,peak strain and shear stiffness are closer to the test results.The research results of the paper are conducive to the accurate evaluation of structural plane in rock engineering.

Geological characteristics and models of fault-foldfracture body in deep tight sandstone of the second member of Upper Triassic Xujiahe Formation in Xinchang structural belt of Sichuan Basin,SW China

In the second member of the Upper Triassic Xujiahe Formation(T_(3)x_(2))in the Xinchang area,western Sichuan Basin,only a low percent of reserves has been recovered,and the geological model of gas reservoir sweet spot remains unclear.Based on a large number of core,field outcrop,test and logging-seismic data,the T_(3)x_(2) gas reservoir in the Xinchang area is examined.The concept of fault-fold-fracture body(FFFB)is proposed,and its types are recognized.The main factors controlling fracture development are identified,and the geological models of FFFB are established.FFFB refers to faults,folds and associated fractures reservoirs.According to the characteristics and genesis,FFFBs can be divided into three types:fault-fracture body,fold-fracture body,and fault-fold body.In the hanging wall of the fault,the closer to the fault,the more developed the effective fractures;the greater the fold amplitude and the closer to the fold hinge plane,the more developed the effective fractures.Two types of geological models of FFFB are established:fault-fold fracture,and matrix storage and permeability.The former can be divided into two subtypes:network fracture,and single structural fracture,and the later can be divided into three subtypes:bedding fracture,low permeability pore,and extremely low permeability pore.The process for evaluating favorable FFFB zones was formed to define favorable development targets and support the well deployment for purpose of high production.The study results provide a reference for the exploration and development of deep tight sandstone oil and gas reservoirs in China.

An Educational GUI-Based Software for Dynamic Analysis of Framed Structural Models

This paper introduces a new version of the open-source educational software, LESM (Linear Elements Structure Model), developed in MATLAB for structural analysis of one-dimensional models such as frames, trusses, and grillages. The updated program includes dynamic analysis, which incorporates inertial and damping effects, time-dependent load conditions, and a transient solver with multiple time integration schemes. The software assumes small displacements and linear-elastic material behavior. The paper briefly explains the theoretical basis for these developments and the reorganization of the source code using Object-Oriented Programming (OOP). The updated Graphical User Interface (GUI) allows interactive use of dynamic analysis features and displays new results such as animations, envelope diagrams of internal forces, phase portraits, and the response of degrees-of-freedom in time and frequency domain. The new version was used in a structural dynamics course, and new assignments were elaborated to improve students’ understanding of the subject.

Dynamics of Advantageous Mutant Spread in Spatial Death-Birth and Birth-Death Moran Models

The spread of an advantageous mutation through a population is of fundamental interest in population genetics. While the classical Moran model is formulated for a well-mixed population, it has long been recognized that in real-world applications, the population usually has an explicit spatial structure which can significantly influence the dynamics. In the context of cancer initiation in epithelial tissue, several recent works have analyzed the dynamics of advantageous mutant spread on integer lattices, using the biased voter model from particle systems theory. In this spatial version of the Moran model, individuals first reproduce according to their fitness and then replace a neighboring individual. From a biological standpoint, the opposite dynamics, where individuals first die and are then replaced by a neighboring individual according to its fitness, are equally relevant. Here, we investigate this death-birth analogue of the biased voter model. We construct the process mathematically, derive the associated dual process, establish bounds on the survival probability of a single mutant, and prove that the process has an asymptotic shape. We also briefly discuss alternative birth-death and death-birth dynamics, depending on how the mutant fitness advantage affects the dynamics. We show that birth-death and death-birth formulations of the biased voter model are equivalent when fitness affects the former event of each update of the model, whereas the birth-death model is fundamentally different from the death-birth model when fitness affects the latter event.

The Need for Structural Adjustment: Was It Essential for African Countries over the Decade of the 80’s? An Econometric Analysis Using Count Data Models

Several economists agree to say that the need for adjustment was essential for African countries over the decade of the 80’s. The econometric analysis of a sample of 28 sub-Saharan African countries, from variables regarded as “representatives” for the adjustment objectives, proves that this assertion cannot be completely rejected.

A Re-Parametrization-Based Bayesian Differential Analysis Algorithm for Gene Regulatory Networks Modeled with Structural Equation Models

Under different conditions,gene regulatory networks(GRNs)of the same gene set could be similar but different.The differential analysis of GRNs under different conditions is important for understanding condition-specific gene regulatory relationships.In a naive approach,existing GRN inference algorithms can be used to separately estimate two GRNs under different conditions and identify the differences between them.However,in this way,the similarities between the pairwise GRNs are not taken into account.Several joint differential analysis algorithms have been proposed recently,which were proved to outperform the naive approach apparently.In this paper,we model the GRNs under different conditions with structural equation models(SEMs)to integrate gene expression data and genetic perturbations,and re-parameterize the pairwise SEMs to form an integrated model that incorporates the differential structure.Then,a Bayesian inference method is used to make joint differential analysis by solving the integrated model.We evaluated the performance of the proposed re-parametrization-based Bayesian differential analysis(ReBDA)algorithm by running simulations on synthetic data with different settings.The performance of the ReBDA algorithm was demonstrated better than another state-of-the-art joint differential analysis algorithm for SEMs ReDNet obviously.In the end,the ReBDA algorithm was applied to make differential analysis on a real human lung gene data set to illustrate its applicability and practicability.

Quantitative Structural Models to Assess Credit Risk on Individuals

Default Probabilities quantitatively measures the credit risk that a borrower will be unable or unwilling to repay its debt. An accurate model to estimate, as a function of time, these default probabilities is of crucial importance in the credit derivatives market. In this work, we adapt Merton’s [1] original works on credit risk, consumption and portfolio rules to model an individual wealth scenario, and apply it to compute this individual default probabilities. Using our model, we also compute the time depending individual default intensities, recovery rates, hazard rate and risk premiums. Hence, as a straight-forward application, our model can be used as novel way to measure the credit risk of individuals.

Nanoindentation Mechanical Properties and Structural Biomimetic Models of Three Species of Insects Wings

Mimicking insect flights were used to design and develop new engineering materials. Although extensive research was done to study various aspects of flying insects. Because the detailed mechanics and underlying principles involved in insect flights remain largely unknown. A systematic study was carried on insect flights by using a combination of several advanced techniques to develop new models for the simulation and analysis of the wing membrane and veins of three types of insect wings, namely dragonfly （Pantala flavescens Fabricius）, honeybee （Apis cerana cerana Fabricius） and fly （Sarcophaga carnaria Linnaeus）. In order to gain insights into the flight mechanics of insects, reverse engineering methods were used to establish three-dimensional geometrical models of the membranous wings, so we can make a comparative analysis. Then nano-mechanical test of the three insect wing membranes was performed to provide experimental parameter values for mechanical models in terms of nano-hardness and elastic modulus. Finally, a computational model was established by using the finite element analysis （ANSYS） to analyze and compare the wings under a variety of simplified load regimes that are concentrated force, uniform line-load and a torque. This work opened up the possibility towards developing an engineering basis for the biomimetic design of thin solid films and 2D advanced engineering composite materials.

The relationship between compartment models and their stochastic counterparts:A comparative study with examples of the COVID-19 epidemic modeling

Deterministic compartment models(CMs)and stochastic models,including stochastic CMs and agent-based models,are widely utilized in epidemic modeling.However,the relationship between CMs and their corresponding stochastic models is not well understood.The present study aimed to address this gap by conducting a comparative study using the susceptible,exposed,infectious,and recovered(SEIR)model and its extended CMs from the coronavirus disease 2019 modeling literature.We demonstrated the equivalence of the numerical solution of CMs using the Euler scheme and their stochastic counterparts through theoretical analysis and simulations.Based on this equivalence,we proposed an efficient model calibration method that could replicate the exact solution of CMs in the corresponding stochastic models through parameter adjustment.The advancement in calibration techniques enhanced the accuracy of stochastic modeling in capturing the dynamics of epidemics.However,it should be noted that discrete-time stochastic models cannot perfectly reproduce the exact solution of continuous-time CMs.Additionally,we proposed a new stochastic compartment and agent mixed model as an alternative to agent-based models for large-scale population simulations with a limited number of agents.This model offered a balance between computational efficiency and accuracy.The results of this research contributed to the comparison and unification of deterministic CMs and stochastic models in epidemic modeling.Furthermore,the results had implications for the development of hybrid models that integrated the strengths of both frameworks.Overall,the present study has provided valuable epidemic modeling techniques and their practical applications for understanding and controlling the spread of infectious diseases.

Investigation on hot deformation behavior and mathematical models of flow stress of high-strength structural steel

The effect of deformation conditions on dynamic recrystallization behavior of Nb,V,Ti microalloyed high-strength structural steel was investigated via high-temperature single pass reduction tests on a MMS-300 thermomechanical simulator,with mathematical models established for flow stress during hot deformation.The results show that the deformation resistance decreases with the increase of temperature and is in power function relationship with the temperature.Meanwhile,it increases with the increase of strain rate and is in log-log relationship with the strain rate.The dynamic recrystallization activation energy of tested steel was determined to be about 329.55 kJ/mol,295.31 kJ/mol at peak and steady states.The prediction models developed for flow stress indicated that they are in good agreement with experimental results.

A Structural Overview through GR(s) Models Characteristics for Better Yearly Runoff Simulation

In rainfall-runoff modelling, a monthly timescale and an annual one are sufficient for the management of deductions. However, to simulate the flow at a large time-step (annual), we generally precede the use of a model working for a finer time-step (daily) while aggregating the desired outputs. The finest time-steps are considered, apriori, as the most performant. By passing from one time-step to another, and in order to work in the desired time-step (annual) and calculate the potential gains or loss, this article proposed a comparative study between the aggregation method of outputs of a modal working at a finer time step, and a method in which we use a conceived model from the beginning. To ensure this comparative and empirical approach, the choice has been focused on (GRs) models to a daily time-step (GR4J), monthly time step (GR2M) and annual time step (GR1A). The modelling platform used is the same for all three models taking into account the specificities of each one: the same data sample, the same optimization method, and the same function criterion are used during the construction of these models. Due to the moving between these time steps, results show that the best way to simulate the annual flow is to use an appropriate and designed modal initially conceived to this time step. Indeed, this simulation seems to be less effective when using a model at a finer time-step (daily).

Evaluation on Spot Weld Models in Structural Dynamic Analysis of Automotive Body in White

Spot weld models are widely used in finite element analysis（FEA） of automotive body in white（BIW） to predict static,dynamic,durability and other characteristics of automotive BIW.However,few researches are done on evaluation of the validity of these spot weld models in structural dynamic analysis of BIW.To evaluate the validity and accuracy of spot weld models in structural dynamic analysis of BIW,two object functions,error function and deviation function,are introduced innovatively.Modal analysis of Two-panel and Double-hat structures,which are the dominated structures in BIW,is conducted,and the values of these two object functions are obtained.Based on the values of object functions,the validity of these spot weld models are evaluated.It is found that the area contact method（ACM2） and weld element connection（CWELD） can give more precise prediction in modal analysis of these two classical structures,thus are more applicable to structural dynamic analysis of automotive BIW.Modal analysis of a classical BIW is performed,which further confirms this evaluation.The error function and deviation function proposed in this research can give guidance on the adaptability of spot weld models in structural dynamic analysis of BIW.And this evaluation method can also be adopted in evaluation of other finite element models in static,dynamic and other kinds of analysis for automotive structures.

Genetic models of structural traps related to normal faults in the Putaohua Oilfield,Songliao Basin

The Putaohua Oilfield is a fault-prolific area and the faults have close relation with structural traps. The genetic models of the structural traps in the Putaohua Oilfield can be divided into two types： individual fault model and multi-fault interaction model. This is based on the description of displacement distribution of typical individual normal faults, the geometry of the footwall and hanging wall, and the analysis of the interaction between faults and the corresponding change in geometry when the faults grow. The individual fault model is that the displacement reaches a maximum at or near the center of fault and decreases toward the fault tips, so a half-graben is formed on the hanging wall of the fault and a half- anticline is formed on the footwall because of the isostatic process. The multi-fault interaction model is that during the growth of faults, they overlap and interact with each other, and accommodation zones are formed in the overlapping segments. The accommodation zones are favorable targets for hydrocarbon exploration, and the trap characteristics are dependent The multi-fault interaction model can be subdivided on the extent of overlap and occurrence of faults. into three types： synthetic accommodation zone, convergent accommodation zone and divergent accommodation zone. Hydrocarbon migration and accumulation models of each type have been developed. The hydrocarbon migration and accumulation models of the traps with different genetic models have their own characteristics in the different stages of fault growth.

Experimental study on stress-strain-temperature models for structural steel

For the research on steel structure in fire,it is very important to determine the properties of structural steel at elevated temperature.Up to now,the high-temperature properties of material is believed to be related to only temperature state,which is not precise enough to simulate the behavior of steel structures under different combinations of heating,cooling,loading,and unloading.To analyze the influence of the temperature-load history on the steel properties,a series of tests were carried out under different temperature-load paths about steel Q235,which is widely used in steel structures in China.In this paper,the method to set the temperature-load paths was introduced;the variety regulation of steel properties changing with temperature was analyzed under different paths;according to experimental results,the formulas of elastic modulus and yield strength at elevated temperature were fitted,and the stress-strain-temperature 3D relationships of structural steel under different paths were presented.

An experimental study on variation of b value during deformation of rock samples with different structural models

A preliminary study of b value of rocks with two kinds of structural models has been made on the base of a new acoustic emission recording system. It shows that b value of the sample decreases obviously when the sample with compressive en echelon faults changes into a tensile one after interchange occurs between stress axis σ1 and σ2. A similar experiment is observed when the sample with tensile en echelon faults changes into that with a bend fault after two segments of the en echelon fault linking up. These facts indicate that the variation of b value may con-tain the information of the regional dominant structural model. Therefore, b-value analyses could be a new method for studying regional dominant structural models.

Smaller & Smarter: Score-Driven Network Chaining of Smaller Language Models

With the continuous evolution and expanding applications of Large Language Models (LLMs), there has been a noticeable surge in the size of the emerging models. It is not solely the growth in model size, primarily measured by the number of parameters, but also the subsequent escalation in computational demands, hardware and software prerequisites for training, all culminating in a substantial financial investment as well. In this paper, we present novel techniques like supervision, parallelization, and scoring functions to get better results out of chains of smaller language models, rather than relying solely on scaling up model size. Firstly, we propose an approach to quantify the performance of a Smaller Language Models (SLM) by introducing a corresponding supervisor model that incrementally corrects the encountered errors. Secondly, we propose an approach to utilize two smaller language models (in a network) performing the same task and retrieving the best relevant output from the two, ensuring peak performance for a specific task. Experimental evaluations establish the quantitative accuracy improvements on financial reasoning and arithmetic calculation tasks from utilizing techniques like supervisor models (in a network of model scenario), threshold scoring and parallel processing over a baseline study.

Large animal models for Huntington's disease research

Huntington'sdisease(HD)isahereditary neurodegenerative disorder for which there is currently no effectivetreatmentavailable.Consequently,the development of appropriate disease models is critical to thoroughly investigate disease progression.The genetic basis of HD involves the abnormal expansion of CAG repeats in the huntingtin(HTT)gene,leading to the expansion of a polyglutamine repeat in the HTT protein.Mutant HTT carrying the expanded polyglutamine repeat undergoes misfolding and forms aggregates in the brain,which precipitate selective neuronal loss in specific brain regions.Animal models play an important role in elucidating the pathogenesis of neurodegenerative disorders such as HD and in identifying potential therapeutic targets.Due to the marked species differences between rodents and larger animals,substantial efforts have been directed toward establishing large animal models for HD research.These models are pivotal for advancing the discovery of novel therapeutic targets,enhancing effective drug delivery methods,and improving treatment outcomes.We have explored the advantages of utilizing large animal models,particularly pigs,in previous reviews.Since then,however,significant progress has been made in developing more sophisticated animal models that faithfully replicate the typical pathology of HD.In the current review,we provide a comprehensive overview of large animal models of HD,incorporating recent findings regarding the establishment of HD knock-in(KI)pigs and their genetic therapy.We also explore the utilization of large animal models in HD research,with a focus on sheep,non-human primates(NHPs),and pigs.Our objective is to provide valuable insights into the application of these large animal models for the investigation and treatment of neurodegenerative disorders.

Genetically modified pigs:Emerging animal models for hereditary hearing loss

Hereditary hearing loss(HHL),a genetic disorder that impairs auditory function,significantly affects quality of life and incurs substantial economic losses for society.To investigate the underlying causes of HHL and evaluate therapeutic outcomes,appropriate animal models are necessary.Pigs have been extensively used as valuable large animal models in biomedical research.In this review,we highlight the advantages of pig models in terms of ear anatomy,inner ear morphology,and electrophysiological characteristics,as well as recent advancements in the development of distinct genetically modified porcine models of hearing loss.Additionally,we discuss the prospects,challenges,and recommendations regarding the use pig models in HHL research.Overall,this review provides insights and perspectives for future studies on HHL using porcine models.

Use of machine learning models for the prognostication of liver transplantation: A systematic review

BACKGROUND Liver transplantation(LT)is a life-saving intervention for patients with end-stage liver disease.However,the equitable allocation of scarce donor organs remains a formidable challenge.Prognostic tools are pivotal in identifying the most suitable transplant candidates.Traditionally,scoring systems like the model for end-stage liver disease have been instrumental in this process.Nevertheless,the landscape of prognostication is undergoing a transformation with the integration of machine learning(ML)and artificial intelligence models.AIM To assess the utility of ML models in prognostication for LT,comparing their performance and reliability to established traditional scoring systems.METHODS Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines,we conducted a thorough and standardized literature search using the PubMed/MEDLINE database.Our search imposed no restrictions on publication year,age,or gender.Exclusion criteria encompassed non-English studies,review articles,case reports,conference papers,studies with missing data,or those exhibiting evident methodological flaws.RESULTS Our search yielded a total of 64 articles,with 23 meeting the inclusion criteria.Among the selected studies,60.8%originated from the United States and China combined.Only one pediatric study met the criteria.Notably,91%of the studies were published within the past five years.ML models consistently demonstrated satisfactory to excellent area under the receiver operating characteristic curve values(ranging from 0.6 to 1)across all studies,surpassing the performance of traditional scoring systems.Random forest exhibited superior predictive capabilities for 90-d mortality following LT,sepsis,and acute kidney injury(AKI).In contrast,gradient boosting excelled in predicting the risk of graft-versus-host disease,pneumonia,and AKI.CONCLUSION This study underscores the potential of ML models in guiding decisions related to allograft allocation and LT,marking a significant evolution in the field of prognostication.

Economic Stress, Precariousness and Risk of High Blood Pressure: A Descriptive Survey of Life Models within Households of the Central Region of Cameroon

Context/Objective: High blood pressure (HBP) currently represents the most widespread chronic non-communicable disease in Cameroon. The increase in its prevalence in the country is the result of multiple factors including economic stress imposed by precariousness, poor living conditions, sources of anxiety, anguish, depression and other behavioral disorders. Economic stress is a globalizing concept that integrates into a purely hermeneutic approach, a particular functioning of the nervous system of an individual who faces employment problems and precarious remuneration conditions. The non-satisfaction by an individual of his basic needs due to insufficient financial means can cause him to become irritable, aggressive, and socially and symbolically isolated, thereby increasing the desire to resort to morbid life models such as excessive consumption of narcotics and other psychoactive substances often associated with high blood pressure. The fight against the emergence of BPH is a complex, multifaceted and multifactorial reality that requires taking into account economic stress. The main objective of this survey is to describe the situation of economic stress within the Cameroonian population, which imposes precariousness and life models at risk of high blood pressure. Specifically, we determined the level of household income and the sources of income. Methods: A cross-sectional survey with a descriptive aim among five hundred households in the Central Region of Cameroon was conducted. A probabilistic technique called simple randomness was used. The number of households to be surveyed was determined indirectly using the Cochrane formula. Data collection in face-to-face mode using a physical questionnaire took place from July 1 to August 31, 2023, after obtaining ethical clearance from the Regional Health Research Ethics Committee, Human from the Center and an administrative authorization for data collection. Regarding their processing, the data was grouped during processing in Excel sheets. Normality and reliability tests of the collected data were carried out. For this, the Chi-square test was used for data with a qualitative value and that of Kolmogorov-Sminorf for data with a quantitative value. Descriptive analysis was possible using R software version 3.2, SPSS version 25.0, XLSTAT 2016, PAST and EXCEL programs from Microsoft Office 2013. Results: The main results highlight economic stress, with 45.60% of households surveyed earning less than US$154 per month;55% of household heads were women in single-parent families;14% of household heads were unemployed, 22% worked in the private sector and 19% were self-employed. This general economic situation leads to precarious living conditions, thereby increasing the risk of high blood pressure among the Cameroonian population.