Applications of Soft Computing Methods in Backbreak Assessment in Surface Mines: A Comprehensive Review

Geo-engineering problems are known for their complexity and high uncertainty levels,requiring precise defini-tions,past experiences,logical reasoning,mathematical analysis,and practical insight to address them effectively.Soft Computing(SC)methods have gained popularity in engineering disciplines such as mining and civil engineering due to computer hardware and machine learning advancements.Unlike traditional hard computing approaches,SC models use soft values and fuzzy sets to navigate uncertain environments.This study focuses on the application of SC methods to predict backbreak,a common issue in blasting operations within mining and civil projects.Backbreak,which refers to the unintended fracturing of rock beyond the desired blast perimeter,can significantly impact project timelines and costs.This study aims to explore how SC methods can be effectively employed to anticipate and mitigate the undesirable consequences of blasting operations,specifically focusing on backbreak prediction.The research explores the complexities of backbreak prediction and highlights the potential benefits of utilizing SC methods to address this challenging issue in geo-engineering projects.

Effect of a two-phase wedge-sliding model on the ingredient drift of a stable mixed fluid and its computing method

A two-phase wedge-sliding model is developed based on the micro-cellular structure and minimum entropy theory of a stable system, and it is used to describe the ingredient distribution of a mixed fluid in a non-uniform stress field and to analyse its phase drift phenomenon. In the model, the drift-inhibition angle and the expansion-inhibition angle are also deduced and used as evaluating indexes to describe the drifting trend of different ingredients among the mixed fluids. For solving above two indexes of the model, a new calculation method is developed and used to compute the phase distributions of multiphase fluid at peak stress and gradient area stress, respectively. As an example, the flow process of grease in a pipe is analysed by simulation method and used to verify the validity of the model.

A fast computing method to distinguish the hyperbolic trajectory of an non-autonomous system

Attempting to find a fast computing method to DHT （distinguished hyperbolic trajectory）, this study first proves that the errors of the stable DHT can be ignored in normal direction when they are computed as the trajectories extend. This conclusion means that the stable flow with perturbation will approach to the real trajectory as it extends over time. Based on this theory and combined with the improved DHT computing method, this paper reports a new fast computing method to DHT, which magnifies the DHT computing speed without decreasing its accuracy.

Computing methods for icosahedral and symmetry-mismatch reconstruction of viruses by cryo-electron microscopy

Three-dimensional（3 D） reconstruction of icosahedral viruses has played a crucial role in the development of cryoelectron microscopy single-particle reconstruction, with many cryo-electron microscopy techniques first established for structural studies of icosahedral viruses, owing to their high symmetry and large mass. This review summarizes the computational methods for icosahedral and symmetry-mismatch reconstruction of viruses, as well as the likely challenges and bottlenecks in virus reconstruction, such as symmetry mismatch reconstruction, contrast transformation function（CTF）correction, and particle distortion.

Dynamic Factor Method of Computing Dynamic Mathematical Model for System Simulation

The computational methods of a typical dynamic mathematical model that can describe the differential element and the inertial element for the system simulation are researched. The stability of numerical solutions of the dynamic mathematical model is researched. By means of theoretical analysis, the error formulas, the error sign criteria and the error relationship criterion of the implicit Euler method and the trapezoidal method are given, the dynamic factor affecting the computational accuracy has been found, the formula and the methods of computing the dynamic factor are given. The computational accuracy of the dynamic mathematical model like this can be improved by use of the dynamic factor.

Development of mathematically motivated hybrid soft computing models for improved predictions of ultimate bearing capacity of shallow foundations

Ultimate bearing capacity(UBC)is a key subject in geotechnical/foundation engineering as it determines the limit of loads imposed on the foundation.The most reliable means of determining UBC is through experiment,but it is costly and time-consuming which has led to the development of various models based on the simplified assumptions.The outcomes of the models are usually validated with the experimental results,but a large gap usually exists between them.Therefore,a model that can give a close prediction of the experimental results is imperative.This study proposes a grasshopper optimization algorithm(GOA)and salp swarm algorithm(SSA)to optimize artificial neural networks(ANNs)using the existing UBC experimental database.The performances of the proposed models are evaluated using various statistical indices.The obtained results are compared with the existing models.The proposed models outperformed the existing models.The proposed hybrid GOA-ANN and SSA-ANN models are then transformed into mathematical forms that can be incorporated into geotechnical/foundation engineering design codes for accurate UBC measurements.

State-of-the-art review of soft computing applications in underground excavations

Soft computing techniques are becoming even more popular and particularly amenable to model the complex behaviors of most geotechnical engineering systems since they have demonstrated superior predictive capacity,compared to the traditional methods.This paper presents an overview of some soft computing techniques as well as their applications in underground excavations.A case study is adopted to compare the predictive performances of soft computing techniques including eXtreme Gradient Boosting(XGBoost),Multivariate Adaptive Regression Splines(MARS),Artificial Neural Networks(ANN),and Support Vector Machine(SVM) in estimating the maximum lateral wall deflection induced by braced excavation.This study also discusses the merits and the limitations of some soft computing techniques,compared with the conventional approaches available.

Neural Network Robust Control Based on Computed Torque for Lower Limb Exoskeleton

The lower limb exoskeletons are used to assist wearers in various scenarios such as medical and industrial settings.Complex modeling errors of the exoskeleton in different application scenarios pose challenges to the robustness and stability of its control algorithm.The Radial Basis Function(RBF)neural network is used widely to compensate for modeling errors.In order to solve the problem that the current RBF neural network controllers cannot guarantee the asymptotic stability,a neural network robust control algorithm based on computed torque method is proposed in this paper,focusing on trajectory tracking.It innovatively incorporates the robust adaptive term while introducing the RBF neural network term,improving the compensation ability for modeling errors.The stability of the algorithm is proved by Lyapunov method,and the effectiveness of the robust adaptive term is verified by the simulation.Experiments wearing the exoskeleton under different walking speeds and scenarios were carried out,and the results show that the absolute value of tracking errors of the hip and knee joints of the exoskeleton are consistently less than 1.5°and 2.5°,respectively.The proposed control algorithm effectively compensates for modeling errors and exhibits high robustness.

Numerical Study on the Aerodynamic and Fluid−Structure Interaction of An NREL-5MW Wind Turbine

A 5-MW wind turbine has been modeled and analyzed for fluid-structure interaction and aerodynamic performance.In this study, a full-scale model of a 5-MW wind turbine is first developed based on a computational fluid dynamics(CFD) approach, in which the unsteady, noncompressible Reynolds Averaged Navier-Stokes(RANS) method is used. The main focus of the study is to analyze the tower shadow effect on the aerodynamic performance of the wind turbine under different inlet flow conditions. Subsequently, the finite element model is established by considering fluid/structure interactions to study the structural stress, displacement, strain distributions and flow field information of the structure under the uniform wind speed. Finally, the fluid-structure interaction model is established by considering turbulent wind and the tower shadow effect. The variation rules of the dynamic response of the one-way and two-way fluid-structure interaction(FSI) models under different wind speeds are analyzed, and the numerical calculation results are compared with those of the centralized mass model. The results show that the tower shadow effect and structural deformation are the main factors affecting the aerodynamic load fluctuation of the wind turbine, which in turn affects the aerodynamic performance and structural stability of the blades. The structural dynamic response of the coupled model shows significant similarity, while the structural displacement response of the former exhibits less fluctuation compared with the conventional centralized mass model. The one-way fluid-structure interaction(FSI)model shows a higher frequency of stress-strain and displacement oscillations on the blade compared with the two-way FSI model.

Preliminary Investigation of Copper(II) Ion Binding or Complex Coordination in Lysozeme Molecules

Hydrophobic Val derivative Schiff base copper(II) complexes and dipeptide (AlaAla, GlyGly) derivative Schiff base copper(II) complexes were introduced into egg white lysozyme. X-ray crystal structure analysis revealed amino acid derivative Schiff base copper(II) complexes were obtained. Herein we discuss primarily on the binding mode of copper(II) of the complexes obtained with egg white lysozyme. The electron density of copper(II) ions was confirmed by X-ray crystal structure analysis. The Val derivative Schiff base copper(II) complex was weakly bound at Arg114 of egg white lysozyme. In other copper(II) complexes, binding of copper(II) ions with dissociated ligands to various residues was observed. The binding sites of copper(II) ions were compared with computational scientific predictions.

A fast computational method for the landing footprints of space-to-ground vehicles

Fast computation of the landing footprint of a space-to-ground vehicle is a basic requirement for the deployment of parking orbits, as well as for enabling decision makers to develop real-time programs of transfer trajectories. In order to address the usually slow computational time for the determination of the landing footprint of a space-to-ground vehicle under finite thrust, this work proposes a method that uses polynomial equations to describe the boundaries of the landing footprint and uses back propagation(BP) neural networks to quickly determine the landing footprint of the space-to-ground vehicle. First, given orbital parameters and a manoeuvre moment, the solution model of the landing footprint of a space-to-ground vehicle under finite thrust is established. Second, given arbitrary orbital parameters and an arbitrary manoeuvre moment, a fast computational model for the landing footprint of a space-to-ground vehicle based on BP neural networks is provided.Finally, the simulation results demonstrate that under the premise of ensuring accuracy, the proposed method can quickly determine the landing footprint of a space-to-ground vehicle with arbitrary orbital parameters and arbitrary manoeuvre moments. The proposed fast computational method for determining a landing footprint lays a foundation for the parking-orbit configuration and supports the design of real-time transfer trajectories.

Predicting Genotype Information Related to COVID-19 for Molecular Mechanism Based on Computational Methods

Novel coronavirus disease 2019(COVID-19)is an ongoing health emergency.Several studies are related to COVID-19.However,its molecular mechanism remains unclear.The rapid publication of COVID-19 provides a new way to elucidate its mechanism through computational methods.This paper proposes a prediction method for mining genotype information related to COVID-19 from the perspective of molecular mechanisms based on machine learning.The method obtains seed genes based on prior knowledge.Candidate genes are mined from biomedical literature.The candidate genes are scored by machine learning based on the similarities measured between the seed and candidate genes.Furthermore,the results of the scores are used to perform functional enrichment analyses,including KEGG,interaction network,and Gene Ontology,for exploring the molecular mechanism of COVID-19.Experimental results show that the method is promising for mining genotype information to explore the molecular mechanism related to COVID-19.

Protein-protein interactions: Methods, databases, and applications in virus-host study

Almost all the cellular processes in a living system are controlled by proteins:They regulate gene expression,catalyze chemical reactions,transport small molecules across membranes,and transmit signal across membranes.Even,a viral infection is often initiated through virus-host protein interactions.Protein-protein interactions(PPIs)are the physical contacts between two or more proteins and they represent complex biological functions.Nowadays,PPIs have been used to construct PPI networks to study complex pathways for revealing the functions of unknown proteins.Scientists have used PPIs to find the molecular basis of certain diseases and also some potential drug targets.In this review,we will discuss how PPI networks are essential to understand the molecular basis of virus-host relationships and several databases which are dedicated to virus-host interaction studies.Here,we present a short but comprehensive review on PPIs,including the experimental and computational methods of finding PPIs,the databases dedicated to virus-host PPIs,and the associated various applications in protein interaction networks of some lethal viruses with their hosts.

Global SH-wave propagation in a 2D whole Moon model using the parallel hybrid PSM/FDM method

We present numerical modeling of SH-wave propagation for the recently proposed whole Moon model and try to improve our understanding of lunar seismic wave propagation. We use a hybrid PSM/FDM method on staggered grids to solve the wave equations and implement the calculation on a parallel PC cluster to improve the computing efficiency. Features of global SH-wave propagation are firstly discussed for a 100-km shallow and900-km deep moonquakes, respectively. Effects of frequency range and lateral variation of crust thickness are then investigated with various models. Our synthetic waveforms are finally compared with observed Apollo data to show the features of wave propagation that were produced by our model and those not reproduced by our models. Our numerical modeling show that the low-velocity upper crust plays significant role in the development of reverberating wave trains. Increasing frequency enhances the strength and duration of the reverberations.Surface multiples dominate wavefields for shallow event.Core–mantle reflections can be clearly identified for deep event at low frequency. The layered whole Moon model and the low-velocity upper crust produce the reverberating wave trains following each phases consistent with observation. However, more realistic Moon model should be considered in order to explain the strong and slow decay scattering between various phases shown on observation data.

Review:Recent Development of High⁃Order⁃Spectral Method Combined with Computational Fluid Dynamics Method for Wave⁃Structure Interactions

The present paper reviews the recent developments of a high⁃order⁃spectral method(HOS)and the combination with computational fluid dynamics(CFD)method for wave⁃structure interactions.As the numerical simulations of wave⁃structure interaction require efficiency and accuracy,as well as the ability in calculating in open sea states,the HOS method has its strength in both generating extreme waves in open seas and fast convergence in simulations,while computational fluid dynamics(CFD)method has its advantages in simulating violent wave⁃structure interactions.This paper provides the new thoughts for fast and accurate simulations,as well as the future work on innovations in fine fluid field of numerical simulations.

Object-Oriented Design for FDTD Visual Scientific Computing

A scheme for general purposed FDTD visual scientific computing software is introduced in this paper using object-oriented design (OOD) method. By abstracting the parameters of FDTD grids to an individual class and separating from the iteration procedure, the visual software can be adapted to more comprehensive computing problems. Real-time gray degree graphic and wave curve of the results can be achieved using DirectX technique. The special difference equation and data structure in dispersive medium are considered, and the peculiarity of parameters in perfectly matched layer are also discussed.

Bio-Inspired Computational Methods for the Polio Virus Epidemic Model

In 2021,most of the developing countries are fighting polio,and parents are concerned with the disabling of their children.Poliovirus transmits from person to person,which can infect the spinal cord,and paralyzes the parts of the body within a matter of hours.According to the World Health Organization(WHO),18 million currently healthy people could have been paralyzed by the virus during 1988–2020.Almost all countries but Pakistan,Afghanistan,and a fewmore have been declared polio-free.The mathematical modeling of poliovirus is studied in the population by categorizing it as susceptible individuals(S),exposed individuals(E),infected individuals(I),and recovered individuals(R).In this study,we study the fundamental properties such as positivity and boundedness of the model.We also rigorously study the model’s stability and equilibria with or without poliovirus.For numerical study,we design the Euler,Runge–Kutta,and nonstandard finite difference method.However,the standard techniques are time-dependent and fail to present the results for an extended period.The nonstandard finite difference method works well to study disease dynamics for a long time without any constraints.Finally,the results of different methods are compared to prove their effectiveness.

Simplified Computation Method of Vibration Characteristics of Jacket Platforms

In this paper a simplified computation method of natural frequency of jacket platforms is given. Shear, bending and rotary inertia effects are considered in the equation, while the jacket shape, rotary inertia and centralized mass of the platform top are all determined by using a coefficient-revising method.

A new correction method for quasi-Keplerian orbits

A pure two-body problem has seven integrals including the Kepler energy,the Laplace vector and the angular momentum vector.However,only five of them are independent.When the five independent integrals are preserved,the two other dependent integrals are naturally preserved from a theoretical viewpoint;but they may not necessarily be from a numerical computational viewpoint.Because of this,we use seven scale factors to adjust the integrated positions and velocities so that the adjusted solutions strictly satisfy the seven constraints.Noticing the existence of the two dependent integrals,we adopt the Newton iterative method combined with singular value decomposition to calculate these factors.This correction scheme can be applied to perturbed two-body and N-body problems in the solar system.In this case,the seven quantities associated with each planet slowly vary with time.More accurate values can be given to the seven slowly-varying quantities by integrating the integral invariant relations of these quantities and the equations of motion.They should be satisfied with the adjusted solutions.Numerical tests show that the new method can significantly reduce the rapid growth of numerical errors for all orbital elements.

Novel Proposal of Bio-based Sewing Timber Joint:Learning from Diatoms

The twenty-first century is one of the most complex in the history of humanity,mainly due to the ecological crisis it is going through.The construction sector generates about 40%of CO2 emissions into the environment;the foregoing should motivate this sector to seek new alternatives to develop new building practices.Taking these current needs into account,this document classifies and presents a multidisciplinary solution that integrates biology,engineering and architecture to develop a new and innovative lightweight timber structure;it divides with a main structure made of timber and an innovative joint system made of bio-polymers connecting all the panels.Through the study of diatoms,it was able to analyze the bio-morphology of the structure,joints and in particular the geometry since they were the inspiration for the design of this structure that presents an innovative and novel design of structural optimization.Through parametric design and digital fabrication,it was able to create a complex geometry that obtains excellent structural behavior.This research discusses and explores how materials,geometry led to the optimization of a structure and how new structures can arise,thanks to biology new solutions can be obtained that are completely sustainable,being a clear example of how to combat the effects of the climate change and in a precise way it highlights the advantages of the bio-design in the architectural design.