Instability mechanism of mining roadway passing through fault at different angles in kilometre-deep mine and control measures of roof cutting and NPR cables

The angle α between the fault strike and the axial direction of the roadway produces different damage characteristics. In this paper, the research methodology includes theoretical analyses, numerical simulations and field experiments in the context of the Daqiang coal mine located in Shenyang, China. The stability control countermeasure of "pre-splitting cutting roof + NPR anchor cable"(PSCR-NPR) is simultaneously proposed. According to the different deformation characteristics of the roadway, the faults are innovatively classified into three types, with α of type I being 0°-30°, α of type II being 30°-60°, and α of type III being 60°-90°. The full-cycle stress evolution paths during mining roadway traverses across different types of faults are investigated by numerical simulation. Different pinch angles α lead to high stress concentration areas at different locations in the surrounding rock. The non-uniform stress field formed in the shallow surrounding rock is an important reason for the instability of the roadway. The pre-cracked cut top shifted the high stress region to the deep rock mass and formed a low stress region in the shallow rock mass. The high prestressing NPR anchor cable transforms the non-uniform stress field of the shallow surrounding rock into a uniform stress field. PSCR-NPR is applied in the fault-through roadway of Daqiang mine. The low stress area of the surrounding rock was enlarged by 3-7 times, and the cumulative convergence was reduced by 45%-50%. It provides a reference for the stability control of the deep fault-through mining roadway.

Mechanism of high-preload support based on the NPR anchor cable in layered soft rock tunnels

The control of large deformation problems in layered soft rock tunnels needs to solve urgently.The roof problem is particularly severe among the deformation issues in tunnels.This study first analyzes the asymmetric deformation modes in layered soft rock tunnels with large deformations.Subsequently,we construct a mechanical model under ideal conditions for controlling the roof of layered soft rock tunnels through high preload with the support of NPR anchor cables.The prominent roles of long and short NPR anchor cables in the support system are also analyzed.The results indicate the significance of high preload in controlling the roof of layered soft rock tunnels.The short NPR anchor cables effectively improve the integrity of the stratified soft rock layers,while the long NPR anchor cables effectively mobilize the self-bearing capacity of deep-stable rock layers.Finally,the high-preload support method with NPR anchor cables is validated to have a good effect on controlling large deformations in layered soft rock tunnels through field monitoring data.

Multi-Strategy Assisted Multi-Objective Whale Optimization Algorithm for Feature Selection

In classification problems,datasets often contain a large amount of features,but not all of them are relevant for accurate classification.In fact,irrelevant features may even hinder classification accuracy.Feature selection aims to alleviate this issue by minimizing the number of features in the subset while simultaneously minimizing the classification error rate.Single-objective optimization approaches employ an evaluation function designed as an aggregate function with a parameter,but the results obtained depend on the value of the parameter.To eliminate this parameter’s influence,the problem can be reformulated as a multi-objective optimization problem.The Whale Optimization Algorithm(WOA)is widely used in optimization problems because of its simplicity and easy implementation.In this paper,we propose a multi-strategy assisted multi-objective WOA(MSMOWOA)to address feature selection.To enhance the algorithm’s search ability,we integrate multiple strategies such as Levy flight,Grey Wolf Optimizer,and adaptive mutation into it.Additionally,we utilize an external repository to store non-dominant solution sets and grid technology is used to maintain diversity.Results on fourteen University of California Irvine(UCI)datasets demonstrate that our proposed method effectively removes redundant features and improves classification performance.The source code can be accessed from the website:https://github.com/zc0315/MSMOWOA.

Anti-Byzantine Attacks Enabled Vehicle Selection for Asynchronous Federated Learning in Vehicular Edge Computing

In vehicle edge computing(VEC),asynchronous federated learning(AFL)is used,where the edge receives a local model and updates the global model,effectively reducing the global aggregation latency.Due to different amounts of local data,computing capabilities and locations of the vehicles,renewing the global model with same weight is inappropriate.The above factors will affect the local calculation time and upload time of the local model,and the vehicle may also be affected by Byzantine attacks,leading to the deterioration of the vehicle data.However,based on deep reinforcement learning(DRL),we can consider these factors comprehensively to eliminate vehicles with poor performance as much as possible and exclude vehicles that have suffered Byzantine attacks before AFL.At the same time,when aggregating AFL,we can focus on those vehicles with better performance to improve the accuracy and safety of the system.In this paper,we proposed a vehicle selection scheme based on DRL in VEC.In this scheme,vehicle’s mobility,channel conditions with temporal variations,computational resources with temporal variations,different data amount,transmission channel status of vehicles as well as Byzantine attacks were taken into account.Simulation results show that the proposed scheme effectively improves the safety and accuracy of the global model.

Incorporating empirical knowledge into data-driven variable selection for quantitative analysis of coal ash content by laser-induced breakdown spectroscopy

Laser-induced breakdown spectroscopy(LIBS)has become a widely used atomic spectroscopic technique for rapid coal analysis.However,the vast amount of spectral information in LIBS contains signal uncertainty,which can affect its quantification performance.In this work,we propose a hybrid variable selection method to improve the performance of LIBS quantification.Important variables are first identified using Pearson's correlation coefficient,mutual information,least absolute shrinkage and selection operator(LASSO)and random forest,and then filtered and combined with empirical variables related to fingerprint elements of coal ash content.Subsequently,these variables are fed into a partial least squares regression(PLSR).Additionally,in some models,certain variables unrelated to ash content are removed manually to study the impact of variable deselection on model performance.The proposed hybrid strategy was tested on three LIBS datasets for quantitative analysis of coal ash content and compared with the corresponding data-driven baseline method.It is significantly better than the variable selection only method based on empirical knowledge and in most cases outperforms the baseline method.The results showed that on all three datasets the hybrid strategy for variable selection combining empirical knowledge and data-driven algorithms achieved the lowest root mean square error of prediction(RMSEP)values of 1.605,3.478 and 1.647,respectively,which were significantly lower than those obtained from multiple linear regression using only 12 empirical variables,which are 1.959,3.718 and 2.181,respectively.The LASSO-PLSR model with empirical support and 20 selected variables exhibited a significantly improved performance after variable deselection,with RMSEP values dropping from 1.635,3.962 and 1.647 to 1.483,3.086 and 1.567,respectively.Such results demonstrate that using empirical knowledge as a support for datadriven variable selection can be a viable approach to improve the accuracy and reliability of LIBS quantification.

Enhancing Cancer Classification through a Hybrid Bio-Inspired Evolutionary Algorithm for Biomarker Gene Selection

In this study,our aim is to address the problem of gene selection by proposing a hybrid bio-inspired evolutionary algorithm that combines Grey Wolf Optimization(GWO)with Harris Hawks Optimization(HHO)for feature selection.Themotivation for utilizingGWOandHHOstems fromtheir bio-inspired nature and their demonstrated success in optimization problems.We aimto leverage the strengths of these algorithms to enhance the effectiveness of feature selection in microarray-based cancer classification.We selected leave-one-out cross-validation(LOOCV)to evaluate the performance of both two widely used classifiers,k-nearest neighbors(KNN)and support vector machine(SVM),on high-dimensional cancer microarray data.The proposed method is extensively tested on six publicly available cancer microarray datasets,and a comprehensive comparison with recently published methods is conducted.Our hybrid algorithm demonstrates its effectiveness in improving classification performance,Surpassing alternative approaches in terms of precision.The outcomes confirm the capability of our method to substantially improve both the precision and efficiency of cancer classification,thereby advancing the development ofmore efficient treatment strategies.The proposed hybridmethod offers a promising solution to the gene selection problem in microarray-based cancer classification.It improves the accuracy and efficiency of cancer diagnosis and treatment,and its superior performance compared to other methods highlights its potential applicability in realworld cancer classification tasks.By harnessing the complementary search mechanisms of GWO and HHO,we leverage their bio-inspired behavior to identify informative genes relevant to cancer diagnosis and treatment.

A new deformable cable for rock support in high stress tunnel:Steel pipe shrinkable energy-absorbing cable

High stress in surrounding rock will lead to serious problems,e.g.,rock burst in hard rock and large deformation in soft rock.The applied support system under high in-situ stress conditions should be able to carry high load and also accommodate large deformation without experiencing severe damage.In this paper,a specially designed energy-absorbing component for rock bolt and cable that can solve the above problems was proposed.The energy-absorbing component can provide support resistance by plastic deformation of the metal including constraint annulus and compression pipe.For practical engineering,two forms were proposed.One was installed in the surrounding rock by reaming,and the other was installed directly outside the surrounding rock.During the dilation of the surrounding rock,the relative displacement of constraint annulus and compression pipe occurs,resulting in deformation resistance.Deformation resistance is transmitted to the rock bolt or cable,providing support resistance.The lab test and numerical simulation showed that the energy-absorbing component can perfectly achieve the large deformation effect,the deformation amount is as high as 694 mm,and the bearing capacity is stable at 367 kN.The field application tests were carried out in the mining roadway of Xinjulong coal mine,and the results showed that the new type of cable can ensure itself not to break under the condition of large deformation of the surrounding rock.The energy-absorbing component has the superiorities of performing large constant resistance and controllable deformation to effectively control the unpredictable disasters such as large deformation in soft rock and rock burst in hard rock encountered in deep strata.

Seismic stability of expansive soil slopes reinforced by anchor cables using a modified horizontal slice method

Earthquake-induced slope failures are common occurrences in engineering practice and pre-stressed anchor cables are an effective technique in maintaining slope stability,especially in areas that are prone to earthquakes.Furthermore,the soil at typical engineering sites also exhibit unsaturated features.Explicit considerations of these factors in slope stability estimations are crucial in producing accurate results.In this study,the seismic responses of expansive soil slopes stabilized by anchor cables is studied in the realm of kinematic limit analysis.A modified horizontal slice method is proposed to semi-analytically formulate the energy balance equation.An illustrative slope is studied to demonstrate the influences of suction,seismic excitations and anchor cables on the slope stability.The results indicate that the stabilizing effect of soil suction relates strongly to the seismic excitation and presents a sine shape as the seismic wave propagates.In higher and steeper slopes,the stabilizing effect of suction is more evident.The critical slip surface tends to be much more shallow as the seismic wave approaches the peak and vice versa.

Global Dynamic Responses and Progressive Failure of Submerged Floating Tunnel Under Cable Breakage Conditions

The Submerged Floating Tunnel(SFT)relies on a tensioned mooring system for precise positioning.The sudden breakage of a single cable can trigger an immediate alteration in the constraint conditions of the tube,inducing a transient heave response within the structure along with a transient increase in cable tension experienced by adjacent cables.In more severe cases,this may even lead to a progressive failure culminating in the global destruction of the SFT.This study used ANSYS/AQWA to establish a numerical model of the entire length SFT for the hydrodynamic response analysis,and conducted a coupled calculation of the dynamic responses of the SFT-mooring line model based on Orca Flex to study the global dynamic responses of the SFT at the moment of cable breakage and the redistribution of cable internal forces.The most unfavorable position for SFT cable breakage was identified,the influence mechanism of cable breakage at different positions on the global dynamic response was revealed,and the progressive chain failure pattern caused by localized cable breakage are also clarified.

A Portfolio Selection Method Based on Pattern Matching with Dual Information of Direction and Distance

Pattern matching method is one of the classic classifications of existing online portfolio selection strategies. This article aims to study the key aspects of this method—measurement of similarity and selection of similarity sets, and proposes a Portfolio Selection Method based on Pattern Matching with Dual Information of Direction and Distance (PMDI). By studying different combination methods of indicators such as Euclidean distance, Chebyshev distance, and correlation coefficient, important information such as direction and distance in stock historical price information is extracted, thereby filtering out the similarity set required for pattern matching based investment portfolio selection algorithms. A large number of experiments conducted on two datasets of real stock markets have shown that PMDI outperforms other algorithms in balancing income and risk. Therefore, it is suitable for the financial environment in the real world.

Analytical modeling and approaches of multihelix cables incorporating with interwire mutual contacts

This study aims to develop an analytical model based on the curve beam theory to capture the mechanical response of a multihelix cable considering the internal contact displacements.Accordingly,a double-helix cable subjected to axial tension and torsion is analyzed,and both the line and point contacts between the neighboring wires and strands are considered via an equivalent homogenized approach.Then,the proposed theoretical model is extended to a hierarchical multihelix cable with mutual contact displacements by constructing a recursive relationship between the high-and low-level multihelix structures.The global tensile stiffness and torsional stiffness of the double-helix cable are successfully evaluated.The results are validated by a finite element(FE)model,and are found to be consistent with the findings of previous studies.It is shown that the contact deformations in multihelix cables significantly affect their equivalent mechanical stiffness,and the contact displacements are remarkably enhanced as the helix angles increase.This study provides insights into the interwire/interstrand mutual contact effects on global and local responses.

Reliability analysis of carbon fiber rod-reinforced umbilical cable under tension using an improved sampling method

The umbilical cable is a vital component of subsea production systems that provide power,chemical agents,control signals et al.,and its requirement for reliability is exceedingly high.However,as the umbilical cable is a composite structure comprising multiple functional units,the reliability analysis of such cables involves numerous parameters that can impact calculation efficiency.In this paper,the reliability analysis of a new kind of umbilical cable with carbon fiber rod under tension is analyzed.The global dynamic analytical model is first established to determine the maximum tension load,then the local analytical model of umbilical cable including each unit are constructed by finite element method(FEM).Based on the mechanical analytical model,the reliability of umbilical cable under tension load is studied using response surface method(RSM)and Monte Carlo method.During the calculation process,a new tangent plane sampling method to calculate the response surface function(RSF)is proposed in this paper,which could make sampling points faster come close to the RSF curve,and it is proved that the calculation efficiency increases about 33%comparing with traditional method.

Case study on the mechanics of NPR anchor cable compensation for large deformation tunnel in soft rock in the Transverse Mountain area,China

A study was conducted to analyze the deformation mechanism of strongly weathered quartz schist in the Daliangshan Tunnel,located in the western Transverse Mountain area.A large deformation problem was experienced during the tunnel construction.To mitigate this problem,a support system was designed incorporating negative Poisson ratio(NPR)anchor cables with negative Poisson ratio effect.Physical model experiments,field experiments,and numerical simulation experiments were conducted to investigate the compensation mechanical behavior of NPR anchor cables.The large deformations of soft rocks in the Daliangshan Tunnel are caused by a high ground stress,a high degree of joint fracture development,and a high degree of surrounding rock fragmentation.A compensation mechanics support system combining long and short NPR anchor cables was suggested to provide sufficient counter-support force(approximately 350 kN)for the surrounding rock inside the tunnel.Comparing the NPR anchor cable support system with the original support system used in the Daliangshan tunnel showed that an NPR anchor cable support system,combining cables of 6.3 m and 10.3 m in length,effectively prevented convergence of surrounding rock deformation,and the integrated settlement convergence value remained below 300 mm.This study provides an effective scientific basis for resolving large deformation problems in deeply buried soft rocks in western transverse mountain areas.

A Heuristic Radiomics Feature Selection Method Based on Frequency Iteration and Multi-Supervised Training Mode

Radiomics is a non-invasive method for extracting quantitative and higher-dimensional features from medical images for diagnosis.It has received great attention due to its huge application prospects in recent years.We can know that the number of features selected by the existing radiomics feature selectionmethods is basically about ten.In this paper,a heuristic feature selection method based on frequency iteration and multiple supervised training mode is proposed.Based on the combination between features,it decomposes all features layer by layer to select the optimal features for each layer,then fuses the optimal features to form a local optimal group layer by layer and iterates to the global optimal combination finally.Compared with the currentmethod with the best prediction performance in the three data sets,thismethod proposed in this paper can reduce the number of features fromabout ten to about three without losing classification accuracy and even significantly improving classification accuracy.The proposed method has better interpretability and generalization ability,which gives it great potential in the feature selection of radiomics.

Analysis of environmental selection pressure of superoxide dismutase in deep-sea sea cucumber

Manganese superoxide dismutase(MnSOD)is an antioxidant that exists in mitochondria and can effectively remove superoxide anions in mitochondria.In a dark,high-pressure,and low-temperature deep-sea environment,MnSOD is essential for the survival of sea cucumbers.Six MnSODs were identified from the transcriptomes of deep and shallow-sea sea cucumbers.To explore their environmental adaptation mechanism,we conducted environmental selection pressure analysis through the branching site model of PAML software.We obtained night positive selection sites,and two of them were significant(97F→H,134K→V):97F→H located in a highly conservative characteristic sequence,and its polarity c hange might have a great impact on the function of MnSOD;134K→V had a change in piezophilic a bility,which might help MnSOD adapt to the environment of high hydrostatic pressure in the deepsea.To further study the effect of these two positive selection sites on MnSOD,we predicted the point mutations of F97H and K134V on shallow-sea sea cucumber by using MAESTROweb and PyMOL.Results show that 97F→H,134K→V might improve MnSOD’s efficiency of scavenging superoxide a nion and its ability to resist high hydrostatic pressure by moderately reducing its stability.The above results indicated that MnSODs of deep-sea sea cucumber adapted to deep-sea environments through their amino acid changes in polarity,piezophilic behavior,and local stability.This study revealed the correlation between MnSOD and extreme environment,and will help improve our understanding of the organism’s adaptation mechanisms in deep sea.

Exploring the feasibility of prestressed anchor cables as an alternative to temporary support in the excavation of super-large-span tunnel

Excavating super-large-span tunnels in soft rock masses presents significant challenges.To ensure safety,the sequential excavation method is commonly adopted.It utilizes internal temporary supports to spatially partition the tunnel face and divide the excavation into multiple stages.However,these internal supports generally impose spatial constraints,limiting the use of large-scale excavation equipment and reducing construction efficiency.To address this constraint,this study adopts the“Shed-frame”principle to explore the feasibility of an innovative support system,which aims to replace internal supports with prestressed anchor cables and thus provide a more spacious working space with fewer internal obstructions.To evaluate its effectiveness,a field case involving the excavation of a 24-m span tunnel in soft rock is presented,and an analysis of extensive field data is conducted to study the deformation characteristics of the surrounding rock and the mechanical behavior of the support system.The results revealed that prestressed anchor cables integrated the initial support with the shed,creating an effective“shed-frame”system,which successively maintained tunnel deformation and frame stress levels within safe regulatory bounds.Moreover,the prestressed anchor cables bolstered the surrounding rock effectively and reduced the excavation-induced disturbance zone significantly.In summary,the proposed support system balances construction efficiency and safety.These field experiences may offer valuable insights into the popularization and further development of prestressed anchor cable support systems.

Using multi-omics analysis to explore diagnostic tool and optimize drug therapy selection for patients with glioma based on cross-talk gene signature

Background:The heterogeneity of prognosis and treatment benefits among patients with gliomas is due to tumor microenvironment characteristics.However,biomarkers that reflect microenvironmental characteristics and predict the prognosis of gliomas are limited.Therefore,we aimed to develop a model that can effectively predict prognosis,differentiate microenvironment signatures,and optimize drug selection for patients with glioma.Materials and Methods:The CIBERSORT algorithm,bulk sequencing analysis,and single-cell RNA(scRNA)analysis were employed to identify significant cross-talk genes between M2 macrophages and cancer cells in glioma tissues.A predictive model was constructed based on cross-talk gene expression,and its effect on prognosis,recurrence prediction,and microenvironment characteristics was validated in multiple cohorts.The effect of the predictive model on drug selection was evaluated using the OncoPredict algorithm and relevant cellular biology experiments.Results:A high abundance of M2 macrophages in glioma tissues indicates poor prognosis,and cross-talk between macrophages and cancer cells plays a crucial role in shaping the tumor microenvironment.Eight genes involved in the cross-talk between macrophages and cancer cells were identified.Among them,periostin(POSTN),chitinase 3 like 1(CHI3L1),serum amyloid A1(SAA1),and matrix metallopeptidase 9(MMP9)were selected to construct a predictive model.The developed model demonstrated significant efficacy in distinguishing patient prognosis,recurrent cases,and characteristics of high inflammation,hypoxia,and immunosuppression.Furthermore,this model can serve as a valuable tool for guiding the use of trametinib.Conclusions:In summary,this study provides a comprehensive understanding of the interplay between M2 macrophages and cancer cells in glioma;utilizes a cross-talk gene signature to develop a predictive model that can predict the differentiation of patient prognosis,recurrence instances,and microenvironment characteristics;and aids in optimizing the application of trametinib in glioma patients.

A Pricing-Based Cooperative Relay Selection Scheme for Reliable Communications

With the rapid development and application of energy harvesting technology,it has become a prominent research area due to its significant benefits in terms of green environmental protection,convenience,and high safety and efficiency.However,the uneven energy collection and consumption among IoT devices at varying distances may lead to resource imbalance within energy harvesting networks,thereby resulting in low energy transmission efficiency.To enhance the energy transmission efficiency of IoT devices in energy harvesting,this paper focuses on the utilization of collaborative communication,along with pricing-based incentive mechanisms and auction strategies.We propose a dynamic relay selection scheme,including a ladder pricing mechanism based on energy level and a Kuhn-Munkre Algorithm based on an auction theory employing a negotiation mechanism,to encourage more IoT devices to participate in the collaboration process.Simulation results demonstrate that the proposed algorithm outperforms traditional algorithms in terms of improving the energy efficiency of the system.

Evaluating the performance of genomic selection on purebred population by incorporating crossbred data in pigs

Genomic selection(GS)has been widely used in livestock,which greatly accelerated the genetic progress of complex traits.The population size was one of the significant factors affecting the prediction accuracy,while it was limited by the purebred population.Compared to directly combining two uncorrelated purebred populations to extend the reference population size,it might be more meaningful to incorporate the correlated crossbreds into reference population for genomic prediction.In this study,we simulated purebred offspring(PAS and PBS)and crossbred offspring(CAB)base on real genotype data of two base purebred populations(PA and PB),to evaluate the performance of genomic selection on purebred while incorporating crossbred information.The results showed that selecting key crossbred individuals via maximizing the expected genetic relationship(REL)was better than the other methods(individuals closet or farthest to the purebred population,CP/FP)in term of the prediction accuracy.Furthermore,the prediction accuracy of reference populations combining PA and CAB was significantly better only based on PA,which was similar to combine PA and PAS.Moreover,the rank correlation between the multiple of the increased relationship(MIR)and reliability improvement was 0.60-0.70.But for individuals with low correlation(Cor(Pi,PA or B),the reliability improvement was significantly lower than other individuals.Our findings suggested that incorporating crossbred into purebred population could improve the performance of genetic prediction compared with using the purebred population only.The genetic relationship between purebred and crossbred population is a key factor determining the increased reliability while incorporating crossbred population in the genomic prediction on pure bred individuals.

Three-dimensional limit variation analysis on the ultimate pullout capacity of the anchor cables based on the Hoek-Brown failure criterion

Only simplified two-dimensional model and a single failure mode are adopted to calculate the ultimate pullout capacity(UPC)of anchor cables in most previous research.This study focuses on a more comprehensive combination failure mode that consists of bond failure of an anchorage body and failure of an anchored rock mass.The three-dimensional ultimate pullout capacity of the anchor cables is calculated based on the Hoek-Brown failure criterion and variation analysis method.The numerical solution for the curvilinear function in fracture plane is obtained based on the finite difference theory,which more accurately reflects the failure state of the anchor cable,as opposed to that being assumed in advance.The results reveal that relying solely on a single failure mode for UPC calculations has limitations,as changes in parameter values not only directly impact the UPC value but also can alter the failure model and thus the calculation method.