Rock mass quality prediction on tunnel faces with incomplete multi-source dataset via tree-augmented naive Bayesian network

Rock mass quality serves as a vital index for predicting the stability and safety status of rock tunnel faces.In tunneling practice,the rock mass quality is often assessed via a combination of qualitative and quantitative parameters.However,due to the harsh on-site construction conditions,it is rather difficult to obtain some of the evaluation parameters which are essential for the rock mass quality prediction.In this study,a novel improved Swin Transformer is proposed to detect,segment,and quantify rock mass characteristic parameters such as water leakage,fractures,weak interlayers.The site experiment results demonstrate that the improved Swin Transformer achieves optimal segmentation results and achieving accuracies of 92%,81%,and 86%for water leakage,fractures,and weak interlayers,respectively.A multisource rock tunnel face characteristic(RTFC)dataset includes 11 parameters for predicting rock mass quality is established.Considering the limitations in predictive performance of incomplete evaluation parameters exist in this dataset,a novel tree-augmented naive Bayesian network(BN)is proposed to address the challenge of the incomplete dataset and achieved a prediction accuracy of 88%.In comparison with other commonly used Machine Learning models the proposed BN-based approach proved an improved performance on predicting the rock mass quality with the incomplete dataset.By utilizing the established BN,a further sensitivity analysis is conducted to quantitatively evaluate the importance of the various parameters,results indicate that the rock strength and fractures parameter exert the most significant influence on rock mass quality.

Deep learning based classification of rock structure of tunnel face

The automated interpretation of rock structure can improve the efficiency,accuracy,and consistency of the geological risk assessment of tunnel face.Because of the high uncertainties in the geological images as a result of different regional rock types,as well as in-situ conditions(e.g.,temperature,humidity,and construction procedure),previous automated methods have limited performance in classification of rock structure of tunnel face during construction.This paper presents a framework for classifying multiple rock structures based on the geological images of tunnel face using convolutional neural networks(CNN),namely Inception-ResNet-V2(IRV2).A prototype recognition system is implemented to classify 5 types of rock structures including mosaic,granular,layered,block,and fragmentation structures.The proposed IRV2 network is trained by over 35,000 out of 42,400 images extracted from over 150 sections of tunnel faces and tested by the remaining 7400 images.Furthermore,different hyperparameters of the CNN model are introduced to optimize the most efficient algorithm parameter.Among all the discussed models,i.e.,ResNet-50,ResNet-101,and Inception-v4,Inception-ResNet-V2 exhibits the best performance in terms of various indicators,such as precision,recall,F-score,and testing time per image.Meanwhile,the model trained by a large database can obtain the object features more comprehensively,leading to higher accuracy.Compared with the original image classification method,the sub-image method is closer to the reality considering both the accuracy and the perspective of error divergence.The experimental results reveal that the proposed method is optimal and efficient for automated classification of rock structure using the geological images of the tunnel face.

Machine learning-based classification of rock discontinuity trace:SMOTE oversampling integrated with GBT ensemble learning

This paper presents a hybrid ensemble classifier combined synthetic minority oversampling technique(SMOTE),random search(RS)hyper-parameters optimization algorithm and gradient boosting tree(GBT)to achieve efficient and accurate rock trace identification.A thirteen-dimensional database consisting of basic,vector,and discontinuity features is established from image samples.All data points are classified as either‘‘trace”or‘‘non-trace”to divide the ultimate results into candidate trace samples.It is found that the SMOTE technology can effectively improve classification performance by recommending an optimized imbalance ratio of 1:5 to 1:4.Then,sixteen classifiers generated from four basic machine learning(ML)models are applied for performance comparison.The results reveal that the proposed RS-SMOTE-GBT classifier outperforms the other fifteen hybrid ML algorithms for both trace and nontrace classifications.Finally,discussions on feature importance,generalization ability and classification error are conducted for the proposed classifier.The experimental results indicate that more critical features affecting the trace classification are primarily from the discontinuity features.Besides,cleaning up the sedimentary pumice and reducing the area of fractured rock contribute to improving the overall classification performance.The proposed method provides a new alternative approach for the identification of 3D rock trace.

Exosomes derived from 3D-cultured MSCs improve therapeutic effects in periodontitis and experimental colitis and restore the Th17 cell/Treg balance in inflamed periodontium

Although mesenchymal stem cell-derived exosomes(MSC-exos)have been shown to have therapeutic effects in experimental periodontitis,their drawbacks,such as low yield and limited efficacy,have hampered their clinical application.These drawbacks can be largely reduced by replacing the traditional 2D culture system with a 3D system.However,the potential function of MSC-exos produced by 3D culture(3D-exos)in periodontitis remains elusive.This study showed that compared with MSC-exos generated via 2D culture(2D-exos),3D-exos showed enhanced anti-inflammatory effects in a ligature-induced model of periodontitis by restoring the reactive T helper 17(Th17)cell/Treg balance in inflamed periodontal tissues.Mechanistically,3D-exos exhibited greater enrichment of miR-1246,which can suppress the expression of Nfat5,a key factor that mediates Th17 cell polarization in a sequence-dependent manner.Furthermore,we found that recovery of the Th17 cell/Treg balance in the inflamed periodontium by the local injection of 3D-exos attenuated experimental colitis.Our study not only showed that by restoring the Th17 cell/Treg balance through the miR-1246/Nfat5 axis,the 3D culture system improved the function of MSC-exos in the treatment of periodontitis,but also it provided a basis for treating inflammatory bowel disease(IBD)by restoring immune responses in the inflamed periodontium.

Face stability analysis of circular tunnels in layered rock masses using the upper bound theorem

An analysis of tunnel face stability generally assumes a single homogeneous rock mass.However,most rock tunnel projects are excavated in stratified rock masses.This paper presents a two-dimensional(2D)analytical model for estimating the face stability of a rock tunnel in the presence of rock mass stratification.The model uses the kinematical limit analysis approach combined with the block calculation technique.A virtual support force is applied to the tunnel face,and then solved using an optimization method based on the upper limit theorem of limit analysis and the nonlinear Hoek-Brown yield criterion.Several design charts are provided to analyze the effects of rock layer thickness on tunnel face stability,tunnel diameter,the arrangement sequence of weak and strong rock layers,and the variation in rock layer parameters at different positions.The results indicate that the thickness of the rock layer,tunnel diameter,and arrangement sequence of weak and strong rock layers significantly affect the tunnel face stability.Variations in the parameters of the lower layer of the tunnel face have a greater effect on tunnel stability than those of the upper layer.

A novel image-based approach for interactive characterization of rock fracture spacing in a tunnel face

This paper presents a novel integrated method for interactive characterization of fracture spacing in rock tunnel sections.The main procedure includes four steps:(1)Automatic extraction of fracture traces,(2)digitization of trace maps,(3)disconnection and grouping of traces,and(4)interactive measurement of fracture set spacing,total spacing,and surface rock quality designation(S-RQD)value.To evaluate the performance of the proposed method,sample images were obtained by employing a photogrammetrybased scheme in tunnel faces.Experiments were then conducted to determine the optimal parameter values(i.e.distance threshold,angle threshold,and number of fracture trace grouping)for characterizing rock fracture spacing.By applying the identified optimal parameters involved in the model,the proposed method could lead to excellent qualitative results to a new tunnel face.To perform a quantitative analysis,three methods(i.e.field,straightening,and the proposed method)were employed in the same study and comparisons were made.The proposed method agrees well with the field measurement in terms of the maximum and average values of measured spacing distribution.Overall,the proposed method has reasonably good accuracy and interactive advantage for estimating the ultimate fracture spacing and S-RQD.It can be a possible extension of existing methods for fracture spacing characterization for two-dimensional(2D)rock tunnel faces.

金属有机框架材料电池隔膜的最新应用

随着新能源行业的快速发展,对于各种不同高性能电池的研究已成为当前的热点。隔膜作为电池的重要组成部分之一,能有效防止电池正负极直接接触并为离子传输提供有利通道。然而,传统聚合物电池隔膜通常存在热稳定性不足、离子输运能力差、电解液浸润性差等问题。金属-有机框架材料(MOFs)作为一种新型的多孔晶体材料,由于其高孔隙率、高比表面积和出色的热稳定性,已成为当前高性能电池隔膜的研究热点。本文综述了各类MOFs/MOFs基材料在电池隔膜中的应用,并全面讨论了MOFs基电池隔膜的优缺点。最后,介绍了MOFs基电池隔膜领域亟待解决的问题以及MOFs在电池隔膜上的发展前景。

Restoring periodontal tissue homoeostasis prevents cognitive decline by reducing the number of Serpina3nhigh astrocytes in the hippocampus

Cognitive decline has been linked to periodontitis through an undetermined pathophysiological mechanism.This study aimed to explore the mechanism underlying periodontitis-related cognitive decline and identify therapeutic strategies for this condition.Using single-nucleus RNA sequencing we found that changes in astrocyte number,gene expression,and cell‒cell communication were associated with cognitive decline in mice with periodontitis.In addition,activation of the NOD-like receptor family pyrin domain containing 3(NLRP3)inflammasome was observed to decrease the phagocytic capability of macrophages and reprogram macrophages to a more proinflammatory state in the gingiva,thus aggravating periodontitis.To further investigate this finding,lipid-based nanoparticles carrying NLRP3 siRNA(NPsiNLRP3)were used to inhibit overactivation of the NLRP3 inflammasome in gingival macrophages,restoring the oral microbiome and reducing periodontal inflammation.Furthermore,gingival injection of NPsiNLRP3 reduced the number of Serpina3nhigh astrocytes in the hippocampus and prevented cognitive decline.This study provides a functional basis for the mechanism by which the destruction of periodontal tissues can worsen cognitive decline and identifies nanoparticle-mediated restoration of gingival macrophage function as a novel treatment for periodontitis-related cognitive decline.

The spatial transcriptomic landscape of human gingiva in health and periodontitis

The gingiva is a key oral barrier that protects oral tissues from various stimuli.A loss of gingival tissue homeostasis causes periodontitis,one of the most prevalent inflammatory diseases in humans.The human gingiva exists as a complex cell network comprising specialized structures.To understand the tissue-specific pathophysiology of the gingiva,we applied a recently developed spatial enhanced resolution omics-sequencing(Stereo-seq)technique to obtain a spatial transcriptome(ST)atlas of the gingiva in healthy individuals and periodontitis patients.By utilizing Stereo-seq,we identified the major cell types present in the gingiva,which included epithelial cells,fibroblasts,endothelial cells,and immune cells,as well as subgroups of epithelial cells and immune cells.We further observed that inflammation-related signalling pathways,such as the JAK-STAT and NF-κB signalling pathways,were significantly upregulated in the endothelial cells of the gingiva of periodontitis patients compared with those of healthy individuals.Additionally,we characterized the spatial distribution of periodontitis risk genes in the gingiva and found that the expression of IFI16 was significantly increased in endothelial cells of inflamed gingiva.In conclusion,our Stereo-seq findings may facilitate the development of innovative therapeutic strategies for periodontitis by mapping periodontitis-relevant genes and pathways and effector cells.

Catalytic conversion and DFT analysis of post DDBD-catalysis system for degradation of toluene,ethyl acetate and acetone with different metal-oxides catalysts

A series of multiphase metal-oxide catalysts(MnOx/γ-Al_(2)O_(3),CuOx/γ-Al_(2)O_(3),FeO_(x/γ)-Al_(2)O_(3),CeO_(x)/γ-Al_(2)O_(3)and LaOx/γ-Al_(2)O_(3))were prepared for plasma-catalyst degradation of multicomponent volatile organic compounds(VOCs,such as toluene,acetone and ethyl acetate).The results reveal that the degradation efficiency(DE)of acetone,toluene and ethyl acetate in the DDBD system can be arranged as follows:Mn_(2)O_(3)/γ-Al_(2)O_(3)>Fe2O3/γ-Al_(2)O_(3)>CuO_(/γ)-Al_(2)O_(3)>CeO_(2)/γ-Al_(2)O_(3)>La_(2)O_(3)/γ-Al_(2)O_(3)>γ-Al_(2)O_(3),and the highest DE(49.5%for acetone,93.3%for toluene and 79.8%for ethyl acetate)is obtained in Mn_(2)O_(3)/γ-Al_(2)O_(3)+double dielectric barrier discharge(DDBD)system at specific input energy(SIE)of 700 J/L.Compared with the other catalysts,Mn_(2)O_(3)/γ-Al_(2)O_(3)also exhibits the most significant inhibitory effect on the production of ozone(O3).On the other hand,CeO_(2)/γ-Al_(2)O_(3)and La_(2)O_(3)/γ-Al_(2)O_(3)catalysts display different catalytic selectivity.Both catalysts can slightly raise the DE of VOCs and remarkably facilitate VOCs’mineralization.The carbon balance(CB)is increased by 30.2%and 38.8%,and the CO_(2)selectivity(SCO_(2))is raised by 70.5%and 11.9%in the CeO_(2)/γ-Al_(2)O_(3)+DDBD system and La_(2)O_(3)/γ-Al_(2)O_(3)+DDBD system at SIE of 700 J/L,respectively.To further understand the catalytic effect of the catalysts,the adsorption of O3on different catalysts was simulated by a DFT model.According to the results of the DFT model,it is found that the adsorption energy of other metal oxides seems to be used as a reference for catalytic effect to a certain extent except CeO_(2).The higher the absolute values of adsorption energy,the higher the improvement of VOCs degradation efficiency by catalyst.

Novel STING-targeted PET radiotracer for alert and therapeutic evaluation of acute lung injury

Acute lung injury(ALI),as a common clinical emergency,is pulmonary edema and diffuse lung infiltration caused by inflammation.The lack of non-invasive alert strategy,resulting in failure to carry out preventive treatment,means high mortality and poor prognosis.Stimulator of interferon genes(STING)is a key molecular biomarker of innate immunity in response to inflammation,but there is still a lack of STING-targeted strategy.In this study,a novel STING-targeted PET tracer,[~(18)F]FBTA,was labeled with high radiochemical yield(79.7±4.3%)and molar activity(32.5±2.9 GBq/μmol).We confirmed that[~(18)F]FBTA has a strong STING binding affinity(K_d=26.86±6.79 nmol/L)and can be used for PET imaging in ALI mice to alert early lung inflammation and to assess the efficacy of drug therapy.Our STING-targeted strategy also reveals that[~(18)F]FBTA can trace ALI before reaching the computed tomography(CT)diagnostic criteria,and demonstrates its better specificity and distribution than[~(18)F]fluorodeoxyglucose([~(18)F]FDG).

Composite electrolytes with efficient Li^(+)transport across ceramic/polymer interface

Over the past decades,solid electrolytes have re-emerged as promising candidates which replace flammable liquid electrolytes for a safe,energy-dense,and reversible storage of electrochemical energy in batteries[1].However,as the main distinct families of solid electrolytes,both polymer and ceramic electrolytes have their individual disadvantages in ionic conductivity,mechanical robustness,and interfacial resistance.

理论探究水溶液条件对TMN_(x)C_(y)催化氮还原性能的增强机制

在电化学氮还原(NRR)合成氨过程中,为了认识真实水环境对过渡金属掺杂氮碳材料催化NRR活性的影响,并筛选出实际催化性能最佳的构型,本研究通过密度泛函理论(DFT)和从头算分子动力学(AIMD)相结合的方法,对一系列不同配位形式的过渡金属掺杂氮碳催化剂(TMN_(x)C_(y), TM=Sc、Ti、V、Cr、Mn、Fe、Co、Ni、Cu、Zn;x=1~4;y=4-x,总共40种构型)催化NRR的热力学稳定性和实际氮还原催化活性进行了系统研究.计算结果表明,本工作所研究的40种催化剂结构均具有较高的热力学稳定性,可作为实际的NRR候选催化剂.其中,利用隐性+显性水溶剂模拟真实水环境时,过渡金属V掺杂形成的VN3C1结构催化NRR酶机制过程的最大吉布斯自由能变值仅为0.35eV(在U=0 V vs. RHE时),表现出最佳的NRR催化性能.总体而言,本研究采用DFT与AIMD相结合的方法,深入地阐述了TMN_(x)C_(y)催化剂的实际NRR催化过程,为实验过程做出了更为贴切的理论预测.

Inhibition of CCL2 by bindarit alleviates diabetes-associated periodontitis by suppressing inflammatory monocyte infiltration and altering macrophage properties

Diabetes-associated periodontitis(DP)aggravates diabetic complications and increases mortality from diabetes.DP is caused by diabetes-enhanced host immune-inflammatory responses to bacterial insult.In this study,we found that persistently elevated CCL2 levels in combination with proinflammatory monocyte infiltration of periodontal tissues were closely related to DP.Moreover,inhibition of CCL2 by oral administration of bindarit reduced alveolar bone loss and increased periodontal epithelial thickness by suppressing periodontal inflammation.Furthermore,bindarit suppressed the infiltration of proinflammatory monocytes and altered the inflammatory properties of macrophages in the diabetic periodontium.This finding provides a basis for the development of an effective therapeutic approach for treating DP.

Built-in piezoelectric field improved photocatalytic performance of nanoflower-like Bi_(2)WO_(6) using low-power white LEDs

Photocatalysis technology has been proved to be a potential strategy for removal of organic dyes,however high-power light sources are generally necessary to initiate photocatalytic reaction.In this work,we employed an excellent photocatalyst of Bi_(2)WO_(6) with visible light harvest and meanwhile an intrinsic ferroelectricity,which realized the efficient degradation of organic dye via the synergetic photopiezocatalysis.Through coupling the illumination by a low-power(9 W)LED and the ultrasonic vibration(120 W)by an ultrasonic cleaner,the nano flower-like Bi_(2)WO_(6) composed of ultrathin nanosheets showed a much more enhanced photopiezocatalysis performance for purification of organic dye than the individual photocatalysis and piezocatalysis.Furthermore,the high mineralization efficiency and the good durability of the Bi_(2)WO_(6) catalyst were demonstrated.The possible mechanism of photopiezocatalysis was finally proposed,where the ultrasound-induced piezoelectric field in Bi_(2)WO_(6) drove photo-generated electrons and holes to diffuse along opposite directions,consequently promoting the separation efficiency of charge carriers.This work indicates that the synergetic photopiezocatalysis by coupling irradiation and ultrasonic vibration is a promising strategy to purify organic pollutants in wastewater.

Highly sensitive vector magnetic fiber sensor based on hyperbolic metamaterials

Hyperbolic metamaterials(HMMs) are novel artificial materials that excite the surface plasmon resonance(SPR) because of their unique hyperbolic dispersion properties. Herein, to the best of our knowledge, we propose the first HMM-based fiber SPR(HMM-SPR) sensor for vector magnetic detection. By selecting the composite materials and structural parameters of the HMM dispersion management, HMM-SPR sensors can achieve a high refractive index sensitivity of 14.43 μm/RIU. Vector magnetic field detection was performed with the HMM-SPR sensor encapsulated with a magnetic fluid. Compared with other ferrofluidbased magnetic field fiber sensors, the proposed sensor shows pronounced advantages in intensity and direction sensitivity of 1.307 nm/Oe and 7.116 nm/°, respectively. The sensor design approach presented in this paper provides an excellent demonstration of HMM-SPR sensors in various applications.

A parathyroid hormone related supramolecular peptide for multi-functionalized osteoregeneration

Supramolecular peptide nanofiber hydrogels are emerging biomaterials for tissue engineering,but it is difficult to fabricate multi-functional systems by simply mixing several short-motif-modified supramolecular peptides because relatively abundant motifs generally hinder nanofiber cross-linking or the formation of long nanofiber.Coupling bioactive factors to the assembling backbone is an ideal strategy to design multi-functional supramolecular peptides in spite of challenging synthesis and purification.Herein,a multi-functional supramolecular peptide,P1R16,is developed by coupling a bioactive factor,parathyroid hormone related peptide 1(PTHrP-1),to the basic supramolecular peptide RADA16-I via solid-phase synthesis.It is found that P1R16 self-assembles into long nanofibers and co-assembles with RADA16-I to form nanofiber hydrogels,thus coupling PTHrP-1 to hydrogel matrix.P1R16 nanofiber retains osteoinductive activity in a dose-dependent manner,and P1R16/RADA16-I nanofiber hydrogels promote osteogenesis,angiogenesis and osteoclastogenesis in vitro and induce multi-functionalized osteoregeneration by intramembranous ossification and bone remodeling in vivo when loaded to collagen(Col)scaffolds.Abundant red blood marrow formation,ideal osteointegration and adapted degradation are observed in the 50%P1R16/Col scaffold group.Therefore,this study provides a promising strategy to develop multi-functional supramolecular peptides and a new method to topically administrate parathyroid hormone or parathyroid hormone related peptides for non-healing bone defects.