Predicting Rock Burst in Underground Engineering Leveraging a Novel Metaheuristic-Based LightGBM Model

Rock bursts represent a formidable challenge in underground engineering,posing substantial risks to both infrastructure and human safety.These sudden and violent failures of rock masses are characterized by the rapid release of accumulated stress within the rock,leading to severe seismic events and structural damage.Therefore,the development of reliable prediction models for rock bursts is paramount to mitigating these hazards.This study aims to propose a tree-based model—a Light Gradient Boosting Machine(LightGBM)—to predict the intensity of rock bursts in underground engineering.322 actual rock burst cases are collected to constitute an exhaustive rock burst dataset,which serves to train the LightGBMmodel.Two population-basedmetaheuristic algorithms are used to optimize the hyperparameters of the LightGBM model.Finally,the sensitivity analysis is used to identify the predominant factors that may incur the occurrence of rock bursts.The results show that the population-based metaheuristic algorithms have a good ability to search out the optimal hyperparameters of the LightGBM model.The developed LightGBM model yields promising performance in predicting the intensity of rock bursts,with which accuracy on training and testing sets are 0.972 and 0.944,respectively.The sensitivity analysis discloses that the risk of occurring rock burst is significantly sensitive to three factors:uniaxial compressive strength(σc),stress concentration factor(SCF),and elastic strain energy index(Wet).Moreover,this study clarifies the particular impact of these three factors on the intensity of rock bursts through the partial dependence plot.

Development and optimization of object detection technology in pavement engineering: A literature review

Due to the rapid advancement of the transportation industry and the continual increase in pavement infrastructure,it is difficult to keep up with the huge road maintenance task by relying only on the traditional manual detection method.Intelligent pavement detection technology with deep learning techniques is available for the research and industry areas by the gradual development of computer vision technology.Due to the different characteristics of pavement distress and the uncertainty of the external environment,this kind of object detection technology for distress classification and location still faces great challenges.This paper discusses the development of object detection technology and analyzes classical convolutional neural network(CNN)architecture.In addition to the one-stage and two-stage object detection frameworks,object detection without anchor frames is introduced,which is divided according to whether the anchor box is used or not.This paper also introduces attention mechanisms based on convolutional neural networks and emphasizes the performance of these mechanisms to further enhance the accuracy of object recognition.Lightweight network architecture is introduced for mobile and industrial deployment.Since stereo cameras and sensors are rapidly developed,a detailed summary of three-dimensional object detection algorithms is also provided.While reviewing the history of the development of object detection,the scope of this review is not only limited to the area of pavement crack detection but also guidance for researchers in related fields is shared.

Building Custom Spreadsheet Functions with Python: End-User Software Engineering Approach

End-user computing empowers non-developers to manage data and applications, enhancing collaboration and efficiency. Spreadsheets, a prime example of end-user programming environments widely used in business for data analysis. However, Excel functionalities have limits compared to dedicated programming languages. This paper addresses this gap by proposing a prototype for integrating Python’s capabilities into Excel through on-premises desktop to build custom spreadsheet functions with Python. This approach overcomes potential latency issues associated with cloud-based solutions. This prototype utilizes Excel-DNA and IronPython. Excel-DNA allows creating custom Python functions that seamlessly integrate with Excel’s calculation engine. IronPython enables the execution of these Python (CSFs) directly within Excel. C# and VSTO add-ins form the core components, facilitating communication between Python and Excel. This approach empowers users with a potentially open-ended set of Python (CSFs) for tasks like mathematical calculations, statistical analysis, and even predictive modeling, all within the familiar Excel interface. This prototype demonstrates smooth integration, allowing users to call Python (CSFs) just like standard Excel functions. This research contributes to enhancing spreadsheet capabilities for end-user programmers by leveraging Python’s power within Excel. Future research could explore expanding data analysis capabilities by expanding the (CSFs) functions for complex calculations, statistical analysis, data manipulation, and even external library integration. The possibility of integrating machine learning models through the (CSFs) functions within the familiar Excel environment.

An engineering classification system for the detached granite blocks distributed on slopes

Gentle slopes with large amounts of granite blocks are widespread in granitic areas with warm and humid climate.These blocks pose a potential risk to the existing and planned infrastructure.The instability type and geometry of these blocks will influence their motility and destructive power to some extent.This study aims at creating a classification system that can indicate both the shape and the instability type of these blocks and then developing a block removal scheme.The classification system was constructed based on the mechanical stability analysis of blocks on an inclined surface.This analysis identified key factors affecting stability,including block shape,block weathering roundness,the existing state of a block on a slope,and the friction between the block and the slope.The achieved work allowed the establishment of a direct correlation between block geometry and their instability types.The availability of this classification system was validated by field data and experimental data in the literature.The proposal to remove blocks identified as the toppling types,such as cylindrical-toppling types,cuboid-toppling types,cube-toppling types,was put forward to avoid the uneconomical problem of a complete clearance.Meanwhile,this classification provides a foundation for further research on the instability probability of each type of block and the development of a more refined blocks’removal scheme.The classification approach adopted in this paper can provide a reference for the classification of other lithological blocks under similar engineering geological conditions.

Engineering versatile Au-based catalysts for solar-to-fuel conversion

Gold(Au) nanostructures(NSs) have been widely employed as cocatalysts to improve the photoactivity of semiconductor materials, while a systematic summary of the engineering approaches of Au NSs to maximize the solar-to-fuel conversion efficiency is still lacking. In this review, the recently developed strategies for elevating the overall photocatalytic performance of Au-based catalysts and the deep physical chemistry mechanisms are highlighted. Firstly, the synthetic approaches of Au NSs are summarized, followed by an elaboration on their multiple functions in improving photoactivity. Afterward, modification strategies of Au NSs used to enhance the photocatalytic efficiency of Au-semiconductor composites,including controlling the Au NSs morphology, size, crystal phase, defect engineering, alloying with different metals, modulating interfacial interaction, and introducing an external field, are summarized and discussed independently. Additionally, advanced characterization techniques that can provide insights into the charge dynamics of the photocatalysts are introduced. Finally, we share our opinion on the challenges and outline potentially promising opportunities and directions for efficient Au-based photocatalysis research moving forward. We sincerely look forward to this review can deliver insightful views to design efficient Au-based photocatalysts and spur certain innovations to other metal-based catalysts.

黄河流域煤炭开发区地下水污染成因分析及防治建议

[研究目的]黄河流域内由于煤炭资源开发导致地下水污染严重,从整体角度对流域内重点煤炭开发区地下水进行归纳总结,为其可持续健康发展和地下水资源改善提供防治建议。[研究方法]从黄河流域战略地位出发,对地下水污染成因和污染模式进行归纳总结。采用改进后的综合水质指数法对流域内九大煤炭基地的地下水进行水质评价。选用主成分分析法赋权指标,并引入改进的内梅罗污染指数法,按照WPI分级法进行评价。[研究结果]通过对黄河流域各重点断面水质评价,得到目前流域内煤炭基地水质结果中Ⅲ、Ⅳ类占比较多,水质较差。分析成因当前流域内存在高矿化度矿井水、酸性矿井水和含特殊组分矿井水污染,同时阐述污染地下水形成的浅层和深层两种污染模式。[结论]对3种矿井水采用相应防治方法,并提出膏体充填技术和微生物修复技术2种防治技术建议,通过应用实际矿山为例,印证污染防治技术能够改善由煤炭开采引发的流域内地下水污染严重的问题,以期研究结果能够对成功推动中国绿色矿山的快速发展具有参考意义。

State-of-the-art advances in vacancy defect engineering of graphitic carbon nitride for solar water splitting

Developing low-cost,efficient,and stable photocatalysts is one of the most promising methods for large-scale solar water splitting.As a metal-free semiconductor material with suitable band gap,graphitic carbon nitride(g-C_(3)N_(4))has attracted attention in the field of photocatalysis,which is mainly attributed to its fascinating physicochemical and photoelectronic properties.However,several inherent limitations and shortcomings—involving high recombination rate of photocarriers,insufficient reaction kinetics,and optical absorption—impede the practical applicability of g-C_(3)N_(4).As an effective strategy,vacancy defect engineering has been widely used for breaking through the current limitations,considering its ability to optimize the electronic structure and surface morphology of g-C_(3)N_(4) to obtain the desired photocatalytic activity.This review summarizes the recent progress of vacancy defect engineered g-C_(3)N_(4) for solar water splitting.The fundamentals of solar water splitting with g-C_(3)N_(4) are discussed first.We then focus on the fabrication strategies and effect of vacancy generated in g-C_(3)N_(4).The advances of vacancy-modified g-C_(3)N_(4) photocatalysts toward solar water splitting are discussed next.Finally,the current challenges and future opportunities of vacancy-modified g-C_(3)N_(4) are summarized.This review aims to provide a theoretical basis and guidance for future research on the design and development of highly efficient defective g-C_(3)N_(4).

Geochemical fingerprints and hydrocarbon potential of Paleocene mudrocks in the Tano Basin, Ghana: insights from biomarkers and stable carbon isotopes

The Paleocene mudrocks in Ghana’s Tano Basin have received limited attention despite ongoing efforts to explore hydrocarbon resources.A thorough geochemical analysis is imperative to assess these mudrocks’petroleum generation potential and formulate effective exploration strategies.In this study,a comprehensive geochemical analysis was carried out on ten Paleocene rock cuttings extracted from TP-1,a discovery well within the Tano Basin.Various analytical techniques,including total organic carbon(TOC)analysis,Rock–Eval pyrolysis,gas chromatography-mass spectrometry,and isotope ratio-mass spectrometry,were employed to elucidate their hydrocar-bon potential and organic facies.Thefindings in this study were subsequently compared to existing geochemical data on Paleocene source rocks in the South Atlantic marginal basins.The results indicated that the Paleocene samples have TOC content ranging from 0.68 to 2.93 wt%.The prevalent kerogen types identified in these samples were Type Ⅱ and Type Ⅲ.Molecular and isotope data suggest that the organic matter found in the Paleocene mudrocks can be traced back to land plants and lower aquatic organisms.These mudrocks were deposited in a transi-tional environment withfluctuating water salinity,charac-terized by sub-oxic redox conditions.Maturity indices,both bulk and molecular,indicated a spectrum of maturity levels within the Paleocene mudrocks,spanning from immature to marginally mature,with increasing maturity observed with greater depth.In comparison,the organic composition and depositional environments of Paleocene source rocks in the Tano Basin closely resemble those found in the Niger Delta Basin,Douala,and Kribi-Campo Basins,the Kwanza Formation in Angola,and certain Brazilian marginal basins.However,it is worth noting that Paleocene source rocks in some of the basins,such as the Niger Delta and Brazilian marginal basins,exhibit rela-tively higher thermal maturity levels compared to those observed in the current Paleocene samples from the Tano Basin.In conclusion,the comprehensive geochemical analysis of Paleocene mudrocks within Ghana’s Tano Basin has unveiled their marginal hydrocarbon generation potential.The shared geochemical characteristics between the Paleocene mudrocks in the Tano Basin and those in the nearby South Atlantic marginal basins offer valuable insights into source rock quality,which is crucial for shaping future strategies in petroleum exploration in this region.

Correlation of work function and stacking fault energy through Kelvin probe force microscopy and nanohardness in diluteα-magnesium

Electronic interactions of the Group 2A elements with magnesium have been studied through the dilute solid solutions in binary Mg-Ca,Mg-Sr and Mg-Ba systems.This investigation incorporated the difference in the‘Work Function'(ΔWF)measured via Kelvin Probe Force Microscopy(KPFM),as a property directly affected by interatomic bond types,i.e.the electronic structure,nanoindentation measurements,and Stacking Fault Energy values reported in the literature.It was shown that the nano-hardness of the solid-solutionα-Mg phase changed in the order of Mg-Ca>Mg-Sr>Mg-Ba.Thus,it was shown,by also considering the nano-hardness levels,that SFE of a solid-solution is closely correlated with its‘Work Function'level.Nano-hardness measurements on the eutectics andΔWF difference between eutectic phases enabled an assessment of the relative bond strength and the pertinent electronic structures of the eutectics in the three alloys.Correlation withΔWF and at least qualitative verification of those computed SFE values with some experimental measurement techniques were considered important as those computational methods are based on zero Kelvin degree,relatively simple atomic models and a number of assumptions.As asserted by this investigation,if the results of measurement techniques can be qualitatively correlated with those of the computational methods,it can be possible to evaluate the electronic structures in alloys,starting from binary systems,going to ternary and then multi-elemental systems.Our investigation has shown that such a qualitative correlation is possible.After all,the SFE values are not treated as absolute values but rather become essential in comparative investigations when assessing the influences of alloying elements at a fundamental level,that is,free electron density distributions.Our study indicated that the principles of‘electronic metallurgy'in developing multi-elemental alloy systems can be followed via practical experimental methods,i.e.ΔWF measurements using KPFM and nanoindentation.

裂纹加筋和缺口加筋对钢加筋板极限强度的影响

This paper numerically evaluates the effect of the crack position on the ultimate strength of stiffened panels.Imperfections such as notches and cracks in aged marine stiffened panels can reduce their ultimate strength.To investigate the effect of crack length and position,a series of nonlinear finite element analyses were carried out and two cases were considered,i.e.,case 1 with thin stiffeners and case 2 with thick stiffeners.In both cases,the stiffeners have the same cross-section area.To have a basis for comparison,the intact panels were modeled as well.The cracks and notches were in the longitudinal and transverse direction and were assumed to be in the middle part of the panel.The cracks and notches were assumed to be through the thickness and there is neither crack propagation nor contact between crack faces.Based on the numerical results,longitudinal cracks affect the behavior of the stiffened panels in the postbuckling region.When the stiffeners are thinner,they buckle first and provide no reserved strength after plate buckling.Thus,cracks in the stiffeners do not affect the ultimate strength in the case of the thinner stiffeners.Generally,when stiffeners are thicker,they affect the postbuckling behavior more.In that case,cracks in the stiffeners affect the buckling and failure modes of the stiffened panels.The effect of notch was also studied.In contrast to the longitudinal crack in stiffeners,a notch in the stiffeners reduces the ultimate strength of the stiffened panel for both slender and thick stiffeners.

Nonlinear phenomena in vibrations of embedded carbon nanotubes conveying viscous fluid

Various nonlinear phenomena such as bifurcations and chaos in the responses of carbon nanotubes(CNTs)are recognized as being major contributors to the inaccuracy and instability of nanoscale mechanical systems.Therefore,the main purpose of this paper is to predict the nonlinear dynamic behavior of a CNT conveying viscousfluid and supported on a nonlinear elastic foundation.The proposed model is based on nonlocal Euler–Bernoulli beam theory.The Galerkin method and perturbation analysis are used to discretize the partial differential equation of motion and obtain the frequency-response equation,respectively.A detailed parametric study is reported into how the nonlocal parameter,foundation coefficients,fluid viscosity,and amplitude and frequency of the external force influence the nonlinear dynamics of the system.Subharmonic,quasi-periodic,and chaotic behaviors and hardening nonlinearity are revealed by means of the vibration time histories,frequency-response curves,bifurcation diagrams,phase portraits,power spectra,and Poincarémaps.Also,the results show that it is possible to eliminate irregular motion in the whole range of external force amplitude by selecting appropriate parameters.

Relational Turkish Text Classification Using Distant Supervised Entities and Relations

Text classification,by automatically categorizing texts,is one of the foundational elements of natural language processing applications.This study investigates how text classification performance can be improved through the integration of entity-relation information obtained from the Wikidata(Wikipedia database)database and BERTbased pre-trained Named Entity Recognition(NER)models.Focusing on a significant challenge in the field of natural language processing(NLP),the research evaluates the potential of using entity and relational information to extract deeper meaning from texts.The adopted methodology encompasses a comprehensive approach that includes text preprocessing,entity detection,and the integration of relational information.Experiments conducted on text datasets in both Turkish and English assess the performance of various classification algorithms,such as Support Vector Machine,Logistic Regression,Deep Neural Network,and Convolutional Neural Network.The results indicate that the integration of entity-relation information can significantly enhance algorithmperformance in text classification tasks and offer new perspectives for information extraction and semantic analysis in NLP applications.Contributions of this work include the utilization of distant supervised entity-relation information in Turkish text classification,the development of a Turkish relational text classification approach,and the creation of a relational database.By demonstrating potential performance improvements through the integration of distant supervised entity-relation information into Turkish text classification,this research aims to support the effectiveness of text-based artificial intelligence(AI)tools.Additionally,it makes significant contributions to the development ofmultilingual text classification systems by adding deeper meaning to text content,thereby providing a valuable addition to current NLP studies and setting an important reference point for future research.

Control system design for a pressure-tube-type supercritical water-cooled nuclear reactor via a higher order sliding mode method

Nuclear power plants exhibit non-linear and time-variable dynamics.Therefore,designing a control system that sets the reactor power and forces it to follow the desired load is complicated.A supercritical water reactor(SCWR)is a fourth-generation conceptual reactor.In an SCWR,the non-linear dynamics of the reactor require a controller capable of control-ling the nonlinearities.In this study,a pressure-tube-type SCWR was controlled during reactor power maneuvering with a higher order sliding mode,and the reactor outgoing steam temperature and pressure were controlled simultaneously.In an SCWR,the temperature,pressure,and power must be maintained at a setpoint(desired value)during power maneuvering.Reactor point kinetics equations with three groups of delayed neutrons were used in the simulation.Higher-order and classic sliding mode controllers were separately manufactured to control the plant and were compared with the PI controllers speci-fied in previous studies.The controlled parameters were reactor power,steam temperature,and pressure.Notably,for these parameters,the PI controller had certain instabilities in the presence of disturbances.The classic sliding mode controller had a higher accuracy and stability;however its main drawback was the chattering phenomenon.HOSMC was highly accurate and stable and had a small computational cost.In reality,it followed the desired values without oscillations and chattering.

Surface wave inversion with unknown number of soil layers based on a hybrid learning procedure of deep learning and genetic algorithm

Surface wave inversion is a key step in the application of surface waves to soil velocity profiling.Currently,a common practice for the process of inversion is that the number of soil layers is assumed to be known before using heuristic search algorithms to compute the shear wave velocity profile or the number of soil layers is considered as an optimization variable.However,an improper selection of the number of layers may lead to an incorrect shear wave velocity profile.In this study,a deep learning and genetic algorithm hybrid learning procedure is proposed to perform the surface wave inversion without the need to assume the number of soil layers.First,a deep neural network is adapted to learn from a large number of synthetic dispersion curves for inferring the layer number.Then,the shear-wave velocity profile is determined by a genetic algorithm with the known layer number.By applying this procedure to both simulated and real-world cases,the results indicate that the proposed method is reliable and efficient for surface wave inversion.

Annual growth of Fagus orientalis is limited by spring drought conditions in Iran’s Golestan Province

Due to the lack of a uniform and accurate defi-nition of‘drought’,several indicators have been introduced based on different variables and methods,and the efficiency of each of these is determined according to their relationship with drought.The relationship between two drought indices,SPI(standardized precipitation index)and SPEI(standard-ized precipitation-evapotranspiration index)in different sea-sons was investigated using annual rings of 15 tree samples to determine the effect of drought on the growth of oriental beech(Fagus orientalis Lipsky)in the Hyrcanian forests of northern Iran.The different evapotranspiration calcula-tion methods were evaluated on SPEI efficiency based on Hargreaves-Samani,Thornthwaite,and Penman-Monteith methods using the step-by-step M5 decision tree regression method.The results show that SPEI based on the Penman-Monteith in a three-month time scale(spring)had similar temporal changes and a better relationship with annual tree rings(R^(2)=0.81)at a 0.05 significant level.Abrupt change and a decreasing trend in the time series of annual tree rings are similar to the variation in the SPEI based on the Penman-Monteith method.Factors affecting evapotranspiration,temperature,wind speed,and sunshine hours(used in the Penman-Monteith method),increased but precipitation decreased.Using non-linear modeling methods,SPEI based on Penman-Monteith best illustrated climate changes affecting tree growth.

Quantification of the adulteration concentration of palm kernel oil in virgin coconut oil using near-infrared hyperspectral imaging

The adulteration concentration of palm kernel oil(PKO)in virgin coconut oil(VCO)was quantified using near-infrared(NIR)hyperspectral imaging.Nowadays,some VCO is adulterated with lower-priced PKO to reduce production costs,which diminishes the quality of the VCO.This study used NIR hyperspectral imaging in the wavelength region 900-1,650 nm to create a quantitative model for the detection of PKO contaminants(0-100%)in VCO and to develop predictive mapping.The prediction equation for the adulteration of VCO with PKO was constructed using the partial least squares regression method.The best predictive model was pre-processed using the standard normal variate method,and the coefficient of determination of prediction was 0.991,the root mean square error of prediction was 2.93%,and the residual prediction deviation was 10.37.The results showed that this model could be applied for quantifying the adulteration concentration of PKO in VCO.The prediction adulteration concentration mapping of VCO with PKO was created from a calibration model that showed the color level according to the adulteration concentration in the range of 0-100%.NIR hyperspectral imaging could be clearly used to quantify the adulteration of VCO with a color level map that provides a quick,accurate,and non-destructive detection method.

Genuine entanglement under squeezed generalized amplitude damping channels with memory

We study genuine entanglement among three qubits undergoing a noisy process that includes dissipation, squeezing,and decoherence. We obtain a general solution and analyze the asymptotic quantum states. We find that most of these asymptotic states can be genuinely entangled depending upon the parameters of the channel, memory parameter, and the parameters of the initial states. We study Greenberger–Horne–Zeilinger(GHZ) states and W states, mixed with white noise,and determine the conditions for them to be genuinely entangled at infinity. We find that for these mixtures, it is possible to start with a bi-separable state(with a specific mixture of white noise) and end with genuine entangled states. However, the memory parameter μ must be very high. We find that in contrast to the two-qubit case, none of the three-qubit asymptotic states for n → ∞ are genuinely entangled.

Determination of Material Parameters of EVA Foam under Uniaxial Compressive Testing Using Hyperelastic Models

The objective of this research was to determine the mechanical parameter from EVA foam and also investigate its behavior by using Blatz-Ko,Neo-Hookean,Mooney model and experimental test.The physical characteristic of EVA foam was also evaluated by scanning electron microscopy(SEM).The results show that Blatz-Ko and Neo-Hookean model can fit the curve at 5%and 8%strain,respectively.The Mooney model can fit the curve at 50%strain.The modulus of rigidity evaluated from Mooney model is 0.0814±0.0027 MPa.The structure of EVA foam from SEM image shows that EVA structure is a closed cell with homogeneous porous structure.From the result,it is found that Mooney model can adjust the data better than other models.This model can be applied for mechanical response prediction of EVA foam and also for reference value in engineering application.

Advancements in enhancing corrosion protection of Mg alloys:A comprehensive review on the synergistic effects of combining inhibitors with PEO coating

Magnesium(Mg)alloys are lightweight materials with excellent mechanical properties,making them attractive for various applications,including aerospace,automotive,and biomedical industries.However,the practical application of Mg alloys is limited due to their high susceptibility to corrosion.Plasma electrolytic oxidation(PEO),or micro-arc oxidation(MAO),is a coating method that boosts Mg alloys'corrosion resistance.However,despite the benefits of PEO coatings,they can still exhibit certain limitations,such as failing to maintain long-term protection as a result of their inherent porosity.To address these challenges,researchers have suggested the use of inhibitors in combination with PEO coatings on Mg alloys.Inhibitors are chemical compounds that can be incorporated into the coating or applied as a post-treatment to further boost the corrosion resistance of the PEO-coated Mg alloys.Corrosion inhibitors,whether organic or inorganic,can act by forming a protective barrier,hindering the corrosion process,or modifying the surface properties to reduce susceptibility to corrosion.Containers can be made of various materials,including polyelectrolyte shells,layered double hydroxides,polymer shells,and mesoporous inorganic materials.Encapsulating corrosion inhibitors in containers fully compatible with the coating matrix and substrate is a promising approach for their incorporation.Laboratory studies of the combination of inhibitors with PEO coatings on Mg alloys have shown promising results,demonstrating significant corrosion mitigation,extending the service life of Mg alloy components in aggressive environments,and providing self-healing properties.In general,this review presents available information on the incorporation of inhibitors with PEO coatings,which can lead to improved performance of Mg alloy components in demanding environments.