Application of multi-algorithm ensemble methods in high-dimensional and small-sample data of geotechnical engineering:A case study of swelling pressure of expansive soils

Geotechnical engineering data are usually small-sample and high-dimensional,which brings a lot of challenges in predictive modeling.This paper uses a typical high-dimensional and small-sample swell pressure(P_(s))dataset to explore the possibility of using multi-algorithm hybrid ensemble and dimensionality reduction methods to mitigate the uncertainty of soil parameter prediction.Based on six machine learning(ML)algorithms,the base learner pool is constructed,and four ensemble methods,Stacking(SG),Blending(BG),Voting regression(VR),and Feature weight linear stacking(FWL),are used for the multi-algorithm ensemble.Furthermore,the importance of permutation is used for feature dimensionality reduction to mitigate the impact of weakly correlated variables on predictive modeling.The results show that the proposed methods are superior to traditional prediction models and base ML models,where FWL is more suitable for modeling with small-sample datasets,and dimensionality reduction can simplify the data structure and reduce the adverse impact of the small-sample effect,which points the way to feature selection for predictive modeling.Based on the ensemble methods,the feature importance of the five primary factors affecting P_(s) is the maximum dry density(31.145%),clay fraction(15.876%),swell percent(15.289%),plasticity index(14%),and optimum moisture content(13.69%),the influence of input parameters on P_(s) is also investigated,in line with the findings of the existing literature.

Precursor engineering enables high-performance all-inorganic CsPbIBr_(2) perovskite solar cells with a record efficiency approaching 13%

All-inorganic CsPbIBr_(2) perovskite has attracted widespread attention in photovoltaic and other optoelectronic devices because of its superior thermal stability.However,the deposition of high-quality solutionprocessed CsPbIBr_(2) perovskite films with large thicknesses remains challenging.Here,we develop a triple-component precursor(TCP) by employing lead bromide,lead iodide,and cesium bromide,to replace the most commonly used double-component precursor(DCP) consisting of lead bromide and cesium iodide.Remarkably,the TCP system significantly increases the solution concentration to 1.3 M,leading to a larger film thickness(~390 nm) and enhanced light absorption.The resultant CsPbIBr_(2) films were evaluated in planar n-i-p structured solar cells,which exhibit a considerably higher optimal photocurrent density of 11.50 mA cm^(-2) in comparison to that of DCP-based devices(10.69 mA cm^(-2)).By adopting an organic surface passivator,the maximum device efficiency using TCP is further boosted to a record efficiency of 12.8% for CsPbIBr_(2) perovskite solar cells.

Groundwater flow through fractured rocks and seepage control in geotechnical engineering: Theories and practices

Groundwater flow through fractured rocks has been recognized as an important issue in many geotechnical engineering practices.Several key aspects of fundamental mechanisms,numerical modeling and engineering applications of flow in fractured rocks are discussed.First,the microscopic mechanisms of fluid flow in fractured rocks,especially under the complex conditions of non-Darcian flow,multiphase flow,rock dissolution,and particle transport,have been revealed through a com-bined effort of visualized experiments and theoretical analysis.Then,laboratory and field methods of characterizing hydraulic properties(e.g.intrinsic permeability,inertial permeability,and unsaturated flow parameters)of fractured rocks in different flow regimes have been proposed.Subsequently,high-performance numerical simulation approaches for large-scale modeling of groundwater flow in frac-tured rocks and aquifers have been developed.Numerical procedures for optimization design of seepage control systems in various settings have also been proposed.Mechanisms of coupled hydro-mechanical processes and control of flow-induced deformation have been discussed.Finally,three case studies are presented to illustrate the applications of the improved theoretical understanding,characterization methods,modeling approaches,and seepage and deformation control strategies to geotechnical engi-neering projects.

High-resolution assessment of retrogressive thaw slump susceptibility in the Qinghai-Tibet Engineering Corridor

Under the rapidly warming climate in the Arctic and high mountain areas,permafrost is thawing,leading to various hazards at a global scale.One common permafrost hazard termed retrogressive thaw slump(RTS)occurs extensively in ice-rich permafrost areas.Understanding the spatial and temporal distributive features of RTSs in a changing climate is crucial to assessing the damage to infrastructure and decision-making.To this end,we used a machine learning-based model to investigate the environmental factors that could lead to RTS occurrence and create a susceptibility map for RTS along the Qinghai-Tibet Engineering Corridor(QTEC)at a local scale.The results indicate that extreme summer climate events(e.g.,maximum air temperature and rainfall)contributes the most to the RTS occurrence over the flat areas with fine-grained soils.The model predicts that 13%(ca.22,948 km^(2))of the QTEC falls into high to very high susceptibility categories under the current climate over the permafrost areas with mean annual ground temperature at 10 m depth ranging from-3 to-1℃.This study provides insights into the impacts of permafrost thaw on the stability of landscape,carbon stock,and infrastructure,and the results are of value for engineering planning and maintenance.

THREE KINDS OF DENTABILITIES IN BANACH SPACES AND THEIR APPLICATIONS

In this paper,we study some dentabilities in Banach spaces which are closely related to the famous Radon-Nikodym property.We introduce the concepts of the weak^(*)-weak denting point and the weak^(*)-weak^(*)denting point of a set.These are the generalizations of the weak^(*)denting point of a set in a dual Banach space.By use of the weak^(*)-weak denting point,we characterize the very smooth space,the point of weak^(*)-weak continuity,and the extreme point of a unit ball in a dual Banach space.Meanwhile,we also characterize an approximatively weak compact Chebyshev set in dual Banach spaces.Moreover,we define the nearly weak dentability in Banach spaces,which is a generalization of near dentability.We prove the necessary and sufficient conditions of the reflexivity by nearly weak dentability.We also obtain that nearly weak dentability is equivalent to both the approximatively weak compactness of Banach spaces and the w-strong proximinality of every closed convex subset of Banach spaces.

A vertical track nonlinear energy sink

Eliminating the effects of gravity and designing nonlinear energy sinks(NESs)that suppress vibration in the vertical direction is a challenging task with numerous damping requirements.In this paper,the dynamic design of a vertical track nonlinear energy sink(VTNES)with zero linear stiffness in the vertical direction is proposed and realized for the first time.The motion differential equations of the VTNES coupled with a linear oscillator(LO)are established.With the strong nonlinearity considered of the VTNES,the steady-state response of the system is analyzed with the harmonic balance method(HBM),and the accuracy of the HBM is verified numerically.On this basis,the VTNES prototype is manufactured,and its nonlinear stiffness is identified.The damping effect and dynamic characteristics of the VTNES are studied theoretically and experimentally.The results show that the VTNES has better damping effects when strong modulation responses(SMRs)occur.Moreover,even for small-amplitude vibration,the VTNES also has a good vibration suppression effect.To sum up,in order to suppress the vertical vibration,an NES is designed and developed,which can suppress the vertical vibration within certain ranges of the resonance frequency and the vibration intensity.

Enhancing Relational Triple Extraction in Specific Domains:Semantic Enhancement and Synergy of Large Language Models and Small Pre-Trained Language Models

In the process of constructing domain-specific knowledge graphs,the task of relational triple extraction plays a critical role in transforming unstructured text into structured information.Existing relational triple extraction models facemultiple challenges when processing domain-specific data,including insufficient utilization of semantic interaction information between entities and relations,difficulties in handling challenging samples,and the scarcity of domain-specific datasets.To address these issues,our study introduces three innovative components:Relation semantic enhancement,data augmentation,and a voting strategy,all designed to significantly improve the model’s performance in tackling domain-specific relational triple extraction tasks.We first propose an innovative attention interaction module.This method significantly enhances the semantic interaction capabilities between entities and relations by integrating semantic information fromrelation labels.Second,we propose a voting strategy that effectively combines the strengths of large languagemodels(LLMs)and fine-tuned small pre-trained language models(SLMs)to reevaluate challenging samples,thereby improving the model’s adaptability in specific domains.Additionally,we explore the use of LLMs for data augmentation,aiming to generate domain-specific datasets to alleviate the scarcity of domain data.Experiments conducted on three domain-specific datasets demonstrate that our model outperforms existing comparative models in several aspects,with F1 scores exceeding the State of the Art models by 2%,1.6%,and 0.6%,respectively,validating the effectiveness and generalizability of our approach.

A Railway Fastener Inspection Method Based on Abnormal Sample Generation

Regular fastener detection is necessary to ensure the safety of railways.However,the number of abnormal fasteners is significantly lower than the number of normal fasteners in real railways.Existing supervised inspectionmethods have insufficient detection ability in cases of imbalanced samples.To solve this problem,we propose an approach based on deep convolutional neural networks(DCNNs),which consists of three stages:fastener localization,abnormal fastener sample generation based on saliency detection,and fastener state inspection.First,a lightweight YOLOv5s is designed to achieve fast and precise localization of fastener regions.Then,the foreground clip region of a fastener image is extracted by the designed fastener saliency detection network(F-SDNet),combined with data augmentation to generate a large number of abnormal fastener samples and balance the number of abnormal and normal samples.Finally,a fastener inspection model called Fastener ResNet-8 is constructed by being trained with the augmented fastener dataset.Results show the effectiveness of our proposed method in solving the problem of sample imbalance in fastener detection.Qualitative and quantitative comparisons show that the proposed F-SDNet outperforms other state-of-the-art methods in clip region extraction,reaching MAE and max F-measure of 0.0215 and 0.9635,respectively.In addition,the FPS of the fastener state inspection model reached 86.2,and the average accuracy reached 98.7%on 614 augmented fastener test sets and 99.9%on 7505 real fastener datasets.

Protected simultaneous quantum remote state preparation scheme by weak and reversal measurements in noisy environments

We discuss a quantum remote state preparation protocol by which two parties, Alice and Candy, prepare a single-qubit and a two-qubit state, respectively, at the site of the receiver Bob. The single-qubit state is known to Alice while the two-qubit state which is a non-maximally entangled Bell state is known to Candy. The three parties are connected through a single entangled state which acts as a quantum channel. We first describe the protocol in the ideal case when the entangled channel under use is in a pure state. After that, we consider the effect of amplitude damping(AD) noise on the quantum channel and describe the protocol executed through the noisy channel. The decrement of the fidelity is shown to occur with the increment in the noise parameter. This is shown by numerical computation in specific examples of the states to be created. Finally, we show that it is possible to maintain the label of fidelity to some extent and hence to decrease the effect of noise by the application of weak and reversal measurements. We also present a scheme for the generation of the five-qubit entangled resource which we require as a quantum channel. The generation scheme is run on the IBMQ platform.

Effects of cross-Kerr coupling on transmission spectrum of double-cavity optomechanical system

We theoretically study the transmission spectrum of the cavity field in a double-cavity optomechanical system with cross-Kerr(CK) effect. The system consists of two tunneling coupling optomechanical cavities with a mechanical resonator as a coupling interface. By doping CK medium into the mechanical resonator, CK couplings between the cavity fields and the mechanical resonator are introduced. We investigate the effects of CK coupling strength on the transmission spectrum of the cavity field, including the transmission rate, nonreciprocity and four-wave mixing(FWM). We find that the transmission spectrum of the probe field can show two obvious transparent windows, which can be widened by increasing the CK coupling strength. For the transmission between the two cavity fields, the perfect nonreciprocity and reciprocity are present and modulated by CK coupling and phase difference between two effective optomechanical couplings. In addition, the effects of the optomechanical and CK couplings on FWM show that the single peak of FWM is split into three symmetrical peaks due to the introduction of the CK effect.

Epitaxial growth of ultrathin gallium films on Cd(0001)

Growth and electronic properties of ultrathin Ga films on Cd(0001) are investigated by low-temperature scanning tunneling microscopy(STM) and density functional theory(DFT) calculations. It is found that Ga films exhibit the epitaxial growth with the pseudomorphic 1×1 lattice. The Ga islands deposited at 100 K show a ramified shape due to the suppressed edge diffusion and corner crossing. Furthermore, the majority of Ga islands reveal flat tops and a preferred height of three atomic layers, indicating the electronic growth at low temperature. Annealing to room temperature leads to not only the growth mode transition from electronic growth to conventional Stranski–Krastanov growth, but also the shape transition from ramified islands to smooth compact islands. Scanning tunneling spectroscopy(STS) measurements reveal that the Ga monolayer exhibits metallic behavior. DFT calculations indicate that all the interfacial Ga atoms occupy the energetically favorable hcp-hollow sites of the substrate. The charge density difference analysis demonstrates that the charge transfer from the Cd substrate to the Ga atoms is negligible, and there is weak interaction between Ga atoms and the Cd substrate. These results shall shed important light on fabrication of ultrathin Ga films on metal substrates with novel physical properties.

Impact of different interaction behavior on epidemic spreading in time-dependent social networks

We investigate the impact of pairwise and group interactions on the spread of epidemics through an activity-driven model based on time-dependent networks.The effects of pairwise/group interaction proportion and pairwise/group interaction intensity are explored by extensive simulation and theoretical analysis.It is demonstrated that altering the group interaction proportion can either hinder or enhance the spread of epidemics,depending on the relative social intensity of group and pairwise interactions.As the group interaction proportion decreases,the impact of reducing group social intensity diminishes.The ratio of group and pairwise social intensity can affect the effect of group interaction proportion on the scale of infection.A weak heterogeneous activity distribution can raise the epidemic threshold,and reduce the scale of infection.These results benefit the design of epidemic control strategy.

Workout Action Recognition in Video Streams Using an Attention Driven Residual DC-GRU Network

Regular exercise is a crucial aspect of daily life, as it enables individuals to stay physically active, lowers thelikelihood of developing illnesses, and enhances life expectancy. The recognition of workout actions in videostreams holds significant importance in computer vision research, as it aims to enhance exercise adherence, enableinstant recognition, advance fitness tracking technologies, and optimize fitness routines. However, existing actiondatasets often lack diversity and specificity for workout actions, hindering the development of accurate recognitionmodels. To address this gap, the Workout Action Video dataset (WAVd) has been introduced as a significantcontribution. WAVd comprises a diverse collection of labeled workout action videos, meticulously curated toencompass various exercises performed by numerous individuals in different settings. This research proposes aninnovative framework based on the Attention driven Residual Deep Convolutional-Gated Recurrent Unit (ResDCGRU)network for workout action recognition in video streams. Unlike image-based action recognition, videoscontain spatio-temporal information, making the task more complex and challenging. While substantial progresshas been made in this area, challenges persist in detecting subtle and complex actions, handling occlusions,and managing the computational demands of deep learning approaches. The proposed ResDC-GRU Attentionmodel demonstrated exceptional classification performance with 95.81% accuracy in classifying workout actionvideos and also outperformed various state-of-the-art models. The method also yielded 81.6%, 97.2%, 95.6%, and93.2% accuracy on established benchmark datasets, namely HMDB51, Youtube Actions, UCF50, and UCF101,respectively, showcasing its superiority and robustness in action recognition. The findings suggest practicalimplications in real-world scenarios where precise video action recognition is paramount, addressing the persistingchallenges in the field. TheWAVd dataset serves as a catalyst for the development ofmore robust and effective fitnesstracking systems and ultimately promotes healthier lifestyles through improved exercise monitoring and analysis.

Novel self-embedding holographic watermarking image encryption protection scheme

For digital image transmission security and information copyright,a new holographic image self-embedding watermarking encryption scheme is proposed.Firstly,the plaintext is converted to the RGB three-color channel,the corresponding phase hologram is obtained by holographic technology and the watermark is self-embedded in the frequency domain.Secondly,by applying the Hilbert transform principle and genetic center law,a complete set of image encryption algorithms is constructed to realize the encryption of image information.Finally,simulation results and security analysis indicate that the scheme can effectively encrypt and decrypt image information and realize the copyright protection of information.The introduced scheme can provide some support for relevant theoretical research,and has practical significance.

Coupling of quasi-localized and phonon modes in glasses at low frequency

Boson peak of glasses,a THz vibrational excess compared to Debye squared-frequency law,remains mysterious in condensed-matter physics and material science.It appears in many different kinds of glassy matters and is also argued to exist in damped crystals.A consensus is that boson peak originates from the coupling of the(quasi)-localized non-phonon modes and the plane-wave-like phonon modes,but the coupling behavior is still not fully understood.In this paper,by modulating the content of localized modes and the frequencies of phonon modes,the coupling is clearly reflected in the localization and anharmonicity of low-frequency vibrational modes.The coupling enhances with increasing cooling rate and sample size.For finite sample size,phonon modes do not fully intrude into the low frequency to form a dense spectrum and they are not sufficiently coupled to the localized modes,thus there is no Debye level and boson peak is ill-defined.This suggestion remains valid in the presence of thermal motions induced by temperature,even though the anharmonicity comes into play.Our results point to the coupling of quasi-localized and phonon modes and its relation to the boson peak.

Analysis of thermal management and anti-mechanical abuse of multi-functional battery modules based on magneto-sensitive shear thickening fluid

Electric vehicles(EVs)have garnered significant attention as a vital driver of economic growth and environmental sustainability.Nevertheless,ensuring the safety of high-energy batteries is now a top priority that cannot be overlooked during large-scale applications.This paper proposes an innovative active protection and cooling integrated battery module using smart materials,magneto-sensitive shear thickening fluid(MSTF),which is specifically designed to address safety threats posed by lithium-ion batteries(LIBs)exposed to harsh mechanical and environmental conditions.The theoretical framework introduces a novel approach for harnessing the smoothed-particle hydrodynamics(SPH)methodology that incorporates the intricate interplay of non-Newtonian fluid behavior,capturing the fluid-structure coupling inherent to the MSTF.This approach is further advanced by adopting an enhanced Herschel-Bulkley(H-B)model to encapsulate the intricate rheology of the MSTF under the influence of the magnetorheological effect(MRE)and shear thickening(ST)behavior.Numerical simulation results show that in the case of cooling,the MSTF is an effective cooling medium for rapidly reducing the temperature.In terms of mechanical abuse,the MSTF solidifies through actively applying the magnetic field during mechanical compression and impact within the battery module,resulting in 66%and 61.7%reductions in the maximum stress within the battery jellyroll,and 31.1%and 23%reductions in the reaction force,respectively.This mechanism effectively lowers the risk of short-circuit failure.The groundbreaking concepts unveiled in this paper for active protection battery modules are anticipated to be a valuable technological breakthrough in the areas of EV safety and lightweight/integrated design.

Anisotropic shearing mechanism of Kangding slate:Experimental investigation and numerical analysis

The shear mechanical behavior is regarded as an essential factor affecting the stability of the surrounding rocks in underground engineering.The shear strength and failure mechanisms of layered rock are significantly affected by the foliation angles.Direct shear tests were conducted on cubic slate samples with foliation angles of 0°,30°,45°,60°,and 90°.The effect of foliation angles on failure patterns,acoustic emission(AE)characteristics,and shear strength parameters was analyzed.Based on AE characteristics,the slate failure process could be divided into four stages:quiet period,step-like increasing period,dramatic increasing period,and remission period.A new empirical expression of cohesion for layered rock was proposed,which was compared with linear and sinusoidal cohesion expressions based on the results made by this paper and previous experiments.The comparative analysis demonstrated that the new expression has better prediction ability than other expressions.The proposed empirical equation was used for direct shear simulations with the combined finite-discrete element method(FDEM),and it was found to align well with the experimental results.Considering both computational efficiency and accuracy,it was recommended to use a shear rate of 0.01 m/s for FDEM to carry out direct shear simulations.To balance the relationship between the number of elements and the simulation results in the direct shear simulations,the recommended element size is 1 mm.

Experimental study on the influences of cutter geometry and material on scraper wear during shield TBM tunnelling in abrasive sandy ground

When shield TBM tunnelling in abrasive sandy ground,the rational design of cutter parameters is critical to reduce tool wear and improve tunnelling efficiency.However,the influence mechanism of cutter parameters on scraper wear remains unclear due to the lack of a reliable test method.Geometry and material optimisation are often based on subjective experience,which is unfavourable for improving scraper geological adaptability.In the present study,the newly developed WHU-SAT soil abrasion test was used to evaluate the variation in scraper wear with cutter geometry,material and hardness.The influence mechanism of cutter parameters on scraper wear has been revealed according to the scratch characteristics of the scraper surface.Cutter geometry and material parameters have been optimised to reduce scraper wear.The results indicate that the variation in scraper wear with cutter geometry is related to the cutting resistance,frictional resistance and stress distribution.An appropriate increase in the front angle(or back angle)reduces the cutting resistance(or frictional resistance),while an excessive increase in the front angle(or back angle)reduces the edge angle and causes stress concentration.The optimal front angle,back angle and edge angle for quartz sand samples areα=25°,β=10°andγ=55°,respectively.The wear resistance of the modelled scrapers made of different metal materials is related to the chemical elements and microstructure.The wear resistances of the modelled scrapers made of 45#,06Cr19Ni10,42CrMo4 and 40CrNiMoA are 0.569,0.661,0.691 and 0.728 times those made of WC-Co,respectively.When the alloy hardness is less than 47 HRC(or greater than 58 HRC),scraper wear decreases slowly with increasing alloy hardness as the scratch depth of the particle asperity on the metal surface stabilizes at a high(or low)level.However,when the alloy hardness is between 47 HRC and 58 HRC,scraper wear decreases rapidly with increasing alloy hardness as the scratch depth transitions from high to low levels.The sensitive hardness interval and recommended hardness interval for quartz sand are[47,58]and[58,62],respectively.The present study provides a reference for optimising scraper parameters and improving cutterhead adaptability in abrasive sandy ground tunnelling.

A Study of the Effect of the Miller Cycle on the Combustion of a Supercharged Marine Diesel Engine

The Miller cycle is a program that effectively reduces NOx emissions from marine diesel engines by lowering the maximum combustion temperature in the cylinder,thereby reducing NOx emissions.To effectively investigate the impact of Miller cycle optimum combustion performance and emission capability under high load conditions,this study will perform a one-dimensional simulation of the performance of a marine diesel engine,as well as a threedimensional simulation of the combustion in the cylinder.A 6-cylinder four-stroke single-stage supercharged diesel engine is taken as the research object.The chassis dynamometer and other related equipment are used to build the test system,carry out the diesel engine bench test,and collect experimental data.The simulation results are compared with the test results,and the error is less than 5%.In this study,the authors will use simulation software to simulate several Miller cycle scenarios designed for early inlet valve closure and analyze the impact of the Miller cycle on combustion and emissions at 100%load conditions.By comparing the flow field distribution of the engine at 1500 r/min condition,it was found that proper EIVC can prolong the ignition latency period and homogeneous fuel-air mixture combustion acceleration,but it can reduce pressure and temperature within the piston chamber and NOx emission.However,the Miller cycle reduces end-of-compression temperatures,which increases combustion duration and exhaust temperatures,making it difficult to improve fuel economy at the optimum fuel consumption point,and closing the intake valves prematurely leads to excessive fuel expenditure.Furthermore,temperature and heat release rate within the piston chamber,NOx,and SOOT generation were significantly enhanced.

The study of intelligent algorithm in particle identification of heavy-ion collisions at low and intermediate energies

Traditional particle identification methods face timeconsuming,experience-dependent,and poor repeatability challenges in heavy-ion collisions at low and intermediate energies.Researchers urgently need solutions to the dilemma of traditional particle identification methods.This study explores the possibility of applying intelligent learning algorithms to the particle identification of heavy-ion collisions at low and intermediate energies.Multiple intelligent algorithms,including XgBoost and TabNet,were selected to test datasets from the neutron ion multi-detector for reaction-oriented dynamics(NIMROD-ISiS)and Geant4 simulation.Tree-based machine learning algorithms and deep learning algorithms e.g.TabNet show excellent performance and generalization ability.Adding additional data features besides energy deposition can improve the algorithm’s performance when the data distribution is nonuniform.Intelligent learning algorithms can be applied to solve the particle identification problem in heavy-ion collisions at low and intermediate energies.