Coupled bio-chemo-hydro-mechanical modeling of microbially induced calcite precipitation process considering biomass encapsulation using a micro-scale relationship

Geomaterials with inferior hydraulic and strength characteristics often need improvement to enhance their engineering behaviors.Traditional ground improvement techniques require enormous mechanical effort or synthetic chemicals.Sustainable stabilization technique such as microbially induced calcite precipitation(MICP)utilizes bacterial metabolic processes to precipitate cementitious calcium carbonate.The reactive transport of biochemical species in the soil mass initiates the precipitation of biocement during the MICP process.The precipitated biocement alters the hydro-mechanical performance of the soil mass.Usually,the flow,deformation,and transport phenomena regulate the biocementation technique via coupled bio-chemo-hydro-mechanical(BCHM)processes.Among all,one crucial phenomenon controlling the precipitation mechanism is the encapsulation of biomass by calcium carbonate.Biomass encapsulation can potentially reduce the biochemical reaction rate and decelerate biocementation.Laboratory examination of the encapsulation process demands a thorough analysis of associated coupled effects.Despite this,a numerical model can assist in capturing the coupled processes influencing encapsulation during the MICP treatment.However,most numerical models did not consider biochemical reaction rate kinetics accounting for the influence of bacterial encapsulation.Given this,the current study developed a coupled BCHM model to evaluate the effect of encapsulation on the precipitated calcite content using a micro-scale semiempirical relationship.Firstly,the developed BCHM model was verified and validated using numerical and experimental observations of soil column tests.Later,the encapsulation phenomenon was investigated in the soil columns of variable maximum calcite crystal sizes.The results depict altered reaction rates due to the encapsulation phenomenon and an observable change in the precipitated calcite content for each maximum crystal size.Furthermore,the permeability and deformation of the soil mass were affected by the simultaneous precipitation of calcium carbonate.Overall,the present study comprehended the influence of the encapsulation of bacteria on cement morphology-induced permeability,biocement-induced stresses and displacements.

Transforming Education with Photogrammetry:Creating Realistic 3D Objects for Augmented Reality Applications

Augmented reality(AR)is an emerging dynamic technology that effectively supports education across different levels.The increased use of mobile devices has an even greater impact.As the demand for AR applications in education continues to increase,educators actively seek innovative and immersive methods to engage students in learning.However,exploring these possibilities also entails identifying and overcoming existing barriers to optimal educational integration.Concurrently,this surge in demand has prompted the identification of specific barriers,one of which is three-dimensional(3D)modeling.Creating 3D objects for augmented reality education applications can be challenging and time-consuming for the educators.To address this,we have developed a pipeline that creates realistic 3D objects from the two-dimensional(2D)photograph.Applications for augmented and virtual reality can then utilize these created 3D objects.We evaluated the proposed pipeline based on the usability of the 3D object and performance metrics.Quantitatively,with 117 respondents,the co-creation team was surveyed with openended questions to evaluate the precision of the 3D object created by the proposed photogrammetry pipeline.We analyzed the survey data using descriptive-analytical methods and found that the proposed pipeline produces 3D models that are positively accurate when compared to real-world objects,with an average mean score above 8.This study adds new knowledge in creating 3D objects for augmented reality applications by using the photogrammetry technique;finally,it discusses potential problems and future research directions for 3D objects in the education sector.

Numerical Analysis of a Spiral-groove Dry-gas Seal Considering Micro-scale Effects

A dry-gas seal system is a non-contact seal technology that is widely used in different industrial applications.Spiral-groove dry-gas seal utilizes fluid dynamic pressure effects to realize the seal and lubrication processes,while forming a high pressure gas film between two sealing faces due to the deceleration of the gas pumped in or out.There is little research into the effects and the influence on seal performance,if the grooves and the gas film are at the micro-scale.This paper investigates the micro-scale effects on spiral-groove dry-gas seal performance in a numerical solution of a corrected Reynolds equation.The Reynolds equation is discretized by means of the finite difference method with the second order scheme and solved by the successive-over-relaxation（SOR） iterative method.The Knudsen number of the flow in the sealing gas film is changed from 0.005 to 0.120 with a variation of film depth and sealing pressure.The numerical results show that the average pressure in the gas film and the sealed gas leakage increase due to micro-scale effects.The open force is enlarged,while the gas film stiffness is significantly decreased due to micro-scale effects.The friction torque and power consumption remain constant,even in low sealing pressure and spin speed conditions.In this paper,the seal performance at different rotor face spin speeds is also described.The proposed research clarifies the micro-scale effects in a spiral-groove dry-gas seal and their influence on seal performance,which is expected to be useful for the improvement of the design of dry-gas seal systems operating in the slip flow regime.

Discharge characteristics of a needle-to-plate electrode at a micro-scale gap

To understand the discharge characteristics under a gap of micrometers,the breakdown voltage and current–voltage curve are measured experimentally in a needle-to-plate electrode at a microscale gap of 3–50 μm in air.The effect of the needle radius and the gas pressure on the discharge characteristics are tested.The results show that when the gap is larger than 10 μm,the relation between the breakdown voltage and the gap looks like the Paschen curve;while below 10 μm,the breakdown voltage is nearly constant in the range of the tested gap.However,at the same gap distance,the breakdown voltage is still affected by the pressure and shows a trend similar to Paschen＇s law.The current–voltage characteristic in all the gaps is similar and follows the trend of a typical Townsend-to-glow discharge.A simple model is used to explain the non-normality of breakdown in the micro-gaps.The Townsend mechanism is suggested to control the breakdown process in this configuration before the gap reduces much smaller in air.

Recent Advances in Computational Simulation of Macro-,Meso-,and Micro-Scale Biomimetics Related Fluid Flow Problems

Over the last decade, computational methods have been intensively applied to a variety of scientific researches and engineering designs. Although the computational fluid dynamics （CFD） method has played a dominant role in studying and simulating transport phenomena involving fluid flow and heat and mass transfers, in recent years, other numerical methods for the simulations at meso- and micro-scales have also been actively applied to solve the physics of complex flow and fluid-interface interactions. This paper presents a review of recent advances in multi-scale computational simulation of biomimetics related fluid flow problems. The state-of-the-art numerical techniques, such as lattice Boltzmann method （LBM）, molecular dynamics （MD）, and conventional CFD, applied to different problems such as fish flow, electro-osmosis effect of earthworm motion, and self-cleaning hydrophobic surface, and the numerical approaches are introduced. The new challenging of modelling biomimetics problems in developing the physical conditions of self-clean hydrophobic surfaces is discussed.

Dechlorination of Chlorinated Aliphatic Compounds by Micro-scale Al-Zn-Mg/Fe Powders as Advanced Zero-valent Iron

Micro-scale Al-Zn-Mg/Fe composite powders (MAF) with high reactivity and good storage properties were prepared by reducing iron onto the surface of Al-Zn-Mg alloy powders. Experimental results show that MAF as advanced zero-valent iron are highly effective for degradation of chlorinated organic compounds. The efficiency of degradation for carbon tetrachloride and perchloroethylene is higher than 99% within a period of 2 h. The efficiency of degradation for trichloroethylene by MAF after storing for one month is equivalent to that by freshly prepared nano-size zero-valent iron particles.

Thermoelastic vibration analysis of micro-scale functionally graded material fluid-conveying pipes in elastic medium

Micro-scale functionally graded material(FGM)pipes conveying fluid have many significant applications in engineering fields.In this work,the thermoelastic vibration of FGM fluid-conveying tubes in elastic medium is studied.Based on modified couple stress theory and Hamilton’s principle,the governing equation and boundary conditions are obtained.The differential quadrature method(DQM)is applied to investigating the thermoelastic vibration of the FGM pipes.The effect of temperature variation,scale effect of the microtubule,micro-fluid effect,material properties,elastic coefficient of elastic medium and outer radius on thermoelastic vibration of the FGM pipes conveying fluid are studied.The results show that in the condition of considering the scale effect and micro-fluid of the microtubule,the critical dimensionless velocity of the system is higher than that of the system which calculated using classical macroscopic model.The results also show that the variations of temperature,material properties,elastic coefficient and outer radius have significant influences on the first-order dimensionless natural frequency.

Fabrication of micro-scale gratings for moiré method with a femtosecond laser

Fabrication of micro gratings using a femtosecond laser exposure system is experimentally investigated for the electron moire method. Micro holes and lines are firstly etched for parameter study. Grating profile is theoretically optimized to form high quality moire patterns. For a demonstration, a parallel grating is fabricated on a specimen of quartz glass. The minimum line width and the distance between two adjacent lines are both set to be 1 μm, and the frequency of grating is 500 lines/ram. The experimental results indicate that the quality of gratings is good and the relative error of the gratings pitch is about 1.5%. Based on molte method, scanning electron microscope （SEM） moire patterns are observed clearly, which manifests that gratings fabricated with the femtosecond laser exposure is suitable for micro scale deformation measurement.

Micro-scale Dispersion of Air Pollutants over an Urban Setup in a Coastal Region

The dispersion is mainly governed by wind field and depends on the planetary boundary layer (PBL) dynamics. Accurate representation of the meteorological weather fields would improve the dispersion assessments. In urban areas representation of wind around the obstacles is not possible for the pollution dispersion studies using Gaussian based modeling studies. It is widely accepted that computational fluid dynamics (CFD) tools would provide reasonably good solution to produce the wind fields around the complex structures and other land scale elements. By keeping in view of the requirement for the micro-scale dispersion, a commercial CFD model PANACHE with PANEPR developed by Fluidyn is implemented to study the micro-scale dispersion of air pollution over an urban setup at Indira Gandhi Centre for Atomic Research (IGCAR), Kalpakkam a coastal station in the east coast of India under stable atmospheric conditions. Meso-scale module of the PANACHE model is integrated with the data generated at the site by IGCAR under RRE (Round Robin Exercise) program to develop the flow fields. Using this flow fields, CFD model is integrated to study the micro-scale dispersion. Various pollution dispersion scenarios are developed using hypothetical emission inventory during stably stratified conditions to understand the micro-scale dispersion over different locations of coastal urban set up in the IGCAR region of Kalpakkam.

Micro-scale FEM Calculation of Concrete Temperature during Production and Casting

A micro-scale finite element method(FEM) was proposed to precisely calculate the heat conduction between mortar and aggregate, and thus to accurately predict the non-uniformity of concrete pouring temperature. The concrete temperature field during vibration was also precisely calculated by accurate description of heat absorption characteristics of different parts of concrete when vibration. Based on the above method, the prediction model was used to predict the pouring temperature of a practical engineering. The comparison between actual results and simulated values shows that this method can be adopted to accurately predict the non-uniformity of concrete pouring temperature and the influence of mechanized vibration on concrete pouring temperature, and thus accurately predict pouring temperature. The control of casting temperature is crucial for preventing concrete fracture. The study provides a new method for predicting the pouring temperature of concrete structures, which has great practical value in engineering application.

Micro-Scale Motion Precision Simulation Method for a New-Type 6-DOF Micro-Manipulation Robot

A new 6-DOF micro-manipulation robot based on 3-PPTTRS parallel mechanisms in combination with flexure hinges is proposed. The design principle of the mechanism is introduced, and the kinematics analysis method based on differentiation is used to get the (inverse) kinematics equations. Then a micro-scale motion precision simulation method is proposed according to finite element analysis (FEA), and the prediction of robot’s motion precision in design phase is realized. The simulation result indicates that the 6-DOF micro-manipulation robot can meet the design specification.

Response of revegetation to climate change with meso- and micro-scale remote sensing in an arid desert of China

The revegetation protection system(VPS)on the edge of the Tengger Desert can be referred to as a successful model of sand control technology in China and even the world,and there has been a substantial amount of research on revegetation stability.However,it is unclear how meso-and micro-scale revegetation activity has responded to climatic change over the past decades.To evaluate the relative influence of climatic variables on revegetation activities in a restored desert ecosystem,we analysed the trend of revegetation change from 2002 to 2015 using a satellite-derived normalized difference vegetation index(NDVI)dataset.The time series of the NDVI data were decomposed into trend,seasonal,and random components using a segmented regression method.The results of the segmented regression model indicate a changing trend in the NDVI in the VPS,changing from a decrease(−7×10−3/month)before 2005 to an increase(0.3×10−3/month)after 2005.We found that precipitation was the most important climatic factor influencing the growing season NDVI(P<0.05),while vegetation growth sensitivity to water and heat varied significantly in different seasons.In the case of precipitation reduction and warming in the study area,the NDVI of the VPS could still maintain an overall slow upward trend(0.04×10−3/month),indicating that the ecosystem is sustainable.Our findings suggest that the VPS has been successful in maintaining stability and sustainability under current climate change conditions and that it is possible to introduce the VPS in similar areas as a template for resistance to sand and drought hazards.

A novel strategy of smart manipulation by micro-scale oscillatory networks of the reactionary zones for enhanced extreme thrust control of the next-generation solid propulsion systems

The main aim of this research is to get a better knowledge and understanding of the micro-scale oscillatory networks behavior in the solid propellants reactionary zones. Fundamental understanding of the micro-and nano-scale combustion mechanisms is essential to the development and further improvement of the next-generation technologies for extreme control of the solid propellant thrust. Both experiments and theory confirm that the micro-and nano-scale oscillatory networks excitation in the solid propellants reactionary zones is a rather universal phenomenon. In accordance with our concept,the micro-and nano-scale structures form both the fractal and self-organized wave patterns in the solid propellants reactionary zones. Control by the shape, the sizes and spacial orientation of the wave patterns allows manipulate by the energy exchange and release in the reactionary zones. A novel strategy for enhanced extreme thrust control in solid propulsion systems are based on manipulation by selforganization of the micro-and nano-scale oscillatory networks and self-organized patterns formation in the reactionary zones with use of the system of acoustic waves and electro-magnetic fields, generated by special kind of ring-shaped electric discharges along with resonance laser radiation. Application of special kind of the ring-shaped electric discharges demands the minimum expenses of energy and opens prospects for almost inertia-free control by combustion processes. Nano-sized additives will enhance self-organizing and self-synchronization of the micro-and nano-scale oscillatory networks on the nanometer scale. Suggested novel strategy opens the door for completely new ways for enhanced extreme thrust control of the solid propulsion systems.

Enhancing Dense Small Object Detection in UAV Images Based on Hybrid Transformer

Transformer-based models have facilitated significant advances in object detection.However,their extensive computational consumption and suboptimal detection of dense small objects curtail their applicability in unmanned aerial vehicle(UAV)imagery.Addressing these limitations,we propose a hybrid transformer-based detector,H-DETR,and enhance it for dense small objects,leading to an accurate and efficient model.Firstly,we introduce a hybrid transformer encoder,which integrates a convolutional neural network-based cross-scale fusion module with the original encoder to handle multi-scale feature sequences more efficiently.Furthermore,we propose two novel strategies to enhance detection performance without incurring additional inference computation.Query filter is designed to cope with the dense clustering inherent in drone-captured images by counteracting similar queries with a training-aware non-maximum suppression.Adversarial denoising learning is a novel enhancement method inspired by adversarial learning,which improves the detection of numerous small targets by counteracting the effects of artificial spatial and semantic noise.Extensive experiments on the VisDrone and UAVDT datasets substantiate the effectiveness of our approach,achieving a significant improvement in accuracy with a reduction in computational complexity.Our method achieves 31.9%and 21.1%AP on the VisDrone and UAVDT datasets,respectively,and has a faster inference speed,making it a competitive model in UAV image object detection.

Masked Autoencoders as Single Object Tracking Learners

Significant advancements have beenwitnessed in visual tracking applications leveragingViT in recent years,mainly due to the formidablemodeling capabilities of Vision Transformer(ViT).However,the strong performance of such trackers heavily relies on ViT models pretrained for long periods,limitingmore flexible model designs for tracking tasks.To address this issue,we propose an efficient unsupervised ViT pretraining method for the tracking task based on masked autoencoders,called TrackMAE.During pretraining,we employ two shared-parameter ViTs,serving as the appearance encoder and motion encoder,respectively.The appearance encoder encodes randomly masked image data,while the motion encoder encodes randomly masked pairs of video frames.Subsequently,an appearance decoder and a motion decoder separately reconstruct the original image data and video frame data at the pixel level.In this way,ViT learns to understand both the appearance of images and the motion between video frames simultaneously.Experimental results demonstrate that ViT-Base and ViT-Large models,pretrained with TrackMAE and combined with a simple tracking head,achieve state-of-the-art(SOTA)performance without additional design.Moreover,compared to the currently popular MAE pretraining methods,TrackMAE consumes only 1/5 of the training time,which will facilitate the customization of diverse models for tracking.For instance,we additionally customize a lightweight ViT-XS,which achieves SOTA efficient tracking performance.

Enhanced Object Detection and Classification via Multi-Method Fusion

Advances in machine vision systems have revolutionized applications such as autonomous driving,robotic navigation,and augmented reality.Despite substantial progress,challenges persist,including dynamic backgrounds,occlusion,and limited labeled data.To address these challenges,we introduce a comprehensive methodology toenhance image classification and object detection accuracy.The proposed approach involves the integration ofmultiple methods in a complementary way.The process commences with the application of Gaussian filters tomitigate the impact of noise interference.These images are then processed for segmentation using Fuzzy C-Meanssegmentation in parallel with saliency mapping techniques to find the most prominent regions.The Binary RobustIndependent Elementary Features(BRIEF)characteristics are then extracted fromdata derived fromsaliency mapsand segmented images.For precise object separation,Oriented FAST and Rotated BRIEF(ORB)algorithms areemployed.Genetic Algorithms(GAs)are used to optimize Random Forest classifier parameters which lead toimproved performance.Our method stands out due to its comprehensive approach,adeptly addressing challengessuch as changing backdrops,occlusion,and limited labeled data concurrently.A significant enhancement hasbeen achieved by integrating Genetic Algorithms(GAs)to precisely optimize parameters.This minor adjustmentnot only boosts the uniqueness of our system but also amplifies its overall efficacy.The proposed methodologyhas demonstrated notable classification accuracies of 90.9%and 89.0%on the challenging Corel-1k and MSRCdatasets,respectively.Furthermore,detection accuracies of 87.2%and 86.6%have been attained.Although ourmethod performed well in both datasets it may face difficulties in real-world data especially where datasets havehighly complex backgrounds.Despite these limitations,GAintegration for parameter optimization shows a notablestrength in enhancing the overall adaptability and performance of our system.

Confusing Object Detection:A Survey

Confusing object detection(COD),such as glass,mirrors,and camouflaged objects,represents a burgeoning visual detection task centered on pinpointing and distinguishing concealed targets within intricate backgrounds,leveraging deep learning methodologies.Despite garnering increasing attention in computer vision,the focus of most existing works leans toward formulating task-specific solutions rather than delving into in-depth analyses of methodological structures.As of now,there is a notable absence of a comprehensive systematic review that focuses on recently proposed deep learning-based models for these specific tasks.To fill this gap,our study presents a pioneering review that covers both themodels and the publicly available benchmark datasets,while also identifying potential directions for future research in this field.The current dataset primarily focuses on single confusing object detection at the image level,with some studies extending to video-level data.We conduct an in-depth analysis of deep learning architectures,revealing that the current state-of-the-art(SOTA)COD methods demonstrate promising performance in single object detection.We also compile and provide detailed descriptions ofwidely used datasets relevant to these detection tasks.Our endeavor extends to discussing the limitations observed in current methodologies,alongside proposed solutions aimed at enhancing detection accuracy.Additionally,we deliberate on relevant applications and outline future research trajectories,aiming to catalyze advancements in the field of glass,mirror,and camouflaged object detection.

Floating Waste Discovery by Request via Object-Centric Learning

Discovering floating wastes,especially bottles on water,is a crucial research problem in environmental hygiene.Nevertheless,real-world applications often face challenges such as interference from irrelevant objects and the high cost associated with data collection.Consequently,devising algorithms capable of accurately localizing specific objects within a scene in scenarios where annotated data is limited remains a formidable challenge.To solve this problem,this paper proposes an object discovery by request problem setting and a corresponding algorithmic framework.The proposed problem setting aims to identify specified objects in scenes,and the associated algorithmic framework comprises pseudo data generation and object discovery by request network.Pseudo-data generation generates images resembling natural scenes through various data augmentation rules,using a small number of object samples and scene images.The network structure of object discovery by request utilizes the pre-trained Vision Transformer(ViT)model as the backbone,employs object-centric methods to learn the latent representations of foreground objects,and applies patch-level reconstruction constraints to the model.During the validation phase,we use the generated pseudo datasets as training sets and evaluate the performance of our model on the original test sets.Experiments have proved that our method achieves state-of-the-art performance on Unmanned Aerial Vehicles-Bottle Detection(UAV-BD)dataset and self-constructed dataset Bottle,especially in multi-object scenarios.

Two-Layer Attention Feature Pyramid Network for Small Object Detection

Effective small object detection is crucial in various applications including urban intelligent transportation and pedestrian detection.However,small objects are difficult to detect accurately because they contain less information.Many current methods,particularly those based on Feature Pyramid Network(FPN),address this challenge by leveraging multi-scale feature fusion.However,existing FPN-based methods often suffer from inadequate feature fusion due to varying resolutions across different layers,leading to suboptimal small object detection.To address this problem,we propose the Two-layerAttention Feature Pyramid Network(TA-FPN),featuring two key modules:the Two-layer Attention Module(TAM)and the Small Object Detail Enhancement Module(SODEM).TAM uses the attention module to make the network more focused on the semantic information of the object and fuse it to the lower layer,so that each layer contains similar semantic information,to alleviate the problem of small object information being submerged due to semantic gaps between different layers.At the same time,SODEM is introduced to strengthen the local features of the object,suppress background noise,enhance the information details of the small object,and fuse the enhanced features to other feature layers to ensure that each layer is rich in small object information,to improve small object detection accuracy.Our extensive experiments on challenging datasets such as Microsoft Common Objects inContext(MSCOCO)and Pattern Analysis Statistical Modelling and Computational Learning,Visual Object Classes(PASCAL VOC)demonstrate the validity of the proposedmethod.Experimental results show a significant improvement in small object detection accuracy compared to state-of-theart detectors.

Rail-Pillar Net:A 3D Detection Network for Railway Foreign Object Based on LiDAR

Aiming at the limitations of the existing railway foreign object detection methods based on two-dimensional(2D)images,such as short detection distance,strong influence of environment and lack of distance information,we propose Rail-PillarNet,a three-dimensional(3D)LIDAR(Light Detection and Ranging)railway foreign object detection method based on the improvement of PointPillars.Firstly,the parallel attention pillar encoder(PAPE)is designed to fully extract the features of the pillars and alleviate the problem of local fine-grained information loss in PointPillars pillars encoder.Secondly,a fine backbone network is designed to improve the feature extraction capability of the network by combining the coding characteristics of LIDAR point cloud feature and residual structure.Finally,the initial weight parameters of the model were optimised by the transfer learning training method to further improve accuracy.The experimental results on the OSDaR23 dataset show that the average accuracy of Rail-PillarNet reaches 58.51%,which is higher than most mainstream models,and the number of parameters is 5.49 M.Compared with PointPillars,the accuracy of each target is improved by 10.94%,3.53%,16.96%and 19.90%,respectively,and the number of parameters only increases by 0.64M,which achieves a balance between the number of parameters and accuracy.