Search Results (116)

Developing effective antibiotics against Gram-negative bacteria remains challenging due to their protective outer membrane. With this study, we investigated the relationship between antibiotic permeation through the OmpF porin of Escherichia coli and antimicrobial efficacy. We measured the relative permeability coefficients (RPCs) through the bacterial porin by liposome swelling assays, including non-antibacterial molecules, and the minimum inhibitory concentrations (MICs) against E. coli. We developed a machine learning (ML) approach by combining classification and regression models to correlate these data sets. Our strategy allowed us to quantify the negative correlation between RPC and MIC values, clearly indicating that increased permeability through OmpF generally leads to improved antimicrobial activity. Moreover, the correlation was remarkable only for compounds with significant permeability coefficients. Conversely, when permeation ability is low, other factors play the most significant role in antimicrobial potency. Importantly, the proposed ML-based approach was set by exploiting the available seminal information from previous investigations in order to keep the number of molecular descriptors to the minimum for greater interpretability. This provided valuable insights into the complex interplay between different molecular properties in defining the overall outer membrane permeation and, consequently, the antimicrobial efficacy. From a practical perspective, the presented approach does not aim at identifying the “golden rule” for boosting antibiotic potency. The automated protocol presented here could be used to inspect, in silico, many alternatives of a given molecular structure, with the output being the list of the best candidates to be then synthesized and tested. This could be a valuable in silico tool for researchers in both academia and industry to rapidly evaluate novel potential compounds and reduce costs and time during the early drug discovery stage. Full article

Prenatal exposure to metals can influence fetal programming via DNA methylation and has been linked to adverse birth outcomes and long-term consequences. Epigenetic clocks estimate the biological age of a given tissue based on DNA methylation and are potential health biomarkers. This study leveraged the Extremely Low Gestational Age Newborn (ELGAN) study (n = 265) to evaluate associations between umbilical cord tissue concentrations of 11 metals as single exposures as well as mixtures in relation to (1) placental epigenetic gestational age acceleration (eGAA) and the (2) methylation status of the Robust Placental Clock (RPC) CpGs. Linear mixed effect regression models were stratified by infant sex. Both copper (Cu) and manganese (Mn) were significantly associated with a decelerated placental eGA of −0.98 (95% confidence interval (CI): −1.89, −0.07) and −0.90 weeks (95% CI: −1.78, −0.01), respectively, in male infants. Cu and Mn levels were also associated with methylation at RPC CpGs within genes related to processes including energy homeostasis and inflammatory response in placenta. Overall, these findings suggest that prenatal exposures to Cu and Mn impact placental eGAA in a sex-dependent manner in ELGANs, and future work could examine eGAA as a potential mechanism mediating in utero metal exposures and later life consequences. Full article

The construction industry is a fundamental sector for the development of countries; however, it produces negative environmental impacts due to the demand for natural resources and the generation of construction and demolition waste (CDW). Therefore, the pursuit of solutions to recycle and reintegrate these wastes, which often accumulate in poorly regulated areas, becomes not only an environmental priority but also an opportunity to transform a problem into an advantage. Utilizing these residues contributes to reducing the pressure on natural resources, minimizes the environmental footprint of the construction sector, and promotes a more sustainable and responsible model that can serve as an example for future generations. The properties of recycled concrete aggregates (RCA) and recycled asphalt pavement (RAP) were determined in order to subsequently obtain the properties of different permeable recycled concrete (RPC) elaborated from a factorial design 2³ with these aggregates. The properties studied were workability, permeability, volumetric weight, compression uniaxial, and bending. Finally, they were studied and correlated with their matrix microstructure by means of TGA and SEM tests, which allowed determining the compounds contained in the various mixtures and their impact on physical–mechanical behavior. The results indicate that RCA and RAP are feasible alternatives for making porous pavements in pedestrian or light traffic areas when recycled aggregates of 3/4” size are included in their matrix, resulting in the optimum dosage of the M5 3/4” mix in this research, whose mechanical properties are: uniaxial compressive strength: 15.39 MPa; flexural strength: 3.12 MPa; permeability: 0.375 cm/s. Full article

Reactive powder concrete (RPC) is widely used in ultra-high-rise buildings, hydropower stations, bridges, and other important infrastructures. To study the dynamic response and damage characteristics of RPC columns and frames considering coupled fire and explosions, an analytical model of RPC columns and frames with coupled fire and explosions was established by using ABAQUS (2021) finite element software. The dynamic response and damage degree of RPC columns under coupled fire and explosions were investigated to reveal the influence laws of parameters such as cross-section size, axial compression ratio, reinforcement rate, and fire duration on the dynamic response of RPC columns at high temperatures. The dynamic response of the frame structure was analyzed when the explosion load was applied to the bottom corner columns, side columns, and top beams, respectively. The results show that the fire severely weakened the blast resistance of RPC columns; the maximum mid-span deformation and residual deformation of RPC columns decreased with the increase in cross-section size and longitudinal bar reinforcement ratio and increased with the increase in fire duration and axial compression ratio. When the explosion load was applied to the corner columns of the bottom floor of the frame, the bottom corner columns were almost completely destroyed, and there was a significant risk of the structure collapsing. Based on the results of the data analysis, a method to enhance the explosion resistance of RC frame structures using RPC materials at high temperatures is proposed. Full article

This study addresses the issue of atmospheric delay correction for the rational polynomial coefficient (RPC) model associated with spaceborne synthetic aperture radar (SAR) imagery under conditions lacking ephemeris data, proposing a novel approach to enhance the geometric positioning accuracy of RPC models. A satellite position inversion method based on the vector-autonomous intersection technique was developed, incorporating ionospheric delay and neutral atmospheric delay models to derive atmospheric delay errors. Additionally, an RPC model reconstruction approach, which integrates atmospheric correction, is proposed. Validation experiments using GF-3 satellite imagery demonstrated that the atmospheric delay values obtained by this method differed by only 0.0001 m from those derived using the traditional ephemeris-based approach, a negligible difference. The method also exhibited high robustness in long-strip imagery. The reconstructed RPC parameters improved image-space accuracy by 18–44% and object-space accuracy by 19–32%. The results indicate that this approach can fully replace traditional ephemeris-based methods for atmospheric delay extraction under ephemeris-free conditions, significantly enhancing the geometric positioning accuracy of SAR imagery RPC models, with substantial application value and development potential. Full article

China has an existing building area of 80 billion square meters, where reinforced concrete structures have a large quantity and a wide surface area. The risk of structures being subjected to blast loading is relatively high. Reactive powder concrete has the specialties of ultra-high toughness, super strength, and a high strength to ponderance ratio. Reinforced concrete (RC) structures strengthened by RPC are called RPC-RC structures, which can easily elevate the explosive load resistance of building structures while also strengthening the building. It is a significant method used in avoiding the collapse of structures under explosive loads. The dynamic reaction and damage evaluation approaches of RPC-RC columns under explosive load have not been deeply studied. For addressing this issue, numerical simulation of RPC strengthened RC columns under explosive load was carried out by LS-DYNA (R10), and the correctness of the numerical simulation was verified by comparing it with relevant experimental results. In this paper, a finite element model of an RPC-RC column was established, and the main factors affecting the anti-explosion performance of an RPC-RC column were studied. The influence of the RPC reinforcement layer parameters (RPC thickness, RPC strength, longitudinal reinforcement ratio, and stirrup ratio) on the dynamic reaction and damage degree of RPC-RC columns was examined. The consequences indicated that the failure mode of the columns after RPC reinforcement can alter from bending shear damage to bending damage. As the thickness and strength of the RPC increases, the longitudinal reinforcement ratio increases, the stirrup ratio increases, and the maximum horizontal deformation of the center point of the RPC reinforced RC columns decreases. For RPC-RC columns with a height of 3–4 m and a width of 300–400 mm under blast loading, columns with an axial compression ratio greater than 0.3 will collapse, while columns with an axial compression ratio less than 0.3 are less likely to collapse. In the light of the calculation outcomes, a formula for reckoning the damage index of RPC-RC columns was proposed, taking into account factors such as proportional distance, axial compression ratio, RPC thickness, longitudinal reinforcement ratio, and stirrup ratio. Full article

The presence of particles leads to varying degrees of mass loss on a metal sealing surface, which severely affects the seal’s lifespan. Understanding the complex wear mechanism and optimizing the surface roughness morphology are particularly important in engineering. By characterizing the surface of the metal (SS 304) with different roughness parameters Ra, Rp, Rpk, Rpc and Rku, the variation mode of mass loss under abrasive wear conditions was revealed. Unlike traditional two-body wear, the involvement of abrasive particles significantly impacts surface Ra and other surface morphologies (asperity peak features). A contact model for metal rough surfaces, distinct from two-body contact, was established to clarify the changes in removal mechanisms. It was found that the change in the contact between the particle and the asperity peak led to a change in the mass loss and guided the appropriate metal roughness range: Ra 0.05 μm and Ra 0.6–0.8 μm. In addition, it was found that the removal of asperity peaks is holistic under low roughness, and only parts of asperity peaks are removed under high roughness. Notably, the metrological methods used in this study supplement existing roughness measurements. By exploring the complex removal mechanism of asperity peaks, micro-scale guidance for surface (texture) design, machining, and optimization is provided. Full article

In this paper, the damage constitutive model of reactive powder concrete (RPC) in uniaxial tension is investigated. The relationship between the uniaxial tensile strength of RPC and the steel fiber admixture was analyzed by preparing RPC specimens with different steel fiber volume admixtures for uniaxial tensile tests, and the stress–strain curves were recorded. The test results show that the uniaxial tensile strength of RPC is significantly enhanced with an increase in steel fiber doping, especially after the doping amount is greater than 2%, showing a linear increase. Based on the classical damage mechanics and Weibull distribution, the damage evolution equation and the constitutive model of RPC uniaxial tension were established. The validation shows that the model can effectively describe the damage process of RPC in uniaxial tension, which provides a theoretical basis for the application of RPC in engineering practice. Full article

Conventional methods for constructing bridge I-beam joints face several challenges, including heavy precast slabs, complicated transportation and lifting procedures, strict accuracy requirements, lengthy construction timelines, and increased safety risks. The use of ultra-thin, high-performance reactive powder concrete (RPC) prefabricated slabs can effectively resolve these issues. However, research in this area is limited, leaving our understanding of the strength and feasibility of ultra-thin RPC slabs for I-beam joints incomplete. Therefore, this study conducts a thorough examination of the strength and safety aspects of these slabs to assess their practical suitability. First, 11 numerical models are generated to evaluate the bearing capacity of ultra-thin RPC slabs, determining key factors such as cracking load, ultimate load, and safety factor according to relevant codes and standards. This establishes a theoretical foundation for practical engineering applications. Next, several sets of ultra-thin RPC slabs that meet material performance criteria are prefabricated to study the mechanical properties under equivalent concentrated load. Finally, two types of in situ temporary construction loads are encountered in the safety calculations of the RPC slabs. This study aims to provide a robust theoretical framework and technical support for the application and advancement of ultra-thin RPC prefabricated slabs in bridge I-beam joints. Full article

In this paper, we propose a 3D Digital Surface Model (DSM) reconstruction method from uncalibrated Multi-view Satellite Stereo (MVSS) images, where Rational Polynomial Coefficient (RPC) sensor parameters are not available. While recent investigations have introduced several techniques to reconstruct high-precision and high-density DSMs from MVSS images, they inherently depend on the use of geo-corrected RPC sensor parameters. However, RPC parameters from satellite sensors are subject to being erroneous due to inaccurate sensor data. In addition, due to the increasing data availability from the internet, uncalibrated satellite images can be easily obtained without RPC parameters. This study proposes a novel method to reconstruct a 3D DSM from uncalibrated MVSS images by estimating and integrating RPC parameters. To do this, we first employ a structure from motion (SfM) and 3D homography-based geo-referencing method to reconstruct an initial DSM. Second, we sample 3D points from the initial DSM as references and reproject them to the 2D image space to determine 3D–2D correspondences. Using the correspondences, we directly calculate all RPC parameters. To overcome the memory shortage problem while running the large size of satellite images, we also propose an RPC integration method. Image space is partitioned to multiple tiles, and RPC estimation is performed independently in each tile. Then, all tiles’ RPCs are integrated into the final RPC to represent the geometry of the whole image space. Finally, the integrated RPC is used to run a true MVSS pipeline to obtain the 3D DSM. The experimental results show that the proposed method can achieve 1.455 m Mean Absolute Error (MAE) in the height map reconstruction from multi-view satellite benchmark datasets. We also show that the proposed method can be used to reconstruct a geo-referenced 3D DSM from uncalibrated and freely available Google Earth imagery. Full article

An innovative form of steel–concrete composite beam, the steel–coarse aggregate reactive powder concrete (CA-RPC) composite beam with uplift-restricted and slip-permitted (URSP) connectors, is introduced in this paper. The aim is to enhance the cracking resistance under negative bending moments, which is a difficult problem for traditional composite beams, and to make the cost lower than using ordinary reactive powder concrete (RPC). An experimental investigation of the behavior of six specimens of simply supported steel–CA-RPC composite beams with URSP connectors under negative bending moments is presented in this paper. The test results validated that the cracking load of steel–CA-RPC composite beams could be approximately three times that of the ordinary steel–concrete composite beams while the bearing capacity and stiffness are almost the same. A numerical model, using the concrete damaged plasticity (CDP) model to simulate the behavior of the CA-RPC material, was proposed and successfully calculated the overall load–displacement relationship of the composite beams with sufficient accuracy compared with the experimental results, and the distribution of cracks and the failure mode of the beams could also be captured by this model. Furthermore, a parametric analysis was carried out to find out how the application of prestress, CA-RPC, and URSP connectors could affect the cracking resistance of the composite beams, and the results indicated that using CA-RPC and prestress made the main contributions and that the usage of URSP could boost the effect of the other two factors. The plastic resistance moment of the beams was also compared with the calculation results using the methods introduced in Eurocode 4, and it was proved that the calculation results were lower than the experimental results by approximately 10%, which meant that the method was reliable for this kind of composite beam. Full article

Digital Surface Model (DSM) generation from high-resolution optical satellite images is an important topic of research in the remote sensing field. In optical satellite imaging systems, the attitude information of the cameras recorded by satellite sensors is often biased, which leads to errors in the Rational Polynomial Camera (RPC) model of satellite imaging. These errors in the RPC model can mislead the DSM generation. To solve the above problems, we propose an automatic DSM generation method from satellite images based on the Double-Penalty bundle adjustment (DPBA) optimization algorithm. In the proposed method, two penalty functions representing the camera’s attitude and the spatial 3D points, respectively, are added to the reprojection error model of the traditional bundle adjustment optimization algorithm. Instead of acting on images directly, the penalty functions are used to adjust the reprojection error model and improve the RPC parameters. We evaluate the performance of the proposed method using high-resolution satellite image pairs and multi-date satellite images. Through some experiments, we compare the accuracy and completeness of the DSM generated by the proposed method, the Satellite Stereo Pipeline (S2P) method, and the traditional bundle adjustment (BA) method. Compared to the S2P method, the experiment results of the satellite image pair indicate that the proposed method can significantly improve the accuracy and the completeness of the generated DSM by about 1–5 m and 20%–60% in most cases. Compared to the traditional BA method, the proposed method improves the accuracy and completeness of the generated DSM by about 0.01–0.05 m and 1%–3% in most cases. The experiment results can be a testament to the feasibility and effectiveness of the proposed method. Full article

Straw fibers are renowned for their cost-effectiveness, sustainability, and durability. They represent a promising natural reinforcement option for reactive powder concrete (RPC). This paper investigated the impact of straw fibers on RPC’s workability, mechanical performance (mechanical strength and flexural toughness), and electrical properties (electrical resistance and AC impedance spectroscopy curves). The straw fiber volumes ranged from 1% to 4.0% of the total RPC volume. Specimens were cured under standard curing conditions for 3, 7, 14, and 28 days. Mechanical and electrical properties of the specimens were tested before chloride salt erosion. The mass loss and ultrasonic velocity loss of the samples were measured under NaCl freeze–thaw cycles (F-Cs). The mass loss, ultrasonic velocity loss, and mechanical strengths loss of the samples were measured under NaCl dry–wet alternations (D-As). The findings indicated that incorporating straw fibers enhanced RPC’s flexural strength, compressive strength, and flexural toughness by 21.3% to 45.76%, −7.16% to 11.62%, and 2.4% to 32.7%, respectively, following a 28-day curing period. The addition of straw fibers could augment the AC electrical resistance of the RPC by 10.17% to 58.1%. The electrical characteristics of the RPC adhered to series conduction models. A power function relationship existed between the electrical resistance and mechanical strengths of the RPC. After 10 NaCl D-As, the mass loss rate, ultrasonic velocity loss rate, flexural strength, and compressive strength loss rates of the RPC decreased by 0.42% to 1.68%, 2.69% to 6.73%, 9.6% to 35.65%, and 5.41% to 34.88%, respectively, compared to blank samples. After undergoing 200 NaCl F-Cs, the rates of mass loss and ultrasonic velocity loss of the RPC decreased by 0.89% to 1.01% and 6.68% to 8.9%, respectively. Full article

The Rational Function Model (RFM) is composed of numerous highly correlated Rational Polynomial Coefficients (RPCs), establishing a mathematical relationship between two-dimensional images and three-dimensional spatial coordinates. Due to the existence of ill-posedness and overparameterization, the estimated RPCs are sensitive to any slight perturbations in the observation data, particularly when handling a limited number of Ground Control Points (GCPs). Recently, Principal Component Analysis (PCA) has demonstrated significant performance improvements in the RFM optimization problem. In the PCA-based RFM, each Principal Component (PC) is a linear combination of all variables in the design matrix. However, some original variables are noise related and have very small or almost zero contributions to the construction of PCs, which leads to the overparameterization problem and makes the RPC estimation process ill posed. To address this problem, in this paper, we propose an Adaptive Sparse Principal Component Analysis-based RFM method (ASPCA-RFM) for RPC estimation. In this method, the Elastic Net sparsity constraint is introduced to ensure that each PC contains only a small number of original variables, which automatically eliminates unnecessary variables during PC computation. Since the optimal regularization parameters of the Elastic Net vary significantly in different scenarios, an adaptive regularization parameter approach is proposed to dynamically adjust the regularization parameters according to the explained variance of PCs and degrees of freedom. By adopting the proposed method, the noise and error in the design matrix can be reduced, and the ill-posedness and overparameterization of the RPC estimation can be significantly mitigated. Additionally, we conduct extensive experiments to validate the effectiveness of our method. Compared to existing state-of-the-art methods, the proposed method yields markedly improved or competitive performance. Full article

Three-dimensional reconstruction based on optical satellite images has always been a research hotspot in the field of photogrammetry. In particular, the 3D reconstruction of building areas has provided great help for urban planning, change detection and emergency response. The results of 3D reconstruction of satellite images are greatly affected by the input images, and this paper proposes an improvement method for 3D reconstruction of satellite images based on the generative adversarial network (GAN) image enhancement. In this method, the perceptual loss function is used to optimize the network, so that it can output high-definition satellite images for 3D reconstruction, so as to improve the completeness and accuracy of the reconstructed 3D model. We use the public benchmark dataset of satellite images to test the feasibility and effectiveness of the proposed method. The experiments show that compared with the satellite stereo pipeline (S2P) method and the bundle adjustment (BA) method, the proposed method can automatically reconstruct high-quality 3D point clouds. Full article