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Previous scholars studied UV aged asphalt performance under constant UV intensity. This study proposed a method of UV aged asphalt based on the time-varying UV intensity, which is more capable of realistically simulating the UV aging of asphalt under natural conditions. The softening point, viscosity, and DSR tests were used to analyze changes in conventional and rheological properties of UV aged asphalt and compared them with RTFO (Rolling Thin-Film Oven) aged asphalt. Fourier transform infrared (FTIR) spectroscopy and scanning electron microscope (SEM) tests were analyzed for the microscopic characteristics. The aging index was also used to evaluate the percentage of asphalt aging attributable to each UV aging cycle. The results show that the softening point is more susceptible to UV radiation, with UV-6 cycles exceeding the softening point increment of RTFO. On the contrary, the viscosity is more susceptible to thermo-oxidative aging, and the viscosity increment of RTFO is 39.3 % higher than that of UV-12 cycles. Based on DSR's test results, the rutting factor magnitude of RTFO was between 9 and 12 UV aging cycles The phase angle results indicate that thermo-oxidative aging is more likely to convert asphalt to elastomer than RTFO asphalt. UV aging process of asphalt produced sulfinyl and, carbonyl and other polar functional groups, asphalt intermolecular forced to enhance the production of a harder oxidation layer, the asphalt surface began to appear cracks. As aging time increases, the cracks become longer and deeper. Practical image recognition technology can identify the areas of the cracks. The crack area follows a linear pattern during cycles 3–9, while the growth rate slows down during cycles 9–12.

This study compared the performance of extracted asphalt and laboratory aged asphalt. The five-year service asphalt mixtures were used for extraction in this study. The laboratory aged asphalt experienced the Rolling thin film oven (RTFO) aging the Pressure aging vessel (PAV) aging with variable duration (5, 10, 15, 20, and 25 hours). By comparing the softening point, viscosity, and rheological properties of both, an aging simulation protocol was established. For laboratory aged asphalt binder, the softening point increases by 3.56 %, 5.68 %, 7.43 %, 8.16 %, and 8.74 % at PAV aging times of 5 h, 10 h, 15 h, 20 h, and 25 h, respectively. The rutting index and viscosity of the aged asphalt gradually increases with the PAV duration. There is a substantial increase in creep recovery rate and a significant decrease in non-recoverable creep compliance for aged asphalt. The rheological aging index demonstrates a linear increasing trend. The sulfoxide (SO) and aromatic (CO) aging indexes of the laboratory-simulated aged asphalt progressively rise with prolonged PAV aging time. The SO and CO aging indexes of the extracted asphalt consistent with RTFO aged asphalt experienced PAV aging 20 and 25 hours. The extracted asphalt can be simulated in the laboratory under standard RTFO conditions and a 22.26 hours PAV duration at a pressure of 2.1 MPa ± 0.1 MPa. This study introduces a methodology for simulating asphalt aging in an authentic service environment by adjusting the laboratory aging test conditions.

Predicting the strength of asphalt mixtures with different specifications under various conditions was a highly challenging task. The standard strength test lacked consideration of multiple factors, resulting in an inability to accurately characterize the properties of the pavement. This paper proposed a strength prediction approach based on influence factor analysis using the back-propagation neural network (BPNN) and support vector machine (SVM). The strength dataset was processed to realize the physical analysis of factors influencing asphalt mixture strength. Stress state (Direct tensile with250mm×50mm×50mm, Uniaxial compression with 100mm×Φ100mm, Indirect tensile with63.5mm×Φ100mm,Four-point bending with 380mm×63.5mm×50mm), temperatures (35 ̊C, 25 ̊C, 15 ̊C, 0 ̊C, −15 ̊C, −25 ̊C), and load rates (0.02 MPa/s, 0.05 MPa/s, 0.1 MPa/s, 0.5 MPa/s) were selected as input features to train the BPNN and SVM. The strength prediction model for asphalt mixture under complex conditions was established by optimizing the parameters of algorithms. The performance of the BPNN and SVM was evaluated and compared by the root mean square error, determination coefficient, and mean absolute percentage deviation. The results show that the asphalt mixture specimen with different specifications under various stress states presents significant discrepancies. The maximum compressive strength is followed by the bending strength, then comes the indirect tensile strength, and the smallest is the direct tensile strength. The difference in the role of asphalt or aggregate is the main reason for the diversity in strength. The increase in temperature leads to asphalt softening, which reduces the strength of the asphalt mixture. The increased loading rate meant the loading time was short cause the strength increased. In addition, the predictive value of the strength under various conditions was consistent with the results of the experiments. The hidden neurons in the BPNN were set to 9, achieving the prediction accuracy is high (R2=0.99). The penalty coefficient of the SVM was set to 500 and the kernel function parameter was set to 300, resulting in the error within 0.02 %. When comparing the performance metrics of BPNN and SVM, it becomes evident that SVM outperforms BPNN in terms of prediction accuracy. Specifically, SVM exhibits a coefficient of determination of 0.9983, a root mean square error of 0.208, and a mean absolute percentage deviation of 0.145, whereas BPNN demonstrates respective values of 0.9979, 0.233, and 0.067. This study lays a theoretical foundation for the digital and intelligent road construction.

The early strength formation of Cement-Stabilized Macadam (CSM) base materials in cold regions is affected by environmental changes. At the same time, the curing temperature of CSM in the actual service environment did not meet the laboratory standard temperature curing environment (ST-CE). This study explores the evolution law of CSM performance and the influence of an early strength agent (ESA-I) on the early performance of CSM under variable temperature curing environment (VT-CE) in cold regions. The actual service environment for CSM curing was simulated in the laboratory, and CSM specimens with five ESA-I dosages of 0%, 5%, 10%, 15%, and 20% were prepared. The effect of ESA-I on the early properties of CSM under VT-CE was analyzed. The micro mechanism of CSM under different curing environments and ESA-I dosages was investigated based on X-ray diffraction, Fourier transform infrared spectroscopy, and Scanning electron microscope. The experimental results indicated that the early strength formation of CSM was slow in VT-CE, and the 7-day unconfined compressive strength did not meet the construction quality control indicators of CSM material under heavy traffic conditions. It was necessary to extend the curing period to meet the construction requirements. Adding ESA-I could enhance the strength, modulus, frost resistance, and fatigue performance of CSM under different curing environments. Compared with the ST-CE, the VT-CE would weaken the improvement effect of ESA-I on the performance of CSM. The recommended range for ESA-I in VT-CE was 10% −15% based on comprehensive compressive strength and resilient modulus indicators. Microscopic experiments show that under a VT-CE, the hydration rate of cement decreases, the Ettringite content in the hydration products is higher, and the microstructure is loose. Adding ESA-I improves the hydration rate of cement under VT-CE, and the microstructure contains more C-S-H hydration products.

This paper established a mathematical model with nonconvex bilinear terms. It formulated the complex material flow in the petroleum refinery scenario based on the concept of the "P model". The mathematical model described the nonlinear constraints such as linear and nonlinear mass and volume intersection flow blending of crude and middle material physical properties. Additionally, it described the complex inflow and outflow in secondary devices as nonlinear constraints such as delta-base structure and physical property transfer. It is highly difficult to determine the direction and quantity of each material in the network of refineries. An improved interior point algorithm with an initial point strategy was proposed to find a high-quality feasible solution in a short time. The real instances from the petroleum refinery were employed to compare and analyze the solutions from the improved algorithm and commercial solver. The experimental results show that the proposed algorithm framework can balance the solution quality and computational efficiency and perform well in different scenarios of refinery material flow networks.

This study normalized the mixture's fatigue behavior at various temperatures, and the strength and fatigue tests of the mixture were conducted. The stress state of the asphalt mixture includes direct tensile, uniaxial compression, and indirect tensile. The Desai yield surface and fatigue path were proposed. And a normalized fatigue characteristics model of the mixture was established. The following conclusions were obtained. With the increases in the loading rate, the strength of the asphalt mixture increased. As the temperature increases, the strength of the mixture is reduced. At various temperatures and rates, the strength forms a closed curved surface. The Desai strength yield surface was established, which forms a closed curved surface. When the loading rate and temperature are below a certain critical line, the asphalt mixture will not undergo strength damage. At a fixed stress state, the fatigue damage path of the mixture was determined. The stress ratio was determined considering the influence of the loading rate. In this way, a normalized model can be described to express the asphalt mixture fatigue properties at various temperatures and stress levels. For the asphalt mixture in an indirect tensile state, the normalized fatigue equation parameter is 4.09. This model is more suitable for reflecting the viscous-elastic behavior of the mixtures than the fatigue equation determined by the notional stress ratio.

The aging degree of reclaimed asphalt pavement (RAP) is complex and diverse, and the current utilization rate of RAP is far from meeting the needs of green cycle development. To explore the effect of Styreneic Methyl Copolymer (SMC) rejuvenator of aging asphalt and the performance of SMC regenerated asphalt mixture containing 50% and 60% RAP, this study used SMC rejuvenator to regenerate SBS-modified asphalt with different aging degrees. The high and low-temperature rheological properties of regenerated asphalt were evaluated by temperature sweep test, multi stress creep recovery test, and bending beam rheometer test. Then, the recommended content of the SMC rejuvenator was obtained by using Matlab to establish the corresponding model. Finally, the road performance of the regenerated mixture with high-content RAP was verified. The test results showed that the recommended content range of the SMC rejuvenator for the aging asphalt with PAV (Pressurized Aging Vessel) 0 h, PAV5h, PAV10h, PAV15h, PAV20h were 1.56∼2.31%, 3.03∼3.36%, 4.269∼4.48%, 5.23∼5.66%, 5.95∼6.91%, respectively. The road performance of the SMC regenerated asphalt mixture with 50% and 60% RAP content met the specification requirements. SMC rejuvenator had no significant negative impact on high-temperature performance, significantly improving low-temperature cracking resistance. SMC regenerator had a positive significance in improving regeneration efficiency.

ObjectiveEleutheroside E (EE) is an anti-inflammatory natural compound derived from the edible medicinal herb Acanthopanax senticosus. This study aims to investigate the underlying mechanism of the anti-osteoporosis action of EE through network pharmacology, molecular docking and gut microbiota.Materials and methodsNetwork pharmacology was used to explore the potential core targets and main pathways mediated by EE in osteoporosis (OP) treatment. Molecular docking was exploited to investigate the interactions between the active anti-OP compounds in EE and the potential downstream targets. Following the multi-approach bioinformatics analysis, ovariectomy (OVX) model was also established to investigate the in vivo anti-OP effects of EE.ResultsThe top 10 core targets in PPI network were TP53, AKT1, JUN, CTNNB1, STAT3, HIF1A, EP300, CREB1, IL1B and ESR1. Molecular docking results that the binding energy of target proteins and the active compounds was approximately between −5.0 and −7.0 kcal/mol, which EE has the lowest docking binding energy with HIF1A. Enrichment analysis of GO and KEGG pathways of target proteins indicated that EE treatment could potentially alter numerous biological processes and cellular pathways. In vivo experiments demonstrated the protective effect of EE treatment against accelerated bone loss, where reduced serum levels of TRAP, CTX, TNF-α, LPS, and IL-6 and increased bone volume and serum levels of P1NP were observed in EE-treated mice. In addition, changes in gut microbiota were spotted by 16S rRNA gene sequencing, showing that EE treatment increased the relative abundance of Lactobacillus and decreased the relative abundance of Clostridiaceae.ConclusionIn summary, these findings suggested that the characteristics of multi-target and multi-pathway of EE against OP. In vivo, EE prevents the onset of OP by regulating gut microbiota and inflammatory response and is therefore a potential OP drug.

An ever-growing demand for depleted natural resources is one of the significant challenges facing the global asphalt pavement industry in building and maintaining global asphalt pavements. Because plastics are ubiquitous in the global economy, they are the latest in a series of high-profile materials to attract attention. Their low material recovery rates and the environmental impact of current disposal methods pose a threat to plastic recycling. Recycling plastic wastes in asphalt pavement is a possible approach to reducing environmental pressure and the demand for depleted natural resources. Many studies have proposed recycling plastic waste in asphalt pavement using dry- and wet-processed technologies. This review aims to comprehensively evaluate the feasibility of various recycled plastics in asphalt pavement concerning the properties of compatibility, storage stability, microstructure, thermo-rheology, and mechanical performance and to identify challenges and recommendations for the future. This review discusses recent developments and the feasibility of using plastic wastes as modifiers or additives to asphalt binders or asphalt mixtures in dry and wet processes, focusing on different materials from waste streams, how to produce such modified materials, and the characteristics of plastic waste modified asphalt, thus contributing to the sustainable management of resources and production of useful paving materials.

Ground tire rubber (GTR) usage in asphalt pavement with the dry process has gained more prominence in recent times. The objective of this work is to investigate the pavement performance of GTR-modified asphalt pavement and polymer-modified asphalt pavement on heavy volume of traffic conditions in Michigan's wet and freeze environment. A suite of laboratory tests was done to evaluate the pavement performance of GTR-modified and polymer-modified asphalt mixtures. To reveal the strain and stress relationship under different frequencies and temperatures, the dynamic modulus test was applied. The Hamburg wheel tracking device (HWTD) was used to assess the high-temperature deformation resistance. The disc-shaped compact tension (DCT) test was used to evaluate the low-temperature cracking characteristics. The characteristics of the asphalt binder were assessed by the dynamic shear rheometer (DSR) for high-temperature properties and the asphalt binder cracking device (ABCD) for low-temperature properties. After the construction, a field noise test was conducted. The experimental results stated that the polymer-modified asphalt mixture and GTR-modified asphalt mixture showed higher dynamic modulus and better ability to prevent cracking than the conventional asphalt mixture at low temperatures, as well as better permanent deformation and stripping resistance than the conventional asphalt mixture. The fracture energy of the GTR-modified hot mix asphalt (HMA) is 13–16 % larger than the polymer-modified HMA. The number of passes to the stripping point of GTR-modified was 510–518 % higher than the conventional HMA. When compared to the field core, the lab-compacted HMA offers superior pavement performance. The extracted asphalt binder test results show the GTR-modified asphalt has better rutting resistance and cracking resistance than polymer-modified asphalt, and the results in the noise test demonstrated that the rubber-modified asphalt pavement mitigated the noise level by 2–3 dB on the road at different vehicle speeds. Moreover, the pavement condition was noticeably enhanced after the reconstruction of the surface course. The total number of passenger tires to be used in this project is about 2270. To summarize, better rutting and cracking properties in asphalt pavement are shown by the project's utilization of rubber technology. And the GTR-modified HMA is comparable to polymer-modified HMA. Therefore, it may be appropriate to utilize rubber technology on high-traffic volume asphalt pavement in Michigan's wet and freeze climate.

A full understanding of bitumen fatigue cracking behavior is extremely important as this phenomenon has a considerable influence on bituminous pavement performance. The current framework for assessing this asphalt binder property is inconsistent in ranking bitumen fatigue performance in terms of the failure definition and damage characteristic curve (DCC) analysis. This study used four different types of asphalt binders: neat asphalt (NA), self-healing thermoplastic polyurethane (STP)-modified bitumen, self-healing poly (dimethyl siloxane) crosslinked with urea bond (IPA1w)-modified bitumen, and styrene–butadiene–styrene (SBS)-modified bitumen (SBSB). All the bitumens were subjected to short-term and long-term aging, and they were also tested by utilizing the linear amplitude sweep (LAS) test and the simplified viscoelastic continuum damage (S-VECD) model. LAS and S-VECD procedures were used to apply the newly proposed and current frameworks in order to analyze bitumen performance. The current framework showed that the bitumens that used a higher number of loading cycles (N) to reach their failure points (Nf) failed to exhibit greater fatigue performances in terms of DCC analysis. The developed framework (mainly based on the damage intensity [S] instead of N) was used to solve the inconsistency between the failure definition and DCC assessment in ranking bitumen performance. Additionally, the current framework (failure criterion) presented two R2 values below 0.1, but the developed framework (failure criterion) showed that all R2 values were greater than 0.9. The developed framework represents a turning point because, for the first time, this type of procedure is mainly being based on S instead of N. Although further tests are needed to confirm its efficiency, it eliminates the inconsistency between the failure definition and DCC assessment.

Abstract Aiming at the difficulty in extracting the features of time–frequency images for the recognition of car engine sounds, we propose a method to recognize them based on a deformable feature map residual network. A deformable feature map residual block includes offset and convolutional layers. The offset layers shift the pixels of the input feature map. The shifted feature map is superimposed on the feature map extracted by the convolutional layers through shortcut connections to concentrate the network to the sampling in the region of interest, and to transmit the information of the offset feature map to the lower network. Then, a deformable convolution residual network is designed, and the features extracted through this network are fused with the Mel frequency cepstral coefficients of car engine sounds. After recalibration by the squeeze and excitation block, the fused results are fed into the fully connected layer for classification. Experiments on a car engine sound dataset show that the accuracy of the proposed method is 84.28%. Compared with the existing state-of-the-art methods, in terms of the accuracy of recognizing car engine sounds under various operating conditions, the proposed method represents an improvement over the method based on dictionary learning and a convolutional neural network.

Coloured pavement provides benefits for improving functionality and safety in pavement infrastructure. In this investigation, a coloured ultra-thin pavement overlay was prepared with a resin-based binder and artificial coloured ceramic aggregates. The overlays were prepared in various binder thicknesses (0.8 mm, 1.2 mm, and 1.6 mm) and aggregate spreading ratios (50 %, 75 %, and 100 %). The specimens were experimentally tested with a wheel abrasion tester followed by the analysis of apparent colour loss, rutting depth, mass loss, and skid resistance in accordance with a proposed comprehensive durability evaluation method. The colour-cooling performance was evaluated with the sunlight simulation test. In addition, the interface properties were tested based on the flexural toughness test, pull-off test, and shear test. The comprehensive durability evaluation results indicated that coloured overlay with 1.2 mm binder depth and 75 % aggregate spreading ratio had the highest durability score among all samples. The sunlight simulation test results verified the positive cooling effect of the coloured overlay on the pavement structure. The bottom-up cracking resistance based on the flexural toughness test was also improved with overlays by comparing that with specimens without covering. The pull-off test and shear test results illustrated that the interface adhesion was correlated to environmental temperature and binder thickness. This study provided a comprehensive durability scoring method for coloured ultra-thin functional overlays, and the optimized material compositions enhanced the durability and interface adhesion, which could be utilized for future field applications of coloured ultra-thin functional overlays and contribute to infrastructure sustainability.

Given the prevalence of part-time farming behaviors in rural households, studying the impact of part-time farming behaviors on grain output is of great practical significance. Using a panel dataset of 5629 Chinese national rural fixed observation point data from 2009 to 2015, this paper used the Propensity Score Matching-Difference in Differences method (PSM-DID) to examine the impact and dynamic effect of rural household’s part-time farming behavior on grain output. In addition, the paper also aims to explore the mechanism of how part-time farming affects grain output by running an OLS in an intermediary effect model. We analyzed the data from two aspects: the effect of rural households’ part-time farming decisions on grain output and the relationship between the income from rural households’ part-time farming and grain output. After accounting for the control variables, the results showed a significant positive correlation between rural households’ part-time farming behavior and grain output, with an influence coefficient of 0.304 tons increase in total grain output. While rural households’ part-time farming behavior inhibited grain output over the short term, it ultimately promoted grain output in the long run. The promotion effect increased with the duration of the rural households’ part-time farming. Moreover, the degree of part-time farming and its impact on grain output are mediated by agricultural labor inputs and agricultural technology inputs. Hence, differences in the employment times of rural households’ part-time farming can lead to varying results in grain output. To improve the efficiency of grain output, this study recommends that the government implement policies that promote orderly rural households’ part-time farming behavior.

MAPK signaling suppresses autoimmunity mediated by the SUMM2 receptor in Arabidopsis. Here Huang et al. show that a trimeric complex consisting of the GPI anchored protein LLG1, and the two receptor-like proteins LET1 and LET2, promotes activation of SUMM2 according to MAPK signaling status.

Purpose – The purpose of this paper is to achieve intelligent superstore site selection. Yonghui Superstores partnered with Cardinal Operations to incorporate a tremendous amount of site-related information (e.g. points of interest, population density and features, distribution of competitors, transportation, commercial ecosystem, existing own-store network) into its store site optimization. Design/methodology/approach – This paper showcases the integration of regression, optimization and machine learning approaches in site selection, which has proven practical and effective. Findings – The result was the development of the “Yonghui Intelligent Site Selection System” that includes three modules: business district scoring, intelligent site engine and precision sales forecasting. The application of this system helps to significantly reduce the labor force required to visit and investigate all potential sites, circumvent the pitfalls associated with possibly biased experience or intuition-based decision making and achieve the same population coverage as competitors while needing only half the number of stores as its competitors. Originality/value – To our knowledge, this project is among the first to integrate regression, optimization and machine learning approaches in site selection. There is innovation in optimization techniques.

Scrap tire rubber and nylon fiber are waste materials that could potentially be recycled and used to improve the mechanical properties of asphalt pavement. The objective of this research was to investigate the properties of scrap tire rubber and nylon fiber (R-F) modified warm mix asphalt mixture (WMA). The high-temperature performance was estimated by the Hamburg wheel-tracking testing (HWTT) device. The low-temperature cracking performance was evaluated by the disk-shaped compact tension (DCT) test and the indirect tensile strength (IDT) test. The stress and strain relationship was assessed by the dynamic modulus test at various temperatures and frequencies. The extracted asphalt binder was evaluated by the dynamic shear rheometer (DSR). Pavement distresses were predicted by pavement mechanistic-empirical (M-E) analysis. The test results showed that: (1) The R-F modified WMA had better high-temperature rutting performance. The dynamic modulus of conventional hot mix asphalt mixture (HMA) was 21.8%~103% lower than R-F modified WMA at high temperatures. The wheel passes and stripping point of R-F modified WMA were 2.17 and 5.8 times higher than those of conventional HMA, respectively. Moreover, the R-F modified warm mix asphalt had a higher rutting index than the original asphalt. (2) R-F modified WMA had better cracking resistance at a low temperature. The failure energy of the R-F modified WMA was 24.3% higher than the conventional HMA, and the fracture energy of the R-F modified WMA was 7.7% higher than the conventional HMA. (3) The pavement distress prediction results showed the same trend compared with the laboratory testing performance in that the R-F modified WMA helped to improve the IRI, AC cracking, and rutting performance compared with the conventional HMA. In summary, R-F modified WMA can be applied in pavement construction.

An accurate estimation of the state of health (SOH) of lithium-ion batteries is essential for the safe and reliable operation of electric vehicles. As a single hidden-layer feedforward neural network, extreme learning machine (ELM) has the advantages of a fast learning speed and good generalization performance. The bat algorithm (BA) is a swarm intelligence optimization algorithm based on bat echolocation for foraging. In this study, BA was creatively applied to improve the ELM neural network, forming a BA-ELM model, and it was applied to SOH estimation for the first time. First, through Pearson and Spearman correlation analysis, six variables were determined as the input variables of the model. The actual remaining capacity of the battery was determined as the output variable. Second, BA was used to optimize the connection weights and bias in ELM to construct the BA-ELM model. Third, the battery data set was trained and tested with BA-ELM, ELM, Elman, back propagation (BP), radial basis function (RBF), and general regression neural network (GRNN) models. Five statistical error indicators, and the radar chart, scatter plot, and violin diagram were used to compare the estimation effects. The results show that the evaluation function of BA-ELM can converge quickly and effectively optimize the network model of ELM. The RMSE of the BA-ELM model was 0.5354%, and the MAE was 0.4326%, which is the smallest among the 6 models. The RMSE values of the other model were 2.27%, 3.53%, 3.07%, 3.86%, 3.24%, respectively, indicating the BA-ELM has good potential for future applications.

The accurate state of charge (SoC) online estimation for lithium-ion batteries is a primary concern for predicting the remaining range in electric vehicles. The Sigma points Kalman Filter is an emerging SoC filtering technology. Firstly, the charge and discharge tests of the battery were carried out using the interval static method to obtain the accurate calibration of the SoC-OCV (open circuit voltage) relationship curve. Secondly, the recursive least squares method (RLS) was combined with the dynamic stress test (DST) to identify the parameters of the second-order equivalent circuit model (ECM) and establish a non-linear state-space model of the lithium-ion battery. Thirdly, based on proportional correction sampling and symmetric sampling Sigma points, an SoC estimation method combining unscented transformation and Stirling interpolation center difference was designed. Finally, a semi-physical simulation platform was built. The Federal Urban Driving Schedule and US06 Highway Driving Schedule operating conditions were used to verify the effectiveness of the proposed estimation method in the presence of initial SoC errors and compare with the EKF (extended Kalman filter), UKF (unscented Kalman filter) and CDKF (central difference Kalman filter) algorithms. The results showed that the new algorithm could ensure an SoC error within 2% under the two working conditions and quickly converge to the reference value when the initial SoC value was inaccurate, effectively improving the initial error correction ability.

This study was initiated to evaluate the performance of asphalt binders and mixtures incorporating linear low-density polyethylene- calcium carbonate (LLDPE-CaCO3) pellet, either with or without titanate coupling agent. The detailed manufacturing process of modifier pellets was displayed. The coupling agent was used to enhance the cross-linking between materials by means of winding up covalent bonds or molecule chains, thus improving the performance of composites. In the preparation of modified bitumen, the preheated asphalt binder was mixed with the modifiers using a high shear mixer at 5000 rpm rotational speed for 45 min. Experimental works were conducted to evaluate the performance of asphalt binders in terms of volatile loss, viscosity, rutting potential, and low temperature cracking. Meanwhile, the asphalt mixtures were tested using the flow number test and tensile strength ratio (TSR) test. The addition of LLDPE-CaCO3 modifiers and coupling agent does not significantly affect the volatile loss of modified asphalt binders. The addition of modifiers and coupling agent has significantly improved the resistance to permanent deformation of asphalt binders. Even though, the addition of LLDPE-CaCO3 modifier and coupling agent remarkably increased the mixture stiffness that contributed to lower rutting potential, the resistance to low temperature cracking of asphalt binder was not adversely affected. The combination of 1% coupling agent with 3% PECC is optimum dosage for asphalt binder to have satisfactory performance in resistance to moisture damage and rutting.

Objective: Eleutheroside E (EE) is an anti-inflammatory natural compound derived from the edible medicinal herb Acanthopanax senticosus. This study aims to investigate the underlying mechanism of the anti-osteoporosis action of EE through network pharmacology, molecular docking and gut microbiota. Materials and methods: Network pharmacology was used to explore the potential core targets and main pathways mediated by EE in osteoporosis (OP) treatment. Molecular docking was exploited to investigate the interactions between the active anti-OP compounds in EE and the potential downstream targets. Following the multi-approach bioinformatics analysis, ovariectomy (OVX) model was also established to investigate the in vivo anti-OP effects of EE. Results: The top 10 core targets in PPI network were TP53, AKT1, JUN, CTNNB1, STAT3, HIF1A, EP300, CREB1, IL1B and ESR1. Molecular docking results that the binding energy of target proteins and the active compounds was approximately between −5.0 and −7.0 kcal/mol, which EE has the lowest docking binding energy with HIF1A. Enrichment analysis of GO and KEGG pathways of target proteins indicated that EE treatment could potentially alter numerous biological processes and cellular pathways. In vivo experiments demonstrated the protective effect of EE treatment against accelerated bone loss, where reduced serum levels of TRAP, CTX, TNF-α, LPS, and IL-6 and increased bone volume and serum levels of P1NP were observed in EE-treated mice. In addition, changes in gut microbiota were spotted by 16S rRNA gene sequencing, showing that EE treatment increased the relative abundance of Lactobacillus and decreased the relative abundance of Clostridiaceae. Conclusion: In summary, these findings suggested that the characteristics of multi-target and multi-pathway of EE against OP. In vivo, EE prevents the onset of OP by regulating gut microbiota and inflammatory response and is therefore a potential OP drug. [ABSTRACT FROM AUTHOR]