Natural aggregate (NA) can be replaced by recycled concrete aggregate (RCA) in the construction industry, which can solve the problem of construction waste disposal and reduce the gap between demand and supply. Various methods that were used to pretreat the RCA from the published research can be divided into the removal of adhered mortar, polymer impregnation, pozzolanic slurry immersion, carbonation or CO 2 curing and bio-deposition method. This paper compared the pretreatment efficiency of these methods and their effects on mechanical properties, shrinkage, and durability of recycled aggregate concrete (RAC) based on statistical analyses. The pretreatment method is a highly attractive candidate for enhancing the mechanical strength and durability and decreasing the drying shrinkage. The effects of pretreatment methods with different parameters, testing age, mixture proportion, and measurement parameters on RAC are analyzed in detail. The findings of this paper can improve the development of eco-friendly concrete incorporating pretreated-RCAs in construction and civil engineering in the future.
This study investigated the combinatorial expression of xanthine dehydrogenase (XDH) and chaperone XdhC from Acinetobacter baumannii and Rhodobacter capsulatus and their applications in decreasing purine content in the beer, beef and yeast. Naturally occurring xdhABC gene clusters of A. baumannii CICC 10254 and R. capsulatus CGMCC 1.3366 as well as two refactored clusters constructed by exchanging their xdhC genes were overexpressed in Escherichia coli and purified to near homogeneity. RcXDH chaperoned by AbXdhC showed nearly the same catalytic performance as that by RcXdhC, except for the decreased substrate affinity. While the AbXDH co-expressed with RcXdhC displayed enhanced acidic adaptation but weakened catalytic activity. All the XDHs degraded purines in beer, beef and yeast extract effectively, indicating potential applications in low-purine foods to prevent hyperuricemia and gout. The study also presents a method for exploiting the better chaperone XdhC and novel XDHs by functional complement activity using existing XdhCs such as RcXdhC.
The dark pigments of beet juice (mainly high-molecular-weight hexose alkaline degradation products, HHADPs) negatively affect the quality of white sugar and should therefore be removed during processing, e.g., through adsorption. Herein, we developed a rosin-based anion adsorbent (RAA) as an effective, regenerable, and green decolorization agent for the removal of HHADPs from beet juice, achieving an equilibrium HHADP adsorption capacity of 6.50 mg/g and a removal efficiency of 89.36%. The investigations of adsorption kinetics, isotherms, thermodynamics, and mechanisms revealed that the adsorption was a spontaneous endothermic multilayer process and mainly corresponded to chemisorption driven by the interactions between the positively charged protonated tertiary amine groups of RAA and the negatively charged carboxylate groups of HHADPs. Our results pave the way to the development of cost-effective and practical adsorbents for an important industrial process, namely beet sugar production.
Concrete is a highly heterogeneous composite material on the microscopic length scale (10⁻⁶ m) to the mesoscopic length scale (10⁻¹ m). The heterogeneous structure of concrete influences its macro mechanical properties. The multiscale approach is an effective method to analyze the mechanical properties of composite material and support the design of material. In this paper, a 3D multiscale model for prediction of tensile strength of concrete is presented. The microstructure of Hydrated Cement Paste (HCP) is generated by the HYMOSTURC and exported to the Abaqus by using Python program. The local background grid method is used to directly generate the meso-scale models of mortar and concrete. An uncoupled multiscale method is applied to transfer parameters from a smaller scale to a larger scale model. In the case of scale overlapping, parameter transfer is carried out through a simplified uncoupled averaging method. Finally, the multiscale model is verified by flexural test of mortar and splitting tensile test of concrete.
Heating and cooling are major resources of Demand Response (DR) to enhance the flexibility and reliability of power grids. In order to maximize their potential, it is necessary to reliably keep track of their operating states in real-time operation. This paper presents a comprehensive state estimation framework for power systems with building thermostats, with a tractable thermodynamic building model and integration of multi-source information from weather, power grid, building systems. Based on the thermodynamic model and state of the buildings, the building temperature trajectories in the next few hours can be accurately predicted, such that the DR potential of the building can be precisely estimated. To jointly estimate the state variables of power grids and state variables in building thermostats with different time scales of dynamics, a holistic estimation framework is developed based on the partial equivalence between the Weighted Least Squares (WLS) estimation problem and the correction stage of the Iterative Extended-Kalman Filter (IEKF). Simulation results show that the proposed framework can accurately track the thermo-electrical states of the system and estimate the DR potential in the presence of measurement noise and bad data.
For the cooling of transformer in the grid, the heat transfer efficiency of mixed convection is relatively low due to the low thermal conductivity and large viscosity of transformer oil. Therefore, this paper aims to study the PCD (periodically changed direction) electrical field to enhance the mixed convective heat transfer of transformer oil. Then, a semi empirical correlation is developed to calculate the Nusselt number affected by electrical field. Specially, the influences of heat flux, electrical field intensity and switching period on the heat transfer characteristics are discussed experimentally. As a result, the result reveals that the mixed convective heat transfer is enhanced increasingly with the decrease of switching time and improvement of voltage. Thus, the PCD electric field can effectively enhance the mixed convection heat transfer. In addition, the mechanism analysis shows that the PCD electrical field leads to the continuous change of fluid mode, and the polarized molecules continuously vibrate and move within a certain range, which leads to the violent disturbance of internal fluid. Meanwhile, the secondary flow induced by the electric field results in the disruption of thermal boundary layer, which strengths the heat transfer. Finally, the predicted results of the semi-empirical correlation are in good agreement with the experimental results, and the effect of electrical field intensity and switching periods are comprehensively considered in the correlation. The present study is of great importance for the transformer cooling.
Medical image segmentation and registration are very important related steps in clinical medical diagnosis. In the past few years, deep learning techniques for joint segmentation and registration have achieved good results in both segmentation and registration tasks through one-way assisted learning or mutual utilization. However, they often rely on large labeled datasets for supervised training or directly use pseudo-labels without quality estimation. We propose a joint registration and segmentation self-training framework (JRSS), which aims to use segmentation pseudo-labels to promote shared learning between segmentation and registration in scenarios with few manually labeled samples while improving the performance of dual tasks. JRSS combines weakly supervised registration and semi-supervised segmentation learning in a self-training framework. Segmentation self-training generates high-quality pseudo-labels for unlabeled data by injecting noise, pseudo-labels screening, and uncertainty correction. Registration utilizes pseudo-labels to facilitate weakly supervised learning, and as input noise as well as data augmentation to facilitate segmentation self-training. Experiments on two public 3D medical image datasets, abdominal CT and brain MRI, demonstrate that our proposed method achieves simultaneous improvements in segmentation and registration accuracy under few-shot scenarios. Outperforms the single-task fully-supervised training state-of-the-art model in the metrics of Dice similarity coefficient and standard deviation of the Jacobian determinant.
Rice (Oryza sativa L.) consumption represents a major route for the exposure to cadmium (Cd) and arsenic (As). Compared with un-amendment soil, silkworm excrement (SE) amendment at a rate of 0.20 % (m/m) reduced the rice grains Cd and As concentrations by 15.99 % and 8.70 %, respectively, with the following mechanisms: 1) increasing the soil pH, electrical conductivity, and organic matter content (for Cd); 2) regarding the total soil bacteria (16S rRNA), SE-0.2 increased the abundances of Chloroflexi and decreased the abundances of Firmicutes, Bacillus, and Clostridium_sensu_stricto_1. Regarding the microorganisms involved in As methylation (arsM) in soil, SE-0.2 increased the abundances of Actinobacteria_d__bacteria, Firmicutes, Chloroflexi, and Rubriviva (for Cd and As); 3) promoting the formation of the iron plaque (for Cd and As). Collectively, SE can remediate Cd- and As-polluted soil and prevent the migration of Cd and As, thus ultimately resulting in decreased Cd and As in rice grains. This provides a new way to improve the utilization rate of agricultural waste resources and ensure the safe production of food.
With users’ increasing knowledge and intellectualization, users’ abnormal electricity consumption behaviors (AECB) are becoming more prevalent. Since access to renewable energy sources leads to a volatile and intermittent electricity load, the existing artificial intelligence methods are challenging to detect the AECB of users. To quickly and accurately identify the AECB of a massive number of users, this work proposes the GoogLeResNet3 network module, which contains fully connected layers, the Inception module, and a residual module. The GoogLeResNet3 network is compared with the GoogLeNet module, ResNet-50, ResNet-101, and 11 other neural networks. The results of the comparison experiments indicate that: the GoogLeResNet3 network with the highest accuracy is 335 s quicker than the second-fast network, and the accuracy is 10.57 % higher than the second-best network at least.
Slow-release fertilizer (SRF) is a new type of fertilizer, which is consistent with the physiological nutrient requirements of crops, but can also improve fertilizer utilization by reducing the total amount of fertilizers and the number of applications. However, its effects on soil fertility and bacteria in sugarcane fields are not clear. This study investigated the proper usage of SRF and the long-term application of SRF on soil fertility and health in sugarcane fields, seven fertilization treatments were set up as follows: SRF + 125 g/t long-acting agent (A), SRF +150 g/t long-acting agent (B), SRF + 235 g/t long-acting agent (C), SRF + 3 kg/t synergists (D), SRF + 8 kg/t synergists (E), SRF + 18 kg/t synergists (F) and traditional fertilization (CK). Traditional and Illumina HiSeq high-throughput sequencing technologies were used to compare and analyze soil enzyme activity, microbial biomass, and other biological traits and bacterial diversity. The results showed that sugarcane yields could be significantly increased by the application of SRFs. Meanwhile, soil fertility, which was represented by the content of total organic matter, nitrogen, phosphorus, and potassium, or the available nitrogen, phosphorus, and potassium, were all significantly improved or similar to those of CK. Moreover, the bioindicators of soil fertility, such as the activities of soil enzymes (β-Glucosidase, aminopeptidase, and phosphatase) and soil microbial biomass (carbon, nitrogen, and phosphorous), were also significantly increased or similar to those of CK in most of the SRFs treatments. In comparison with CK, soil microbial diversity, richness, and soil bacterial community structures were not significantly altered in sugarcane fields under different SRFs applications. In addition, although unclassified_f__Acetobacteraceae, Humibacter, and norank_f__Caulobacteraceae were the unique dominant bacterial genera in CK, some soil bacterial genera, such as unclassified__Subgroup_6, unclassified__Subgroup_2 and unclassified__IMCC26256 were also enriched as the special soil dominant bacterial genera in all or parts of the SRFs treatments. i.e., soil bacterial community structures also were not destroyed by SRFs application which compared to CK. In comparison to CK, cane yields increased significantly over three years, but soil fertility and health in sugarcane fields could also be improved or maintained by SRFs applications. All of the above results indicate that the traditional fertilization method for sugarcane production can be completely replaced by SRFs application. Furthermore, based on the cane yields and the prices of sugarcane and fertilizers in the local area, the treatment C can be concluded as the most cost-effective SRFs usage among the six SRFs treatments: A to F.
To attenuate the heat transfer deterioration of supercritical methane under high heat flux in mini channel, dimple structure is employed and studied numerically. Flow friction factor (f), heat transfer coefficient (HTC), Nusselt number (Nu) and performance evaluation criteria (PEC) are adopted to compare the heat transfer and flow characteristics between dimple and smooth channel. Besides, local velocity, turbulence kinetic energy (TKE), temperature and HTC are discussed. The results indicate that higher heat flux could deteriorate the heat transfer of supercritical methane resulted from the buoyancy and flow acceleration effect. While dimple enhances tremendously the heat transfer performance under high heat flux with enhanced factor up to 2.2, and there exists an optimal inlet temperature for the enhanced factor. On the other hand, the friction factor is worsened. However, PEC is positive up to 1.9 increased by the heat-mass ratio and mass flux. For enhanced mechanism, dimple induces vortex and raises the TKE, which strengthens the heat transfer between wall and bulk fluid.
Due to the nitrogen atmosphere during the preparation of h-BN, N atoms are inevitably introduced into h-BN crystals to form impurities. An in-depth understanding of the adsorption and diffusion behaviors of N impurities in h-BN will be conductive to further improve and formulate effective crystal growth strategies. In this study, the effects of N impurities on the structural stability, electronic structure and optical properties of h-BN were systematically studied by first-principles calculations. The results show that N impurities tend to be adsorbed at the top site of surface N atoms (TN) to form stable covalent bonds with host N atoms. The covalent N–N bond is along the inter-layer direction with a bond length of 1.57 Å, and the two N atoms are equidistantly distributed on both sides of the atomic layer. Within the h-BN lattice, the top site above the B–N bond (M) shares the same formation energy as the TN sites, which provides another stable adsorption site for N impurities. The diffusion of N impurities on the h-BN surface needs to overcome a large energy barrier of 2.64 eV. Relatively, N impurities are more prone to inter-layer permeability diffusion, which overcome a much lower diffusion barrier of 1.74 eV. In this case, the impurity N atoms do not diffuse directly through the atomic layers of h-BN, but the host N atoms with which they form covalent bonds leave the equilibrium lattice site and diffuse into the inner layers. When N impurities are adsorbed on the surface or inside of the h-BN lattice, the impurity energy levels are clearly introduced into the electronic band gap. Fortunately, these defect levels do not impair the optical absorption performance of the h-BN system. In contrast, in the crystal plane direction, N impurities can significantly enhance the optical response intensity of h-BN to ultraviolet light.
Plasma membranes, as the network for transferring information and constituents between cells and their microenvironment, play important roles in physiological and pathological processes. As the first defensive barriers of cells, cell membranes take further actions through changes in shapes, morphologies or components once they sense damages from exterior poison stimulations. Thus, the changes of plasma membranes are the direct indication for apoptosis induced by drugs, and non-destructive visualization of these changes is of great significance. Various fluorescent probes for real-time and in situ monitoring these changes of plasma membranes have been explored for more than 20 years. However, a concise review on this specific and important topic is still lacking, which may hamper the development of this field. In this review, the typical fluorescent probes for visualization the changes of plasma membranes during apoptosis in the past decades have been summarized. The representative examples have been classified into five categories according to the biological changes, which included the changes of phosphatidylserine (PS), changes in membrane composition, changes in membrane permeability, changes in morphologies and lipid order in plasma membranes. The basic principle, design strategies, sensing mechanisms and the applications of these fluorescent sensors that have been achieved were discussed. The perspectives and challenges with respect to this field were also presented. We anticipated that this review can promote the development of the probes for sensing the changes of plasma membranes during apoptosis, and facilitate the studies of drug discovery and other relative biomedical fields.
Nowadays, lithium-ion batteries are widely used in electric vehicles as the power source and its safety attracts increasing attention. Particularly, the thermal runaway is a highest risk needed to be solved. In order to effectively inhibit thermal runaway propagation, an efficient and energy-saving battery thermal management system is proposed in this study, which integrates phase change cooling, nanofluid cooling and heat insulation materials. Firstly, the system model is established by integrating electrochemical model, thermal model and fluid model, and the validity of the model is analyzed. Then the cooling performance of the system under two working conditions is discussed. In the normal heat dissipation condition, Scheme 6 can effectively inhibit thermal runaway propagation and reduce the maximum battery temperature from 1013.50 K to 328.34 K. In the extreme condition, Scheme 6 can reduce the maximum battery temperature from 980.51 K to 380.34 K, and the heat in the system can be taken away in time. In addition, the effects of the volume fraction of nanoparticles and the flow rate of nanofluids on the cooling performance are studied. Finally, for purpose of further improving the cooling performance, uniform-precision rotatable central composite design is used to establish the regression equation and determine the best combination factors. Comparing with Scheme 6, the maximum battery temperature in the improved scheme is reduced by 23%, and the economic index is reduced by 22%.
Photocatalytic technology has been widely studied because of using solar energy to degrade pollutants with high efficiency. However, rapid recombination of the photogenerated charges results in low efficiency and instability. In this study, a novel MIL-101(Fe)/Bi2WO6 heterojunction with high efficient for degradation of organic pollutants from wastewater under visible-light irradiation was synthesized. A ¹O2 dominant Fenton assisted photocatalytic system was constructed by superimposing the photo-fenton effect of MIL-101(Fe) itself. The photocatalytic degradation efficiency of MIL-101(Fe)/Bi2WO6-3% heterojunction under visible light irradiation is 10.0 times that of Bi2WO6. Specifically, the designed MIL-101(Fe)/Bi2WO6 photocatalyst showed excellent organic degradation performance under visible light. The results of this work not only supply a novel angle to the design and develop Z-scheme heterojunction photocatalysts but also identify its vital application value in the field of environmental remediation.
Practical engineering signals usually show the characteristics of non-stationary and nonlinear, while time-frequency (TF) analysis is an effective means to deal with such signals. To accurately capture the transient features of fast varying signals, an iterative reassignment method is proposed. Firstly, the limitations of the synchrosqueezing transform (SST), time-reassigned synchrosqueezing transform (TSST), and the reassignment method (RM) in signal processing are discussed. Then, an iterative process is applied to the RM method, and the implementation process of the algorithm is introduced. Finally, several simulated signals and two sets of experimental data are employed to verify the effectiveness of the proposed method. The results show that the proposed method can not only deal with harmonic-like signals, but also characterize impulsive-like signals. By comparisons, it is shown that the proposed method has the better ability to extract transient features of strongly time-varying signals and strongly time-varying signals.
Laser welding of Mg to Al dissimilar metals is of great significance in various industries, such as automobile, electrical, electronics, and energy systems, which promotes lightweight composite structure. However, Mg-Al intermetallic compounds (IMCs) have tremendous detrimental effects on the dissimilar metal joining of Mg to Al. In this work, dissimilar Mg/Al alloys joints with CoCrFeNi medium-entropy alloy (MEA) powders interlayer were achieved by the laser welding process, and the effect of different laser powers on the microstructure and mechanical properties of the joints was analyzed, reaching a maximum tensile shear strength of 100.12 MPa. The formation mechanism of the interface was investigated by experimental investigations and thermodynamic calculations. In addition, a numerical simulation of the laser welding process was carried out to reveal the temperature field of the molten pool and the flow change process. Based on the law of multi-component HEA phase formation, the interfacial metallurgical products were calculated and analyzed. The results of the energy dispersive spectroscopy show that the molten CoCrFeNi MEA powders reacted with Al to form Al-rich AlxCoCrFeNi phase, due to a more negative generation enthalpy compared to Mg-Al IMCs. With the increase of laser power, the Al-rich AlxCoCrFeNi phase mixed layer went through four states of agglomeration, continuity, interruption and dispersion, while the IMCs at the interface gradually increased and finally tend to be continuity. Due to the physical barrier of MEA and formation of Al-rich AlxCoCrFeNi phase, the formation of the Mg-Al IMCs phase was effectively suppressed, which improved the shear properties of the joints. The material- microstructure-property relations was established in this work, which facilitated the development of laser welding technology for Mg and Al alloys.
In medical image segmentation tasks, fully-supervised learning has been a huge success by using abundant labeled data. However, it is time-consuming and expensive for technicians to label medical images. In this paper, we propose a novel framework for semi-supervised medical image segmentation, named Uncertainty-aware Pseudo-label and Consistency. Our framework is made up of the student–teacher models. The supervised loss on labeled data and the consistency loss on both labeled and unlabeled data are weighted and combined to optimize the models. Our method combines the recent state-of-the-art semi-supervised methods, which are consistency regularization and pseudo-labeling. More importantly, we calculate the Kullback–Leibler variance between the student model’s prediction and the teacher model’s prediction as uncertainty estimation, and directly use the uncertainty to rectify the learning of noisy pseudo-labels, instead of setting a fixed threshold to filter the pseudo-labels. Experiments on the Left Atrium dataset show that our method can efficiently utilize unlabeled data to achieve high performance and outperform other state-of-the-art semi-supervised methods. In addition, we have also analyzed its difference from conventional methods of consistency regularization and pseudo-labeling in semi-supervised medical image segmentation. Code is available in https://github.com/GXU-GMU-MICCAI/UPC-Pytorch.
Steinberg conjectured in 1976 that every planar graph with no cycles of length four or five is 3-colorable. This conjecture is disproved by constructing a planar graph with no cycles of length four or five but intersecting triangles. Jin et al. proved that plane graphs without 4- and 5-cycles and without ext-triangular 7-cycles are 3-colorable [SIAM J. Discrete Math. 31 (3) (2017) 1836–1847]. In this paper, we point out a mistake of their proof and give an improved proof.
Minimizing the catalyst cost is critical to achieving long-term economic feasibility for obtaining bio-chemicals directly from biomass. In the present study, low cost ZSM-5 was synthesized via hydrothermal crystallization of the activated aluminosilicate gel without adding any organic ammonium templates and additional seed crystals. The activation process promoted the depolymerization of the silica-alumina species and the formation of active pasty gel containing nucleation precursors, consequently accelerating the subsequent crystallization reaction. The addition of eucalyptus sawdust (ES) in the synthesis process increased the BET (Brunauer-Emmett-Teller) surface area and acidity of the resultant zeolites. The ES-mediated ZSM-5 (AGZ-10) achieved a higher benzene, toluene, ethylbenzene and xylene (BTEX) carbon yield (18.9 %) from co-cracking of ES and waste plastics (WP) (ES/WP mass ratio at 1:1) at 500 °C and exhibited better synergistic effect and anti-coking ability compared to the commercial ZSM-5. The results provide an efficient synthetic strategy to produce low-cost zeolites, and a promising technique to effectively utilize biomass and plastic wastes.
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