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Fine particulate matter (PM

PurposeThe purpose is to explore the essential reasons for the differences between book awakening phenomena, to develop the critical factors in awakening the slumbering collections and to provide a reliable basis for maximizing book value and optimizing collection allocation.Design/methodology/approachThe research employs the integrated learning algorithm XGBoost to measure driving factors. In the process of book circulation, the characteristics of collections and readers are worthy of attention. Therefore, this study also carries out feature selection and model construction from the two dimensions of books and readers.FindingsThe results show that reader features have a stronger impetus for the collection awakening phenomenon than collection features. Among reader features, education level, gender and major subject are the main factors, which are followed closely by the activity level; among collection features, publication date and price are the main driving factors. The indicators of book popularity are not significant, whose effect on the phenomenon of collection awakening is almost negligible.Originality/valueThis study aims to augment the theory of zero circulation from the theoretical level and, for the first time, seeks to define the phenomenon of collection awakening. This study attempts to present novel ideas for research in the field of libraries and to provide references for optimizing collection and maximizing the value of books.

Learning and extracting high-level features from point cloud is the key to improving the segmentation performances on point clouds for many networks. At present, many networks present very deep structures to extract high-level features for 3D perception. However, we argue that even better results can be achieved by (i) building feature vectors that integrates multi-scale geometric features, and (ii) exerting discriminative constraints on the learning of mid-levels features. In this paper, we propose a Multi-scale Neighborhood Feature Extraction and Aggregation Model (MNFEAM) to enhance feature extraction for point cloud learning. We try to first extract multi-scale neighborhood information for each input point and then aggregate local information of a mid-level locality feature space, and finally integrate the aggregated local and the global feature vectors. A new discriminative loss function is designed to strengthen the coarse semantics on mid-levels features so that the semantic abstraction process can be improved and accelerated. We improve the performances of three popular networks for point cloud segmentation using the proposed MNFEAM on standard 3D datasets.

In this research, the microstructure evolution, mechanical properties, and creep mechanisms of Mg-12Gd-1MM-0.6Zr (wt%) alloy under different conditions were systematically studied using scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray diffraction (XRD), and tensile creep tests. Regarding the microstructure of the as-cast sample, the average grain size is about 42 μm, and the eutectic compounds were determined to be Mg5(Gd0.8MM0.2). During homogenization, these eutectic compounds gradually dissolve, and Mg12MM particles are precipitated. During hot extrusion, complete dynamic recrystallization (DRX) occurs, resulting in equiaxial grains with an average grain size of about 12 μm and the formation of streamlines consisting of Mg12MM particles along the extrusion direction (ED). After T5 treatment (225 °C for 7 h), a large number of β′(Mg7Gd) phases are precipitated on the {11–20}α habit plane and are interconnected, forming an interlaced network structure. The ultimate tensile strength (Rm = 405 MPa) and yield strength (RP0.2 = 288 MPa) of the T5 sample are significantly higher than those of the as-extruded sample (Rm = 289 MPa, RP0.2 = 185 MPa), but the elongation (A = 4%) was remarkably lower than that of the as-extruded sample (A = 18%). When crept at 225 °C under 100 MPa, the steady-state creep rates of the as-cast, as-extruded, and T5 samples are 1.59 × 10–8, 1.08 × 10–8, and 1.40 × 10–8 s–1, respectively, and their total strains within 100 h are respectively breaking, 0.81%, and 0.92%, indicating that the as-extruded alloy exhibits the best creep resistance. TEM analysis reveals that, during the creep process of the T5 sample, the β′ particles coarsen and the precipitate-free zones (PFZs) widen, which increase the steady-state creep rate and the total strain within 100 h as compared with the as-extruded sample.

Sulfate is a major PM

Ambient PM

Reducing sulfur poisoning is significant for maintaining the catalytic efficiency and durability of heterogeneous catalysts. We screened PdAu nanoclusters with specific Pd : Au ratios based on Monte Carlo simulations and then carried out density functional calculations to reveal how to reduce sulfur poisoning via alloying. Among various nanoclusters, the core-shell structure Pd13Au42 (Pd@Au) exhibits a low adsorption energy of SO2 (-0.67 eV), comparable with O2 (-0.45 eV) and lower than CO (-1.25 eV), thus avoiding sulfur poisoning during the CO catalytic oxidation. Fundamentally, the weak adsorption of SO2 originates from the negative d-band center of the shell and delocalized charge distribution near the Fermi level, due to the appropriate charge transfer from the core to shell. Core-shell nanoclusters with a different core (Ni, Cu, Ag, Pt) and a Pd@Au slab model were further constructed to validate and extend the results. These findings provide insights into designing core-shell catalysts to suppress sulfur poisoning while optimizing catalytic behaviors.

Recently, shadow detectors based on Deep Convolutional Neural Networks (DCNN) have lifted the detection performances to a new height. However, the correct detection of mild and very dark shadows remains to be a difficult task. In this work, we propose a new model called Double-stream Atrous Network (DSAN) for shadow detection. It has a double-streams framework: the pooling stream extracts high-level features and the residual stream incorporates low-level features with high-level feature maps from the pooling stream in each single step. We also design new modules such as Atrous Convolution Module (ACM), Multi-layer Atrous Pooling Module (MLAPM), and Cross-stream Residual Module (CSRM) to extract shadow features effectively. On two shadow datasets, our DSAN outperforms several popular shadow detectors based on DCNN.

The microstructure and thermophysical properties of Mg−2Zn−xCu alloys (x=0.5, 1.0 and 1.5, at.%) were investigated through microstructural and thermophysical characterization, heat treatment, and first-principles calculations. It was found that the addition of Cu had influence on the microstructure and thermophysical properties of the alloy. As the Cu content increased, the content of the MgCuZn phase increased in the as-cast alloys along with the electrical and thermal conductivities. After solution treatment, the eutectic structure partially decomposed and Zn atoms dissolved into the matrix, leading to the decrease in both the electrical and thermal conductivities of the alloy. During the early stages of the aging treatment, solute atoms precipitated from the matrix, thus increasing the electrical conductivity of the alloy. After aging for 24 h, the thermal conductivity of Mg−2Zn−1.5Cu alloy reached the maximum of 147.1 W/(m·K). The thermostable MgCuZn phases were responsible for increasing the electrical and thermal conductivities. Smaller amounts of Zn atoms dissolved in the matrix resulted in smaller lattice distortion and higher conductivities. The first-principles calculations findings also proved that the MgCuZn phases had very high conductance.

The morphology and crystal structure of the precipitates in Mg−7Gd−3Y−1Nd−1Zn−0.5Zr (wt.%) alloy with fine plate-like 14H-LPSO structures aged at 240 °C were investigated using transmission electron microscopy (TEM) and high-angle annular dark-field scanning transmission electron microscopy (HAADF-STEM). Fine plate-like 14H-LPSO structures precipitate after heat treatment at 500 °C for 2 h, and β-type phases precipitate after the alloy is aged at 240 °C. The long-period atomic stacking sequence of 14H-LPSO structures along the [0001]α direction is ABABCACACACBABA. After being aged at 240 °C for 2 h, the β-type phases are the ordered solution clusters, zig-zag GP zones, and a small number of β′ phases. The peak hardness is obtained at 240 °C for 18 h with a Brinell hardness of 112, the β-type phases are β’ phases and local RE-rich structures. After being aged at 240 °C for 100 h, the β-type phases are β’, β1 and β’F phases. β′ phases nucleate from the zig-zag GP zones directly without β″ phases, and then transform into β1 phase by β’→β’F→β1 transformations. The Zn not only can form LPSO structure, but also is the constituent element of β1 phases. LPSO structures have a certain hindrance to the coarsening of β’ and β1 along 〈0001〉α.

The effects of minor Zn (0.2 at%, 0.4 at%, 0.6 at%) on the microstructures and mechanical properties of Mg-1.4Gd-1.2Y-0.15Zr (at%) alloys were systematically explored. Results reveal that increasing Zn content leads to the increase of the intergranular phases and the change of their composition from Mg24(Gd,Y)5 phase and (Mg,Zn)3(Gd,Y) phase to 18R-LPSO phase and (Mg,Zn)3(Gd,Y) phase. Mg24(Gd,Y)5 phase is body-centered cubic structure and shares the same lattice constant with Mg24Y5 while (Mg,Zn)3(Gd,Y) phase is face-centered cubic structure with lattice constant of 0.72 nm, slightly lower than Mg3Gd. 18R-LPSO structure is identified to be monoclinic with c-axis not strictly vertical to the bottom surface but 93.5°. The growth patterns of intergranular phases change from the divorced growth to coupled growth as compositions change. Moreover, the mechanical performance improves with Zn rising, ascribed to the decrease of brittle phases at grain boundaries and the increase of LPSO structure phases.

A method of recovering the potential of the fourth-order binomial operator on a half-interval [1/2, 1] using a known potential on another half-interval [0, 1/2] and the eigenvalues of the self-adjoint boundary problem on the whole interval [0, 1] is proposed.

Soil heavy metal contamination was a global environmental issue that posed adverse impacts on ecological and human health risks. The controlling of soil heavy metal is mainly focused on the emission source and pipe-end treatment, less is known about the intermediate controlling process. The migration route of heavy metals exhibited the spatial evolution of pollutants from the sources to the pipe-end, which provided the more reasonable location for the target-oriented treatment of soil heavy metal. Here, we proposed a new view of heavy metal similarity, which quantitatively expressed how closely of the contaminations between the study area and the test areas. We found that the similarity of different heavy metals was unequally distributed across locations that were related with five main sources, namely agricultural activities, natural sources, traffic emissions, industrial activities, and other sources. Based on the similarity, a state-of-the-art machine learning method was applied to delineate the migration route of soil heavy metals. Thereinto, As was concentrated around livestock farms, and its migration route was close to the water system. Cd migration route was over-dispersed in the areas where located mine fields and chemical plants. Migration routes of Hg and Pb were along rivers, which were related to agricultural activities and natural sources. Overall, the perspective on similarity and migration routes provided theoretical basis and method to alleviate soil heavy metal pollution at regional scale and can be extended across largescale regions.

PM

Thermomechanical treatment T10 (extension twinning + aging treatment) can largely enhance the precipitation strengthening effect of magnesium alloys. In this study, dislocation structure evolution and precipitation behavior during T10 treatment of an AZ80 extruded bar were analyzed mainly by two-beam diffraction in TEM. At a compressive strain of 1% in the extrusion direction (ED), a typical dislocation configuration, including basal I1 stacking faults (SFs) and 〈c + a〉 dislocations, has been established in extension twins. As the strain reaches 7%, the volume fraction of extension twins increases to more than 90% at which point high dense I1 SFs and 〈c + a〉 dislocations occur. After aging for 2 h at 150 °C for the 7% strained sample, masses of basal I1 SFs and 〈c + a〉 dislocations remain in the extension twins and can act as effective nucleation sites and solute fast-diffusion channels for continuous precipitates. Consequently, the precipitates in extension twins become highly dense.

Rare earth elements and the LPSO phase play a significant part in the optimal regulation of the comprehensive performance of the Mg-9Y-1MM-1Zn-0.6Zr (WE91B) alloy, but the high strength of the alloy also brings problems such as difficulties in processing deformation. To determine optimal machining parameters suitable for WE91B, the homogenized alloy was investigated in constant temperature and equal rate compression using the Gleeble-3500 thermal simulation measurement instrument, to study the hot deformation behavior of WE91B alloy in the temperature range of 693-783 K, strain rates of 0.001-1s−1 and maximum deformation of 60%. The deformation activation energy was calculated, the constitutive equation of WE91B alloy was constructed, and the processing map of the alloy was established according to the dynamic material model. The results show that the flow stress curves of the alloy exhibit typical dynamic recrystallization characteristics and the flow stress increases significantly with the increase in strain rate and decrease in deformation temperature. The hot deformation activation energy of WE91B alloy is about 331.605 kJ/mol. From the two aspects of processing map and microstructures after plastic deformation, the optimal processing process of WE91B alloy is T = 783 K and έ = 0.05 s−1.

In the development process of intelligent construction, the safety assessment of prestressed steel structures as an important research direction has become more and more attractive in academia. Digital twins (DTs) is the key technology to realize intelligent construction. The virtual and real interaction of the DTs can provide an efficient management and control mechanism for the construction process. This research proposes an intelligent safety assessment method of prestressed steel structures based on DTs. In this research method, the structural safety assessment is divided into two aspects: performance analysis and maintenance. By analyzing the characteristics of the construction safety assessment, a DTs framework for construction safety assessment is built. Driven by the DTs framework, a physical space model and a virtual space model are constructed. On the basis of virtual and actual interaction, multidimensional information fusion of time and space is carried out to realize the analysis of structural safety performance. On this basis, the paper establishes a Bow-tie model for the maintenance modeling of unsafe construction events. Moreover, the theoretical method formed is applied to the construction of a symmetrical structure (wheel–spoke cable truss). The validity of the method is verified by comparing the cable force calculated by the theoretical method and measured on site. The assessment method driven by the DTs ensures the structural safety and improves the intelligence level of safety management and control of the structure construction.

The inverse problem for the discrete analogue of the exterior transmission eigenvalue problem with a spherically symmetric index of refraction is considered. For the absorbing media, we show that t...

As a plant organ with the largest surface area, leaves are the main place where photosynthesis and respiration take place. High-throughput phenotyping of crop leaves is of great significance for breeding, growth monitoring, and increasing crop yield. Due to the highly complex and diversified plant structures, automated leaf segmentation and phenotypic feature extraction remain to be challenging tasks. In this article, we propose a novel five-stage framework that comprises multiview stereo point cloud reconstruction, preprocessing, stems removal in canopy, leaf segmentation, and leaf phenotypic feature extraction to carry out leaf phenotyping on two types of ornamentals-Maranta arundinacea and Dieffenbachia picta. The phenotypic traits such as the leaf area, leaf length, width, and leaf inclination angle for each single leaf are calculated and compared with ground truths. The experimental results show that the average accuracy of calculated leaf area of the two species reached 96.8% and 97.8%, respectively. The average errors of both the calculated leaf length and width of Maranta arundinacea are less than 4.0%, and for Dieffenbachia picta, the average errors of calculated leaf length and width are both no higher than 4.7%. The average errors of calculated leaf inclination angle for the two plant species are 2.9° and 3.0°, respectively.

This paper deals with the Eigenvalue estimates for Laplace operators with anti-Kirchhoff conditions on compact trees. The results include lower estimates for λ k ( Γ ) involving the edge lengths or diameter of Γ when Γ is a star, and upper estimates for λ k ( Γ ) involving the average edge length or the diameter of Γ when Γ is a general tree. We get the upper eigenvalue estimates by proving a monotonicity principle for the eigenvalues with respect to the domain (graph). Two geometric versions of the Ambartsumian theorem for − Δ Γ are also proven.

Atmospheric particulate matter pollution has attracted much wider attention globally. In recent years, the development of atmospheric particle collection techniques has put forwards new demands on the real-time source apportionments techniques. Such demands are summarized, in this paper, as how to set up new restraints in apportionment and how to develop a non-linear regression model to process complicated circumstances, such as the existence of secondary source and similar source. In this study, we firstly analyze the possible and potential restraints in single particle source apportionment, then propose a novel three-step self-feedback long short-term memory (SF-LSTM) network for approximating the source contribution. The proposed deep learning neural network includes three modules, as generation, scoring and refining, and regeneration modules. Benefited from the scoring modules, SF-LSTM implants four loss functions representing four restraints to be followed in the apportionment, meanwhile, the regeneration module calculates the source contribution in a non-linear way. The results show that the model outperforms the conventional regression methods in the overall performance of the four evaluation indicators (residual sum of squares, stability, sparsity, negativity) for the restraints. Additionally, in short time-resolution analyzing, SF-LSTM provides better results under the restraint of stability.

Automatic leaf segmentation, as well as identification and classification methods that built upon it, are able to provide immediate monitoring for plant growth status to guarantee the output. Although 3D plant point clouds contain abundant phenotypic features, plant leaves are usually distributed in clusters and are sometimes seriously overlapped in the canopy. Therefore, it is still a big challenge to automatically segment each individual leaf from a highly crowded plant canopy in 3D for plant phenotyping purposes. In this work, we propose an overlapping-free individual leaf segmentation method for plant point clouds using the 3D filtering and facet region growing. In order to separate leaves with different overlapping situations, we develop a new 3D joint filtering operator, which integrates a Radius-based Outlier Filter (RBOF) and a Surface Boundary Filter (SBF) to help to separate occluded leaves. By introducing the facet over-segmentation and facet-based region growing, the noise in segmentation is suppressed and labeled leaf centers can expand to their whole leaves, respectively. Our method can work on point clouds generated from three types of 3D imaging platforms, and also suitable for different kinds of plant species. In experiments, it obtains a point-level cover rate of 97% for Epipremnum aureum, 99% for Monstera deliciosa, 99% for Calathea makoyana, and 87% for Hedera nepalensis sample plants. At the leaf level, our method reaches an average Recall at 100.00%, a Precision at 99.33%, and an average F-measure at 99.66%, respectively. The proposed method can also facilitate the automatic traits estimation of each single leaf (such as the leaf area, length, and width), which has potential to become a highly effective tool for plant research and agricultural engineering., 24 Pages, 18 Figures, 7 Tables. Intends to submit to an open-access journal

The submicron particulate matter (PM

This paper deals with the spectral properties of self-adjoint Schrödinger operators with δʹ-type conditions on infinite regular trees. Firstly, we discuss the semi-boundedness and self-adjointness of this kind of Schrödinger operator. Secondly, by using the form approach, we give the necessary and sufficient condition that ensures that the spectra of the self-adjoint Schrödinger operators with δʹ-type conditions are discrete.

A double-chain quaternary ammonium salt of dioctyl dimethyl ammonium chloride (DDAC8) was first used as a low-cost template to prepare the mesostructured zirconia nanomaterials in an alcohol-thermal system. The material was calcinated to obtain pure tetragonal crystalline zirconia ( t -zirconia) and thus has significantly improved original structure properties, including a narrow pore size distribution of 4–7 nm and a high surface area of 195 m 2 /g. The t -zirconia hierarchical nanoparticle aggregate material is different from the smooth microsphere material obtained using the single chain quaternary ammonium salt template. The characteristic rough surface morphology results from the grid distribution form of the DDAC8 micelles in the ethanol solution and caused the formation of more defect sites, which demonstrate the potential for possible catalytic applications. A formation mechanism of the mesoporous zirconia nanomaterial in the alcohol-thermal system of the surfactants is reasonably explained. The synthesis system may provide a new approach to the preparation of mesoporous nanomaterials.