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Pseudo threshold pressure gradient (PTPG) is one of the main factors affecting the oil recovery of heterogeneous low-permeability and tight reservoirs. When the pressure gradient between the supply boundary and the bottom hole is less than PTPG, the crude oil in the formation cannot flow. In order to accurately measure the relationship between PTPG and permeability, a novel experimental device has been designed and a series of core flooding experiments using brine and oil have been carried out. The new experimental device eliminates the influence of rubber sleeve in core holder and core end-effect on the results, and greatly improves the accuracy of pressure measurement. The experimental results show the PTPG has a power function relationship with permeability, and has no obvious relationship with the viscosity of the fluid. Based on the equation for PTPG and permeability of crude oil and brine fitted by the experimental data, a new heterogeneous reservoir mathematical model considering PTPG were established. The influence of the coefficient of permeability variation, average permeability, and the production rate on oil recovery factor have been studied by this model. The results clearly show that the coefficient of permeability variation has the greatest influence on oil recovery factor, which represents inhomogeneity in low-permeability and tight reservoirs. Also, when the coefficient of permeability variation is less than a certain value (about 0.2 when average permeability is 1mD, in our model), there is only a slight decline in oil recovery factor, and when coefficient of permeability variation is larger than this value, the oil recovery factor decreases rapidly. The finding is consistent with oil field production and helpful to determine whether to take EOR(enhanced oil recovery) measures for a low-permeability heterogeneous reservoirs.

Abrasive flow precision machining technology is an effective technology for precision machining special-shaped curved surface products in the mechanical field. In order to explore the vortex formation mechanism of special inner curved surface machined by abrasive flow precision machining, taking the polygonal spiral curved surface tube as the research object, the large eddy simulation method is used to analyze the precision machining behavior of abrasive flow and the effect of vortex cluster on the wall under different inlet pressure. The motion law of abrasive is expounded, the formation mechanism of the fluid vortex is revealed, and the mechanism of abrasive micro-cutting is discussed. The results show that the inlet pressure of abrasive flow plays an important role in the machining quality of abrasive flow. With the increase of inlet pressure, the precision machining quality of abrasive flow is improved accordingly. Properly increasing the inlet pressure of the abrasive flow can effectively improve the inner surface machining quality of the polygonal spiral curved surface tube, and the abrasive flow precision machining technology can obtain the ideal surface quality and improve the surface accuracy of the machined workpiece.

Plain medium carbon steel with multiscale ferrite (α) + cementite (θ) lamellar structure was produced by warm rolling and subsequently annealing. The effect of annealing time on microstructural evolution was studied via scanning electron microscope and electron backscatter diffraction. The obtained results showed that the feature of multiscale lamellar microstructure remained virtually the same with annealing times from 10 to 60 minutes, while the increase of annealing time destabilizes the stability of the microstructure. The specimen annealed for 30 minutes demonstrated good combination of strength and ductility because of the improvement of strain strengthening ability. The strain hardening behavior was described by instantaneous strain hardening exponent ( $$ n_{i} $$ ) and modified Crussard–Jaoul (C–J) model. Furthermore, the respective strengthening contribution of grain refinement, sub-grain strengthening and cementite particles to the yield strength was investigated in detail.

As a flexible processing technology, the abrasive flow machining (AFM) technology can effectively finish the complex cavities and inner channels surface. In order to improve the inner surface quality of the engine fuel nozzle, optimize the ejection quality and atomization performance of the nozzle, and realize the precision machining surface of the nozzle, this paper explores the precision machining behavior and surface quality control of the nozzle by AFM containing magnetic particles through theoretical analysis, numerical simulation, and experimental research. After machining, the Ra value of inner surface roughness of the nozzle bore area was significantly improved, from the initial 0.336 μm to 0.065 μm. Based on the experimental statistical analysis, the significant factors affecting AFM were established, the optimal process combination of AFM was established, and the prediction of AFM on the inner surface of the nozzle was realized. Then, the quantitative cutting of nozzle and the quality control of AFM are realized.

In practice, charts are widely stored as bitmap images. Although easily consumed by humans, they are not convenient for other uses. For example, changing the chart style or type or a data value in a chart image practically requires creating a completely new chart, which is often a time-consuming and error-prone process. To assist these tasks, many approaches have been proposed to automatically extract information from chart images with computer vision and machine learning techniques. Although they have achieved promising preliminary results, there are still a lot of challenges to overcome in terms of robustness and accuracy. In this paper, we propose a novel alternative approach called Chartem to address this issue directly from the root. Specifically, we design a data-embedding schema to encode a significant amount of information into the background of a chart image without interfering human perception of the chart. The embedded information, when extracted from the image, can enable a variety of visualization applications to reuse or repurpose chart images. To evaluate the effectiveness of Chartem, we conduct a user study and performance experiments on Chartem embedding and extraction algorithms. We further present several prototype applications to demonstrate the utility of Chartem.

Submodular maximization is well studied in the fields of data mining and machine learning. We study the submodular maximization subject to a cardinality constraint k for large scale scenarios applications under two combined settings. One is that all elements arrive in a streaming fashion, and the other is that some elements may be invalid at last. For this problem, which is called streaming robust submodular maximization (SRSM) problem, we explore an approximation algorithm, returning a subset S from the ground set V with a limit size, such that it represents V and is robust to a broken set E well. Our algorithm only makes one pass over data, and achieves a constant-factor 0.1224 approximation guarantee, independent of the cardinality constraint parameter k . Based on the algorithm for SRSM problem, we continue to discuss this problem over sliding windows, in which we are asked to obtain a proper set that only considers the last W elements, and derive an algorithm with a constant ( 0 . 0612 − ϵ ) -approximation guarantee. At last we also propose numerical experiments on some applications to well demonstrate our algorithm for SRSM problem over sliding windows.

Li3VO4 particles anchored on reduced graphene oxide hybrid (Li3VO4/rGO) are firstly fabricated through a solvent thermal method at low temperature and atmospheric pressure. In the Li3VO4/rGO, rGO reduces the particle size of Li3VO4 and improves the electronic conductivity of the hybrid, resulting in excellent reaction kinetics and high pseudocapacitive charge storage of 69.3%, giving rise to superb electrochemical performance as anode for Li-ion batteries. After 200 cycles at 0.15 A g−1, the Li3VO4/rGO electrode delivers high discharge capacity of 410.7 mAh g−1, which is distinctly improved than that of pristine Li3VO4 obtained under similar condition (274.1 mAh g−1 after 200 cycles). Moreover, the Li3VO4/rGO electrode also exhibits excellent rate capability, showing high discharge capacity of 249.5 mAh g−1 at a specific current of 1.6 A g−1. The low temperature and atmospheric pressure fabrication approach is easy to scale up, which is advantageous for practical application.

In the spherical k-means problem (SKMP), which is a well-studied clustering problem in text mining, we are given an n-point set D in d-dimensional unit sphere S d , and an integer k ≤ n . The goal is to find a center subset S ⊂ S d with | S | ≤ k that minimizes the sum of cosine dissimilarity measure for each point in D to the nearest center. We prove that any γ-approximation algorithm for the k-means problem (KMP) can be adapted to the SKMP with 2γ-approximation ratio. It follows that there is a local search ( 18 + ϵ ) -approximation algorithm for the SKMP, by leveraging the classical local search ( 9 + ϵ ) -approximation algorithm for the KMP. Therefore, an interesting problem arises, that is whether there exists an approximation algorithm using local search scheme directly for the SKMP. In this paper, we present a local search approximation algorithm for the SKMP and prove its performance guarantee is ( 2 ( 4 + 7 ) + ϵ ) . We also conduct numerical computation to show the efficiency of the local search approximation algorithm by single-swap operation in the end.

Sodium vanadate, Na3VO4, is prepared for the first time as a new anode material for lithium-ion batteries (LIBs), showing satisfactory capacity, an ultra-long cycle life and excellent rate capability. When cycling at a current of 0.1 A g−1, the Na3VO4 electrode delivers a high reversible capacity of 535.2 mA h g−1 after 610 cycles. Then cycling at a current of 1 A g−1, the Na3VO4 electrode still retains a discharge capacity of 208 mA h g−1 after 2000 cycles. After rate performance testing from 0.1 to 1.6 A g−1 over 210 cycles, the Na3VO4 electrode retains a satisfactory capacity of 350 mA h g−1, when the current returns to 0.1 A g−1. The superior comprehensive electrochemical performance and low cost of Na3VO4 endow it with great potential for practical applications in LIBs.

Fractured-vuggy carbonate reservoirs have complex geological structures including pores, caves and fractures, which causes frequent working system adjustments and makes the production prediction extremely challenging. Currently, the most widely used methods in such prediction are the water drive characteristic curve methods and machine learning based models. However, frequent working system adjustments (such as well shut-in) could make water drive characteristic curve unstable, which provides unsatisfactory accuracy of the prediction model. In this paper, by integrating the variational mode decomposition (VMD) and gated recurrent unit (GRU), a novel machine learning based prediction model termed VMD-GRU is proposed to address the limitations of the water drive characteristic curve methods. The time-series production data are firstly decomposed by using VMD into several sub-series that represent different characteristics of the data. GRU is used to establish autoregression model, which can extract the inner characteristics of each sub-series and make prediction. The final prediction outputs are obtained by aggregating prediction result of each GRU model. The proposed VMD-GRU model is verified with the real-world production data from the Tahe oilfield of China. The experimental results demonstrate that the proposed VMD-GRU model outperforms the existing production prediction models for fractured-vuggy carbonate reservoirs.

Li3VO4 is regarded as a promising anode material for Li-ion batteries due to its advantages of safety and low volumetric variation. However, unscalable synthesis makes the practical application of Li3VO4 challenging. Herein, state-of-the-art Li3VO4/N doped C porous microspheres comprising Li3VO4 nanoparticles encapsulated in interconnected N doped C networks (LVO/NC PMSs) are designed and synthesized via a scalable spray drying approach. Ultra-high capacity, ultra-long cycle life and prominent rate capability are achieved in the LVO/NC PMS electrode. When cycling at 0.1 A g−1, it delivers a reversible capacity of 610 mA h g−1 after 200 cycles. At a high constant charge current of 2.0 A g−1, it delivers stable cycling over 5000 cycles at a high discharge current of 4.0 A g−1, and the reversible capacity is completely restored after 150 cycles of rate performance testing with discharge current from 0.2 to 4.0 A g−1. Moreover, Li3V2(PO4)3 microspheres (LVP MSs) are also prepared using the scalable spray drying approach, and a new all-vanadate-based LVO//LVP full cell is designed for the first time. The full cell delivers a high energy density of 300 W h kg−1 which is higher than that of most of the Li4Ti5O12 and LVO-based full cells, and a long lifespan (340.7 mA h g−1 after 1000 cycles at 1.0 A g−1) which meets the requirements of commercialization. The scalable synthesis of LVO/NC PMSs and LVP MSs with excellent performance and the configuration of the LVO//LVP full cell with high energy density and long cycle life may pave the way for LVO for practical applications.

A mononuclear complex Cu(L1)2·CH3OH·H2O (L1: 3,5-dibromo-L-tyrosine) was synthesized and characterized by IR, elemental analysis, and X-ray diffraction single-crystal analysis. Each Cu2+ ion is fou...

Self-assembled Fe3O4 nanoparticles are attracting more and more interests in biomedical field, such as dual photoacoustic devices and magnetic resonance imaging. At present, however, the preparation of self-assembled Fe3O4 nanoparticles mainly relies on the modifications of some toxic polymers, such as 4-vinylpyridine, polyacrylonitrile, and phenol formaldehyde resin. Additionally, the synthetic methods used were too complicated. The biological toxicity and complex methods significantly limit the biomedical applications of self-assembled Fe3O4 nanoparticles. In this work, natural polysaccharide chitosan was used to modify Fe3O4 nanoparticles. The self-assembled Fe3O4 nanochains were obtained easily by controlling the ratio of chitosan and Fe3O4 nanoparticles. The formation mechanism of Fe3O4 nanochains was proposed and demonstrated by experiments and numerical simulation. It is found that the thickness of chitosan is a key factor that affects the magnetic field distributions and magnetic attraction of Fe3O4 nanoparticles. The prepared chitosan-modified Fe3O4 nanochains would be a promising candidate with good biocompatibility for biomedical applications.

The simple homodinuclear M-M single bonds for group II and XII elements are difficult to obtain as a result of the fulfilled s2 electronic configurations, consequently, a dicationic prototype is often utilized to design the M+ −M+ single bond. Existing studies generally use sterically bulky organic ligands L− to synthesize the compounds in the L− −M+ −M+ −L− manner. However, here we report the design of Mg-Mg and Zn-Zn single bonds in two ligandless clusters, Mg2B7− and Zn2B7−, using density functional theory methods. The global minima of both of the clusters are in the form of M22+(B73−), where the M-M single bonds are positioned above a quasi-planar hexagonal B7 moiety. Chemical bonding analyses further confirm the existence of Mg-Mg and Zn-Zn single bonds in these clusters, which are driven by the unusually stable B73− moiety that is both σ and π aromatic. Vertical detachment energies of Mg2B7− and Zn2B7− are calculated to be 2.79 eV and 2.94 eV, respectively, for the future comparisons with experimental data.

A novel growth phenomenon and mechanism in which carbon nanocoils (CNCs) were successfully synthesized using multiple stacked catalyst particles are described in this paper. In this study, the α-Fe2O3/SnO2 with a particle size distribution of 20–65 nm is prepared using a one-pot hydrothermal reaction. By increasing the areal number density of the catalyst nanoparticles on the substrates, the morphology of as-grown CNCs changes from twisted to spring-like, and the average line diameter of the CNC gradually increases. Fe-containing particles (Fe and Fe3C) are the main catalytic components in a Fe/Sn catalyst system, decomposing C2H2 and depositing a carbon filament. The existence of Sn and SnO2 reduce the catalytic activity of Fe-containing particles. The mechanism of CNC growth is attributed to the multiple particles at the tip of a CNC with different catalytic activities corresponding to different growth rates of the carbon filaments. This proposed strategy using catalysts with variable densities has shown to be a simple and prospective pathway toward the controllable synthesis of CNCs. More importantly, this new interpretation of the CNC growth mechanism and synergistic effect between binary catalysts will promote fundamental research into the growth mechanisms for CNCs or other chiral materials.

In this paper, we consider the $$\tau $$ -relaxed soft capacitated facility location problem ( $$\tau $$ -relaxed SCFLP), which extends several well-known facility location problems like the squared metric soft capacitated facility location problem (SMSCFLP), soft capacitated facility location problem (SCFLP), squared metric facility location problem and uncapacitated facility location problem. In the $$\tau $$ -relaxed SCFLP, we are given a facility set $$\mathcal {F}$$ , a client set and a parameter $$\tau \ge 1$$ . Every facility $$i \in \mathcal {F}$$ has a capacity $$u_i$$ and an opening cost $$f_i$$ , and can be opened multiple times. If facility i is opened l times, this facility can be connected by at most $$l u_i$$ clients and incurs an opening cost of $$l f_i$$ . Every facility-client pair has a connection cost. Under the assumption that the connection costs are non-negative, symmetric and satisfy the $$\tau $$ -relaxed triangle inequality, we wish to open some facilities (once or multiple times) and connect every client to an opened facility without violating the capacity constraint so as to minimize the total opening costs as well as connection costs. As our main contribution, we propose a primal-dual based $$(3 \tau + 1)$$ -approximation algorithm for the $$\tau $$ -relaxed SCFLP. Furthermore, our algorithm not only extends the applicability of the primal-dual technique but also improves the previous approximation guarantee for the SMSCFLP from $$11.18+ \varepsilon $$ to 10.

Strengthening the attached old cement mortar of recycled aggregate (RA) is a common approach to enhance the RA properties. In this study, four representative chemical enhancing-technologies were chosen. Through the analysis of physical properties, mechanical properties, and shape indexes, the changes of performance before and after strengthening were tested, so as to grasp the influence of chemical enhancing-technologies on the performance of RA. The results indicated that the chemical strengthening methods had obvious influence on the physical properties of RA, such as density and water absorption. Due to the different chemical enhancing mechanism, the influence trend and range were varied. The mechanical properties of RA such as crushing value and abrasion value were obviously improved by chemical enhancement. However, chemical enhancement had an effect on the shape of RA, which will slightly reduce the angularity index and sphericity index. The texture index of RA with C-3 and C-7 decreased sharply, but texture index increased after abrasion, and the discrepancy of aggregate texture index of different enhancing methods is significantly reduced. The microanalysis exhibited that the aggregate surface structure with C-3 and H was large, but there were a lot of fine structures on the aggregate surface with C-7 and N-P. Different chemical enhancing methods have different effects on the mechanical performance and microstructure of RA, but it should be noted that the fluctuation of RA performance tends to increase.

Submodular optimization has been well studied in combinatorial optimization. However, there are few works considering about non-submodular optimization problems which also have many applications, such as experimental design, some optimization problems in social networks, etc. In this paper, we consider the maximization of non-submodular function under a knapsack constraint, and explore the performance of the greedy algorithm, which is characterized by the submodularity ratio $$\beta $$ and curvature $$\alpha $$ . In particular, we prove that the greedy algorithm enjoys a tight approximation guarantee of $$ (1-e^{-\alpha \beta })/{\alpha }$$ for the above problem. To our knowledge, it is the first tight constant factor for this problem. We further utilize illustrative examples to demonstrate the performance of our algorithm.

Three Co(II) metal–organic frameworks (Co-MOFs) build by 4,4′-bis(imidazolyl)diphenyl ether (bidpe), m-Phthalic acid (H2mpa), 5-Nitroisophthalic acid (H2nmpa) and 5-sulfonatobenzene-1,3-dicarboxylate (H2smpa), namely: [Co(mpa)(bidpe)]n (1), [Co(nmpa)(bidpe)]n (2), {[Co1.5(smpa)(bidpe)2]·1.5H2O·0.25DMA}n (3), were synthesized under same solvothermal condition. The compounds have been characterized by elemental analysis, IR spectroscopy, single-crystal X-ray diffraction, thermogravimetric (TG) analysis and powder X-ray diffraction (PXRD). The single-crystal X-ray diffraction analysis reveals that compound 1 shows a 4-connected sql net with Schlafli point symbol (44·62). Compound 2 displays a 3, 5-connected net with Schlafli point symbol (42·67·8)(42·6). Compound 3 possesses a 3-connected net with Schlafli point symbol (42·6). The photocatalytic investigations indicate that compounds 1–3 afford good photocatalytic capabilities in degradation of methylene blue (MB) and methylene violet (MV). Furthermore, the possible photocatalytic mechanisms have been discussed.

This experimental study is dedicated to the measurements of gas permeability under confinement and to preliminary characterizations of salt rock poromechanical behavior. Salt porosity and permeability under loading are recorded either with argon or hydrogen gas. Permeability measurements carried out with both gases led to similar values. Poromechanical effects occur but they are weak and strongly dependent on the level of confining pressure. Furthermore, triaxial tests have been used to investigate the evolution of permeability of damaged salt samples with time and confinement. These experimental characterizations are finally analysed in the context of gas storage applications.

Spherical k-means clustering as a known NP-hard variant of the k-means problem has broad applications in data mining. In contrast to k-means, it aims to partition a collection of given data distributed on a spherical surface into k sets so as to minimize the within-cluster sum of cosine dissimilarity. In the paper, we introduce spherical k-means clustering with penalties and give a $$2\max \{2,M\}(1+M)(\ln k+2)$$-approximation algorithm. Moreover, we prove that when against spherical k-means clustering with penalties but on separable instances, our algorithm is with an approximation ratio $$2\max \{3,M+1\}$$ with high probability, where M is the ratio of the maximal and the minimal penalty cost of the given data set.

In this paper, we consider the uniform capacitated k-means problem (UC-k-means), an extension of the classical k-means problem (k-means) in machine learning. In the UC-k-means, we are given a set $$\mathcal {D}$$ of n points in d-dimensional space and an integer k. Every point in the d-dimensional space has an uniform capacity which is an upper bound on the number of points in $$\mathcal {D}$$ that can be connected to this point. Every two-point pair in the space has an associated connecting cost, which is equal to the square of the distance between these two points. We want to find at most k points in the space as centers and connect every point in $$\mathcal {D}$$ to some center without violating the capacity constraint, such that the total connecting costs is minimized. Based on the technique of local search, we present a bi-criteria approximation algorithm, which has a constant approximation guarantee and violates the cardinality constraint within a constant factor, for the UC-k-means.

In this paper, we investigate the interactions between a base station (BS) and a full-duplex (FD) active eavesdropper in the uplink of multi-cell massive multiple-input multiple-output (MIMO) systems. Focusing on the resistance against simultaneous eavesdropping and jamming, we introduce a beam extraction method based on spatial information to eliminate pilot contamination, from which a beam-domain (BD) distributed receiving scheme is proposed to suppress the jamming mixed in users’ signals. From a practical point of view, we use the perspective of a hierarchical game to analyze the interactions without the prior information of the eavesdropper. The results show that with the help of the proposed scheme, the performance of the FD-mode attack is inferior to that of passive eavesdropping. A statistical upper bound of eavesdropping is given in a closed-form, which is mainly determined by the direction and distance of the eavesdropper relative to users. Then, a joint power and combining matrix optimization algorithm is proposed to improve the secrecy capacity of the system.

Vehicular Ad-hoc Network (VANET) is a significant component of intelligent transportation system, which facilitates vehicles to share sensitive information and corporate with others. However, due to its unique characteristics, such as openness, dynamic topology and high mobility, VANET suffers from various attacks. This paper proposes an anti-attack trust management scheme in VANET called AATMS to evaluate the trustworthiness of vehicles. With the help of AATMS, vehicles in VANET can avoid malicious vehicles and cooperate with trusted vehicles. The idea of AATMS is mainly inspired by TrustRank algorithm, which is used to combat web spams. In this paper, we calculate local trust and global trust, which indicate the local and global trust relationships among vehicles. First, Bayesian inference is adopted to calculate local trust of vehicles based on historical interactions. Then we select a small set of seed vehicles according to local trust and some social factors. Once we identify the reputable seed vehicles, we use the local trust link structure of vehicles to evaluate the global trust of all vehicles. The simulation results show that AATMS can efficiently identify trustworthy and untrustworthy vehicles in VANET even under malicious attacks.

Electro-Fenton-like processes are widely used in removal of antibiotics in sewage owing to their excellent degradation efficiency for organics. However, the degradation mechanisms of antibiotics in electro-Fenton-like systems are still unclear caused by the non-real time detection of current methods. An electrochemical-mass spectrometry (EC-MS) method was developed for in situ analysis and real-time monitoring of electrochemical degradation of antibiotics in this study. In EC-MS, ITO glass was used as an electrochemical reactor and counter electrode, which is positioned in front of the MS inlet. A platinum slice as a working electrode was placed over the ITO glass. When different high voltages were applied on the electrodes, the electrochemical degradation of organics started and a charged spray was generated for MS detection. To enhance the electrochemical reaction efficiency, the voltage difference was optimized. As a proof-of-concept, the degradation of ciprofloxacin (CIP) in an electro-Fenton-like system was monitored, and five intermediates (m/z 330, 304, 348, 346 and 362) different from those obtained in other removal processes were detected and verified. Furthermore, six new characteristic intermediates (m/z 364, 305, 293, 277, 275 and 280) were captured. Meanwhile, the compounds were identified by high-resolution mass spectrometry and tandem mass spectrometry. Extracted ion chromatography (EIC) of several characteristic intermediates was performed for further discussion of the degradation pathway. Based on the data acquired, the degradation mechanism of CIP by an electro-Fenton-like process was proposed for the first time. This work provided a useful tool for mechanism research in electrochemical advanced oxidation processes.

On account of increasing demand for large-scale energy storage devices, sodium-ion batteries have shown great potential to replace lithium-ion batteries owing to the abundant reserve, low cost and environmental benignity of sodium resources. Here, we report a facile electrospinning method to fabricate a hybrid material of ultrasmall Fe7Se8 nanoparticles/N-doped carbon nanofibers (Fe7Se8/N-CNFs). As an anode material in sodium-ion batteries, the Fe7Se8/N–CNF hybrid exhibits superior rate property with a specific capacity of 286.3 mAh g−1 at a current density of 20 A g−1, outperforming other metal selenides. Such an excellent performance originates from the synergistic effects of ultrasmall Fe7Se8 nanoparticles as well as the unique and interconnected network of N-rich CNFs, which offer short Na+ diffusion distance, provide efficient electrolyte diffusion paths, supply numerous defects and active sites for Na+ adsorption, enhance surface pseudocapacitive behavior, improve the electronic conductivity, and thus facilitate the electron/ion transport and electrochemical reaction kinetics.

Fact sheets with vivid graphical design and intriguing statistical insights are prevalent for presenting raw data. They help audiences understand data-related facts effectively and make a deep impression. However, designing a fact sheet requires both data and design expertise and is a laborious and time-consuming process. One needs to not only understand the data in depth but also produce intricate graphical representations. To assist in the design process, we present DataShot which, to the best of our knowledge, is the first automated system that creates fact sheets automatically from tabular data. First, we conduct a qualitative analysis of 245 infographic examples to explore general infographic design space at both the sheet and element levels. We identify common infographic structures, sheet layouts, fact types, and visualization styles during the study. Based on these findings, we propose a fact sheet generation pipeline, consisting of fact extraction, fact composition, and presentation synthesis, for the auto-generation workflow. To validate our system, we present use cases with three real-world datasets. We conduct an in-lab user study to understand the usage of our system. Our evaluation results show that DataShot can efficiently generate satisfactory fact sheets to support further customization and data presentation.

Ultraviolet (UV) radiation causes asphalt aging and thus influences its performance. In this research, a composite light stabilizer was prepared to increase the light stability of matrix as...

Spreadsheet table detection is the task of detecting all tables on a given sheet and locating their respective ranges. Automatic table detection is a key enabling technique and an initial step in spreadsheet data intelligence. However, the detection task is challenged by the diversity of table structures and table layouts on the spreadsheet. Considering the analogy between a cell matrix as spreadsheet and a pixel matrix as image, and encouraged by the successful application of Convolutional Neural Networks (CNN) in computer vision, we have developed TableSense, a novel end-to-end framework for spreadsheet table detection. First, we devise an effective cell featurization scheme to better leverage the rich information in each cell; second, we develop an enhanced convolutional neural network model for table detection to meet the domain-specific requirement on precise table boundary detection; third, we propose an effective uncertainty metric to guide an active learning based smart sampling algorithm, which enables the efficient build-up of a training dataset with 22,176 tables on 10,220 sheets with broad coverage of diverse table structures and layouts. Our evaluation shows that TableSense is highly effective with 91.3% recall and 86.5% precision in EoB-2 metric, a significant improvement over both the current detection algorithm that are used in commodity spreadsheet tools and state-of-the-art convolutional neural networks in computer vision.

Automatic Exploratory Data Analysis (EDA) focuses on automatically discovering pieces of knowledge in the form of interesting data patterns. However, the conveyed knowledge by these suggested data patterns are disjointed or lack organization. Therefore, it is difficult for users to gain structured knowledge, and as the number of suggested patterns grows, these stand-alone patterns are less likely to motive users to conduct follow-up analysis, which hinders it from being effectively utilized to facilitate EDA. In this paper, we propose MetaInsight, a structured representation of knowledge extracted from multi-dimensional data aiming to facilitate EDA automatically and effectively. Specifically, we propose a novel formulation of basic data pattern to capture essential characteristics of raw data distribution to achieve knowledge extraction. Then based on the mined Homogeneous Data Patterns (HDP) and inter-pattern similarity, MetaInsight is identified by categorizing basic data patterns (within an HDP) into commonness(es) and exceptions thus achieving structured knowledge representation. The commonness(es) and exceptions concretize the knowledge obtained by induction and validation processes which are two typical analysis mechanisms conducted in EDA. We propose a novel scoring function to quantify the usefulness of MetaInsight, an effective and efficient mining procedure and a ranking algorithm to automatically discover high-quality MetaInsights from multi-dimensional data. We demonstrate the effectiveness and efficiency of MetaInsights (w.r.t. facilitating EDA) through evaluation on real-world datasets and user studies on both expert users and non-expert users.

The problem of maximizing a normalized monotone non-submodular set function subject to a cardinality constraint arises in the context of extracting information from massive streaming data. In this paper, we present four streaming algorithms for this problem by utilizing the concept of diminishing-return ratio. We analyze these algorithms to obtain the corresponding approximation ratios, which generalize the previous results for the submodular case. The numerical experiments show that our algorithms have better solution quality and competitive running time when compared to an existing algorithm.

Inspired by the collective behavior of natural living systems, the collective behavior of micromotors has become the research highlight. Although great progress has been made, it is still challenging to control the collective behavior of micromotors. In this paper, we demonstrate a novel near-infrared (NIR) light-powered micromotor consisting of a polystyrene microsphere and a polydopamine core-shell structure (PS@PDA) with concentration-dependent motion direction reversal and dynamic swarming behavior. Among others, a single micromotor exhibits negative phototaxis, whereas a group of micromotors shows positive phototaxis, which can be attributed to the competition between the thermophoretic force and hydrodynamic drag caused by the thermal buoyancy. In addition, because of the reversible hydrogen bonding and π-π stacking interactions between the adjacent PS@PDA micromotors, they form aggregation as a result of the positive phototaxis with dynamically controllable shapes tuned by the irradiation position, which makes them potentially attractive for in-solution calligraphy and painting. It is anticipated that the current study may not only provide a new strategy to control the collective behavior of the micromotors, but also promote their application in the practical field.

Environmental friendly and non-toxic polylactic acid (PLA) foam exhibits a promising perspective in many areas. However, PLA shows very poor foaming ability. Herein, silane-modified glass fiber (GF) was compounded with PLA to improve its foaming ability. Thanks to the increased melt viscoelasticity and melt strength, and the enhanced crystallization by adding GF, PLA/GF composites show dramatically improved foaming ability, characterized by widened processing window, increased expansion ratio, and improved cellular uniformity. Compared with pure PLA foam, PLA/GF composite foam shows dramatically enhanced mechanical properties in compressive strength and modulus. For foams with the same expansion ratio of 20-fold, the incorporation of 10 wt% GF led to increased compressive strength and modulus by 44.8% and 92.0%, respectively. The PLA/GF composite foam with an expansion ratio of 24.2-fold has a low thermal conductivity of 31.4 mW/m·K, which is comparable with the excellent thermal insulation performance of commercial polymer foams.

Four novel Co(II)-MOFs based on the 3,3′-azodibenzoic acid (H2L), namely [CoL (bimb)0.5]n (1), {[CoL (bimmb)]·DMF}n (2), [CoL (bimmb)0.5]n (3), and [CoL (bbibp)]n (4) (bimb = 1,4-bisimidazole, bimmb = 4-bis(imidazole-1-ylmethyl)benzene, bbibp = 4,4′-bis(benzoimidazo-1-yl)biphenyl and DMF = N, N′-dimethylformamide) have been synthesized under solvothermal conditions and characterized by elemental analysis, IR spectra, powder X-ray diffraction (PXRD), thermogravimetric (TG) analysis and single crystal X-ray diffraction. Single crystal X-ray diffraction analysis shows that complex 1 and complex 3 possesses the same structure as uninodal 6-connected 3D (three-dimensional) pcu topology framework with the point symbol (412.63), whereas complex 2 possesses an uninodal 3-connected hcb topology framework with the point symbol (63) and complex 4 displays a 4-connected sql topology framework with the point symbol of (44.62). In addition, the photocatalytic degradation properties of the four complexes have been investigated. Significantly, complex 4 is the best photocatalyst for the degradation of three organic dyes (MV/MB/RhB). Complex 2 is the good photocatalyst for the degradation of two organic dyes (RhB/MB). Complex 3 is the good photocatalyst for the degradation of two organic dyes (MV/MB) and complex 1 is the good photocatalyst for the degradation of only one organic dyes (MV).

Rapid analysis of pharmaceutical preparations is of great interest in clinical studies with benefits for identifying counterfeit drugs. In this study, droplet spray ionization tandem mass s...

Polylactic acid (PLA) is a biodegradable polymer derived from renewable resources, showing potentials in replacing traditional petroleum-based polymers, yet its brittleness and low thermal-resistance limits its applications. Thus, glass fibers (GF) combined with heat treatment were used to prepare high-performance PLA. Differential scanning calorimetry (DSC) and X-ray diffraction (XRD) were employed to analyze crystallization behavior of PLA/GF composite. Tensile, flexural and impact tests were conducted to investigate mechanical properties, and heat deflection temperature was measured to evaluate thermal resistance. GF can coincidently enhance strength, rigidity, and toughness of PLA. Isothermal heat treatment can further improve the mechanical properties regardless of GF content. Compared with neat PLA, the tensile strength, flexural modulus, and impact strength can be increased by 162.5%, 266.4%, 232.5%, respectively, in the presence of 20 wt% GF after isothermal heat treatment, and meanwhile heat deflection temperature can be increased from 50.6 °C to 148.8 °C. Both DSC and XRD analysis results indicated that GF can significantly enhance crystallization of PLA. Thus, not only GF but also enhanced crystallization led to the outstanding mechanical performance of PLA/GF composites. While GF shows little effect on thermal resistance, heat treatment can remarkably improve thermal stability, in particular for PLA/GF composites.

In this paper, we investigate the problem of minimizing the ratio of normalized non-negative monotone non-submodular set function f and normalized non-negative monotone set function g. We take advantage of the greedy technique and get a performance guarantee depending on the generalized curvature and inverse generalized curvature of f, as well as the submodularity ratio of g. Our results generalize the works of Bai et al. (Algorithms for optimizing the ratio of submodular functions. In: Proceedings of the 33rd International Conference on Machine Learning, 2016) and Qian et al. (Optimizing ratio of monotone set functions. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence, 2017).

Objective To observe the effects of fuzhengbutu (strengthening antipathogenic qi and tonifying the earth) acupuncture–moxibustion therapy on walking function in the patients with post-stroke hemiplegia. Methods A total of 57 patients with hemiplegia after ischemic stroke treated in our hospital from January 2018 through to October 2018 were collected as the study objects. According to random number table, they were divided into a treatment group (29 cases) and a control group (28 cases). In the treatment group, the fuzhengbutu acupuncture–moxibustion therapy combined with rehabilitation treatment was adopted. In the control group, the rehabilitation treatment was given. The persistent walking time and pause time in PIERENSTEP gait measurable training and test system as well as the score of Berg balance scale (BBS) were compared between the two groups. Results In the treatment group, the persistent walking time after treatment(1.47 ± 0.28) s was shorter obviously versus before treatment (2.12 ± 0.38) s, indicating the significant difference (P 0.05). In the control group, the persistent walking time after treatment (1.88 ± 0.22) s was shorter obviously versus before treatment (2.18 ± 0.30) s, indicating the significant difference (P 0.05). Before treatment, the differences were not significant in the persistent walking time and pause duration in the patients of either group (P > 0.05). After treatment, the persistent walking time in the treatment group was shorter obviously than the control group (P 0.05). Before treatment, there was no difference in BBS score between the two groups (P > 0.05). In the treatment group, BBS score (42.79 ± 2.78) after treatment was higher than (35.86 ± 2.64) before treatment, indicating the significant difference (P Conclusion Based on rehabilitation treatment, Fuzhengbutu acupuncture–moxibustion therapy effectively improves the walking function of the patients with hemiplegia after ischemic stroke.

Quasi-static and dynamic experiments were carried out to examine the influence of liquid nitrile rubber (LNBR) on the compressive behavior of epoxy resin. The quasi-static tests were performed on an electronic universal machine under strain rates of 10 −4 /s and 10 −3 /s, while a Split Hopkinson Pressure Bar (SHPB) system was adopted to conduct the dynamic tests for strain rates up to 5600/s. The standard Zhu-Wang-Tang (ZWT) nonlinear viscoelastic model was selected to predict the elastic behavior of LNBR/epoxy composites under a wide range of strain rates in this study. After several necessary derivations and data fittings, a set of ZWT model parameters for the tested materials were finally achieved. Meanwhile, the dynamic response of a typical high-overload measurement system encapsulated by LNBR/epoxy composites under impact loading was simulated by LS-DYNA, where the ZWT model was used to simulate the mechanical behavior of LNBR/epoxy composites with model parameters obtained from the experiments. It was concluded that both the resistance to failure and protective effect of epoxy resin could be enhanced by 10% LNBR additive. However, 25% LNBR additive would decrease the mechanical properties of the epoxy resin.

Percolative poly(vinylidene fluoride) (PVDF) composites with high dielectric constant and low dielectric loss was successfully achieved by incorporating reduced graphene oxide (rGO) decorated with magnetic iron oxide (Fe3O4) (rGO@Fe3O4) nano-fillers. The morphology, structure, thermal, electrical and magnetic properties of the composites were investigated systematically by using SEM, XRD, FTIR, TGA, DSC, impedance analyzer and magnetometer, respectively. The presence of a tiny amount of poly(sodium 4-styrene sulfonate) (PSSNa) facilitated the homogeneous distribution of rGO@Fe3O4 nanoparticles and prevented the formation of an electrical conductive network within the PVDF matrix, resulting in an excellent dielectric performance of the rGO@Fe3O4/PVDF nano-composites. With 1.0 wt% rGO@Fe3O4 incorporated in PVDF, the nanocomposite exhibited a high dielectric constant of 1297 and a low dielectric loss of 0.26 at 100 Hz. Percolation model revealed that such improvements could be attributed to the synergistic effect of the inorganic Fe3O4 and rGO, whereby the electric charges were accumulated by forming many mini-capacitors in the bulk PVDF matrix. The structure-property behavior of the final nanocomposites was discussed.

In this paper, we study the sum of squares facility location problem (SOS-FLP) which is an important variant of k-means clustering. In the SOS-FLP, we are given a client set $$ \mathcal {C} \subset \mathbb {R}^p$$ and a uniform center opening cost $$f>0$$ . The goal is to open a finite center subset $$F \subset \mathbb {R}^p$$ and to connect each client to the closest open center such that the total cost including center opening cost and the sum of squares of distances is minimized. The SOS-FLP is introduced firstly by Bandyapadhyay and Varadarajan (in: Proceedings of SoCG 2016, Article No. 14, pp 14:1–14:15, 2016) which present a PTAS for the fixed dimension case. Using local search and scaling techniques, we offer the first constant approximation algorithm for the SOS-FLP with general dimension. We further consider the discrete version of SOS-FLP, in which we are given a finite candidate center set with nonuniform opening cost comparing with the aforementioned (continue) SOS-FLP. By exploring the structures of local and optimal solutions, we claim that the approximation ratios are $$7.7721+ \epsilon $$ and $$9+ \epsilon $$ for the continue and discrete SOS-FLP respectively.

Three novel coordination polymers (CPs) based on transition metals, namely {[Cu(bpt)2(H2O)2]·2NO3}n (1), {[Cu(bpt)(SO4)(H2O)]·H2O}n (2) and [Cd(bpt)(SO4)]n (3) (tib = 1,3,5-tris(1-imidazolyl)benzene and bpt = 2,5-bis(4-pyridyl)-1,3,4-thiadiazole), have been designed and successfully synthesized under solvothermal conditions. All of these three polymers were characterized by single crystal X-ray diffraction, elemental analysis, IR spectra, powder X-ray diffraction (PXRD), and thermogravimetric analysis (TGA). Single-crystal X-ray diffraction analysis reveals that complex 1 possesses a uninodal 4-connected 3D (three-dimensional) spl topology framework with the point symbol (44·62). Complex 2 showing a 1D zigzag chain, which is connected by a sulfate anion to form a 2D network, is further assembled into a 3D supramolecular structure, whereas complex 3 displays a 3D 3,5-connected tfz-d topology with chiral inorganic 2-D layers. Moreover, the photocatalytic degradation of the three complexes has been investigated. Significantly, complex 1 is the best photocatalyst for the degradation of organic dyes (MB/MV/RhB).

Four novel Zn(II) metal–organic frameworks (MOFs) including {[Zn(L)0.5(bimb)]·2H2O·0.5(CH3)2NH}n (1), {[Zn(L)0.5(bimmb)]·2H2O}n (2), {[Zn(L)0.5(btdpe)]·H2O}n (3), and [Zn(L)0.5(bidpe)]n (4) (H4L = 5,5′-(1,4-xylylenediamino) diisophthalic acid, bimb = 1,4-bis(imidazol-1-yl)-butane, bimmb = 1,4-bis(imidazol-1-ylmethyl)benzene, btdpe = 4,4′-bis(4H-1,2,4-triazol-4-yl)diphenyl ether, and bidpe = 4,4′-bis(imidazolyl)diphenyl ether) were solvothermally prepared and further characterized by elemental analysis, IR spectroscopy, single-crystal X-ray diffraction, thermogravimetric (TG) analysis and powder X-ray diffraction (PXRD). Single-crystal X-ray diffraction analysis showed that complex 1 features a 3-fold interpenetrated bbf topological framework with the Schlafli symbol of (64·82)(66)2. Complex 2 displayed an unprecedented 2-nodal (4,4)-connected 3D framework with the Schlafli symbol of (86), while complexes 3 and 4 showed 3-nodal (2,4,4)-connected (4·8·104)2(4)2(84·10·12)-unprecedented frameworks. Luminescence studies revealed that complexes 1–4 can be used as high selectivity luminescence sensors for sensing Fe(III) cations, Cr(VI) anions (Cr2O72− and CrO42−) and nitroaromatic compounds. Furthermore, a possible sensing mechanism was investigated in this paper.

We investigate the maximum directed cut (MaxDC) problem by designing a spectral partitioning algorithm. Given a directed graph with nonnegative arc weights, we wish to obtain a bipartition of the vertices such that the total weight of the directed cut arcs is maximized. Relaxing the MaxDC problem as a quadratic program allows us to explore combinatorial properties of the optimal solution, leading to a 0.272-approximation algorithm via the technique of spectral partitioning rounding.

The \begin{document}$k$\end{document} -center clustering is one of the well-studied clustering problems in computer science. We are given a set of data points \begin{document}$P\subseteq R^d$\end{document} , where \begin{document}$R^d$\end{document} is \begin{document}$d$\end{document} dimensional Euclidean space. We need to select \begin{document}$k≤ |P|$\end{document} points as centers and partition the set \begin{document}$P$\end{document} into \begin{document}$k$\end{document} clusters with each point connecting to its nearest center. The goal is to minimize the maximum radius. We consider the so-called online \begin{document}$k$\end{document} -center clustering model where the data points in \begin{document}$R^d$\end{document} arrive over time. We present the bi-criteria \begin{document}$(\frac{n}{k}, (\log\frac{U^*}{L^*})^2)$\end{document} -competitive algorithm and \begin{document}$(\frac{n}{k}, \logγ\log\frac{nγ}{k})$\end{document} -competitive algorithm for semi-online and fully-online \begin{document}$k$\end{document} -center clustering respectively, where \begin{document}$U^*$\end{document} is the maximum cluster radius of optimal solution, \begin{document}$L^*$\end{document} is the minimum distance of two distinct points of \begin{document}$P$\end{document} , \begin{document}$γ$\end{document} is the ratio of the maximum distance of two distinct points and the minimum distance of two distinct points of \begin{document}$P$\end{document} and \begin{document}$n$\end{document} is the number of points that will arrive in total.

Five novel Cd(II) metal–organic frameworks (Cd-MOFs), namely [Cd6(L)4(1,3-bit)2(H2O)4]n (1), [Cd(HL)(4,4′-bbibp)]n (2), [Cd3(L)2(1,4-bimb)2(H2O)2]n (3), [Cd(HL)(4,4′-bidpe)]n (4) and [Cd3(L)2(1,1′-bbi)(H2O)4]n (5) (flexible H3L = 5-(2-carboxylphenoxy)isophthalic acid, rigid 1,3-bit = 1,3-bis(l-imidazoly)toluene, rigid 4,4′-bbibp = 4,4′-bis(benzoimidazo-1-ly)biphenyl, semi-flexible 1,4-bimb = 1,4-bis(imidazol-l-ylmethyl)benzene, semi-flexible 4,4′-bidpe = 4,4′-bis(imidazolyl)diphenyl ether, and flexible 1,1′-bbi = 1,1′-(1,4-butanediyl)bis(imidazole)) were synthesized employing a rational design approach under solvothermal conditions and further characterized by elemental analysis, IR spectra, thermogravimetric (TG) analysis, powder X-ray diffraction (PXRD), and single-crystal X-ray diffraction. Diversity architectural topologies have been generated by adjusting the N-donor ligands. They exhibit varied structures: 1 displays an unprecedented 6-nodal (3,3,4,4,8,8)-c 3D topology framework with the point Schlafli symbol of (3·4·5)2(32·42·52·66·78·87·9)(32·63·7)(4·64·8)(410·611·87)(43)2; 2 shows a 2-fold interpenetrating 4-connected cds topology with the point Schlafli symbol of (65·8); 3 exhibits an unprecedented 2-nodal (3,8)-c 3D topology network with the point Schlafli symbol of (42·5)2(44·56·610·75·82·9); 4 shows a 4-connected sql topology with the point Schlafli symbol of (44·62); 5 displays a tfz-d topology 2-nodal (3,8)-c 3D network with the point Schlafli symbol of (43)2(46·618·84). The luminescence studies reveal that MOFs 1–5 display highly sensitive and selective fluorescence sensing towards Fe3+ cations, CrO42− anions and molecular acetone. Meanwhile, the targeted dye adsorption properties of MOFs 1–5 are investigated.

In order to understand the influence of the vibration mixing method on the performance of cement-stabilized aggregate mixture (CSAM), in this study, an unconfined compressive strength test, drying shrinkage test and the influence of gradation characteristics on compressive strength were used to systematically analyze the performance of CSAM based on vibration mixing. At the same time, the influence mechanism of vibration mixing on the interface transition zone of cement aggregate was analyzed by use of a scanning electron microscope (SEM), and the influence of the mixing method on cement dispersion uniformity was studied by the ethylene diamine tetraacetic acid (EDTA) titration method. The results show that 7 days of unconfined compressive strength and crack resistance of the CSAM with vibration mixing is better than those of the ordinary mixing, and the aggregate grade has a great influence on the compressive strength of the CSAM. Vibration mixing can improve the micro-water-cement ratio uniformity of CSAM and significantly improve the bonding condition of the interface transition zone between cement paste and aggregate, thus enhancing the structural compactness of CSAM. The dispersion uniformity of cement under the vibration mixing is better than that of ordinary mixing.

With the rapid deployment of cloud platforms, high service reliability is of critical importance. An industrial cloud platform contains a huge number of disks, and disk failure is a common cause of service unreliability. In recent years, many machine learning based disk failure prediction approaches have been proposed, and they can predict disk failures based on disk status data before the failures actually happen. In this way, proactive actions can be taken in advance to improve service reliability. However, existing approaches treat each disk individually and do not explore the influence of the neighboring disks. In this paper, we propose Neighborhood-Temporal Attention Model (NTAM), a novel deep learning based approach to disk failure prediction. When predicting whether or not a disk will fail in near future, NTAM is a novel approach that not only utilizes a disk’s own status data, but also considers its neighbors’ status data. Moreover, NTAM includes a novel attention-based temporal component to capture the temporal nature of the disk status data. Besides, we propose a data enhancement method, called Temporal Progressive Sampling (TPS), to handle the extreme data imbalance issue. We evaluate NTAM on a public dataset as well as two industrial datasets collected from millions of disks in Microsoft Azure. Our experimental results show that NTAM significantly outperforms state-of-the-art competitors. Also, our empirical evaluations indicate the effectiveness of the neighborhood-ware component and the temporal component underlying NTAM as well as the effectiveness of TPS. More encouragingly, we have successfully applied NTAM and TPS to Microsoft cloud platforms (including Microsoft Azure and Microsoft 365) and obtained benefits in industrial practice.

Modern search engines retrieve results mainly based on the keyword matching techniques, and thus fail to answer analytical queries like "apps with more than 1 billion monthly active users" or "population growth of the US from 2015 to 2019", which requires numerical reasoning or aggregating results from multiple web pages. Such analytical queries are very common in the data analysis area, the expected results would be structured tables or charts. In most cases, these structured results are not available or accessible, they scatter in various text sources. In this work, we build AnaSearch, a search system to support analytical queries, and return structured results that can be visualized in the form of tables or charts. We collect and build structured quantitative data from the unstructured text on the web automatically. With AnaSearch, data analysts could easily derive insights for decision making with keyword or natural language queries. Specifically, we build AnaSearch under the COVID-19 news data, which makes it easy to compare with manually collected structured data.

Background Experimental and epidemiological studies have linked antibiotics use to gut dysbiosis-mediated risk of chronic metabolic diseases. However, whether adiposity is linked to antibiotic exposure in elderly remains inadequately understood. Objective To investigate the association between internal exposure of antibiotics and adiposity in elderly by using a biomonitoring method. Methods We included 990 participants (≥ 60 years) from the baseline survey of the Cohort of Elderly Health and Environment Controllable Factors in Lu’an city, China, from June to September 2016. Forty-five antibiotics and two metabolites in urine were monitored through liquid chromatography-electrospray tandem mass spectrometry (HPLC-MS/MS). Creatinine-corrected urinary concentrations were used to assess antibiotic exposure levels. Body mass index (BMI), waist circumference (WC) and body fat percentage (BFP) were used as indicators of adiposity. Multiple linear regression and binary logistic regression analyses were used to analyze the association of antibiotic concentrations with obesity-related indices. Subsequently, a gender-stratified analysis was performed. Results Of the included elderly, 50.7% were defined as having overweight/ obesity, 59.8% as having central preobesity/obesity, and 37.5% as having slightly high/high BFP. Linear regression analysis revealed that a 1-unit increase in the logarithmic transformation of norfloxacin concentrations was related with an increase of 0.29 kg/m2 (95% CI: 0.02–0.04), 0.99 cm (95% CI：0.24–1.75), and 0.69% (95% CI：0.21–1.17) in BMI, WC, and BFP, respectively. Compared with the control group, exposure to doxycycline (tertile 2: odds ratio, 2.06 [95% CI: 1.12–3.76]) and norfloxacin (tertile 2: 2.13 [1.05–4.29]; tertile 3: 2.07 [1.03–4.17]) had BMI-based overweight/obesity risk. Additionally, ciprofloxacin (tertile 2: 2.06 [1.12–3.76]), norfloxacin (tertile 3: 2.95 [1.34–6.49]), and florfenicol (tertile 3: 1.84 [1.07–3.14]) were related to WC-based central preobesity/obesity risk. Norfloxacin (tertile 3: 2.54 [1.23–5.24]) was positively associated with a slightly high/high BFP risk. Gender-stratified analysis demonstrated an increased adiposity risk in women compared with men. Conclusions Our research provided an evidence that exposure to specific types of antibiotics (tetracyclines and fluoroquinolones) probably from the food chain contributed to obesity in elderly. Prospective cohort studies with larger sample size are warrented to explore the causation.