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In view of the extremely complex logistics level, long logistics cycle, high-risk coefficient, extremely fuzzy and uncertain evaluation information of cruise construction logistics allocation, an improved intuitionistic fuzzy TOPSIS method is proposed to evaluate the risk of cruise construction logistics allocation. Firstly, on the basis of empirical data analysis and research interviews, risk sources and risk assessment criteria are determined, and different weights are given to experts according to their importance. Then, in order to reduce the fuzziness and uncertainty of risk source information, an intuitionistic fuzzy weighted arithmetic average (IFWAA) operator is used to aggregate the experts’ opinions, and the intuitionistic fuzzy aggregation decision risk matrix is obtained. The objective weight of risk assessment criteria is introduced to balance the impact of subjective weight on the final results, to improve the accuracy of the evaluation results. Finally, according to the relative closeness coefficient between each risk source and the positive-ideal solution, the priority of the risk sources of cruise construction logistics allocation is obtained, and the corresponding risk control measures are put forward. Compared with other methods and sensitivity analysis, the effectiveness and applicability of this method are verified.

There is a recent trend of applying multi-agent reinforcement learning (MARL) to train an agent that can cooperate with humans in a zero-shot fashion without using any human data. The typical workflow is to first repeatedly run self-play (SP) to build a policy pool and then train the final adaptive policy against this pool. A crucial limitation of this framework is that every policy in the pool is optimized w.r.t. the environment reward function, which implicitly assumes that the testing partners of the adaptive policy will be precisely optimizing the same reward function as well. However, human objectives are often substantially biased according to their own preferences, which can differ greatly from the environment reward. We propose a more general framework, Hidden-Utility Self-Play (HSP), which explicitly models human biases as hidden reward functions in the self-play objective. By approximating the reward space as linear functions, HSP adopts an effective technique to generate an augmented policy pool with biased policies. We evaluate HSP on the Overcooked benchmark. Empirical results show that our HSP method produces higher rewards than baselines when cooperating with learned human models, manually scripted policies, and real humans. The HSP policy is also rated as the most assistive policy based on human feedback., Comment: The first two authors share equal contributions. This paper is accepted by ICLR 2023

As a fundamental problem in computer vision, multi-view stereo (MVS) aims at recovering the 3D geometry of a target from a set of 2D images. Recent advances in MVS have shown that it is important to perceive non-local structured information for recovering geometry in low-textured areas. In this work, we propose a Hierarchical Prior Mining for Non-local Multi-View Stereo (HPM-MVS). The key characteristics are the following techniques that exploit non-local information to assist MVS: 1) A Non-local Extensible Sampling Pattern (NESP), which is able to adaptively change the size of sampled areas without becoming snared in locally optimal solutions. 2) A new approach to leverage non-local reliable points and construct a planar prior model based on K-Nearest Neighbor (KNN), to obtain potential hypotheses for the regions where prior construction is challenging. 3) A Hierarchical Prior Mining (HPM) framework, which is used to mine extensive non-local prior information at different scales to assist 3D model recovery, this strategy can achieve a considerable balance between the reconstruction of details and low-textured areas. Experimental results on the ETH3D and Tanks \& Temples have verified the superior performance and strong generalization capability of our method. Our code will be released.

Aiming at the problem of multiobjective transportation decision-making, a fuzzy compromise method with an improved S-type membership function is proposed. This method not only considers a single objective and evaluates it marginally but also evaluates the overall objective as a whole. First of all, a multiobjective transportation decision-making model is established. Then, each objective function is mapped to a function on the interval [0,1] through the S-type membership function, thereby transforming the multiobjective transportation linear programming model into a multiobjective transportation fuzzy compromise programming model, and its satisfaction is expressed by the global utility function. Finally, through two examples, the results of the example algorithm are compared with the results in the literature, highlighting the superiority of the method. The experimental results show that in the multiobjective transportation decision-making problem, the fuzzy compromise method of the S-type membership function has better flexibility and effectiveness.

Background: Ursodeoxycholic acid (UDCA) is currently used for the treatment of primary biliary cholangitis (PBC), but some people do not respond well to UDCA. It reported that the combination of fenofibrate and UDCA can improve the clinical indices in these patients. However, more high-quality evidence is needed to improve guideline recommendations.Methods: Through an updated meta-analysis, studies included were valued by the Cochrane Evaluation Manual and Robins-I. Biochemical and clinical indicator changes in UDCA-refractory PBC patients receiving combination therapy were analyzed by Revman 5.42. Then, we explored the influence of fenofibrate dose and the effectiveness and safety of long-term application by retrospective cohort study.Results: Our meta-analysis included nine publications with a total of 389 patients, including 216 treated with UDCA alone and 173 who received combination therapy. The meta-analysis showed that combination therapy was more effective than UDCA monotherapy in decreasing biochemical parameters, such as ALP, GGT, IgM, and TG. However, the occurrence of pruritus and adverse events was slightly higher with combination therapy than with UDCA monotherapy. A total of 156 patients were included in our cohort study: 68 patients underwent UDCA monotherapy, and 88 patients underwent combination therapy. Among UDCA-refractory patients, fenofibrate add-on therapy significantly improved the ALP normalization rate.Conclusion: The combination of fenofibrate and UDCA can decrease biochemical parameters, of UDCA-refractory PBC patient. Furthermore, the efficacy and safety of long-term combination therapy were also confirmed in our cohort study.

ObjectiveThis study aimed to evaluate the value of fetal magnetic resonance imaging (MRI) in the prenatal diagnosis of spinal neural tube defects.MethodsFrom August 2018 to January 2021, 56 fetuses with suspected spinal cord neural tube defects were treated by prenatal ultrasound in the Neurosurgery Department of the First Medical Center of the People's Liberation Army General Hospital. Fetal MRI was performed within 72 h after ultrasound diagnosis. Forty singleton fetuses were selected. Magnetic resonance examination was performed within 1 month after birth, and the diagnostic coincidence rates of prenatal ultrasound and fetal magnetic resonance examination in the prenatal diagnosis of spinal cord neural tube defects were compared and analyzed using postnatal magnetic resonance examination as the standard.ResultsThe coincidence rates of prenatal ultrasound and fetal MRI for the prenatal diagnosis of spina bifida were 71.4% (20/28) and 39.2% (11/28), respectively, and the difference was statistically significant. The coincidence rates of prenatal ultrasound and fetal MRI in the diagnosis of intraspinal lipoma were 52.6% (10/19) and 73.7% (14/19), respectively, and the difference was statistically significant.ConclusionFetal MRI has an advantage over prenatal ultrasound in detecting intraspinal lipoma. Prenatal ultrasound has an advantage over fetal MRI in detecting spina bifida.

It is generally believed that higher dietary diversity is associated with better health status. The dietary diversity of individuals may change with age; however, evidence on the trajectory of change in the long-term and whether it is related to all-cause mortality is still scant. In this study, we used data from the China Health and Nutrition Survey (CHNS) collected in five follow-ups between 2004 and 2015 to explore the association between changes in dietary diversity scores (DDS) and all-cause mortality, as well as the dynamic change in DDS with age. In total, 6,737 subjects (aged between 30 and 60 at enrollment) were included in the analysis. Latent Class Trajectory Modeling (LCTM) was used to explore the different trajectories of DDS changes among participants. Four classes were identified: class 1 with the lowest average DDS (3.0) that showed a gradual decline during the follow-ups; class 2 with relatively low DDS (4.0) that experienced slight growth; class 3 with medium DDS (5.2) that also demonstrated similar growth rate to class 2; and class 4 with the highest DDS (6.7) maintained at a high level. Cox proportional hazards regression models were applied to investigate the association between the DDS trajectories and the risk of death. Only class 4, which was characterized by the highest and stable DDS, had significant reduced risk of all-cause mortality of 71.0% (hazard ratio [HR]: 0.29; 95% confidence interval [CI]: 0.10–0.83), 68% (HR: 0.32; 95% CI: 0.11–0.89), and 66.0% (HR: 0.34; 95% CI: 0.12–0.94), compared to classes 1, 2, and 3, respectively, while the first three classes showed no significant inter-class differences. When considering the average DDS during the study period, each point of increase in DDS corresponded to a 22% reduced risk of mortality (HR: 0.78; 95% CI: 0.69–0.89). In summary, reaching and maintaining a higher DDS was associated with a decreased risk of all-cause mortality. Therefore, promoting diversified eating and increasing the accessibility of varieties of foods should be paid more attention from policymakers and be more emphasized in dietary guidelines.

We present a novel real-time visual odometry framework for a stereo setup of a depth and high-resolution event camera. Our framework balances accuracy and robustness against computational efficiency towards strong performance in challenging scenarios. We extend conventional edge-based semi-dense visual odometry towards time-surface maps obtained from event streams. Semi-dense depth maps are generated by warping the corresponding depth values of the extrinsically calibrated depth camera. The tracking module updates the camera pose through efficient, geometric semi-dense 3D-2D edge alignment. Our approach is validated on both public and self-collected datasets captured under various conditions. We show that the proposed method performs comparable to state-of-the-art RGB-D camera-based alternatives in regular conditions, and eventually outperforms in challenging conditions such as high dynamics or low illumination., accepted in the 2022 IEEE International Conference on Robotics and Automation (ICRA), Philadelphia (PA), USA

The original Leray's problem concerns the well-posedness of weak solutions to the steady incompressible Navier-Stokes equations in a distorted pipe, which approach to the Poiseuille flow subject to the no-slip boundary condition at spacial infinity. In this paper, the same problem with the Navier-slip boundary condition instead of the no-slip boundary condition, is addressed. Due to the complexity of the boundary condition, some new ideas, presented as follows, are introduced to handle the extra difficulties caused by boundary terms. First, the Poiseuille flow in the semi-infinite straight pipe with the Navier-slip boundary condition will be introduced, which will be served as the asymptotic profile of the solution to the generalized Leray's problem at spacial infinity. Second, a solenoidal vector function defined in the whole pipe, satisfying the Navier-slip boundary condition, having the designated flux and equalling to the Poiseuille flow at large distance, will be carefully constructed. This plays an important role in reformulating our problem. Third, the energy estimates depend on a combined $L^2$ estimate of the gradient and the stress tensor of the velocity.

Additional file 1: Table S1. The risk of bias domains (ROBINS-I) of included studies.

It has been a recent trend to leverage the power of supervised learning (SL) towards more effective reinforcement learning (RL) methods. We propose a novel phasic approach by alternating online RL and offline SL for tackling sparse-reward goal-conditioned problems. In the online phase, we perform RL training and collect rollout data while in the offline phase, we perform SL on those successful trajectories from the dataset. To further improve sample efficiency, we adopt additional techniques in the online phase including task reduction to generate more feasible trajectories and a value-difference-based intrinsic reward to alleviate the sparse-reward issue. We call this overall algorithm, PhAsic self-Imitative Reduction (PAIR). PAIR substantially outperforms both non-phasic RL and phasic SL baselines on sparse-reward goal-conditioned robotic control problems, including a challenging stacking task. PAIR is the first RL method that learns to stack 6 cubes with only 0/1 success rewards from scratch., Comment: Accepted at ICML 2022

This thesis explores and explains key factors influencing the adoption of artificial intelligence (AI) by auditing firms through the lens of the Technology-Organisation-Environment (TOE) framework. Using the multiple-case study method, primary data was collected through semi-structured interviews with decision-makers at auditing firms; this was complemented by secondary data, such as firms’ transparency reports and media news. The data analysis identified anomalies that did not align with previous studies, revealing the specificity of the AI adoption compared to that of other forms of technology. The findings show that firms’ adoption is influenced by the affordance of AI, technological barriers to adoption, formal linkage, linking agents, firm resources and readiness, firm size and scope, the regulatory environment, industrial competition, and the client’s level of acceptance. This study contributes to the literature by providing a better understanding of AI adoption at the firm level, thus filling the gaps in the extant research, which lacks a comprehensive and in-depth understanding of auditing firms’ AI adoption. The findings refine and strengthen the TOE framework to explain how an emerging technology, AI, is adopted in auditing firms.

Objective:To perform a meta-analysis of published randomized controlled trials (RCTs) to assess the effects of curcumin supplementation with different formulations on anthropometric and cardiometabolic indices in patients with metabolism-related diseases (MRDs). Methods: Six databases, including PubMed, Embase, Web of Science, China national knowledge internet (CNKI), Wanfang and China Biology Medicine (CBM), were systematically searched to find relevant articles from 2011 to July 2021. The effect sizes were expressed as weighted mean difference (WMD) with 95% confidence intervals (CI). Between-study heterogeneity was assessed using I2. Subgroup analysis was conducted to find possible sources of heterogeneity. Curcumin formulations in this study were divided as low bioavailability, high bioavailability and nanocurcumin. Results: Of the retrieved 1585 articles, 31 were included in the final analysis. Combined effect sizes suggested a significant effect of curcumin supplementation on reduced body weight (BW) (WMD: −0.94 kg, 95% CI: −1.40, −0.47) and body mass index (BMI) (WMD: −0.40 kg/m2, 95% CI: −0.60, −0.19), respectively. The results also showed significant improvements of fasting plasma glucose (FPG) (WMD: −0.50 mg/dL, 95% CI: −0.72, −0.28), glycosylated hemoglobin (Hb1Ac) (WMD: −0.42%, 95% CI: −0.57, −0.26), insulin (INS) (WMD: −1.70 μIU/mL, 95%CI: −2.03, −1.38), homeostasis model assessment-insulin resistance (HOMA-IR) (WMD: −0.71, 95%CI: −1.11, −0.31), high-density lipoprotein cholesterol (HDL-C) (WMD: 1.73 mg/dL, 95%CI: 0.78, 2.68) and high sensitivity C-reactive protein (Hs-CRP) (WMD: −1.11, 95%CI: −2.16, −0.05). Nanocurcumin showed a greater reduction in FPG (WMD: −1.78 mg/dL, 95% CI: −2.49, −1.07), INS (WMD: −1.66 μIU/mL, 95% CI: −3.21, −0.11), TC (WMD: −12.64 mg/dL (95% CI: −23.72, −1.57) and LDL-C (WMD: −8.95 mg/dL, 95% CI: −16.51, −1.38). The dose-effect analysis showed that there were trends of first rising and then falling between the supplemented curcumin dose and BW, BMI, LDL-C, Hb1Ac, which were clearly distinguished at 80 mg/d due to the strong effect of nanocurcumin on outcomes. A slow upward trend between the dose of curcumin supplementation and HDL-C. No relationships between dose and outcomes were found for FPG and insulin, except for nanocurcumin at 80 mg/d. Conclusions: Our study showed some significant beneficial effects of curcumin supplementation on improving BW, BMI, and the levels of FPG, Hb1Ac, HOMA-IR, HDL-C and Hs-CRP in patients with MRDs. Nanocurcumin may have a greater effect on the reduction of FPG, INS, TC and LDL-C than other curcumin formulations. Considering the potential bias and limitations of studies included, further quality studies with larger sample sizes are needed to confirm these results.

Additional file 2: Table S2. The Risk Of Bias In Non-randomized Studies-of Interventions (ROBINS-I) assessment tool.

With the remarkable increase in the number of vehicles worldwide, the disposal of scrap tire is a pressing issue. Pyrolysis is one treatment option to recover resources from the scrap tire, by whic...

In this paper, a method of realizing large-area atmospheric pressure glow discharge (APGD) plasma in ambient air was investigated. The dielectric barrier discharge (DBD) type was adopted, and the electrode arrangement of a tubular array was designed to achieve large-area APGD (about $1000 \times 60$ mm $^{2}$ ). Optical emission spectroscopy (OES) was used to identify active particles and calculate gas temperature. The results show that the seed electrons can react with N2, O2, and H2O in the air to produce N2 (C-B), N2+, OH, and O3. The gas temperature was calculated to be 410 K–514 K, which is suitable for low-added-value materials treatment. The homogeneity of the plasma temperature was also verified by an infrared camera. Furthermore, a prototype for the wool surface modification was also investigated. The scanning electron microscope (SEM) micrographs and wettability test were adopted, which shows that the plasma can damage the squamae of the natural wool to improve the wettability greatly without burning the wool fiber.

To observe the effect of proximal and distal acupoint catgut-embedding on the contents of prostaglandin EFemale Wistar rats were randomly divided into control, model, proximal catgut-embedding and distal catgut-embedding groups, with 8 rats in each group. The PD model was established by subcutaneous injection of estradiol ben-zoate (0.5 mg/rat on the first day and 10th day, and 0.2 mg/rat from the 2nd to the 9th day) and intraperitoneal injection of oxytocin (2 U/rat, on the 11th day). Catgut-embedding was applied at bilateral "Guanyuan"(CV4) and "Ciliao"(BL32) in the proximal catgut-embedding group, and bilateral "Sanyinjiao" (SP6) and "Diji"(SP8) in the distal catgut-embedding group. After the treatment, the body writhing times in 30 min and uterine mass were recorded, PGE2 and PGF2αin uterus and IL-2 in serum were assayed by using ELISA, the activity of NK cell in the spleen was detected using MTT colorimetry.Following modeling, the body writhing times of the model group was increased than that of the control group (Both proximal and distal acupoint catgut-embeding can increase PGE

Bandit problems with linear or concave reward have been extensively studied, but relatively few works have studied bandits with non-concave reward. This work considers a large family of bandit problems where the unknown underlying reward function is non-concave, including the low-rank generalized linear bandit problems and two-layer neural network with polynomial activation bandit problem. For the low-rank generalized linear bandit problem, we provide a minimax-optimal algorithm in the dimension, refuting both conjectures in [LMT21, JWWN19]. Our algorithms are based on a unified zeroth-order optimization paradigm that applies in great generality and attains optimal rates in several structured polynomial settings (in the dimension). We further demonstrate the applicability of our algorithms in RL in the generative model setting, resulting in improved sample complexity over prior approaches. Finally, we show that the standard optimistic algorithms (e.g., UCB) are sub-optimal by dimension factors. In the neural net setting (with polynomial activation functions) with noiseless reward, we provide a bandit algorithm with sample complexity equal to the intrinsic algebraic dimension. Again, we show that optimistic approaches have worse sample complexity, polynomial in the extrinsic dimension (which could be exponentially worse in the polynomial degree).

Most existing 3D geometry copy detection research focused on 3D watermarking, which first embeds ``watermarks'' and then detects the added watermarks. However, this kind of methods is non-straightforward and may be less robust to attacks such as cropping and noise. In this paper, we focus on a fundamental and practical research problem: judging whether a point cloud is plagiarized or copied to another point cloud in the presence of several manipulations (e.g., similarity transformation, smoothing). We propose a novel method to address this critical problem. Our key idea is first to align the two point clouds and then calculate their similarity distance. We design three different measures to compute the similarity. We also introduce two strategies to speed up our method. Comprehensive experiments and comparisons demonstrate the effectiveness and robustness of our method in estimating the similarity of two given 3D point clouds., Comment: submitted for review

This paper presents new \emph{variance-aware} confidence sets for linear bandits and linear mixture Markov Decision Processes (MDPs). With the new confidence sets, we obtain the follow regret bounds: For linear bandits, we obtain an $\tilde{O}(poly(d)\sqrt{1 + \sum_{k=1}^{K}\sigma_k^2})$ data-dependent regret bound, where $d$ is the feature dimension, $K$ is the number of rounds, and $\sigma_k^2$ is the \emph{unknown} variance of the reward at the $k$-th round. This is the first regret bound that only scales with the variance and the dimension but \emph{no explicit polynomial dependency on $K$}. When variances are small, this bound can be significantly smaller than the $\tilde{\Theta}\left(d\sqrt{K}\right)$ worst-case regret bound. For linear mixture MDPs, we obtain an $\tilde{O}(poly(d, \log H)\sqrt{K})$ regret bound, where $d$ is the number of base models, $K$ is the number of episodes, and $H$ is the planning horizon. This is the first regret bound that only scales \emph{logarithmically} with $H$ in the reinforcement learning with linear function approximation setting, thus \emph{exponentially improving} existing results, and resolving an open problem in \citep{zhou2020nearly}. We develop three technical ideas that may be of independent interest: 1) applications of the peeling technique to both the input norm and the variance magnitude, 2) a recursion-based estimator for the variance, and 3) a new convex potential lemma that generalizes the seminal elliptical potential lemma., Comment: 31 pages

Smooth solutions of the stationary Navier-Stokes equations in an infinitely long pipe, equipped with the Navier-slip or Navier-Hodge-Lions boundary condition, are considered in this paper. Three main results are presented. First, when equipped with the Navier-slip boundary condition, it is shown that, $W^{1,\infty}$ axially symmetric solutions with zero flux at one cross section, must be swirling solutions: $u=(- C x_2, C x_1,0)$, and $x_3-$periodic solutions must be helical solutions: $u=(-C_1x_2,C_1x_1,C_2)$. Second, also equipped with the Navier-slip boundary condition, if the swirl or vertical component of the axially symmetric solution is independent of the vertical variable $x_3$, solutions are also proven to be helical solutions. In the case of the vertical component being independent of $x_3$, the $W^{1,\infty}$ assumption is not needed. In the case of the swirl component being independent of $x_3$, the $W^{1,\infty}$ assumption can be relaxed extensively such that the horizontal radial component of the velocity, $u_r$, can grow exponentially with respect to the distance to the origin. Also, by constructing a counterexample, we show that the growing assumption on $u_r$ is optimal. Third, when equipped with the Navier-Hodge-Lions boundary condition, we can show that if the gradient of the velocity grows sublinearly, then the solution, enjoying the Liouville-type theorem, is a trivial shear flow: $(0,0,C)$., Comment: Compared to the previous version, title is chagned. Two new resutls is added and one result is improved. Also one new contributed author is added

A computer-assisted argument is given, which provides existence proofs for periodic orbits in state-dependent delayed perturbations of ordinary differential equations (ODEs). Assuming that the unperturbed ODE has an isolated periodic orbit, we introduce a set of polynomial inequalities whose successful verification leads to the existence of periodic orbits in the perturbed delay equation. We present a general algorithm, which describes a way of computing the coefficients of the polynomials and optimizing their variables so that the polynomial inequalities are satisfied. The algorithm uses the tools of validated numerics together with Chebyshev series expansion to obtain the periodic orbit of the ODE as well as the solution of the variational equations, which are both used to compute rigorously the coefficients of the polynomials. We apply our algorithm to prove the existence of periodic orbits in a state-dependent delayed perturbation of the van der Pol equation.

This paper studies model-based bandit and reinforcement learning (RL) with nonlinear function approximations. We propose to study convergence to approximate local maxima because we show that global convergence is statistically intractable even for one-layer neural net bandit with a deterministic reward. For both nonlinear bandit and RL, the paper presents a model-based algorithm, Virtual Ascent with Online Model Learner (ViOlin), which provably converges to a local maximum with sample complexity that only depends on the sequential Rademacher complexity of the model class. Our results imply novel global or local regret bounds on several concrete settings such as linear bandit with finite or sparse model class, and two-layer neural net bandit. A key algorithmic insight is that optimism may lead to over-exploration even for two-layer neural net model class. On the other hand, for convergence to local maxima, it suffices to maximize the virtual return if the model can also reasonably predict the size of the gradient and Hessian of the real return., Comment: Updated Figure 1 and its caption

We study the motion of an incompressible fluid in an $n+1$-dimensional infinite pipe $\,\La\,$ with an $L$-periodic shape in the $z=x_{n+1}$ direction. We set $\,x=(x_1,x_2,\cdots,x_{n})$, and $z=x_{n+1}$. We denote by $\Sigma_z$ the cross section of the pipe at the level $z\,,$ and by $v_z$ the $n+1$ component of the velocity. Fluid motion is described by the evolution Stokes or Navier-Stokes equations together with the non-slip boundary condition $\bv=\,0\,$. We look for solutions $\bv(x,z,t)$ with a given, arbitrary, $T-$time periodic total flux $\,\int_{\Sigma_z} \,v_z(x,z,t)\,dx=g(t)\,,$ which should be simultaneously $T$-periodic with respect to time and $L$-periodic with respect to $z\,.$ We prove existence and uniqueness of the solution to the above problems. The results extend those proved in reference \cite{B-05}, where the cross sections were independent of $z$. The argument is presented through a sequence of steps. We start by considering the linear, stationary, $z-$periodic Stokes problem. Then we study the double periodic evolution Stokes equations, which is the heart of the matter. Finally, we end with the extension to the full Navier-Stokes equations., Comment: 28 pages

The deep-learning-based image restoration and fusion methods have achieved remarkable results. However, the existing restoration and fusion methods paid little research attention to the robustness problem caused by dynamic degradation. In this paper, we propose a novel dynamic image restoration and fusion neural network, termed as DDRF-Net, which is capable of solving two problems, i.e., static restoration and fusion, dynamic degradation. In order to solve the static fusion problem of existing methods, dynamic convolution is introduced to learn dynamic restoration and fusion weights. In addition, a dynamic degradation kernel is proposed to improve the robustness of image restoration and fusion. Our network framework can effectively combine image degradation with image fusion tasks, provide more detailed information for image fusion tasks through image restoration loss, and optimize image restoration tasks through image fusion loss. Therefore, the stumbling blocks of deep learning in image fusion, e.g., static fusion weight and specifically designed network architecture, are greatly mitigated. Extensive experiments show that our method is more superior compared with the state-of-the-art methods.

Additional file 1: Table S1. Composition of gender and occupation sample size (absolute values and percentage) for different kidney injury indicators. Table S2. Data distribution characteristics of age, gender, and occupational exposure subgroups of U-Cd and responder in different data sets. Table S3. Study-ID and corresponding references for urinary cadmium (U-Cd) and β2-microglobulin (β2-MG). Table S4. Study-ID and corresponding references for urinary cadmium (U-Cd) and N-acetyl-β-glucosidase (NAG).Table S5. Study-ID and corresponding references for urinary cadmium (U-Cd) and microalbumin (mALB). Table S6. Study-ID and corresponding references for urinary cadmium (U-Cd) and retinol binding Protein (RBP).

Partial reconfiguration is a technology that the different bitstream is loaded into reconfigurable region to realize the different functions. Partial reconfiguration be able to make reconfigurable time shorten greatly and implement time division multiplex access of logic resources. JTAG (Joint Test Action Group) is an essential module of FPGA (Field Programmable Gate Array), accomplishing testing, configuration and soon. As the result of supporting user-defined instruction, JTAG circuit has the good extensibility. In order to implement partial reconfiguration of logic resources, this paper puts forward dynamic reconfiguration instruction-DRP, and designs the JTAG circuit of accomplishing dynamic reconfiguration.

Er-doped TiO2 thin films have been prepared by dual-frequency magnetron co-sputtering with two targets composed of TiO2 and Er2O3. The structural and optical properties of these thin films were investigated using various characterization techniques. X-ray photoelectron spectroscopy measurements show that the content of Er increases when the power on Er2O3 target increases. X-ray diffraction patterns indicate that the anatase TiO2 peaks become weaker with the increasing power on Er2O3 target and the films tend to form amorphous structure. Scanning electron microscopy results show that the grain size decreases with the rise in Er content. UV–vis spectrophotometric analyses reveal that the values of band gap decrease from 3.35 ± 1.3% eV to 3.02 ± 1.3% eV with the increasing amount of Er from 0 at.% to 3.2 at.%. The red shift in the optical adsorption edge may contribute to a higher photocatalytic activity of TiO2 thin films.

Nonylphenol (NP) has provoked much environmental concern because of their weak estrogenic activities; however, few data are available on the environmental levels of the chemical in China.Environmental or river samples were assayed for NP by high-performance liquid chromatography (HPLC) technique.The concentration for NP measured in Xiangjiang River, ranging from 0.174 to 3.411 μg/L with a mean value of 1.73 μg/L, was lower than the Water Quality Criteria for NP of the US (6.6 μg/L); however, the NP concentration was maintained at a higher level compare to the developed countries and other civil cities. NP concentration in downstream water was markedly higher than that both in midstream and upstream water. Tissue accumulation of NP was observed in aquatics. A ratio of mean concentration of NP in aquatic (chlamys farreri and hemiculter leucisculus) to that in river water was 241 and 1087, respectively. The presence of NP in tap water in two urban districts of Zunyi was common with a detectable rate reached 100.0%. Mean NP concentration in terminal tap water in Huichuan district was six times as high as Honghuagang district, which was above Standards for the Drinking Water Quality for Phenols of China (2 μg/L).The pollution of NP in Xiangjiang River, tap water, and aquatic in Zunyi belongs to moderate or severe level in the world.

Existing image fusion methods pay few research attention to image fusion efficiency and network architecture. However, the efficiency and accuracy of image fusion has an important impact in practical applications. To solve this problem, we propose an \textit{efficient autonomous evolution image fusion method, dubed by AE-Netv2}. Different from other image fusion methods based on deep learning, AE-Netv2 is inspired by human brain cognitive mechanism. Firstly, we discuss the influence of different network architecture on image fusion quality and fusion efficiency, which provides a reference for the design of image fusion architecture. Secondly, we explore the influence of pooling layer on image fusion task and propose an image fusion method with pooling layer. Finally, we explore the commonness and characteristics of different image fusion tasks, which provides a research basis for further research on the continuous learning characteristics of human brain in the field of image fusion. Comprehensive experiments demonstrate the superiority of AE-Netv2 compared with state-of-the-art methods in different fusion tasks at a real time speed of 100+ FPS on GTX 2070. Among all tested methods based on deep learning, AE-Netv2 has the faster speed, the smaller model size and the better robustness., Comment: Some mistakes have been fixed

This paper focuses on developing efficient and robust evaluation metrics for RANSAC hypotheses to achieve accurate 3D rigid registration. Estimating six-degree-of-freedom (6-DoF) pose from feature correspondences remains a popular approach to 3D rigid registration, where random sample consensus (RANSAC) is a de-facto choice to this problem. However, existing metrics for RANSAC hypotheses are either time-consuming or sensitive to common nuisances, parameter variations, and different application scenarios, resulting in performance deterioration in overall registration accuracy and speed. We alleviate this problem by first analyzing the contributions of inliers and outliers, and then proposing several efficient and robust metrics with different designing motivations for RANSAC hypotheses. Comparative experiments on four standard datasets with different nuisances and application scenarios verify that the proposed metrics can significantly improve the registration performance and are more robust than several state-of-the-art competitors, making them good gifts to practical applications. This work also draws an interesting conclusion, i.e., not all inliers are equal while all outliers should be equal, which may shed new light on this research problem.

We study the multinomial logit (MNL) bandit problem, where at each time step, the seller offers an assortment of size at most $K$ from a pool of $N$ items, and the buyer purchases an item from the assortment according to a MNL choice model. The objective is to learn the model parameters and maximize the expected revenue. We present (i) an algorithm that identifies the optimal assortment $S^*$ within $\widetilde{O}(\sum_{i = 1}^N \Delta_i^{-2})$ time steps with high probability, and (ii) an algorithm that incurs $O(\sum_{i \notin S^*} K\Delta_i^{-1} \log T)$ regret in $T$ time steps. To our knowledge, our algorithms are the first to achieve gap-dependent bounds that fully depends on the suboptimality gaps of all items. Our technical contributions include an algorithmic framework that relates the MNL-bandit problem to a variant of the top-$K$ arm identification problem in multi-armed bandits, a generalized epoch-based offering procedure, and a layer-based adaptive estimation procedure., Comment: 32 pages

Osteoporosis is a growing health problem worldwide. Health beliefs and self-efficacy in regard to osteoporosis may be important targets in preventive behaviours. This study aimed to describe beliefs around osteoporosis held by orthopaedic nurses as well as the nurses' self-efficacy and the associations between knowledge, health beliefs and self-efficacy. A cross-sectional study was conducted with nurses working in orthopaedic departments in 13 tertiary hospitals in Hunan Province, China. Participants completed a questionnaire consisting of the Osteoporosis Health Belief Scale (OHBS), the Osteoporosis Self-Efficacy Scale (OSES) and a survey of their socio-demographic characteristics. A total of 558 nurses completed the survey (response rate 95%). The nurses’ scores regarding their perception of the susceptibility to and seriousness of osteoporosis were moderately low (mean OHBS score = 124.72, range 45–171; SD = 14.71), while their corresponding self-efficacy scores were moderately high (mean OSES score = 72.01, range 0–120; SD = 23.15). Knowledge regarding osteoporosis was positively associated with self-efficacy (r = 0.232, p = 0.000) but not with health beliefs (r = 0.030, p = 0.484). The findings of this study revealed moderately high self-efficacy and moderate health beliefs related to osteoporosis among orthopaedic nurses. Osteoporosis knowledge was not associated with health beliefs or self-efficacy. The findings indicate that educational programs should not only focus on knowledge improvement but also changes in health beliefs to improve actual behaviour in practice.

Viscous damping wall (VDW) is an effective velocity-dependent damper, which can dissipate earthquake energy by shear strain of viscous material. The damping force equation of VDW can only be obtained from regression of VDW dynamic test results, as velocity exponent of power-law material can not be obtained from rheometers. This study designed a Dynamic Sandwich-Type Shear (DSTS) test which matches the engineering working condition of VDW, and conducted a series of experiments with different frequencies and strain amplitudes. Simple data procession methods were proposed to calculate velocity exponent and storage/loss modulus of polymer from experimental data. Comparison with methods adapted from those for VDW formula validated that radial linear regression method was proper to separate stress components and pivotal point method was accurate to evaluate velocity exponent. The velocity exponents obtained varied from 0.8 to 1, with various loading frequency and strain amplitude. Finally, the differences between the results of storage/loss modulus in DSTS test and ARES test were compared. Due to the extrusion effect caused by engineering working condition, the storage and loss modulus obtained by DSTS test was larger than the modulus obtained by ARES test.

To assess the association of indoor environmental risk factors with respiratory function among adults in an acid rain-plagued city in China where coal use is frequent. The subjects were randomly selected in the winter season. Information on selected home environmental factors was collected through administered questionnaires. Additionally, pulmonary function tests, including Forced Vital Capacity (FVC), Forced Expiratory Volume in 1 s (FEV1), FEV1/FVC and Peak Expiratory Flow Rate (PEFR) were also performed in participants. This study showed that, among a variety of risk factors, coal fuel use, cooking oil fumes and active and passive smoking exposure together with asthma in childhood were important factors for deterioration of pulmonary function among adults in the winter season (p < 0.05). Additionally, subjects whose kitchen was located in the living room or bedroom, who opened their windows only occasionally or never, who noted the presence of cooking oil fumes and pests, whose bedroom was shared by 3 or more residents and who kept pets tended to exhibit lower values of FVC, FEV1 and PEFR values compared with non-exposed counterparts (p < 0.05). This study demonstrated impaired pulmonary function among adults who were exposed to indoor risk factors, such as coal fires and cigarette smoking compared to non-users in the winter season and emphasizes the need for public health efforts to decrease exposure to indoor air pollution.

Although copper sulfate (CuSO4) as a valuable desulfurizer is commonly used in the laboratory, it is not applied in industry due to its high cost and lack of practical regeneration method. In this study, a method for regeneration of the absorption product copper sulfide (CuS) under mild conditions is put forward. In the presence of sodium chloride (NaCl), CuSO4 could be regenerated smoothly in acid solution and be recycled for the absorption of hydrogen sulfide (H2S). Specifically, under the conditions of 0.30 mol/L chloride ion (Cl−), 1.8% acidity at 323 K, for 5.5 h, the regeneration efficiency will be higher than 99%. Moreover, extensive experimental studies showed that the addition of Cl− would not observably influence the absorption efficiency of H2S. These results reveal the potential for developing a novel, efficient and low-cost desulfurization technology.

With the continuous development of social economy, energy and environmental issues deserve more and more attention. In order to improve the new energy’s consumption, this paper proposes an optimized social income strategy for clean energy consumption based on transmission channel constraints. The main method combines the method of cooperative game to maximize the value of social income, so as to determine the optimal combination of power generation, then taking into account the constraints of transmission channels and other issues, and then solving the problem of cross-regional clean energy trade with the goal of maximal social benefits. At the same time, the proceeds from the cooperative game can be distributed to each power transaction entity through the method of nuclear solution.

Solution-processed white organic light-emitting diodes (WOLEDs) with silica-coated silver nanocubes (Ag@SiO2 NCs) incorporated at the interface of a hole transporting layer and emission layer are studied. The concentration of Ag@SiO2 NCs is varied to investigate the effect of Ag@SiO2 NCs on the performances of WOLEDs. Owing to the sharp edges and corners, Ag NCs greatly improve the radiative rate and emission intensity of nearby blue excitons. The blue emission at different Ag@SiO2 NC concentrations determines the performance of the WOLEDs. The emission of the orange excitons is strengthened by the high concentration of Ag@SiO2 NCs, which slightly influences the device performance. On the other hand, the SiO2 shell and some SiO2 nanospheres coexisting with Ag NCs reduce the hole transporting, improving the carrier balance in the WOLEDs. The experimental and simulated results also show that excessive Ag@SiO2 NCs may cause a rough film surface, unbalanced carrier injection, and fluorescence quenching, which decreases the device performance. The optimized WOLED with a proper concentration of Ag@SiO2 NCs has a peak current efficiency of 53.9 cd/A, acquiring a significant enhancement factor of 77.3% compared to the control device without Ag@SiO2 NCs.

A low level of knowledge and awareness of specifics regarding osteoporosis among orthopedic nurses can compromise the quality of patient education and nursing care. There are significant variations in education levels among Chinese nurses, however, little is known about the awareness of osteoporosis among orthopedic nurses in China. The objective of this study was to assess the level of osteoporosis knowledge and identify factors that correlated with knowledge levels. A cross-sectional survey was conducted among orthopedic nurses at 13 tertiary hospitals in Hunan Province, China. Knowledge was assessed by the Osteoporosis Knowledge Assessment Test (OKAT). A total of 558 nurses completed the survey and 530 valid questionnaires were returned with a response rate of 95%. Each question had three response options of “True”, “False” and “Don't know.” The mean of scores from all OKAT questions was 11.4 (range 2–17; SD = 2.5). Factors associated with a better knowledge included age (36–45 years) marital status (married) and education (bachelor degree or above). Only 93 nurses (18%) had previously attended formal training regarding osteoporosis. Orthopedic nurses had moderate-to-low levels of knowledge regrading osteoporosis. The survey highlighted the potential areas for improvement, especially among younger single nurses with lower educational levels. In-depth education may contribute to improving service quality and offering better tutoring for fragility fracture patients through increased awareness and knowledge.

Correspondence selection aiming at seeking correct feature correspondences from raw feature matches is pivotal for a number of feature-matching-based tasks. Various 2D (image) correspondence selection algorithms have been presented with decades of progress. Unfortunately, the lack of an in-depth evaluation makes it difficult for developers to choose a proper algorithm given a specific application. This paper fills this gap by evaluating eight 2D correspondence selection algorithms ranging from classical methods to the most recent ones on four standard datasets. The diversity of experimental datasets brings various nuisances including zoom, rotation, blur, viewpoint change, JPEG compression, light change, different rendering styles and multi-structures for comprehensive test. To further create different distributions of initial matches, a set of combinations of detector and descriptor is also taken into consideration. We measure the quality of a correspondence selection algorithm from four perspectives, i.e., precision, recall, F-measure and efficiency. According to evaluation results, the current advantages and limitations of all considered algorithms are aggregately summarized which could be treated as a "user guide" for the following developers.

Matching corresponding features between two images is a fundamental task to computer vision with numerous applications in object recognition, robotics, and 3D reconstruction. Current state of the art in image feature matching has focused on establishing a single consistency in static scenes; by contrast, finding multiple consistencies in dynamic scenes has been under-researched. In this paper, we present an end-to-end optimization framework named "iterative clustering with Game-Theoretic Matching" (ic-GTM) for robust multi-consistency correspondence. The key idea is to formulate multi-consistency matching as a generalized clustering problem for an image pair. In our formulation, several local matching games are simultaneously carried out in different corresponding block pairs under the guidance of a novel payoff function consisting of both geometric and descriptive compatibility; the global matching results are further iteratively refined by clustering and thresholding with respect to a payoff matrix. We also propose three new metrics for evaluating the performance of multi-consistency image feature matching. Extensive experimental results have shown that the proposed framework significantly outperforms previous state-of-the-art approaches on both singleconsistency and multi-consistency datasets.

In this note we extend a 2018 result of Bardos and Titi \cite{BT} to a new class of functional spaces $C^{0,��}_��(\bar��)$. It is shown that weak solutions $\,u\,$ satisfy the energy equality provided that $u\in L^3((0,T);C^{0,��}_��(\bar��))$ with $��\geq\frac{1}{3}$ and $��>0$. The result is new for $\,��= \,\frac{1}{3}\,.$ Actually, a quite stronger result holds. For convenience we start by a similar extension of a 1994 result of Constantin, E, and Titi, \cite{CET}, in the space periodic case. The proofs follow step by step those of the above authors. For the readers convenience, and completeness, proofs are presented in a quite complete form., 13 pages

Multi-modal transportation network is relatively complex, involving many transportation links and transshipment nodes. Transshipment delay risk should not be ignored. Comprehensive consideration of transshipment delay risks at nodes, rational utilization of node transshipment delay time and optimization of vehicles allocation are the key problems that this paper trying to solve. From the research sight of transshipment process at nodes, the transshipment delay risk of multi-modal transport is analyzed and transshipment delay risk value of multi-modal transport node is taken into the optimization objectives for vehicles allocation. With consideration of the effect of transshipment delay on vehicles allocation in transportation sections, the number of allocating different vehicles allocation in each transportation section is also discussed in the perspective of multi-modal transportation system planning. While meeting the freight demands and connecting all sorts of transportation effectively, operating costs and the transshipment delay risk can be minimized. This paper also provides a new thought of vehicles allocation for multi-modal transportation and guides for multi-modal transportation risk management as well.

SRAM-based FPGA has become a core device in space application. However, based on CMOS technology, SRAM-based FPGA is sensitive for SEU effect. JTAG circuit is a significant module of SRAM-based FPGA, executing boundary-scan test and global configuration function. SEU effect can result in function disturbance of JTAG circuit. To adopt reasonable harden strategies for JTAG circuit, the paper puts forward an experimental scheme to evaluate the sensitivity of each module for SEU. Simulation results illustrate that the sensitivity of TAP Controller module is highest, 60% or more, compared with other modules. Finally, the paper proposes corresponding harden measures for sensitive module.

Titanium dioxide/single layer graphene (TiO2/SLG) composites have been widely studied due to the role of graphene acting as an electron-acceptor, which can suppress the recombination of photogenerated electron–hole pairs from TiO2 upon light irradiation. In this study, a novel route for the synthesis of TiO2/SLG composite films using controlled experiment chemical vapor deposition combined with magnetron sputtering is reported for the first time. The structure and morphology of the SLG and TiO2/SLG composites were characterized by scanning electron microscopy, Raman spectroscopy, transmission electron microscopy, X-ray diffraction and X-ray photoelectron spectroscopy. The results showed that the introduction of SLG layer influenced the crystalline structure, morphology and photocurrent of the TiO2 films. The interaction between TiO2 and SLG and p-doping of SLG were demonstrated by the G band splitting and upshift of the 2D band. With the higher quality of SLG as the interface of the TiO2/SLG, the composite films exhibited improved photocurrent and reached the highest of about 3.25 μA/cm−2. The results suggested the high separation efficiency of photogenerated electron-hole pairs, which could inspire continuous researches of energy conversion and environmental applications.