Search

The evaluation of community disaster resilience is of great practical importance for building low-risk, sustainable, and disaster-resistant cities. With 12 communities in Luoyang as the objects, this paper adopts entropy weight TOPSIS and obstacle diagnosis to study the community disaster resilience of Luoyang from seven dimensions, such as demographic characteristics, economic development, and infrastructure. The results of the study are as follows: (1) the community disaster resilience of Luoyang (0.48) is at a medium level. Community capital is the main influencing factor of community disaster resilience. Government governance, community capacity, and community intelligence are the components that need attention in the construction of Luoyang’s community disaster resilience. (2) The community disaster resilience of Luoyang presents a decreasing trend from rural to urban areas. Moreover, communities with high disaster resilience are less than communities with low disaster resilience. (3) The obstacle to community disaster resilience of Luoyang focuses on population, economic development, and infrastructure. In addition, community trust, community dependence, popularization and intellectualization of disaster prevention information, and disaster information sharing also significantly restrict the construction of Luoyang community disaster resilience. (4) According to the results of sensitivity analysis, the entropy weight TOPSIS evaluation results are less sensitive. Moreover, changing the weight value, weight method, and evaluation method will not lead to major changes in the rankings.

The aim of multiprojector interreflection compensation is to modify input images to remove complex physical stray-light effects (interreflection) from a multiprojector immersive system. This is an important but often ignored problem, which can lead to degradation of a projection image. Traditional methods usually address this problem by computing a matrix inversion. These traditional methods often ignore issue of the clarity of the generated images. In this paper, we describe a method for learning the inversion using a deep convolutional neural network (CNN), named Superresolution Compensation Net (SRCN). SRCN consists of four convolution layers to learn interactions of global light, six convolution layers, and two transposed convolution layers to extract multilevel features and generate compensation images. We also used a subpixel convolution layer to increase the resolution. To make compensation images more consistent with human visual perception, we used a perceptual loss, which compares the differences between feature maps on the VGG16 network. We implemented an immersive projector-camera display prototype (Pro-Cam) and calculated the quality index of the compensation images and the projection results. Our method achieved better results than previous methods in both objective evaluations and subjective visual perception.

In order to predict the surface subsidence scientifically in solid filling mining, it is necessary to establish a complete subsidence prediction model and parameter system according to the evolution law of overburden structure and strata movement characteristics. Mine pressure monitoring and borehole peeping show that the overburden in solid filling mining is mainly a bending zone with relatively complete layered structure, and the overburden only develops a certain height of fault zone near the roof, without collapse. The results show that the surface subsidence pattern of solid filling mining can still be described by probability integral model, and the parameter system of the surface subsidence prediction model based on “equivalent mining height” is further discussed. Finally, the prediction model of surface subsidence established in this paper is applied to an engineering example, and good results are achieved.

This paper based on the Gaussian particle filter (GPF) deals with the attitude estimation of UAV. GPF algorithm has better estimation accuracy than the general nonlinear non-Gaussian state estimation and is usually used to improve the system’s real-time performance whose noise is specific such as Gaussian noise during the mini UAV positioning and navigation. The attitude estimation algorithm is implemented on FPGA to verify the effectiveness of the Gaussian particle filter. Simulation results have illustrated that the GPF algorithm is effective and has better real-time performance than that of the particle filter.

Objective. In renal ischemia/reperfusion injury (RIRI), nuclear factor κB (NF-κB) initiates the expression of multiple genes involved in inflammatory disease. Inhibition of NF-κB-mediated inducible nitric oxide synthase (iNOS) expression can ameliorate RIRI. Vagus nerve stimulation (VNS) protects against various organs I/R injury. The present study was designed to elucidate the protective effect of VNS on RIRI and its influence on iNOS protein expression. Methods. Eighteen male Sprague-Dawley rats were randomly allocated into the sham group, the I/R group, and the VNS+I/R group, 6 rats per group. An RIRI model was induced by a right nephrectomy and blockade of the left renal pedicle vessels for 45 min. After 6 h of reperfusion, the blood samples and renal samples were collected. The VNS treatment was performed throughout the I/R process in the VNS+I/R group using specific parameters (20 Hz, 0.1 ms in duration, square waves) known to produce a small but reliable bradycardia. Blood was used for evaluation of renal function and inflammatory state. Renal injury was evaluated via TUNEL staining. Renal samples were harvested to evaluate renal oxidative stress, NF-κB p65 levels, and iNOS protein expression. Results. The VNS treatment reduces serum creatinine (Cr) and blood urea nitrogen (BUN) levels. Simultaneously, the levels of tumor necrosis factor alpha (TNF-α), interleukin 6 (IL-6), and interleukin 1-beta (IL-1β) were significantly increased in the I/R group, but VNS treatment markedly ameliorated this inflammatory response. Furthermore, the VNS ameliorated oxidant stress and renal injury, indicated by a decrease in 3-nitrotyrosine (3-NT) formation and MDA and MPO levels and an increase in the SOD level compared to that in the I/R group. Finally, the VNS also significantly decreases NF-κB p65, iNOS, and nitrite/nitrate levels compared to that in the I/R group. Conclusion. Our findings indicate that NF-κB activation increased iNOS expression and promoted RIRI and that VNS treatment attenuated RIRI by inhibiting iNOS expression, oxidative stress, and inflammation via NF-κB inactivation.

Background and aims Dehydroepiandrosterone (DHEA) supplementation has been investigated in patients with altered cortisol levels and is proposed to ameliorate the metabolic profile related to adipose tissue. However, further research is warranted and evidence is no compelling for liver safety. Hence, we aimed to meta-analyze the effects of DHEA supplementation on circulating levels of cortisol, liver enzymes, and adipokines. Methods We searched literature published in PubMed, Web of Science, Embase, and Scopus, until December 2020. We obtained overall results using the generic inverse of variance method with a random-effects model. Results Through 10 arms, serum cortisol levels decreased significantly after DHEA supplementation [weighted mean difference (WMD): -53.581 nmol/L, 95% confidence interval (CI): -88.2, -18.9, p = 0.002], without significant heterogeneity (I2 = 36%, p = 0.117). In contrast, any significance was noted for adiponectin (WMD: -0.045 ug/ml, 95% CI: -0.56, 0.47; p = 0.865), leptin (WMD: -2.55 ug/ml, 95% CI: -6.2, 1.06; p = 0.166), aspartate transaminase (AST) (WMD: -3.7 U/L, 95% CI: -10.35, 2.95; p = 0.276), and alanine aminotransferase (ALT) (WMD: -1.7 U/L, 95% CI: -3.45, 0.06; p = 0.058). Conclusion DHEA supplementation decreased circulating cortisol but did not alter adiponectin, leptin, AST, and ALT levels. Hence, DHEA supplementation could be considered as an adjunct in the management of hypercortisolemia and is safe for the liver.

The role of green supplier evaluation and selection (GSES) in supply chain management is increasingly appreciated due to the intensification of competition, raising public consciousness, and environmental issues. To improve GSES, a large number of approaches have been proposed in the past decades. However, few attempts have been made to systematically review and classify the literature in this field. The objective of this study is to afford a comprehensive review of the studies which aim to develop models and methods in helping enterprises to assess and select the right green suppliers. To achieve this goal, a total of 193 journal articles extracted from the Scopus database over the period of 2009 to 2020 were chosen and reviewed. These publications were classified into ten categories based on their adopted GSES models and analyzed concerning the evaluation criteria, criteria weighting methods, and performance evaluation methods. Moreover, a bibliometric analysis was conducted according to the frequency of supplier selection methods, citation number, publication year, journal, country, and application area. This study supports practitioners, managers, and researchers in effectively recognizing and applying the GSES models to enhance organizational competitiveness and provides an insight into its state of the art.

As the key metering equipment in the smart grid, the measurement accuracy and stability of electronic transformer are important for the normal operation of power system. In order to solve the problem that there is no effective way to predict the error developing trend of electronic transformer, this paper proposed two kinds of short-term prediction methods for electronic transformer error based on the backpropagation neural network and the Prophet model, respectively. First, preprocessing and visualization operation are performed on the original error data. Then, the data fitting and short-term prediction of electronic transformer error are made on the basis of the backpropagation neural network and the Prophet model, and the fitting and prediction results of the two methods are compared and analysed in combination with four evaluation indexes. Finally, the Prophet model is adopted to simulate the development trend and periodic fluctuation of error, and the reason for fluctuation is analysed. The simulation results show that the Prophet model is more suitable for the prediction of electronic transformer measurement error than the backpropagation neural network.

For the decryption of the fully homomorphic encryption (FHE) over the integers with the message space ZQ, Nuida and Kurosawa proposed a Q4λ-multiplicative-degree circuit to compute it at Eurocrypt 2015, where λ is the security parameter and the message size Q is a constant. Since the degree of the decryption circuit is polynomial in Q, the range of the message size Q is limited. In this work, we solve this open problem as long as Q is large enough (larger than λ). We represent the decryption circuit as a arithmetic polynomial of multiplicative degree 108·λ log3λ, which is independent of the message size Q except a constraint Q>λ. Moreover, the bootstrapping process requires only O(λ·log⁡λ) number of multiplications to implement the decryption circuit, which is significantly lower than O(λ4) of Nuida and Kurosawa’s work. We also show the efficiency of the FHE scheme with message space ZQ compared to the FHE scheme with binary message space. As a result, we have that the former is preferable.

Decision making trial and evaluation laboratory (DEMATEL) is considered as an effective method for the identification of cause-effect chain components of a complex system. It deals with evaluating interdependent relationships among factors and finding the critical ones through a visual structural model. Over the recent decade, a large number of studies have been done on the application of DEMATEL and many different variants have been put forward in the literature. The objective of this study is to review systematically the methodologies and applications of the DEMATEL technique. We reviewed a total of 346 papers published from 2006 to 2016 in the international journals. According to the approaches used, these publications are grouped into five categories: classical DEMATEL, fuzzy DEMATEL, grey DEMATEL, analytical network process- (ANP-) DEMATEL, and other DEMATEL. All papers with respect to each category are summarized and analyzed, pointing out their implementing procedures, real applications, and crucial findings. This systematic and comprehensive review holds valuable insights for researchers and practitioners into using the DEMATEL in terms of indicating current research trends and potential directions for further research.

Community detection is an important task in network analysis, in which we aim to find a network partitioning that groups together vertices with similar community-level connectivity patterns. Bipartite networks are a common type of network in which there are two types of vertices, and only vertices of different types can be connected. While there are a range of powerful and flexible methods for dividing a bipartite network into a specified number of communities, it is an open question how to determine exactly how many communities one should use, and estimating the numbers of pure-type communities in a bipartite network has not been completed. In our paper, we propose a method named as “biCNEQ” (bipartite network communities number estimation based on quality of filtering coefficient), which ensures that communities are all pure type, for estimating the number of communities in a bipartite network. This paper makes the following contributions: (1) we show how a unipartite weighted network, which we call similarity network, can be projected from a bipartite network using a measure of correlation; (2) we reveal the relation between the similarity correlation and community’s edges in the vertices of a unipartite network; (3) we design a measure of the filtering quality named QFC (quality of filtering coefficient) to filter the similarity network and construct a binary network, which we call approximation network; and (4) the number of communities in each type of unipartite networks is estimated using Riolo’s method with the approximation network as input. Finally, the proposed biCNEQ is demonstrated by both synthetic bipartite networks and a real-world network, and the results show that it can determine the correct number of communities and perform better than two classical one-mode projection methods.

Summary In this work, three kinds of natural gas-based power generation processes for CO2 capture and storage, that is, natural gas-combined cycle with pre-combustion decarburization (NGCC-PRE), NGCC-PRE with calcium-looping process, and NGCC-PRE with chemical-looping combustion (NGCC-CLC), are analyzed by Aspen Plus. The effects of two decisive variables (i.e., steam-to-natural gas (S/NG) ratio and oxygen-to-natural gas (O/NG) ratio) on the thermodynamic performances of individual process, such as energy and exergy efficiencies, are investigated systematically. Based on simulation outcomes, all the three processes are favored by operating at S/NG = 2.0 and O/NG = 0.65. Furthermore, comparisons of individual system efficiencies and exergy destruction contributor are herein involved. The results show that the highest system efficiencies and lowest exergy destruction are achieved in the NGCC-CLC process. In addition, capital investment, dynamic payback period, net present value, and internal rate of return are used for deciding the economic feasibility and surely are involved in this work for comparison purpose. Copyright © 2016 John Wiley & Sons, Ltd.

Site selection for electric vehicle charging stations (EVCSs) is the process of determining the most suitable location among alternatives for the construction of charging facilities for electric vehicles. It can be regarded as a complex multicriteria decision-making (MCDM) problem requiring consideration of multiple conflicting criteria. In the real world, it is often hard or impossible for decision makers to estimate their preferences with exact numerical values. Therefore, Pythagorean fuzzy set theory has been frequently used to handle imprecise data and vague expressions in practical decision-making problems. In this paper, a Pythagorean fuzzy VIKOR (PF-VIKOR) approach is developed for solving the EVCS site selection problems, in which the evaluations of alternatives are given as linguistic terms characterized by Pythagorean fuzzy values (PFVs). Particularly, the generalized Pythagorean fuzzy ordered weighted standardized distance (GPFOWSD) operator is proposed to calculate the utility and regret measures for ranking alternative sites. Finally, a practical example in Shanghai, China, is included to demonstrate the proposed EVCS sitting model, and the advantages are highlighted by comparing the results with other relevant methods.

Comprehensive geriatric assessment (CGA) is considered the cornerstone of good practice, as it identifies need across multiple domains such as social, physical and psychological. The interRAI home care (interRAI-HC), probably the most well-researched and supported community-based CGA has been implemented globally, often at considerable expense. Policy-makers, managers and clinicians anticipate significant gains in health outcomes following such investment; however, the implementation of CGA is often undertaken in the absence of community service development. This study sought to compare the interRAI-HC with an existing CGA [the Support Needs Assessment (SNA)] in community-dwelling older people. A randomised controlled trial was undertaken from January 2006 to January 2007 comparing the interRAI-HC and the SNA in 316 people (65+) referred for assessment of needs with follow-up at 1 and 4 months. Outcomes included health-related quality of life, physical function, social support, cognitive status, mood and health service usage as well as identified need. The study found that significantly more support needs were identified using the interRAI-HC compared to the SNA. More social and carer support were recommended by SNA and more rehabilitation and preventive health screens were recommended by interRAI-HC. Despite these differences, the mean healthcare use was similar at 4 months, although interRAI-HC participants had more Emergency Department presentations and hospital admissions. No statistically significant differences between groups were reported in terms of outcomes. In conclusion, the interRAI-HC was found to identify more unmet support needs than the SNA though resulted in no favourable outcomes for the older person or their carer. The study highlights the need to invest attention around the service context to maximise outcomes based on identified needs.

To diagnose the implosion of a laser-driven-fusion target such as the symmetry, the hydrodynamic instability at the interface, a high-resolution, large field-of-view kilo-electron-volt X-ray imaging is required. A Kirkpatrick-Baez (K-B) microscope is commonly used, but its field of view is limited to a few hundred microns as the resolution decreases rapidly with the increase of the field of view. A higher resolution could be realized by using a Fresnel zone plate (FZP) for imaging. Presented in this work is a numerical study on the imaging properties of an FZP at Ti-Kα wavelength of 0.275 nm, and a comparison to a K-B imager. It is found that the FZP can realize not only a resolution better than 1 µm, but also a field-of-view larger than 20 mm when the FZP is illuminated by X-rays of spectral bandwidth less than 1.75%. These results indicate the feasibility of applying the FZP in high-resolution, large field-of-view X-ray imaging.

To address the unreliability and inefficiency of the existing algorithms for Bluetooth scatternet formation, this paper proposes a new efficient algorithm, Bluetooth Growing Network (BGN) algorithm, which constructs Bluetooth scatternet by adding reserve links among its branch nodes so that the resulting scatternet can maintain a high degree of connectivity while avoiding exccessive redundant links. Extensive simulation results demonstrate that the scatternet constructed by BGN algorithm is efficient in terms of the number of bridge nodes, average data transmission distance, network reliablity and overall network traffic.

Perception of head pose is useful for many face-related tasks such as face recognition, gaze estimation, and emotion analysis. In this paper, we propose a novel random forest based method for estimating head pose angles from single face images. In order to improve the effectiveness of the constructed head pose predictor, we introduce feature weighting and tree screening into the random forest training process. In this way, the features with more discriminative power are more likely to be chosen for constructing trees, and each of the trees in the obtained random forest usually has high pose estimation accuracy, while the diversity or generalization ability of the forest is not deteriorated. The proposed method has been evaluated on four public databases as well as a surveillance dataset collected by ourselves. The results show that the proposed method can achieve state-of-the-art pose estimation accuracy. Moreover, we investigate the impact of pose angle sampling intervals and heterogeneous face images on the effectiveness of trained head pose predictors.

The exact nature of the comorbidity between cardiovascular disease (CVD) and major depressive disorder (MDD) is poorly understood. The proposed mechanisms include various biochemical and molecular pathways as well as health behaviors such as physical inactivity. One possible link between MDD and CVD is increased platelet activity and blood viscosity. Recently, it was discovered that platelets express functional subtype of alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptors, for example, glutamate receptor 1 (GluR1). Here, I propose that this type of AMPA receptor could play a role in comorbid MDD and CVD, and antidepressants may interfere with platelet activation via direct or indirect effects on platelet GluR1 phosphorylation. Testing this hypothesis could provide a novel view on the pathobiological mechanisms of comorbid MDD and CVD. With respect to the recently discovered role of AMPA receptors in regulating platelet activation and thrombosis, it appears that the information about the putative effects of psychoactive AMPA-modifying drugs on platelet AMPA receptors would be critical in evaluating the putative effects of such drugs on CVD.