Search

Background Recently, accumulating data have supported that long non-coding RNAs (lncRNAs) may contribute to tumorigenesis. LncRNA LINC00511 (LINC00511) has been proved to serve as an oncogene in several tumors. However, as a novel lncRNA, the crucial role and potential mechanism of LINC00511 in LUSC is largely unknown. Methods Here, we performed a differential gene expression analysis of the LINC00511 in LUSC using data from TCGA database. Loss-of-functional assays were used to gain further insights into the latter function of LINC00511 on the malignant phenotypes in vitro. Meanwhile, qRT-PCR, western blot, dual-luciferase reporter, and RIP assays were utilized to highlight the molecular basis of LINC00511 in LUSC. Results LINC00511 was upregulated in LUSC tissues in TCGA database compared to adjacent non-tumor counterparts, and its expression level was strongly associated with tumor stage. LINC00511 deficiency significantly suppressed LUSC cell proliferation and migration. Furthermore, mechanistic investigation demonstrated that LINC00511 accelerated LUSC progression partially through its up-regulation of TADA1 via targeting miR-150-5p. Conclusions our study highlights that LINC00511 facilitates LUSC progression via sequestering miR-150-5p and targeting TADA1, suggesting a need for development of a strategy for therapeutic targeting of LINC00511 in LUSC.

Objective This study aimed to evaluate the roles of the ratio of log(serum CA125 level)/PCI in epithelial ovarian cancer. Methods This is a retrospective study. Data were retrieved for patients with epithelial ovarian cancer who received primary debulking surgeries (PDS) between January 2014 and December 2017 in Zhongnan Hospital of Wuhan University. The PCI and CA125 were determined retrospectively using surgical reports, histological findings, and intraoperative photographic documentation. Survival analysis and ROC curves were applied to evaluate the roles of the ratio of log(serum CA125 level)/PCI in epithelial ovarian cancer. Results A total of 69 patients were included. Of these, serous ovarian cancer and mucinous carcinoma accounted for 63.8% (n=44) and 31.9% (n=22), respectively. The remaining patients had clear cell carcinoma (2.9%, n=2) and endometrioid carcinoma ( 1.4%, n= 1). Kaplan-Meier survival analysis showed that log(serum CA125 level)/PCI (log-rank p=0.018) were prognostic factors for OS. Cox regression analysis, otherwise, suggested that only stages were an independent factor of PFS (P=0.02, 95% CI 0.043-0.763); outcomes of cytoreductive surgery could only affect OS significantly (P=0.009, 95% CI 1.639-31.016). Binary logistic regression discovered that only log(serum CA125 level)/PCI was an independent risk factor of PDS. We further used the ROC curve to find that log(serum CA125 level)/PCI could correctly predict the resectability of PDS with AUC 0.781. Conclusion The ratio of log(CA125)/PCI that combined the tumor burden and characteristics of peritoneal carcinoma of ovarian origin can predict the resectability of PDS in epithelial ovarian cancer.

Hydrogen peroxide (H2O2) is a typical reactive peroxide that is widely used in food preservation but may cause serious harm to human health. Thus, a simple, efficient and accurate method for H2O2 assay is essential. In this work, a dual Eu-MOFs-based ratiometric fluorescence sensor, a logic gate-based advanced analytical method, and a dual MOFs-loaded paper microchip for visual H2O2 assay were constructed. The H2O2 sensor was response-sensitive (limit of detection: 0.14 μM) and linearly responsive in the concentration range of 0.001–1 mM (r > 0.99). Then, an advanced AND gate logic device was established to monitor H2O2 concentration. Moreover, the paper-based microchip realized the point-of-care visual H2O2 assay via ratiometric chromaticity assisted by a portable smartphone-based visual assay device. The microchip consumes only 5 μL of sample and demonstrates long-term stability for over 30 days along with response-sensitivity. Both the MOF dispersion-based ratiometric fluorescence sensor and the paper microchip realized an accurate and precise assay of H2O2 (the recovery was 88.2–105.5% and RSD < 9.3%). To the best of our knowledge, this is the first report of a dual MOFs-based ratiometric fluorescent H2O2 sensor, an advanced logic gate-based analytical method, and a paper-based microchip for visual point-of-care H2O2 assay.

Disproportionate progressive collapse is a structural failure mode with low probability and high consequences. Due to this nature, progressive collapse design is usually treated as a secondary design; namely, a design check after the design for primary loadings. Various design procedures have been recommended, notably the alternate load path method through nonlinear pushdown analyses. However, most of these design procedures are iterative by nature to satisfy the design acceptable criteria. To improve the design efficiency, this study presented an innovative design technique that allows to directly determine the proper amount of reinforcement in beams and slabs of a reinforced concrete (RC) structure in order to withstand the gravitational loads under column removal scenarios. Inspired from fire-induced progressive collapse research, the proposed method employs a virtual thermal pushdown analysis, in which the temperature increase affects only the strength of reinforced steel. With carefully developed strength-temperature relationships of the rebars, the virtual thermal analysis produces a displacement-temperature curve that represents the genuine nonlinear relationship between the amount of reinforcement and structural performance (often represented by vertical displacement). This curve is then used to directly determine the amount of reinforcement at the prescribed performance target. Two beam-column sub-assemblage examples and the Alfred P. Murrah Federal Building were analyzed and redesigned to demonstrate the effectiveness of the proposed method. All three examples showed that the proposal virtual thermal pushdown method can accurately determine the appropriate amount of reinforcements to meet the performance target.

Parkinson's disease (PD) is a neurodegenerative disorder, which is characterized by microglia activation and dopaminergic neurons affected by inflammatory processes. Inflammation has been recognized to be necessary for initiation and progress of PD. Emerging evidence indicates that NLRP3 inflammasome complex is involved in the recognition and execution of host inflammatory response. Stress is acknowledged to be a predisposing and precipitating factor in some neurodegenerative diseases. However, it is unknown whether chronic unpredictable mild stress (CUMS) sensitized microglia to pro-inflammatory stimuli. In this study, in vivo experiments are used to evaluate the effects of CUMS on lipopolysaccharide (LPS)-induced microglia activation and NLRP3 inflammasome activation. The results showed that CUMS pretreatment for 14 days significantly aggravated the behavioral dysfunction of PD rats, increased the activation of microglia. Pretreatment with CUMS for 14 days increased the levels of interleukin-1β (IL-1β), interleukin-6 (IL-6), and tumor necrosis factor-a (TNF-a) in the serum, and increased the expression of NLRP-3, ASC, Casepase-1 in the substantia nigra of PD rats. Our data showed that pretreatment with CUMS for 14 days increased the microglia activation and the DA neurons damage, and the mechanisms may be associated with the acceleration of the inflammatory response and activation of NLRP3 inflammasome.

The fabrication and luminescent properties of novel Na2YMg2V3O12:Eu3+ phosphors produced by conventional solid-state reactions were investigated. Self-activated emission of the Na2YMg2V3O12 host produces a broad emission band ranging from 400 to 700 nm with a maximum peak at 530 nm, ascribed to the charge transfer in the (VO4)3− groups. Excitation with near-UV (365 nm) light causes the Na2YMg2V3O12:Eu3+ phosphors to emit bright red light, including both the broad emission band of the (VO4)3− groups and the sharp emission peaks of Eu3+ ions. At a quenching concentration of 0.03 mol, the Eu3+ ion emission peaks were located at 597, 613, 654 and 710 nm. As-prepared Na2YMg2V3O12:Eu3+ phosphors also exhibited stable emission at high temperatures. Furthermore, a designed and packaged white-light-emitting diode (WLED) lamp, including the obtained phosphors, commercial (Ba,Sr)2SiO4:Eu2+ green phosphors, BaMgAl10O17:Eu2+ blue phosphors and a near-ultraviolet (n-UV) chip, emitted bright white light with a good chromaticity coordinate of (0.3068, 0.3491), a satisfactory colour rendering index of 88.20 and a properly correlated colour temperature of 4460.52 K. These results indicate the potential of this Na2YMg2V3O12:Eu3+ phosphor as a red-emitting phosphor for solid-state illumination.

The training process of deep learning model is costly. As such, deep learning model can be treated as an intellectual property (IP) of the model creator. However, a pirate can illegally copy, redistribute or abuse the model without permission. In recent years, a few Deep Neural Networks (DNN) IP protection works have been proposed. However, most of existing works passively verify the copyright of the model after the piracy occurs, and lack of user identity management, thus cannot provide commercial copyright management functions. In this paper, a novel user fingerprint management and DNN authorization control technique based on backdoor is proposed to provide active DNN IP protection. The proposed method can not only verify the ownership of the model, but can also authenticate and manage the user's unique identity, so as to provide a commercially applicable DNN IP management mechanism. Experimental results on CIFAR-10, CIFAR-100 and Fashion-MNIST datasets show that the proposed method can achieve high detection rate for user authentication (up to 100% in the three datasets). Illegal users with forged fingerprints cannot pass authentication as the detection rates are all 0 % in the three datasets. Model owner can verify his ownership since he can trigger the backdoor with a high confidence. In addition, the accuracy drops are only 0.52%, 1.61 % and -0.65% on CIFAR-10, CIFAR-100 and Fashion-MNIST, respectively, which indicate that the proposed method will not affect the performance of the DNN models. The proposed method is also robust to model fine-tuning and pruning attacks. The detection rates for owner verification on CIFAR-10, CIFAR-100 and Fashion-MNIST are all 100% after model pruning attack, and are 90 %, 83 % and 93 % respectively after model fine-tuning attack, on the premise that the attacker wants to preserve the accuracy of the model.

The popularity of various social platforms has prompted more people to share their routine photos online. However, undesirable privacy leakages occur due to such online photo sharing behaviors. Advanced deep neural network (DNN) based object detectors can easily steal users' personal information exposed in shared photos. In this paper, we propose a novel adversarial example based privacy-preserving technique for social images against object detectors based privacy stealing. Specifically, we develop an Object Disappearance Algorithm to craft two kinds of adversarial social images. One can hide all objects in the social images from being detected by an object detector, and the other can make the customized sensitive objects be incorrectly classified by the object detector. The Object Disappearance Algorithm constructs perturbation on a clean social image. After being injected with the perturbation, the social image can easily fool the object detector, while its visual quality will not be degraded. We use two metrics, privacy-preserving success rate and privacy leakage rate, to evaluate the effectiveness of the proposed method. Experimental results show that, the proposed method can effectively protect the privacy of social images. The privacy-preserving success rates of the proposed method on MS-COCO and PASCAL VOC 2007 datasets are high up to 96.1% and 99.3%, respectively, and the privacy leakage rates on these two datasets are as low as 0.57% and 0.07%, respectively. In addition, compared with existing image processing methods (low brightness, noise, blur, mosaic and JPEG compression), the proposed method can achieve much better performance in privacy protection and image visual quality maintenance.

Deep neural network (DNN) based face recognition systems have been widely applied in various identity authentication scenarios. However, recent studies show that the DNN models are vulnerable to backdoor attacks. An attacker can embed backdoors into the neural network by modifying its internal structure or poisoning the training set. In this way, the attacker can login into the system as the victim, while the normal use of the system by legitimate users will not be affected. However, the backdoors used in existing attacks are visually perceptible (black-frame glasses or purple sunglasses), which will arouse humans' suspicions thus lead to the failure of the attacks. In this paper, we propose a novel backdoor attack method, BHF2 (Backdoor Hidden as Facial Features), where the attacker can embed the backdoors as the inherent facial features. The proposed method can greatly enhance the concealment of the injected backdoor, which makes the backdoor attacks more difficult to be discovered. Besides, the BHF2 method can be launched under the black-box conditions, where the attacker is completely unaware of the target face recognition system. The proposed backdoor attack method can be applied in those rigorous identity authentication scenarios where the users are not allowed to wear any accessories. Experimental results show that the BHF2 method can achieve high attack success rate (up to 100%) on the state-of-the-art face recognition model, DeepID1, while the normal working performance of the system has hardly been affected (the recognition accuracy of the system has only dropped by 0.01% at the lowest).

Irregular structures are known to be more prone to disproportionate collapse (DC) than structures with regular horizontal and vertical layouts. Despite this, many public, commercial and institutional buildings have to be designed with various irregularities in structural layout due to architectural and aesthetic considerations. While extensive experimental and numerical studies have been carried out to advance the basic understanding of DC behaviors of regular structures, limited research has been carried out on irregular structures. On the other hand, alternate load path method has been recommended by several design codes and standards for DC design, but the large number of potential column removal scenarios involved poses a real challenge for the wide use of the method. This paper presents a quantitative measure referred to as irregularity index, or Π , that can be used to quickly identify the worst column removal scenarios for design check of DC resistance of reinforced concrete (RC) frame structures. Based upon a solid understanding of various competing failure mechanisms and DC resistance of RC frame structures, the irregularity index was derived from the maximum load factors between the yielding load and the limit load at the end of tensile membrane action stage. For verification and validation of the proposed Π , a 3D nonlinear finite element model was developed in OpenSees and calibrated by beam-slab sub-assemblage experiments. Various numerical experiments were carried out to verify the effectiveness of the Π . The very high correlation coefficient between the calculated irregularity indices and DC resistance obtained from finite element analyses showed that the proposed irregularity index can quickly identify the worst column removal scenarios in various irregular structures including setback buildings and large-bottom-space structures. Sensitivity analysis further verified the reliability of the proposed irregularity index.

It is highly desirable to red shift the emission of Y3Al5O12:Ce3+ phosphor to obtain a warmer correlated color temperature (CCT) in applications for blue-light pumped white-light emitting diodes (w...

Modular integrated Construction (MiC) is a game changing construction approach which could significantly increase construction efficiency, quality, and sustainability. For steel MiC structures, modules are manufactured in the factories and assembled at the construction sites through inter-module connections. The concern of the vulnerability of these connections under abnormal hazards leading to disproportionate progressive collapse will impede industrial applications of MiC structures in the construction community. This study investigates the structural robustness of corner-supported modular steel buildings with the focus on different inter-module connections. A sub-structure extracted from a five-storey modular building is analysed by high fidelity finite element analysis under a corner column removal scenario. The load redistribution mechanism and failure modes are investigated thoroughly, based on which the simplified macro model is proposed where connections are modelled with rotational springs. Good consistence is found for the simplified model and the detailed FE analysis in terms of the pushdown curve. It is also found that the inter-module connection types will affect the beam-column joint properties which dominate the progressive collapse resistance of MiC structures. Therefore, attention should be paid when selecting different inter-module connections. The current rigid beam-column joint connection may lead to nonconservative evaluation.

Purpose The purpose of this paper is to design and fabricate a three-dimensional (3D) bionic airflow sensing array made of two multi-electrode piezoelectric metal-core fibers (MPMFs), inspired by the structure of a cricket’s highly sensitive airflow receptor (consisting of two cerci). Design/methodology/approach A metal core was positioned at the center of an MPMF and surrounded by a hollow piezoceramic cylinder. Four thin metal films were spray-coated symmetrically on the surface of the fiber that could be used as two pairs of sensor electrodes. Findings In 3D space, four output signals of the two MPMFs arrays can form three “8”-shaped spheres. Similarly, the sensing signals for the same airflow are located on a spherical surface. Originality/value Two MPMF arrays are sufficient to detect the speed and direction of airflow in all three dimensions.

The novel COVID-19 outbreak has affected more than 200 countries and territories as of March 2020. Given that patients with cancer are generally more vulnerable to infections, systematic analysis of diverse cohorts of patients with cancer affected by COVID-19 is needed. We performed a multicenter study including 105 patients with cancer and 536 age-matched noncancer patients confirmed with COVID-19. Our results showed COVID-19 patients with cancer had higher risks in all severe outcomes. Patients with hematologic cancer, lung cancer, or with metastatic cancer (stage IV) had the highest frequency of severe events. Patients with nonmetastatic cancer experienced similar frequencies of severe conditions to those observed in patients without cancer. Patients who received surgery had higher risks of having severe events, whereas patients who underwent only radiotherapy did not demonstrate significant differences in severe events when compared with patients without cancer. These findings indicate that patients with cancer appear more vulnerable to SARS-CoV-2 outbreak. Significance: Because this is the first large cohort study on this topic, our report will provide much-needed information that will benefit patients with cancer globally. As such, we believe it is extremely important that our study be disseminated widely to alert clinicians and patients. This article is highlighted in the In This Issue feature, p. 747

A new optical thermometer material—Er3+-doped SrNb2O6—based on the fluorescence intensity ratio was synthesized and investigated by both theoretical calculation and experimental measurement, combining X-ray diffractometry (XRD), XRD Rietveld refinement, scanning electron microscopy, X-ray photon emission spectroscopy, photoluminescence (PL) and PL excitation of qualitative and quantitative analysis, to develop a novel green-emitting phosphor with excellent thermometry sensitivity. The Er3+ ions occupied 4e sites of SrNb2O6, and the stable activator occupation induces luminescence properties of emissions at 525, 531, and 550 nm. The SrNb2O6: Er3+ phosphor possesses an outstanding thermal sensitivity in the measurement region of 298–523 K, where Sa and Sr can achieve 6.15 × 10−3 K−1 and 1093.2711/T2, respectively, at 323 K. The optical thermal properties originated from the thermal excitation and formation of quashing thermal equilibrium between 2H11/2 and 4S3/2 levels, leading to photons on 4S3/2 → 4I15/2 transfer process thermally populate onto 2H11/2 → 4I15/2 transfer process. This work will lay a foundation for further exploration of activator suitability in columbite phosphors, making it practicable to foreseeably improve thermal sensitivity and PL for emergent applications in optical thermometer.

The determination of threshold and the construction of thresholding function would directly affect the signal denoising quality in wavelet transform denoising techniques. However, some deficiencies exist in the conventional methods, such as fixed threshold value and the inflexible thresholding functions. To overcome the defects of the traditional wavelet thresholding techniques, a modified particle swarm optimization (MPSO) algorithm-based parametric wavelet thresholding approach is proposed for signal denoising. Firstly, a kind of parametric wavelet thresholding function construction method is proposed on the basis of conventional thresholding functions. With mathematical derivation, the properties of the constructed function are proved. Three dynamic adjustment strategies are then employed to modify the PSO algorithm. The mean square error (MSE) between the original signal and the reconstructed signal is minimized by the MPSO algorithm. Finally, the performances of the proposed approach and the existing methods are simulated by denoising four benchmark signals with different noise levels. The simulation results show that the proposed MPSO-based parametric wavelet thresholding can obtain lower MSE, higher signal-to-noise ratio, and noise suppression ratio compared to the other algorithms. Besides, the denoising visual results also indicate the superiority of the proposed approach in terms of the signal denoising capability.

The novel COVID-19 outbreak has affected more than 200 countries and territories as of March 2020. Given that patients with cancer are generally more vulnerable to infections, systematic analysis of diverse cohorts of patients with cancer affected by COVID-19 are needed. We performed a multi-center study including 105 cancer patients and 536 age-matched non-cancer patients confirmed with COVID-19. Our results showed COVID-19 patients with cancer had higher risks in all severe outcomes. Patients with hematological cancer, lung cancer, or with metastatic cancer (stage IV) had the highest frequency of severe events. Non-metastatic cancer patients experienced similar frequencies of severe conditions to those observed in patients without cancer. Patients who received surgery had higher risks of having severe events, while patients with only radiotherapy did not demonstrate significant differences in severe events when compared to patients without cancer. These findings indicate that cancer patients appear more vulnerable to SARS-COV-2 outbreak. Since this is the first large cohort study on this topic, our report will provide the much-needed information that will benefit global cancer patients. As such, we believe it is extremely important that our study be disseminated widely to alert clinicians and patients. Citation Format: Meng-Yuan Dai, Dian-bo Liu, Miao Liu, Fu-Xiang Zhou, Gui-Ling Li, Zhen Chen, Zhi-An Zhang, Hua-You Md, Meng Wu, Qi-Chao Zheng, Yong Xiong, Hui-Hua Xiong, Chun Wang, Chang-Chun Chen, Fei Xiong, Yan Zhang, Ya-Qin Peng, Si-Ping Ge, Bo Zhen, Ting-Ting Yu, Ling Wang, Hua Wang, Yu Liu, Ye-Shan Chen, Jun-Hua Mei, Xiao-Jia Gao, Zhu-Yan Li, Li-Juan Gan, Can He, Zhen Li, Yu-Ying Shi, Yu-Wen Qi, Jing Yang, Daniel G. Tenen, Li Chai, Lorelei Ann Mucci, Mauricio Santillana, Hongbing Cai. Patients with cancer appear more vulnerable to SARS-COV-2: A multi-center study during the COVID-19 outbreak [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr CT406.

Associations between vitamin D receptor (VDR) gene polymorphisms and allergic diseases were already reported by many publications. The aim of this meta-analysis was to clarify associations between VDR gene polymorphisms and allergic diseases by combing the results of all relevant publications. Eligible publications were searched from Pubmed, Embase, WOS and CNKI. We used Review Manager to combine the results of individual studies. Twenty-one studies were included in this study. Combined results proved that VDR rs1544410 BsmI (over-dominant comparison: p = .04, OR = 1.14, 95% CI 1.01–1.29; allele comparison: p = .03, OR = 1.11, 95% CI 1.01–1.22) and rs731236 TaqI (dominant comparison: p = .01, OR = 1.18, 95% CI 1.04–1.33) polymorphisms were both associated with allergic diseases. In subgroup analyses by type of disease, we confirmed positive results for rs1544410 BsmI polymorphism in both asthma and atopic dermatitis, and for rs731236 TaqI polymorphism in atopic dermatitis. Besides, in subgroup by ethnicity of participants, we observed significant associations with allergic diseases for rs7975232 ApaI polymorphism in Caucasians, for rs1544410 BsmI polymorphism in Asians and Caucasians, and for rs731236 TaqI polymorphism in Asians. We also investigated associations between VDR rs2228570 FokI polymorphism and allergic diseases, yet no any positive results were detected for this polymorphism. If we only focused on asthma, then positive findings were detected for rs1544410 BsmI polymorphism in Caucasians, and for rs731236 TaqI polymorphism in Asians. Collectively, this meta-analysis proved that VDR rs7975232 ApaI, rs1544410 BsmI and rs731236 TaqI gene polymorphisms may confer susceptibility to allergic diseases in certain populations.

Background Cervical cancer is the most common cancer among women in Nepal. The prevalence of human papillomavirus (HPV) 16 and or HPV 18 among women with cervical pre-cancer and cancer is higher than the incidence of HPV in the world population. The population-based epidemiological data of HPV in the general population in most parts of the country remains unknown. The objective of this study was to assess the prevalence and type distribution of HPV infection and association of abnormal cytology with high risk HPV infection among women in mid-western rural, Nepal. Methods A population-based cross sectional study was conducted in Jumla, one of the most remote districts in Nepal. A total of 1050 cervical samples were collected from married and non- pregnant women aged 20–65 years during mobile Cervical Cancer Screening Clinics conducted from May 2016 to January 2017. The presence of HPV DNA was firstly confirmed by HPV consensus PCR using PGMY09/PGMY11 designed primers, then HPV positive samples were further genotyped by the membrane hybridization method to detect the 21 high-risk HPV (HR-HPV) and low-risk HPV types. The prevalence of HR-HPV among women with normal and abnormal cytology was calculated. Data were analyzed using SPSS software for Windows. P

In health monitoring of long-span structures, proper arrangement of sensors is a key point because of the need to acquire effective structural health information with limited testing resources. This study proposes a novel approach called dual-structure coding and mutation particle swarm optimization (DSC-MPSO) algorithm for the sensor placement. The cumulative effective modal mass participation factor is firstly derived to select the main contributions modes. A novel method combining dual-structure coding with the mutation operator is then utilized to determine the optimal sensors configurations. Finally, the feasibility of the DSC-MPSO algorithm is verified by optimizing the sensors locations for a long-span cable-stayed bridge. The effective independence method, genetic algorithm and standard particle swarm optimization algorithm are taken as contrast experiments. The simulation results show that the proposed algorithm in this paper could improve the convergence speed and precision. Accordingly, the method is effective in solving optimal sensor placement problems.

Excitation makes a great influence on the wavelet energy distribution of the response signal, this deficiency leads that the traditional structural damage identification method based on wavelet energy has a low precision. In order to solve this problem, a new structural damage identification method based on wavelet packet energy entropy (WPEE) of impulse response is presented in this paper. Firstly, natural excitation technique (NExT) is adopted to extract structural impulse response. Then, WPEE of the impulse response is computed, and the change rate of WPEE is used to construct the structural damage index. An experiment of damage identification on a pile structure is provided to verify the effectiveness of the proposed method. Experiment results show that this method can accurately identify the single damage and multi-damage.

The simulation of electromagnetic fields distribution around high-speed railway catenary on the platform by the finite element method. The research is based on simulation models as classification material properties of human body parts, and presents subdivision mesh sub-model method to calculate and simulate the frequency electromagnetic fields around the human, and giving a comprehensive evaluation on the safety of electromagnetic environment around high-speed railway according to national standard limit.

Due to simple calculation and good denoising effect, wavelet threshold denoising method has been widely used in signal denoising. In this method, the threshold is an important parameter that affects the denoising effect. In order to improve the denoising effect of the existing methods, a new threshold considering interscale correlation is presented. Firstly, a new correlation index is proposed based on the propagation characteristics of the wavelet coefficients. Then, a threshold determination strategy is obtained using the new index. At the end of the paper, a simulation experiment is given to verify the effectiveness of the proposed method. In the experiment, four benchmark signals are used as test signals. Simulation results show that the proposed method can achieve a good denoising effect under various signal types, noise intensities, and thresholding functions.

Animproved particle filter algorithm which based on mean shift algorithm isintroduced. The algorithm makes the particles move towards the high likelihoodregion in posterior distribution with the effect of mean shift algorithm,increases the efficiency of the particles moving, and reduces the phenomenon ofdegradation and dilution of particles

Due to the interference of strong vibration signal, a big error exists in modal parameter identification based on traditional ARMA model. In order to solve this problem, a new method for modal parameter identification based on natural excitation technique (NExT) and ARMA model is presented. Firstly, NExT is used to obtain cross-correlation function of different response sequences. Then, ARMA model of cross-correlation function is established, and the parameters of ARMA model are estimated by least squares method. Finally, the modal parameter identification is achieved using the relationship between the ARMA parameters and structural modal parameters. Experiment shows that the identification results of our method are more accurate than the traditional ARMA model.

The reuse of produced water generated by natural gas extraction from Marcellus Shale for hydraulic fracturing is the dominant management option in Pennsylvania (PA), USA. The advantages and disadvantages of this management approach are reviewed and discussed together with long-term concerns and technology development needs. Abandoned mine drainage is a promising alternative make-up water, but high sulfate concentrations will lead to barite precipitation once it is mixed with the produced water. Bench-scale studies were conducted to optimize barite separation from this mixture that meets the finished water quality criteria for sulfate. Conventional separation processes are very effective in removing these solids but radium (Ra) co-precipitation may be a concern for their disposal in municipal landfills. If the produced water volume exceeds the reuse capacity for hydraulic fracturing, lime–soda ash softening can be used to remove divalent cations, including radium, to enable the production of pure salts using subsequent thermal processes.

Wavelet basis selection is an important part in the wavelet denoising of structural vibration signal. However, some defects are present in the existing methods, such as large computation and a single optimal index. In order to solve these problems, a new selection method based on multiple index is proposed in this paper. Firstly, the wavelet basis category which suits for the vibration signal denoising is determined by analyzing the characteristics of wavelet basis and vibration signal. Then, a multiple index evaluation function is constructed by mean square error indicator (MSE), signal-to-noise ratio indicator (SNR) and correlation coefficient indicator (ρ), the weights of index are received by analytic hierarchy process (AHP), the wavelet basis with biggest evaluation function value is considered as optimal wavelet basis. At the end of the paper, a experiment is provided to verify the effectiveness of the new method, the results show that the new method is better than the other four methods in MSE, SNR and ρ index.

Optimal sensor placement is a key issue in the structural health monitoring of large-scale structures. However, some aspects in existing approaches require improvement, such as the empirical and unreliable selection of mode and sensor numbers and time-consuming computation. A novel improved particle swarm optimization (IPSO) algorithm is proposed to address these problems. The approach firstly employs the cumulative effective modal mass participation ratio to select mode number. Three strategies are then adopted to improve the PSO algorithm. Finally, the IPSO algorithm is utilized to determine the optimal sensors number and configurations. A case study of a latticed shell model is implemented to verify the feasibility of the proposed algorithm and four different PSO algorithms. The effective independence method is also taken as a contrast experiment. The comparison results show that the optimal placement schemes obtained by the PSO algorithms are valid, and the proposed IPSO algorithm has better enhancement in convergence speed and precision.

Background: Although step counters are popularly employed for physical rehabilitation in chronic obstructive pulmonary disease (COPD) patients, their effectiveness is inconsistent and even questioned. This meta-analysis aimed to investigate whether step counter use increases physical activity or improves exercise capacity in COPD patients. Methods: Electronic databases were searched for randomized controlled trials that assessed the efficacy of step counter use in increasing physical activity or in improving exercise capacity. Data were aggregated using a random-effects model to get the overall effect sizes [standard mean difference (SMD) with 95% confidence interval (CI)], and subgroup analyses were performed. Results: A total of 15 trials enrolling 1316 patients with moderate to severe COPD were included. Step counter use increased physical activity compared with controls (SMD = 0.57, 95% CI 0.31–0.84), which is equal to a magnitude of 1026 steps/day in daily steps. It also enhanced exercise capacity with an effect size of 0.30 (95% CI 0.16–0.45), approximating to a magnitude of 11.6 m in the 6-min walking distance. Step counter use could augment physical activity (SMD = 0.64, 95% CI 0.19–1.08) and exercise capacity (SMD = 0.32, 95% CI 0.01–0.62) for patients receiving pulmonary rehabilitation. Yet it cannot enhance physical activity or exercise capacity in patients with severe COPD or among studies with intervention durations ⩾6 months (both p > 0.50). Conclusions: Step counter use increases physical activity and improves exercise capacity in COPD patients, at least in the short term, which supports the notion of recommending step counter use in COPD management.

Optimal sensor placement (OSP) is an important part in the structural health monitoring. Due to the ability of ensuring the linear independence of the tested modal vectors, the minimum modal assurance criterion (minMAC) is considered as an effective method and is used widely. However, some defects are present in this method, such as the low modal energy and the long computation time. A new OSP method named IAGA-MMAC is presented in this study to settle the issue. First, a modified modal assurance criterion (MMAC) is proposed to improve the modal energy of the selected locations. Then, an improved adaptive genetic algorithm (IAGA), which uses the root mean square of off-diagonal elements in the MMAC matrix as the fitness function, is proposed to enhance computation efficiency. A case study of sensor placement on a numerically simulated wharf structure is provided to verify the effectiveness of the IAGA-MMAC strategy, and two different methods are used as contrast experiments. A comparison of these strategies shows that the optimal results obtained by the IAGA-MMAC method have a high modal strain energy, a quick computational speed, and small off-diagonal elements in the MMAC matrix.

Formation of mineral scales in industrial and municipal systems causes service interruption, performance decline, and often a complete system failure. Control of mineral scales may include costly water purification to remove scale-forming components or a simple addition of antiscaling chemicals. Although the use of antiscalants has very long history, it still relies more on experience and empirical solutions than on fundamental understanding of mechanisms by which these chemicals can reduce or prevent scale formation. Challenges for scale control include fairly complex interactions in aqueous systems, changing water chemistry, pH and temperature changes, and application of corrosion and biofouling strategies that could compromise the effectiveness of antiscalants. It is generally accepted that antiscalants do not completely eliminate scaling tendency but can inhibit the nucleation and/or crystallization phases. The main mechanisms by which antiscalants can control the formation of mineral precipitates include threshold inhibition, crystal modification, sequestration (chelation), or dispersion. It is desirable to utilize antiscalants that rely on more than one mechanism to ensure reliable performance. The dominant classes of antiscaling chemicals nowadays include polyphosphates, organophosphonates, and various polyelectrolytes. Depending on the application in question, there are several experimental approaches that may be utilized to evaluate the optimal control strategy that is compatible with other goals of water treatment. They range from simple beaker tests to recirculating systems with and without heated surfaces, electrochemical impedance spectroscopy, and dynamic tube blocking experiments for high pressure and temperature applications. It is important to note that these experimental systems are effective in demonstrating relative performance of different antiscaling approaches but that the final adjustments have to be performed during field applications. Case studies discussed in this chapter include the application of specific chemicals for the control of mineral scales in cooling systems using secondary-treated municipal wastewater, scaling control in reverse osmosis desalination, and inhibition of barium sulfate scaling on the production casing during unconventional shale gas extraction.

Flowback water from natural gas extraction in Marcellus Shale contains very high concentrations of inorganic salts and organic chemicals. Potential reuse of this water in subsequent hydraulic-fracturing operations may be limited by high concentrations of divalent cations (e.g., Ba, Sr, and Ca). Kinetics of barite and celestite precipitation in flowback waters from different well sites was evaluated in this study. Ba reacted rapidly with sulfate and reached equilibrium within 30 min, whereas Sr reacted slowly and took days to reach equilibrium. Equilibrium concentrations of Ba and Sr predicted by thermodynamics models were compared with experimental results. Activity corrections based on the Pitzer equation provided the best agreement with experimental data for both Ba and Sr. Comparison of barite and celestite precipitation kinetics in actual and synthetic flowback water revealed that there was no observable impact of organics and other minor components in actual flowback water on barite precipita...

A new image encryption algorithm is proposed based on scrambling chaotic sequence in Contourlet domain. Firstly, several sub-band images are obtained by using multiple-level decomposition of Contourlet transform. Then, combing with coupled logistic map in nonlinear dynamics system, according to some methods, we can get scrambled chaotic sequence by using keys and give the operation of xor to the scrambled chaotic sequence and the coefficients of low-order in low frequency sub-band. At the same time, we use Arnold scrambling to process the high-order coefficients of low frequency sub-band. At last, the encryption image is obtained by means of anti-process of low frequency coefficients and anti-transform of Contourlet. The experimental results suggest that the algorithm proposed not only has the advantages of good encryption effects, big key space and consistency of decrypted and original image, but also can resist the attack of JPEG compression, the effects of noise and cropping to some extent.

The lack of heuristic information is the fundamental reason that affects the attribute selection in data mining. Spatial hypersurface plays a very important role in the classification problem which reflects the characteristic of class attribute and condition attributes. In this paper, a new measure for determining the importance level of the attributes based on partial derivative distribution of the output corresponding to the inputs is presented. For more convenience, we present a new SVM model called TCA-SSVM which could use simple algorithm to solve the optimization problem to acquire the classification hypersurface. The proposed approach is experimentally evaluated in two datasets and the results prove that it can improve the validity of the problem and lead to interesting results.

Spatial hypersurface plays a very important role in the classification problem. In SVM, classification hypersurface is emphasized because of the direct induction of the support vectors, so the hypersurface reflecting the relation between categorical attribute and condition attributes acquired by SVM promotes the classification effect. In traditional SVM solution algorithms, objective function is a strictly convex unconstrained optimization problem, but is not differentiable due to x+, which precludes the most used optimization algorithms. This paper presents a new TCA-Smooth technology which used a segment of circular arc tangent to the given plus function x+ to approximate the original un-differentiable model, thus the traditional SVM model is converted into a differentiable model. The proposed approach is experimentally evaluated in three datasets that are benchmarks for data mining applications and in a real-world dataset, leading to interesting results.

Classification hypersurface plays a very important role in classification problem. In SVM (support vector machine), classification hypersurface is emphasized because of the direct induction of the support vectors. In this paper, a measure of determining the importance level of the attributes based on classification hypersurface acquired by SVM is proposed. In traditional SVM solution algorithms, objective function is a strictly convex unconstrained optimization problem, but it is undifferentiable due to x+ . Therefore, the most used optimization algorithms are precluded. This paper presents a new technology to approximate the original undifferentiable model, so that the traditional SVM model is converted into a differentiable model. The proposed approach is experimentally validated in the datasets that are benchmarks for data mining applications.

Anaphora occurs throughout discourse or dialogue. Their high frequencies make anaphora resolution one key problem in discourse processing which attract attention of increasing researchers. In the paper, according to features of Chinese personal pronoun we present an approach which is based on corpus. It adopts maximum entropy method. Then we discovered characteristic of pronoun anaphora referring to inhuman is different form that referring to people. So the paper put forward a new approach about processing the Chinese personal pronoun anaphora. The approach divided the anaphora resolution system into two parts, the PARS subsystem and the IARS subsystem. They will separately process the personal pronoun anaphora referring to people and that referring to inhuman. The paper described the design and the realization of the system, tests the new system in the Chinese Tree Bank and evaluates the arithmetic in the round. The experiment demonstrates that the method achieves the desired result.

How to deal with various uncertainties and evolution has been a critical problem for further development of AI. The well-developed mathematical logic is too rigid and it can only solve certain problems. It is a new challenge for logics to make mathematical logic more flexible and can contain various uncertainties and evolution. This has been studied by the academic community for several years and has made some breakthroughs at some isolated points, but a systematic theory has not come into being. The flexible propositional logics is put forward on the basis of our study of the general logical rules in real world, which can include quite a few kinds of known uncertainty reasoning models, such as probability, belief, likelihood, certainty, possibility and fuzzy reasoning, and so on. Moreover, it lays the theoretical basis for the study of more complex uncertainty problems and evolution problems.

With the fast development of modern sciences, opinions that people think about the natural system are basically changing, that is to say, turning from ideal simplification to objective complexity. Universal logic puts forward the conceptions of general correlation and general self-correlation, which is used to describe the flexibility of correlation of objective things exactly, and establish the architecture of universal logic. This paper analyzes the principles of universal logic and the correlation of complex system, defines the conceptions of "whole correlation" and "local correlation", and sets up the reasoning model of complex system based on universal logic, moreover, illuminates the applied steps with an example on control system of welding process. The paper states: this reasoning model strongly integrates the natural intuition and operating feasibility, and it gives the implementation procedure of universal logic in engineering applied fields, especially the flexible control for complex system firstly.