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Infrared thermal technology plays a vital role in the health condition monitoring of gearbox. In the traditional infrared thermal technology-based methods, Gaussian pyramid is applied as the feature extraction approach, which has disadvantages of noise influence and information missing. Focus on such disadvantages, an improved multi-scale decomposition method combined with convolutional neural network is proposed to extract the fault features of the multi-scale infrared images in this paper. It can enlarge the data length at large scales, and thus reduce the fluctuations of feature values and reserve the fault information. The effectiveness of the proposed method is validated using the experiment infrared data of one industrial gearbox. Results demonstrate that our proposed method has the best performance comparing with five methods.

Fault diagnosis of rotary machinery plays a significant role in the prognostic and health management system, which aims to identify the root causes of system failures and provide effective information for prognostics and maintenance. Recently, Lempel–Ziv complexity (LZC) method has been employed for fault diagnosis of rotary machinery. However, one actual problem is that LZC fails to account for the multiscale information inherent in measured vibration signals. We first introduce a method to compute the multi-scale LZC for a signal. However, the variance of LZC values becomes larger as the scale factor increases. To solve this actual problem, this paper proposes refined composite multi-scale Lempel-Ziv complexity (RCMLZC) to estimate the complexity. We find that the proposed RCMLZC method consistently yields better performance when analyzing three simulated noisy and impulsive signals. Based on RCMLZC, a novel intelligent fault diagnosis method is designed to recognize various fault types of rotating machinery. Comparative experiments are performed to confirm the effectiveness of proposed method including single fault and compound fault working conditions. Experimental results indicate that RCMLZC is more accurate than multi-scale LZC, multi-scale entropy, and LZC in extracting fault features from vibration signal and that RCMLZC performs best to recognize the various fault types of rotary machinery.

Rotating machinery is widely applied in industrial applications. Fault diagnosis of rotating machinery is vital in manufacturing system, which can prevent catastrophic failure and reduce financial losses. Recently, Deep Learning (DL)-based fault diagnosis method becomes a hot topic. Convolutional Neural Network (CNN) is an effective DL method to extract the features of raw data automatically. This paper develops a fault diagnosis method using CNN for InfRared Thermal (IRT) image. First, IRT technique is utilized to capture the IRT images of rotating machinery. Second, the CNN is applied to extract fault features from the IRT images. In the end, the obtained features are fed into the Softmax Regression (SR) classifier for fault pattern identification. The effectiveness of the proposed method is validated using two different experimental data. Results show that the proposed method has a superior performance in identification various faults on rotor and bearings comparing with other deep learning models and traditional vibration-based method. Keywords: Convolutional neural network, Feature extraction, Infrared thermography (IRT), Intelligent fault diagnosis, Rotating machinery

Heart development is controlled by a complex transcriptional network composed of transcription factors and epigenetic regulators. Mutations in key developmental transcription factor MESP1 and chromatin factors, such as PRC1 and cohesin components, have been found in human congenital heart diseases (CHDs), although their functional mechanism during heart development remains elusive. Here, we find that MESP1 interacts with RING1A/RING1, the core component of PRC1. RING1A depletion impairs human cardiomyocyte differentiation, and cardiac abnormalities similar to those in patients with MESP1 mutations were observed in Ring1A knockout mice. Mechanistically, MESP1 associates with RING1A to activate cardiogenic genes through promoter-enhancer interactions regulated by cohesin and CTCF and histone acetylation mediated by p300. Importantly, CHD mutations of MESP1 significantly affect such mechanisms and impair target gene activation. Together, our results demonstrate the importance of MESP1-RING1A complex in heart development and provide insights into the pathogenic mechanisms of CHDs caused by mutations in MESP1, PRC1, and cohesin components.

Superionic conductors, which exhibit liquid-like phonon transport but crystal-like carrier transport, have attracted great attention and broad research interest in the thermoelectric community. Ag2Te is a superionic conductor; however, its small band gap and large Ag vacancy formation energy impede its application as a prominent p-type thermoelectric material. In this work, synergistic optimization of the thermoelectric performance of Ag2Te through Cu substitution is realized through a combination of experimental and theoretical efforts. For the electrical transport, Cu substitution systematically increases the band gap of Ag2Te and reduces the cation vacancy formation energy. These two beneficial effects simultaneously increase the electrical conductivity and suppress the bipolar effect, thereby greatly enhancing the p-type electrical transport properties of Ag2Te. For the thermal transport, alloying Cu2Te with Ag2Te significantly reduces the thermal conductivity through not only point defect scattering but also softening of the interatomic interactions. The latter is attributed to the relatively small Cu atoms vibrating in the oversized 8c sites. This two-fold optimization results in maximum thermoelectric figure of merit zT values of over 1.3 at 773 K for both Ag1.2Cu0.8Te and AgCuTe, demonstrating the great potential of Ag2−xCuxTe as a promising p-type thermoelectric material system. Keywords: Ag2Te, Band gap, Carrier concentration, Thermoelectric material

Fault diagnosis of rotating machinery is crucial to improve safety, enhance reliability and reduce maintenance cost. The manual feature extraction and selection of traditional fault diagnosis methods depend on signal processing skills and expert experience, which is labor-intensive and time-consuming. As a typical intelligent fault diagnosis method, the convolutional neural network automatically learns features from original data, but it is extremely difficult to design and train a deep network architecture. This paper proposes a fault diagnosis scheme combined of hierarchical symbolic analysis (HSA) and convolutional neural network (CNN), which achieves laborsaving and timesaving preliminary feature extraction and accomplishes automatically feature learning with simplified network architecture. Firstly, hierarchical symbolic analysis is employed to extract features from original signals. The extracted features are able to identify different health conditions under various operating conditions. Then, convolutional neural network instead of human labor is used to learn the complex non-linear relationship between features and health conditions automatically. The architecture of CNN diagnosis model is simple and convenient to implement. Finally, a centrifugal pump dataset and a motor bearing dataset are adopted to validate the effectiveness of the proposed method. The diagnosis results show that the proposed method exhibits superior performance compared with shallow methods and deep learning methods.

Vehicle routing problem (VRP) is one of the widely researched areas in transportation science, mainly due to the potential cost savings and service improvement opportunities which brings to organizations involved in physical distribution of goods. In this paper, we develop a multi-depot green vehicle routing problem (MDGVRP) by maximizing revenue and minimizing costs, time and emission, and then, apply an improved ant colony optimization (IACO) algorithm that aims to efficiently solve the problem. The IACO model developed in this research uses an innovative approach in updating the pheromone that results in better solutions. The results achieved through the IACO demonstrate satisfying performance, which have higher solution quality when compared to the conventional ACO. The IACO algorithm used in this paper demonstrated a good level of responsiveness and simplicity when solving MDGVRP with multiple objectives.

This paper presents a novel signal processing scheme by combining an improved Vold-Kalman filter and the multi-scale sample entropy (IVKF-MSSE) for planetary gearboxes under non-stationary working conditions. In this scheme, we propose a method based on the characteristic frequency ratio (CFR) to select the VKF bandwidth. First, a CFR is adopted to select a VKF bandwidth with the largest CFR value as the optimal VKF bandwidth. Second, IVKF is used to extract fault-induced information under time-varying speed conditions. Because an optimal bandwidth is used in VKF, the feature extraction capability of VKF is enhanced. Then, the MSSE is applied to extract gearbox fault features. After that, the Laplacian score (LS) approach is introduced to refine the fault features by sorting the scale factors. At the end, the selected features are fed into the least square support vector machine (LSSVM) for effective fault pattern identification. Simulation and experimental vibration signals are employed to evaluate the effectiveness of the proposed method. Results show that the proposed method outperforms the auto-regressive AR-MSSE, VKF-MSSE and EEMD-MSSE in identifying fault types of planetary gearboxes.

The frequency domain electromagnetic sounding method is one of the important means of exploring mineral resources. This paper combined tensor controlled source audio frequency magnetotelluric (CSAMT) and audio frequency magnetotelluric(AMT) methods to investigate Xiarihamu copper-nickel ore in Qinghai Province. Spliced tensor CSAMT high frequency (200 Hz-8533 Hz) and AMT low frequency (1 Hz-200 Hz) apparent resistivity and impedance phase data, as well as spliced data, were used for a 2D inversion and to generate a 2D geoelectric model. Coupled with the known geological, geophysical, and drilling data, the inversion results were analyzed. The results show that the combined use of tensor CSAMT and AMT can overcome the inherent defects of these methods, such as limited exploration depth and the "dead band" effect. This method produced good results in terms of exploring the underground electrical structure, finding the low resistivity body, and determining the thickness of the overburden, as well as the occurrence and the buried depth of the ore body. These results provide an effective method and exploration strategy to find similar potential deposits in the surrounding areas. (C) 2018 Elsevier B.V. All rights reserved.

Multiscale symbolic dynamic entropy (MSDE) has been recently proposed to characterize the dynamical behavior of time series, which has merits of high computational efficiency and robustness to noise comparing with multiscale sample entropy (MSE) and multiscale permutation entropy (MPE). However, the variance of the MSDE values increases as the length of a time series becomes shorter using multiscale analysis. To address this shortcoming, a new method, namely refined composite multi-scale symbolic dynamic entropy (RCMSDE), is proposed to extract the fault information of rotating machinery. Then, Laplacian score (LS) is utilized to reduce the dimension of eigenvectors. In the end, the selected features are taken as the input of the improved support vector machine based on binary tree (ISVM-BT) for fault type identification. The effectiveness of the proposed method is validated using both the simulation and two experimental tests. Results demonstrate that the proposed method generates highest classification accuracy in comparison with existing methods such as MSDE, refined composite multiscale sample entropy (RCMSE) and refined composite multiscale permutation entropy (RCMPE).

We examine the effect of restricting executives’ outside job opportunities on corporate tax avoidance and tax risk, using a natural experiment of the staggered adoption and rejection of the inevitable disclosure doctrine (IDD). Based on a difference-in-differences analysis, we find strong evidence that the IDD decreases effective tax rates. We also find that the IDD reduces tax risk though the evidence is weaker. The effect is generally more pronounced when the risk of dismissal due to poor performance is higher. Additional analyses show that the IDD increases the use of tax haven operations. Finally, a path analysis shows that the increase in tax avoidance contributes about 6% to 7% of the total effect of the IDD on firm value. Overall, the results suggest that career concerns motivate executives to reduce their firms’ tax burden using low risk strategies.

The entropy-based method has been demonstrated to be an effective approach to extract the fault features by estimating the complexity of signals, but how to remove the strong background noises in analyzing early weak impulsive signal remains unexplored. To solve this problem, this paper proposes symbolic fuzzy entropy (SFE) based on symbolic dynamic filtering and fuzzy entropy to eliminate the noises and improve the calculation efficiency. The main idea of SFE is to use symbolic dynamic filtering to remove the noise-related fluctuations while significantly simplifying the circulation calculation, thereby, generating better performance in resisting the background noises and high computation efficiency. The superiority of SFE is verified via two simulated signals and other three entropy methods. For comprehensive feature description, we further extend SFE into multiscale analysis by incorporating with the coarse gaining process, called MSFE. Experimental results demonstrate that the proposed MSFE method has the best performance in extracting weak fault characteristics compared with three existing MSE, MFE, and MPE methods.

Early fault diagnosis of rolling bearings is crucial to operating and maintenance cost reduction of the equipment with bearings. This paper aims to propose a novel early fault feature extraction method based on the proposed hierarchical symbol dynamic entropy (HSDE) and the binary tree support vector machine (BT-SVM). Multiscale symbolic dynamic entropy (MSDE) has been recently proposed to characterize the dynamical behavior of time series. MSDE has several merits comparing with multiscale sample entropy (MSE) and multiscale permutation entropy (MPE), such as high computational efficiency and robustness to noise. However, MSDE only utilizes the fault information in the low frequency components and consequently the fault information hidden in the high frequency components is discarded. To address this shortcoming, a new method, namely HSDE, is proposed to extract the fault information in the high frequency components. Then, the BT-SVM is utilized to automatically complete the fault type identification. The effectiveness of the proposed method is validated using simulated and experimental vibration signals. Meanwhile, a comparison is conducted between MPE, hierarchical permutation entropy (HPE), MSE, hierarchical sample entropy (HSE), MSDE and HSDE. Results show that the proposed method performs best to recognize the early fault types of rolling bearings.

The fault diagnosis of planetary gearboxes is crucial to reduce the maintenance costs and economic losses. This paper proposes a novel fault diagnosis method based on adaptive multi-scale morphological filter (AMMF) and modified hierarchical permutation entropy (MHPE) to identify the different health conditions of planetary gearboxes. In this method, AMMF is firstly adopted to remove the fault-unrelated components and enhance the fault characteristics. Second, MHPE is utilized to extract the fault features from the denoised vibration signals. Third, Laplacian score (LS) approach is employed to refine the fault features. In the end, the obtained features are fed into the binary tree support vector machine (BT-SVM) to accomplish the fault pattern identification. The proposed method is numerically and experimentally demonstrated to be able to recognize the different fault categories of planetary gearboxes.

The problem of industrial pollution has increased globally at presently. Countries across the globe are aware of the concept of sustainable implementation in industries from a local to the global level to reduce pollution. Recent research supported this by focusing on Small and Medium Enterprises (SMEs) rather than Multinational Corporations (MNC). Sustainability development is a key to ensure control of the hazards and pollution in traditional activities. In India, many SMEs are involved in manufacturing varied products and services for customers. The auto component manufacturing sector is playing as an important role in the Indian economy by creating numerous job opportunities. Based on this, researchers gave special attention to auto components SMEs in sustainable development (SD) because of pollution which harms the environment. Analysis of SD is an essential activity for industries to ensure environmental improvement. Analyzing the indicators for SD helps confirm the development of sustainability in industries. However, analyzing the indicators for SD is a challenge for managers and needs detailed analysis and significant effort. Indicator analysis is a vague concept for managers who lack alternatives to evaluate SD in their industry. The aim of this present paper is to develop a set of measurement indicators for SD from literature. This paper, with the help of discussions with experts and Decision-making Trial and Evaluation Laboratory (DEMATEL) methodology analyzes sustainable development indicators in the auto components sectors. Fifteen SD indicators were considered for this study from a relevant literature background. These SD indicators indicate the industries’ sustainable development. Based on their interest, a questionnaire for this study was circulated among 35 leading auto components manufacturing SMEs in North India. This study will identify the influential indicator for SD based on experts' judgments through a pairwise comparison matrix. The results from the study clearly states that Indian auto component manufacturing industries believe that among the fifteen listed indicators, carbon management indicator helps in measuring sustainable development. Due to this reason, embedded or embodied carbon (SI2) indicator plays an influential role. Finally, the study concludes with insights about the future path of sustainability performance in a developing country like India.

The non-contact alternating current (AC) impedance method is widely used in the study of electrochemical properties of cement-based materials,the instrument of this paper provide a new way for the study of pore structure of cement-based materials. Due to the loss of magnetic materials and the parasitic capacitance of measuring circuit, the ratio difference and phase difference of current sensor inevitably exit. Traditionally the standard resistor calibration method is used to determine the ratio error and phase error under different frequencies based on polynomial fitting (FT). In this paper, a machine-learning support vector machine (SVM) model is used to predict the calibration data with a small test sample. For the accuracy assessment, SVM model has the best comprehensive performance among FT, artificial neural network (ANN). The actual impedance test of typical cement-based materials under different frequencies is given and the results verify the excellent performance of SVM.

Health condition identification of planetary gearboxes is crucial to reduce the downtime and maximize productivity. This paper aims to develop a novel fault diagnosis method based on modified multi-scale symbolic dynamic entropy (MMSDE) and minimum redundancy maximum relevance (mRMR) to identify the different health conditions of planetary gearbox. MMSDE is proposed to quantify the regularity of time series, which can assess the dynamical characteristics over a range of scales. MMSDE has obvious advantages in the detection of dynamical changes and computation efficiency. Then, the mRMR approach is introduced to refine the fault features. Lastly, the obtained new features are fed into the least square support vector machine (LSSVM) to complete the fault pattern identification. The proposed method is numerically and experimentally demonstrated to be able to recognize the different fault types of planetary gearboxes.

Geophysical electromagnetic (EM) surveys for imaging the subsurface structure under lakes, rivers, or other water bodies have attracted increasing attention. Based on forward modeling and field experiments, a boat-towed audio-frequency magnetotelluric (AMT) multi-channel observation system for shallow fresh water was developed preliminarily. Through the development of water electrodes and the streamer cable, a boat-towed multi-channel data acquisition cable was developed. We carried out a variety of different comparative experiments, such as water- and land-AMT experiments, different polar-distances, and acquisition time-length experiments, and observation experiments at different time, in ditches or rivers around the Qilihai Wetland, Tianjin, China. We also carried out profile detection experiments using this system in southern Baiyang Lake, Xiongan New Area, China. The experimental results demonstrate that the proposed water–AMT system can be utilized to characterize subsurface structure underneath shallow water areas, advancing the potential of geophysical surveys on rivers or lakes.

In this study, we investigated the contamination of 123 vegetables and 130 fruits with 11 fungicides in the mid-western region of China. The index of food safety (IFS) and risk coefficient (R) were used to assess the health hazards of the fungicides to consumers. The results showed that the qualified rates of pesticide residues in fruits and vegetables were 100 % (130/130) and 98.37 % (121/123), respectively. The detection rate of pesticides in celery, cowpea, peach, and grape was higher than 70 %. The amount of difenoconazole and procymidone pesticides exceeded the standard in vegetables, while carbendazim, difenoconazole, and tebuconazole were the most common pesticides in fruits and vegetables. The IFS of all detected pesticides in the fruits and vegetables was less than 1, indicating an acceptable safety risk of the pesticide residues. The R of difenoconazole and procymidone in the vegetables was moderate, which should be considered in the key control risk factors and continuously monitored to protect people's health.

Modeling of vapor-liquid equilibrium for the aqueous solutions of formaldehyde and electrolyte is required for the extractive distillation of formaldehyde-containing multicomponent mixtures using salt as entrainers. Here a revised physicochemical model, which integrates the advantages of the physicochemical model for the vapor-liquid and the chemical equilibria in aqueous formaldehyde solution and the LIFAC model for the phase behavior in mixed-solvent electrolyte systems, has been proposed. The new vapor-liquid equilibrium data in (HCHO–H2O–salt) were systematically measured and utilized to determine newly-introduced model parameters. Comparisons of model calculated and experimental measured values proved that the model presented here provides a reliable method for correlating the vapor-liquid equilibria data in (HCHO–H2O–salt) system. Furthermore, the model established in this work was used to study salts effect on vapor-liquid equilibria. Both the model calculations and the VLE experiments uncover that Zn(NO3)2 is the most promising among all of the entrainers investigated.

The discovery of superconductivity of MgB2 has attracted considerable research attention. Further exploration and discovery of MgB2-like superconductors are important to determine the properties of MgB2 and enhance our understanding of the superconducting mechanism. Four MgB2-like MgXB4 (X = Al, Li, Na, K) superconductors based on an MgB2 structure were designed in this study. The electronic structure, phonon dispersion relationship, Eliashberg spectral function, electron-phonon coupling constant (λ), and superconductivity of MgXB4 were investigated using a first-principles approach. The results indicated that MgXB4 (X = Al, Li, Na, K) were dynamically stable and demonstrated metallic characteristics. Further study of the Eliashberg spectral function and electron-phonon coupling constant indicated that these compounds were superconductors. The calculated superconducting transition temperatures (TC) of MgXB4 (X = Al, Li, Na, K) were 0.12, 22.7, 28.1, and 30.4 K, respectively.

A novel rolling bearing fault diagnosis method based on hierarchical fuzzy entropy (HFE), Laplacian score (LS) and improved support vector machine based binary tree (ISVM-BT) is proposed in this paper. Focus on the difficulty of extracting fault feature from the non-linear and non-stationary vibration signal under complex operating conditions, HFE method is utilized for fault feature extraction. Compared with multi-scale fuzzy entropy (MFE) method, HFE method considers both the low and high frequency components of the vibration signals, which can provide a much more accurate estimation of entropy. Besides, Laplacian score (LS) method is introduced to refine the fault feature by sorting the scale factors. Subsequently, the obtained features are fed into the multi-fault classifier ISVM-BT to automatically fulfill the fault pattern identifications. The experimental results demonstrate that the proposed method is effective in recognizing the different categories and severities of rolling bearings faults.

A new bearing vibration feature extraction method based on multiscale permutation entropy (MPE) and improved support vector machine based binary tree (ISVM-BT) is put forward in this paper. Local mean decomposition (LMD), a new self-adaptive time–frequency analysis method, is utilized to decompose the roller bearing vibration signal into a set of product functions (PFs) and then MPE method is used to characterize the complexity of the principal PF component in different scales. After the feature extraction, a new pattern recognition approach called ISVM-BT is introduced to accomplish the fault identification automatically, which has the priority of high recognition accuracy compared with other classifiers. Besides, the Laplacian score (LS) is introduced to refine the fault feature by sorting the scale factors. Finally, the rolling bearing fault diagnosis method based on LMD, MPE, LS and ISVM-BT is proposed and the experimental results indicate the proposed method is effective in identifying the different categories of rolling bearings.

This paper presents a new rolling bearing fault diagnosis method based on local mean decomposition (LMD), improved multiscale fuzzy entropy (IMFE), Laplacian score (LS) and improved support vector machine based binary tree (ISVM-BT). When the fault occurs in rolling bearings, the measured vibration signal is a multi-component amplitude-modulated and frequency-modulated (AM–FM) signal. LMD, a new self-adaptive time-frequency analysis method can decompose any complicated signal into a series of product functions (PFs), each of which is exactly a mono-component AM–FM signal. Hence, LMD is introduced to preprocess the vibration signal. Furthermore, IMFE that is designed to avoid the inaccurate estimation of fuzzy entropy can be utilized to quantify the complexity and self-similarity of time series for a range of scales based on fuzzy entropy. Besides, the LS approach is introduced to refine the fault features by sorting the scale factors. Subsequently, the obtained features are fed into the multi-fault classifier ISVM-BT to automatically fulfill the fault pattern identifications. The experimental results validate the effectiveness of the methodology and demonstrate that proposed algorithm can be applied to recognize the different categories and severities of rolling bearings.

A new method called intrinsic characteristic-scale decomposition (ICD) is proposed in this paper, which is particularly suitable for processing the nonlinear and non-stationary time series. When fault occurs in gearbox and rolling bearing, the measured vibration signals would exactly present non-stationary characteristics. ICD, a new self-adaptive time-frequency analysis method, can decompose the non-stationary signal into a series of product components (PCs). Therefore, it is possible to diagnose gearbox and rolling bearing fault. In this paper, the ICD method is introduced and the decomposition performance is compared with LMD method. The results demonstrate that ICD has the advantages at least in running time, alleviating the mode mixing problem and restraining the end effect. The ICD method is applied to the practical gear and rolling bearing fault diagnosis. The results demonstrate that the proposed method is effective in the fault signature analysis of the rotating machinery.

A demodulation technique based on improved local mean decomposition (LMD) is investigated in this paper. LMD heavily depends on the local mean and envelope estimate functions in the sifting process. It is well known that the moving average (MA) approach exists in many problems (such as step size selection, inaccurate results and time-consuming). Aiming at the drawbacks of MA in the smoothing process, this paper proposes a new self-adaptive analysis algorithm called optimized LMD (OLMD). In OLMD method, an alternative approach called rational Hermite interpolation is proposed to calculate local mean and envelope estimate functions using the upper and lower envelopes of a signal. Meanwhile, a reasonable bandwidth criterion is introduced to select the optimum product function (OPF) from pre-OPFs derived from rational Hermite interpolation with different shape controlling parameters in each rank. Subsequently, the orthogonality criterion (OC) is taken as the product function (PF) iterative stopping condition. The effectiveness of OLMD method is validated by the numerical simulations and applications to gearbox and roller bearing fault diagnosis. Results demonstrate that OLMD method has better fault identification capacity, which is effective in rotating machinery fault diagnosis. A novel time-frequency analysis method called OLMD is presented in this paper.OLMD can weaken the mode mixing problem in traditional LMD.The simulation and experimental results validate the reliability and feasibility of the proposed methodology.

Increasing population and urbanization motivates the capability of consuming more fashion goods than ever. This push creates more momentum on global companies to focus on clothing sectors. Recent advancements, including globalization and e-commerce, have made this sector as one of the top businesses worldwide. Top clothing brands made several strategies to satisfy the stakeholders to sustain in this hot, profitable business. This results in practicing more sustainable strategies, including corporate social responsibility in their clothing business throughout their operations, including the supply chain. However, most of the developed nations are consumers of textiles, which are produced and processed by any of the developing and under developing nations. Meanwhile, achieving sustainability in clothing business includes promoting sustainability in the whole chain of suppliers. Pressure from developed nations urges developing nations to promote sustainable practices in their operations. Several studies discussed the CSR related strategies in textile sectors but failed to explore their critical success factors based on their region. With this concern, this study attempts to study the critical success factors of CSR in textile industries situated in one of the developing nations, China. This study collected the critical success factors from literature and validated with the field experts; then the same were evaluated with the assistance of Chinese textile case industrial managers. Decision-making trial and evaluation laboratory tool has been used to evaluate the influential critical success factors of CSR to promote CSR through motivating those most influential success factors. These results could help the Chinese textile industrial managers to further extend strong roots on CSR implementation. Finally, this study sheds some light on future opportunities that exist within Chinese contexts with the implementation of CSR. Revisión por pares Revisión por pares

The levels of multi-pesticide residues in highly consumed types of vegetables in western regions of China were investigated in this study. Vegetable samples (506) were collected from local markets from 2010 to 2013, and the concentrations of 21 types of organophosphorus (OP) and pyrethroid (PYR) pesticides from the samples were determined by gas chromatography–mass spectrometry (GC–MS). Ten OP pesticides were found in concentrations ranging from 0.0008 to 4.0544 mg/kg. Five types of PYR pesticides were determined to have concentrations in the range of 0.0009 to 6.0827 mg/kg. There were no residues in 69.76% of the samples. A portion (25.49%) of the samples contained pesticide residues less than or equal to the maximum residue limits (MRLs), and 4.94% of samples contained pesticide residues greater than their MRLs. This study provides useful information on the current contamination status of a key agricultural area in China from 2010 to 2013 and highlights the need for regular monitoring of pesticide residues in a greater number of vegetable samples and for longer periods, especially in leafy vegetables, to protect consumers.

A demodulation technique based on improvement empirical mode decomposition (EMD) is investigated in this paper. Firstly, the problem of the envelope line in EMD is introduced and the drawbacks of two classic interpolation methods, cubic spline interpolation method and cubic Hermite interpolation method are discussed; then a new envelope interpolation method called optimized rational Hermite interpolation method (O-EMD) is proposed, which has a shape controlling parameter compared with the cubic Hermite interpolation algorithm. At the same time, in order to improve the envelope approximation accuracy of local mean, the parameter determining criterion is put forward and an optimization with Genetic Algorithm (GA) is applied to automatic select the suitable shape controlling parameter in each sifting process. The effectiveness of O-EMD method is validated by the numerical simulations and an application to gear fault diagnosis. Results demonstrate that O-EMD method can improve the reliability and accuracy significantly compared with traditional EMD method.

A simple, stable and sensitive electrogenerated chemiluminescence (ECL) detector was developed. It was based on tris(2,2-bipyridyl)ruthenium(II) (Ru(bpy)32+) immobilized on the surface of a Pt wire with Nepem-105D ion exchange solution. The detector was prepared by inserting a Pt wire with immobilized Ru(bpy)32+ (working electrode) into a capillary tube, followed by inserting another Pt wire (counter electrode) in this tube and sealing. ECL behavior was investigated using ofloxacin as an analyte. Under optimal conditions, stable ECL intensity was obtained. This detector has been used in HPLC-ECL for the determination of multiple target fluoroquinolone residues in milk. There is no post column reagent addition, which would dilute the analytes, potentially leading to chromatographic band-broadening. The system is very simple with low dead volume, low baseline and background noise, together with high sensitivity and stability. The as-prepared ECL detector, when was used for the determination of ofloxacin, pefloxacin, enrofloxacin and difloxacin in milk, demonstrated adequate sensitivity to allow quantification of trace FQ levels in commercial milk samples. One or more of the target FQ analytes were present at levels above the LOD of the new ECL detector in each and every one of the 22 milk samples analysed.

Fe3O4/polypyrrole (PPy) core/shell nanocomposite, with Fe3O4 nanoparticle as core and PPy as shell, could be facilely synthesized via in situ chemical oxidative polymerization of pyrrole monomers on the surface of Fe3O4 nanoparticles. The results indicate that core/shell nanocomposite consists of Fe3O4 core with the mean diameter of 100 nm and adjacent PPy shell with a thickness of about 70 nm. The as-prepared Fe3O4/PPy core/shell nanocomposite exhibits a saturated magnetization of 20.1 emu/g and coercivity value of 368.3 Oe, respectively. The electromagnetic characteristics of Fe3O4/PPy core/shell nanocomposite were also investigated with a vector network analyzer in the 2–18 GHz range. The absorbing peak position moves to lower frequency with increasing the thicknesses of samples. The value of the minimum reflection loss is −22.4 dB at 12.9 GHz for Fe3O4/PPy core/shell nanocomposite with a thickness of 2.3 mm, and a broad peak with a bandwidth lower than −10 dB is about 5 GHz. Such strong absorption is attributed to better electromagnetic matching due to the existence of PPy and the special core/shell structure.

Ni/ZnS core/shell nanocomposites with both magnetic and luminescent properties were prepared via a two-step approach with an initial synthesis of Ni nanoparticles and then as templates for the deposition of ZnS nanoparticles. The structure and properties of as-synthesized samples were characterized by XRD, TEM, VSM and PL. Studies indicate that typical nanocomposites consist of Ni (core) with 80–130 nm diameter and adjacent ZnS (shell) with a thickness of about 30–50 nm. The value of magnetization saturation of nanocomposites significantly decreases after coating with ZnS as a result of the nonmagnetic coating affecting. The emission spectrum of nanocomposites shows a small red shift as compared with that of pure ZnS. The possible reason for this phenomenon may be ascribed to the change of size, morphology and crystallinity of ZnS sample after coating.

β-Ga 2 O 3 nanoribbons have been prepared by a two-step gas flow control, involving adjusting the pressure inside the tube before and after the formation of the nanoribbons. In different temperature areas, separated nanoribbons and nanowires were obtained, which grew via solid–liquid–vapor–solid (SLVS) and vapor–liquid–solid (VLS) mechanisms, respectively. The samples obtained were characterized by means of scanning electron microscopy, transmission electron microscopy, micro-Raman scattering, and photoluminescence. It was found that Ga 2 O 3 nanoribbons had a perfect monoclinic single-crystal structure with a [002] growth direction. The Ga 2 O 3 nanoribbons give ultraviolet, cyan and green light emission at room temperature under excitation at 254 and 325 nm.

We demonstrated that ZnFe2O4/polypyrrole core-shell nanoparticles could be facilely synthesized via in situ chemical oxidative polymerization of pyrrole monomers on the surface of ZnFe2O4 nanoparticles. The shell thickness of core-shell nanoparticles could be easily controlled by adjusting the amount of pyrrole monomers. The phase structures, morphologies and properties of the as-prepared products were investigated by XRD, TEM, SEM, VSM, and FTIR spectra. Magnetic studies revealed that the saturation magnetization (Ms) and coercivity (Hc) of ZnFe2O4/PPy core-shell nanoparticles is 17.8 emu/g and 130 Oe, respectively. The electromagnetic characteristics of products showed that ZnFe2O4/PPy core-shell nanoparticles exhibit excellent microwave absorption performance than ZnFe2O4 nanoparticles, such as more powerful absorbing property and wider electromagnetic wave absorbing frequency band due to the proper matching of the permittivity and the permeability of ZnFe2O4/PPy core-shell nanoparticles.

Monodisperse Zn ferrite nanospheres and its Ba substitution nanosheets were successfully prepared by a facile solvothermal method. The size, morphology, and structure of the as-prepared products were investigated in detail by X-ray diffraction (XRD), transmission electron microscopy (TEM), scanning electron microscopy (SEM), high-resolution transmission electron microscopy (HRTEM), and inductively coupled plasma atomic emission spectroscopy (ICP-AES). Magnetic studies revealed that Zn ferrite nanospheres are ferrimagnetic with a saturation magnetization ( M s ) of 49.6 emu /g and M s increased from 49.6 emu /g to 84.2 emu /g when Ba2+ was substituted into Zn ferrite to form BaZn ferrite nanosheets. To explain the variations of morphology and magnetic properties, the strong preferential occupancy of cations in chemically equivalent A and B sites and the metastable cation distribution in nanoparticles are invoked.

Denervation in man often results in shiny, dry, thin skin. A previous study has shown that the epidermis of glabrous skin in the rat becomes approximately 40% thinner within 1 week following sciatic nerve transection, but which nerve fiber type or types influence epidermal thickness is unknown. In this study, we compared the effects on the epidermis of selective sensory, motor, and sympathetic denervation. Protein gene product 9.5 and calcitonin gene-related peptide immunocytochemical staining were used to determine the extent of denervation of epidermis, dermis, and sweat glands in the footpads. Epidermal thickness of the glabrous plantar skin of the foot was measured. To verify the specificity and reliability of each animal model, the relevant regions of the peripheral nervous system were examined by light or electron microscopy or both. Epidermal thickness decreased significantly following sciatic nerve transection (58% of control, P < 0.05) and dorsal root ganglionectomy (59%; P < 0.05). The thickness also decreased following lumbar ventral rhizotomy (61%; P < 0.01), destruction of lumbar spinal motor neurons (66%; P < 0.05), and botulinum toxin-induced paralysis of the tibialis anterior and gastrocnemius muscles (70%; P < 0.05). A slight decrease followed dorsal rhizotomy (84%; P < 0.01). In contrast, no significant alterations in epidermal thickness were detected following sham operation and sympathectomy. Epidermal thinning was paralleled by reductions in the amounts of transcripts for glyceraldehyde-3-phosphate dehydrogenase and beta-actin. These results suggest that selective loss of both sensory and motor fibers to the hind limb can contribute to reducing epidermal thickness in rat foot glabrous skin.