•Unknown fault feature extraction of rolling bearing is realized under variable speed conditions.•Statistical complexity measuresselects the optimalintrinsic mode function component.•Compared with ...spectral amplitude modulation have optimal performance.
As the main transmission components of rotating machinery, rolling bearings have important research significance for fault diagnosis and state detection. However, the operating environment of mechanical equipment is complex, and the fault characteristic information of rolling bearings is often unknown. Through the complex transmission path, the bearing seat vibration sensor picks up the fault vibration response is weaker, and it is often submerged by strong noise. When the speed and load of rotating mechanical equipment change, the fault characteristic of rolling bearings is more obvious under variable speed conditions. The fault diagnosis problem of rolling bearings needs to be solved urgently under strong noise background, variable speed, and unknown fault characteristics. Therefore, this paper studies an unknown fault feature extraction method of variable speed rolling bearing based on statistical complexity measures (SCM). Order analysis preprocesses the variable speed vibration signal of rolling bearings. It is convenient for subsequent fault feature extraction and analysis. The SCM selects the optimal intrinsic mode function (IMF) component corresponding to the Empirical mode decomposition (EMD) decomposition, and it is also evaluated index for the optimal response of stochastic resonance. Therefore, the adaptive frequency shift stochastic resonance effectively extracts the unknown fault features of rolling bearings under strong noise background.
The photochemical production of fuels using sunlight is an innovative way for meeting the quickly increasing energy demands. One of the largest challenges is to develop high‐performance ...photocatalysts that can meet the requirements of practical applications. Owing to their intriguing localized surface plasmon resonances, noble metal nanoparticles and nanostructures show a great potential for enhancing the photocatalytic efficiency and thereby have attracted rapidly growing interest recently. Here, for the first time, the latest achievements in the utilization of plasmons in driving CO2 reduction and N2 fixation into high‐value products are comprehensively described. The involved plasmonic enhancement mechanisms in the two types of reactions are fully illustrated. A particular emphasis is given to the outlook on the direction and prospects for future work in this topic.
Photocatalytic solar‐to‐fuel conversion is of high potential in tackling energy shortage and environmental issues. The emerging applications of plasmons in the enhancement of the conversion efficiency toward practical relevance have recently gathered much attention. A focused overview of the recent studies in the use of plasmons for the photocatalytic CO2 reduction and N2 fixation is provided.
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•Membranes for dehydration of Acetic acid are evaluated to address separation tradeoff.•The roadmap for potential novel membranes for dehydration of acetic acid is presented.•The ...prospects and challenges for scale up as acid-resistant membranes are proposed.
Acetic acid is an essential intermediate chemical and belongs to the top 50 commodities of the chemical industry. Dehydration of acetic acid/water mixture is an indispensable process for the final acetic acid product. As an effective alternative technique for energy-intensive distillation, pervaporation is getting significant attention for this target separation due to its high efficiency, low energy requirements, and eco-friendly nature. In the present review, the advances in pervaporation membranes, especially polymeric membranes, composite membranes, zeolite membranes, and sol–gel derived ceramic membranes are covered for dehydration of acetic acid/water mixture. The developed strategies to overcome the tradeoff between membrane hydrophilicity and acid resistance are discussed for each kind of membrane with a particular focus on tuning framework Si/Al ratio and Al atom spatial distribution of zeolite membranes, and development of hybrid silica membranes. Thus, pervaporation membranes performance is described along with future prospects and challenges for their commercialization as acid-resistant membranes. In addition to this, we have critically discussed the use of hollow fiber technology for the dehydration of acetic acid and stressed the need of acid-resistant membranes for esterification reactions to improve the yield of the final product.
The fixation of atmospheric N2 to NH3 is an essential process for sustaining life. One grand challenge is to develop efficient catalysts to photofix N2 under ambient conditions. Herein we report an ...all-inorganic catalyst, Au nanocrystals anchored on ultrathin TiO2 nanosheets with oxygen vacancies. It can accomplish photodriven N2 fixation in the “working-in-tandem” pathway at room temperature and atmospheric pressure. The oxygen vacancies on the TiO2 nanosheets chemisorb and activate N2 molecules, which are subsequently reduced to NH3 by hot electrons generated from plasmon excitation of the Au nanocrystals. The apparent quantum efficiency of 0.82% at 550 nm for the conversion of incident photons to NH3 is higher than those reported so far. Optimizing the absorption across the overall visible range with the mixture of Au nanospheres and nanorods further enhances the N2 photofixation rate by 66.2% in comparison with Au nanospheres used alone. This work offers a new approach for the rational design of efficient catalysts toward sustainable N2 fixation through a less energy-demanding photochemical process compared to the industrial Haber–Bosch process.
PurposeThe purpose of this paper is to investigate the relationship between e-marketing (eM) and consumers’ buying behavior particularly exploratory buying behavior tendencies (EBBT) with moderating ...effect of a gender in the context of China.Design/methodology/approachStructural equation modeling using SPSS/AMOS was majorly applied to ascertain the relationship and hypotheses testing. First, the correlation of eM toward EBBT is examined using five factors: internet marketing (IM), e-mail marketing (EMa), intranet marketing (IMa), extranet marketing (EM), and mobile marketing (MM). Second, the relationship of each dimension of the eM model is determined autonomously to ensure the importance of such emerging technologies in marketing communications. Third, the effect of gender as a moderator is measured. To this end, primary data were collected through random distribution of the questionnaires among 1,600 consumers particularly students of the universities between February 2016 and August 2016 within North China.FindingsThe findings revealed that eM has a significant correlation on consumers’ EBBT. The comprehensive analysis of each factor of eM, i.e., IM, EMa, IMa, EM, and MM is positively correlated to EBBT. The present study revealed that gender did not moderate among the relationships of eM and EBBT. Additionally, study furnishes practical directions on how managers can utilize such emerging and revolutionary technologies in marketing activities to probe, understand, and reinforce consumers’ buying behavior.Research limitations/implicationsThe research has limitations related to geographical location and sample size which thus limits the widespread generalization.Practical implicationsThis study affirmed that organizations must engage the consumers using such technologies that are more likely acceptable by consumers in the present customer-oriented and digital era. The marketers must engage consumers the way they wish to be engaged by developing appropriate promotional strategies. The study provides possible implications both theoretical and managerial along with a contribution to the literature of eM and consumers’ buying behavior.Social implicationsUnderstanding the emerging technologies may furnish valuable insights for individuals to work well within Chinese SMEs.Originality/valueThe topic of eM has acknowledged as an evolving concept which is gaining an intense concern of both academician and practitioners. Therefore, more research mainly empirical work is still needed to probe the insights of eM across the globe. This study attempts to fulfill such need with empirical evidence together with an in-depth examination of eM determinants, collectively and autonomously.
Despite high sodium storage capacity and better reversibility, metal sulfides suffer from relatively low conductivity and severe volume change as anode materials of sodium-ion batteries (SIBs). ...Introducing a conductive carbon matrix is an efficient method to enhance their sodium storage performance. Herein, we present iron sulfide (Fe7S8) nanoparticles anchored on nitrogen-doped graphene nanosheets fabricated through a combined strategy of solvothermal and postheating process. The as-prepared composite exhibits appealing cycling stability (a high discharge capacity of 393.1 mA h g–1 over 500 cycles at a current density of 400 mA g–1 and outstanding high-rate performance of 543 mA h g–1 even at 10 A g–1). Considering the excellent sodium storage performance, this composite is quite hopeful to become a potential candidate as anode materials for future SIBs.
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Phenol and its derivatives from various man-made activities pose threats to public health and aquatic ecosystems. A number of technologies (e.g., adsorption, oxidation, and biological ...methods) have been proposed and tested to remove phenolic compounds from waste water. Among these technologies, membrane separation is considered one of the most efficient tools for abating phenolic compounds from waste water because of low capital cost, easy scalability, and ecofriendly production with the lowest emission of noxious compounds. In this review, we aim to address the potent role of membrane technology by evaluating its performance in separating various phenolic compounds from industrial effluents.
Vector-matrix multiplication dominates the computation time and energy for many workloads, particularly neural network algorithms and linear transforms (e.g, the Discrete Fourier Transform). ...Utilizing the natural current accumulation feature of memristor crossbar, we developed the Dot-Product Engine (DPE) as a high density, high power efficiency accelerator for approximate matrix-vector multiplication. We firstly invented a conversion algorithm to map arbitrary matrix values appropriately to memristor conductances in a realistic crossbar array, accounting for device physics and circuit issues to reduce computational errors. The accurate device resistance programming in large arrays is enabled by close-loop pulse tuning and access transistors. To validate our approach, we simulated and benchmarked one of the state-of-the-art neural networks for pattern recognition on the DPEs. The result shows no accuracy degradation compared to software approach (99 % pattern recognition accuracy for MNIST data set) with only 4 Bit DAC/ADC requirement, while the DPE can achieve a speed-efficiency product of 1,000× to 10,000× compared to a custom digital ASIC.
The limitations of the Haber–Bosch reaction, particularly high-temperature operation, have ignited new interests in low-temperature ammonia-synthesis scenarios. Ambient N2 electroreduction is a ...compelling alternative but is impeded by a low ammonia production rate (mostly <10 mmol gcat –1 h–1), a small partial current density (<1 mA cm–2), and a high-selectivity hydrogen-evolving side reaction. Herein, we report that room-temperature nitrate electroreduction catalyzed by strained ruthenium nanoclusters generates ammonia at a higher rate (5.56 mol gcat –1 h–1) than the Haber–Bosch process. The primary contributor to such performance is hydrogen radicals, which are generated by suppressing hydrogen–hydrogen dimerization during water splitting enabled by the tensile lattice strains. The radicals expedite nitrate-to-ammonia conversion by hydrogenating intermediates of the rate-limiting steps at lower kinetic barriers. The strained nanostructures can maintain nearly 100% ammonia-evolving selectivity at >120 mA cm–2 current densities for 100 h due to the robust subsurface Ru–O coordination. These findings highlight the potential of nitrate electroreduction in real-world, low-temperature ammonia synthesis.
The supervised deep learning methods applied in mineral prospectivity mapping usually need sufficient samples for training models. However, mineralization is a rare event. Insufficient known mineral ...deposits cannot meet the sample requirement of supervised learning methods, resulting in lower predictive accuracies and poor generalization abilities. For the purpose of solving this issue, this paper adopted a data augmentation method to make mineral prospectivity prediction of gold deposit in the Fengxian Region, China. This data augmentation method adopted cropping operations to generate sufficient training samples without changing spatial directions of geological data. Meanwhile, this paper utilized the continuous buffer distance method to quantify faults and anticline axes, overcoming the loss of geological information caused by using the discrete buffer distance mode. To prove the effectiveness of the data augmentation method, this paper utilized three different convolutional neural networks (LeNet, AlexNet, and VggNet) to extract relationships between multisource ore-indicating factors and mineral deposits. In addition, this paper discussed effects of different parameters on predictive performances. According to series of comparisons, the LeNet model outperformed other models, achieving superior values of accuracy (91.38%), Kappa coefficient (0.8119), and AUC (0.958). Moreover, the LeNet model successfully caught 81.8% of known gold deposits within 18.6% of the study area. The delineated high potential areas offer intuitive guides for exploring more gold deposits in the Fengxian region. The proposed data augmentation method is available for mineral prospectivity modeling by supervised deep learning methods for the areas of lower exploration degrees. For mineral prospectivity modeling based on convolutional neural networks, utilizing the continuous buffer distance to transform faults and anticline axes into predictor variables of the image form is conducive to improve the predictive performance than utilizing the discrete buffer distance.
•To solve the issue of mineral prospectivity prediction in lower developed areas.•A cropping technology of data augmentation was used to expand training samples.•Continuous buffer distance retains more geological information on images for CNN.•Convolutional neural networks were utilized to build mineral prediction models.•The obtained model can catch more known deposits in smaller predicting areas.
