[Article currently freely available to all at the DOI link below] A question arising from the COVID-19 crisis is whether the merits of cases for climate policies have been affected. This article focuses on carbon pricing, in the form of either carbon taxes or emissions trading. It discusses the extent to which relative costs and benefits of introducing carbon pricing may have changed in the context of COVID-19, during both the crisis and the recovery period to follow. In several ways, (...) the case for introducing a carbon price is stronger during the COVID-19 crisis than under normal conditions. Oil costs are lower than normal, so we would expect less harm to consumers compared to normal conditions. Governments have immediate need for diversified new revenue streams in light of both decreased tax receipts and greater use of social safety nets. Finally, supply and demand shocks have led to already destabilized supply-side activities, and carbon pricing would allow this destabilization to equilibrate around greener production for the long-term. The strengthening of the case for introducing carbon pricing now is highly relevant to discussions about recovery measures, especially in the context of policy announcements from the European Union and United States House of Representatives. Key Policy Insights: • Persistently low oil prices mean that consumers will face lower pain from carbon pricing than under normal conditions. • Many consumers are more price-sensitive during the COVID-19 context, which suggests that a greater relative burden from carbon prices would fall upon producers as opposed to consumers than under normal conditions. • Carbon prices in the COVID-19 context can introduce new revenue streams, assisting with fiscal holes or with other green priorities. • Carbon pricing would contribute to a more sustainable COVID-19 recovery period, since many of the costs of revamping supply chains are already being felt while idled labor capacity can be incorporated into firms with lower carbon-intensity. (shrink)
As a result of the increasing public attention to environmental crises, corporate environmental actions and their effects are a current research hotspot. This study examines how two types of corporate environmental actions influence consumers’ perceptions of environmental legitimacy and subsequent purchase intentions. Using experimental method, this study finds that substantial environmental action induces significantly higher perceptions of environmental legitimacy than symbolic environmental action, this effect can be attenuated by corporate environmental reputation, and consumer-based environmental legitimacy has a significantly positive effect (...) on consumers’ purchase intentions. These findings have interesting implications for both researchers and practitioners involved in green marketing. (shrink)
Biomedical ontologies are emerging as critical tools in genomic and proteomic research where complex data in disparate resources need to be integrated. A number of ontologies exist that describe the properties that can be attributed to proteins; for example, protein functions are described by Gene Ontology, while human diseases are described by Disease Ontology. There is, however, a gap in the current set of ontologies—one that describes the protein entities themselves and their relationships. We have designed a PRotein Ontology (PRO) (...) to facilitate protein annotation and to guide new experiments. The components of PRO extend from the classification of proteins on the basis of evolutionary relationships to the representation of the multiple protein forms of a gene (products generated by genetic variation, alternative splicing, proteolytic cleavage, and other post-translational modification). PRO will allow the specification of relationships between PRO, GO and other OBO Foundry ontologies. Here we describe the initial development of PRO, illustrated using human proteins from the TGF-beta signaling pathway. (shrink)
Electromechanical actuators are more and more widely used as actuation devices in flight control system of aircrafts and helicopters. The reliability of EMAs is vital because it will cause serious accidents if the malfunction of EMAs occurs, so it is significant to detect and diagnose the fault of EMAs timely. However, EMAs often run under variable conditions in realistic environment, and the vibration signals of EMAs are nonlinear and nonstationary, which make it difficult to effectively achieve fault diagnosis. This paper (...) proposed a fault diagnosis method of electromechanical actuators based on variational mode decomposition multifractal detrended fluctuation analysis and probabilistic neural network. First, the vibration signals were decomposed by VMD into a number of intrinsic mode functions. Second, the multifractal features hidden in IMFs were extracted by using MFDFA, and the generalized Hurst exponents were selected as the feature vectors. Then, the principal component analysis was introduced to realize dimension reduction of the extracted feature vectors. Finally, the probabilistic neural network was utilized to classify the fault modes. The experimental results show that this method can effectively achieve the fault diagnosis of EMAs even under diffident working conditions. Simultaneously, the diagnosis performance of the proposed method in this paper has an advantage over that of EMD-MFDFA method for feature extraction. (shrink)
Whilst previous studies indicate perceived company ethicality as a driver of job seekers’ job-pursuit intentions, it is poorly understood how and why ethical market signals actually affect their application decisions. Perceptions of company ethicality result from market signals that are either within the control of the company and from market signals that are beyond the company’s control. Building on communication and information processing theories, this study therefore considers both types of ethical market signals, and examines the psychological mechanisms through which (...) they affect job seekers’ intention to apply for a job. The results from a controlled online experiment show that both types of ethical market signals increase job seekers’ job-pursuit intentions. These relationships are mediated by applicants’ attitude towards the job advertisement, their perceptions of corporate employment image and self-referencing. Consequently, the present study alerts practitioners to consider the effects of company-controlled and non-company-controlled ethical market signals, particularly when aiming to recruit highly-qualified millennial candidates. (shrink)
This paper presents a human-computer interaction model with a three layers learning mechanism in a pervasive environment. We begin with a discussion around a number of important issues related to human-computer interaction followed by a description of the architecture for a multi-agent cooperative design system for pervasive computing environment. We present our proposed three- layer HCI model and introduce the group formation algorithm, which is predicated on a dynamic sharing niche technology. Finally, we explore the cooperative reinforcement learning and fusion (...) algorithms; the paper closes with concluding observations and a summary of the principal work and contributions of this paper. (shrink)
A superheterodyne receiver is a type of device universally used in a variety of electronics and information systems. Fault detection and diagnosis for superheterodyne receivers are therefore of critical importance, especially in noise environments. A general purpose fault detection and diagnosis scheme based on observers and residual error analysis was proposed in this study. In the scheme, two generalized regression neural networks are utilized for fault detection, with one as an observer and the other as an adaptive threshold generator; faults (...) are detected by comparing the residual error and the threshold. Then, time and frequency domain features are extracted from the residual error for diagnosis. A probabilistic neural network acts as a classifier to realize the fault diagnosis. Finally, to mimic electromagnetic environments with noise interference, simulation model under different fault conditions with noise interferences is established to test the effectiveness and robustness of the proposed fault detection and diagnosis scheme. Results of the simulation experiments proved that the presented method is effective and robust in simulated electromagnetic environments. (shrink)
Objectives: The corona virus disease-2019 pandemic spread globally, and we aimed to investigate the psychosocial impact on healthcare workers in China during the pandemic.Methods: In this systematic review and meta-analysis, we searched seven electronic databases for cross-sectional studies on psychosocial impact on HWs in relation to COVID-19 from January 1, 2020 to October 7, 2020. We included primary studies involving Chinese HWs during the pandemic, and data were extracted from the published articles. Our primary outcome was prevalence of anxiety, depression, (...) and stress disorders. We pooled prevalence value with their 95% confidence interval using random effect models and assessed study quality on the basis of an 11-item checklist recommended by the Agency for Healthcare Research and Quality. The study protocol was registered in PROSPERO.Results: We identified 25 articles comprising a total of 30,841 completed questionnaires and 22 studies for meta-analysis. The prevalence of anxiety, depression, and stress disorders was 34.4%, 31.1%, and 29.1% for HWs. The pooled prevalence of anxiety disorders for HWs from late January to early February was 46.4%, significantly higher than those in mid-term February and after late February. The pooled prevalence of depression disorders for HWs from late January to early February was 46.5%, significantly higher than those in mid-term February and after late February. HWs working in Hubei Province had a higher prevalence of anxiety and a lower prevalence of depression than those working in other regions. Nurses had a higher prevalence of anxiety and depression than other HWs.Conclusions: About one-third of HWs in China suffered anxiety, depression, and stress at the early epidemic of COVID-19. HWs in Hubei Province, especially nurses, had a higher prevalence of psychological disorders. During the pandemic, a negative psychological state may persist in a proportion of Chinese HWs, fluctuating with the control of the pandemic. The long-term impact should continue to be observed. Attention should be paid to HWs for their psychological impact due to the pandemic.Systematic Review Registration: The study protocol was registered with PROSPERO. (shrink)
The rapid popularity of mobile shopping makes people’s lives more convenient, but it also makes it easier for customers to change providers. How to use marketing stimulus to retain customers has become an urgent concern for mobile sales companies. However, the theoretical researches in this field are not enough. For this reason, this study used the methods of literature review and structural equation to explore the effects of mobile marketing design factors on the continual intention of consumers in mobile shopping (...) by using the S-O-R model and its extended theories. The conclusions of the research showed that interface quality of mobile sales terminal and integrity of mobile sales terminal had significant positive impacts on consumption emotion; sales promotion in mobile sales terminal had a significant positive impacts on continual intention of mobile shopping; consumption emotion had a significant positive effect on continual intention of consumers in mobile shopping; consumption emotion played a significant mediating role in the relationship between interface quality of mobile sales terminal and continual intention of mobile shopping and between integrity of mobile sales terminal and continual intention of mobile shopping. The conclusions could not only enrich the theories of mobile shopping behavior but also provide guidance for companies to carry out mobile marketing activities and allocate marketing resources rationally. (shrink)
The anaerobic treatment process is a complicated multivariable system that is nonlinear and time varying. Moreover, biogas production rates are an important indicator for reflecting operational performance of the anaerobic treatment system. In this work, a novel model fuzzy wavelet neural network based on the genetic algorithm that combines the advantages of the genetic algorithm, fuzzy logic, neural network, and wavelet transform was established for prediction of effluent quality and biogas production rates in a full-scale anaerobic wastewater treatment process. Moreover, (...) the dataset was preprocessed via a self-adapted fuzzy c-means clustering before training the network and a hybrid algorithm for acquiring the optimal parameters of the multiscale GA-FWNN for improving the network precision. The analysis results indicate that the FWNN with the optimal algorithm had a high speed of convergence and good quality of prediction, and the FWNN model was more advantageous than the traditional intelligent coupling models in prediction accuracy and robustness. The determination coefficients R2 of the FWNN models for predicting both the effluent quality and biogas production rates were over 0.95. The proposed model can be used for analyzing both biogas production rates and effluent quality over the operational time period, which plays an important role in saving energy and eliminating pollutant discharge in the wastewater treatment system. (shrink)
For speaker tracking, integrating multimodal information from audio and video provides an effective and promising solution. The current challenges are focused on the construction of a stable observation model. To this end, we propose a 3D audio-visual speaker tracker assisted by deep metric learning on the two-layer particle filter framework. Firstly, the audio-guided motion model is applied to generate candidate samples in the hierarchical structure consisting of an audio layer and a visual layer. Then, a stable observation model is proposed (...) with a designed Siamese network, which provides the similarity-based likelihood to calculate particle weights. The speaker position is estimated using an optimal particle set, which integrates the decisions from audio particles and visual particles. Finally, the long short-term mechanism-based template update strategy is adopted to prevent drift during tracking. Experimental results demonstrate that the proposed method outperforms the single-modal trackers and comparison methods. Efficient and robust tracking is achieved both in 3D space and on image plane. (shrink)
This paper addresses the synchronization issue for the drive-response fractional-order memristor‐based neural networks via state feedback control. To achieve the synchronization for considered drive-response FOMNNs, two feedback controllers are introduced. Then, by adopting nonsmooth analysis, fractional Lyapunov’s direct method, Young inequality, and fractional-order differential inclusions, several algebraic sufficient criteria are obtained for guaranteeing the synchronization of the drive-response FOMNNs. Lastly, for illustrating the effectiveness of the obtained theoretical results, an example is given.
In this paper, taking both white noises and colored noises into consideration, a nonlinear stochastic SIRS epidemic model with regime switching is explored. The threshold parameter R s is found, and we investigate sufficient conditions for the existence of the ergodic stationary distribution of the positive solution. Finally, some numerical simulations are also carried out to demonstrate the analytical results.
The cheongsam, the typical national apparel of the internal and external harmonious unity, is known as the representative of the Chinese clothing culture. It has expressed the virtuous, elegant, and gentle temperament of the Chinese women through flowing melody, rakish picturesque conception, and strong poetic emotion. The paper studies several aspects of the origin, evolution, techniques and communication to let China and the world know better about cheongsam, the national apparel of China.
Knowing how to improve urban consumers’ well-being is of great importance for sustainable urban development and has become a research hotspot in the field of service marketing, which is evolving from functionality-focused view into experience-focused view. This study explored the mechanism and boundary conditions of experiential marketing on urban consumers’ well-being with a survey data collected from 256 consumers in the catering service industry in China. The results showed that experiential marketing had a significant positive impact on consumer well-being, experiential (...) value played a partial mediation role between experiential marketing and consumer well-being, value proposition engagement moderated the relationship between experiential value and consumer well-being, and value proposition engagement moderated the mediation role of experiential value between experiential marketing and consumer well-being. This study complements the literature of transformative service by revealing a complex mechanism relating to the effects of experiential marketing on urban consumers’ well-being and provides theoretical guidance for service enterprises to improve their offerings. (shrink)
Accurate measurement of coalbed methane content is the foundation for CBM resource exploration and development. Machine-learning techniques can help address CBM content prediction tasks. Due to the small amount of actual measurement data and the shallow model structure, however, the results from traditional machine-learning models have errors to some extent. We have developed a deep belief network -based model with the input as continuous real values and the activation function as the rectified linear unit. We first calculated a variety of (...) seismic attributes of the target coal seam to highlight the features of the coal seam, then we preprocessed the original attribute features, and finally developed the performance of the DBN model using the preprocessed features. We used 23,374 training data to train our model, 23,240 for pretraining, and 134 for fine-tuning. For the purpose of demonstrating the advantages of the DBN model, we compared it with two typical machine-learning models, including the multilayer perceptron model and the support vector regression model. These two models were trained based on the same labeled training data. The results, obtained from different models, indicated that the DBN model has the least error, which means that it is more accurate than the other two models when used to predict CBM content. (shrink)
Most binaural speech source localization models perform poorly in unprecedentedly noisy and reverberant situations. Here, this issue is approached by modelling a multiscale dilated convolutional neural network. The time-related crosscorrelation function and energy-related interaural level differences are preprocessed in separate branches of dilated convolutional network. The multiscale dilated CNN can encode discriminative representations for CCF and ILD, respectively. After encoding, the individual interaural representations are fused to map source direction. Furthermore, in order to improve the parameter adaptation, a novel semiadaptive (...) entropy is proposed to train the network under directional constraints. Experimental results show the proposed method can adaptively locate speech sources in simulated noisy and reverberant environments. (shrink)
Dealing with the fixed-time flocking issue is one of the most challenging problems for a Cucker–Smale-type self-propelled particle model. In this article, the fixed-time flocking is established by employing a fixed-time stability theorem when the communication weight function has a positive infimum. Compared with the initial condition-based finite-time stability, an upper bound of the settling time in this paper is merely dependent on the design parameters. Moreover, the size of the final flocking can be estimated by the number of particles (...) and the initial states of the system. In addition, a sufficient condition is formulated to guarantee that all particles do not collide during the process of the flocking. These results can give a reasonable explanation to some flocking phenomena such as bird flocks, fish schools, or human group behaviors. Finally, three numerical examples are granted to display the performance of the obtained results. (shrink)
With monitoring the acoustic emission phenomenon caused by rock deformation and failure, microseismic monitoring has been widely used in the development of unconventional oil and gas fields. Due to the complex environment and diversity types of noise, the signal energy of surface microseismic monitoring is weak, and the signal-to-noise ratio of raw data is very low. In the process of data processing, a lot of human resources needs to discriminate the first break picking because of the low SNR, and it (...) directly affects the error of microseismic event location. In the paper, we proposed the Regularization to Stein Unbiased Risk Estimation algorithm based on the Continuous Wavelet Transform to separate the signal from the noise in different decomposition levels. The regularization factor is adaptive change of different geology and fracturing engineering, which is related to shale brittleness, fracturing pressure and displacement. As a result, the threshold from R-SURE algorithm is multiresolution in different level, and the SNR could be improved effectively. In addition, we established the threshold discriminant for picking up first break wave of low signal-to-noise ratio data combined with Akaike Information Criterion and characteristic function, which compared the maximum absolute value in the time window. The method has good robustness and low computational complexity. The first arrival is automatically and accurately judged, which improves the accuracy of events location. We have successfully applied these methods to the surface microseismic monitoring of shale gas fracturing in several wells in southwest of China. The SNR of raw data has been improved, the Effective Stimulated Reservoir Volume and the performance of gas production are predicted with the results, which provides important technical support for shale gas development in the area. (shrink)
With monitoring of the acoustic emission phenomenon caused by rock deformation and failure, microseismic monitoring has been widely used in the development of unconventional oil and gas fields. Due to the complex environment and diversity types of the noise, the signal energy of surface microseismic monitoring is weak and the signal-to-noise ratio of raw data is very low. In the process of data processing, many human resources are needed to discriminate the first-break picking because of the low S/N, and this (...) directly affects the error of microseismic event location. We have adopted the regularization to Stein unbiased risk estimation algorithm based on the continuous wavelet transform to separate the signal from the noise in different decomposition levels. The regularization factor is the adaptive change of the different geology and fracturing engineering, which is related to shale brittleness, fracturing pressure, and displacement. As a result, the threshold from the R-SURE algorithm is multiresolution in different levels, and the S/N could be improved effectively. In addition, we established the threshold discriminant for picking up the first-break wave of low-S/N data combined with the Akaike information criterion and characteristic function, which compared the maximum absolute value in the time window. The method has good robustness and low computational complexity. The first arrival is automatically and accurately judged, which improves the accuracy of the event location. We successfully applied these methods to the surface microseismic monitoring of shale gas fracturing in several wells in southwest China. The S/N of the raw data has been improved, the effective stimulated reservoir volume and the performance of the gas production are predicted with the results, which provides important technical support for shale gas development in the area. (shrink)