Carbon Nanotube Fibers Prepared by Activating Deactivated Iron Particles in Floating Catalyst Chemical Vapor Deposition Tail Gas

Catalysts can determine the structure and properties of carbon nanotube (CNT) fibers fabricated using the floating catalyst chemical vapor deposition (FCCVD) method. The tail gas left over when CNT fibers are fabricated by the FCCVD method has been proven to contain deactivated iron nanoparticles, as well as carbide gas and hydrogen. This study demonstrates that the deactivated iron nanoparticles in tail gas can be successfully activated in a double furnace system under certain conditions. CNT fibers can be successfully prepared using the activated iron nanoparticles by adding the precursor without the catalyst. These CNT fibers are composed of double-walled carbon nanotubes (DWNTs) and have low density, high strength, and electrical conductivity

Performance Analysis of Integrated Sensing and Communications Under Gain-Phase Imperfections

This paper evaluates the performance of uplink integrated sensing and communication systems in the presence of gain and phase imperfections. Specifically, we consider multiple unmanned aerial vehicles (UAVs) transmitting data to a multiple-input-multiple-output base-station (BS) that is responsible for estimating the transmitted information in addition to localising the transmitting UAVs. The signal processing at the BS is divided into two consecutive stages: localisation and communication. A maximum likelihood (ML) algorithm is introduced for the localisation stage to jointly estimate the azimuth-elevation angles and Doppler frequency of the UAVs under gain-phase defects, which are then compared to the estimation of signal parameters via rotational invariance techniques (ESPRIT) and multiple signal classification (MUSIC). Furthermore, the Cramer-Rao lower bound (CRLB) is derived to evaluate the asymptotic performance and quantify the influence of the gain-phase imperfections which are modelled using Rician and von Mises distributions, respectively. Thereafter, in the communication stage, the location parameters estimated in the first stage are employed to estimate the communication channels which are fed into a maximum ratio combiner to preprocess the received communication signal. An accurate closed-form approximation of the achievable average sum data rate (SDR) for all UAVs is derived. The obtained results show that gain-phase imperfections have a significant influence on both localisation and communication, however, the proposed ML is less sensitive when compared to other algorithms. The derived analysis is concurred with simulations.Comment: 38 pages, 7 figure

Design, Modelling, and Control of a Reconfigurable Rotary Series Elastic Actuator with Nonlinear Stiffness for Assistive Robots

In assistive robots, compliant actuator is a key component in establishing safe and satisfactory physical human-robot interaction (pHRI). The performance of compliant actuators largely depends on the stiffness of the elastic element. Generally, low stiffness is desirable to achieve low impedance, high fidelity of force control and safe pHRI, while high stiffness is required to ensure sufficient force bandwidth and output force. These requirements, however, are contradictory and often vary according to different tasks and conditions. In order to address the contradiction of stiffness selection and improve adaptability to different applications, we develop a reconfigurable rotary series elastic actuator with nonlinear stiffness (RRSEAns) for assistive robots. In this paper, an accurate model of the reconfigurable rotary series elastic element (RSEE) is presented and the adjusting principles are investigated, followed by detailed analysis and experimental validation. The RRSEAns can provide a wide range of stiffness from 0.095 Nm/deg to 2.33 Nm/deg, and different stiffness profiles can be yielded with respect to different configuration of the reconfigurable RSEE. The overall performance of the RRSEAns is verified by experiments on frequency response, torque control and pHRI, which is adequate for most applications in assistive robots. Specifically, the root-mean-square (RMS) error of the interaction torque results as low as 0.07 Nm in transparent/human-in-charge mode, demonstrating the advantages of the RRSEAns in pHRI

Functional and effective connectivity analysis of drug-resistant epilepsy: a resting-state fMRI analysis

ObjectiveEpilepsy is considered as a neural network disorder. Seizure activity in epilepsy may disturb brain networks and damage brain functions. We propose using resting-state functional magnetic resonance imaging (rs-fMRI) data to characterize connectivity patterns in drug-resistant epilepsy.MethodsThis study enrolled 47 participants, including 28 with drug-resistant epilepsy and 19 healthy controls. Functional and effective connectivity was employed to assess drug-resistant epilepsy patients within resting state networks. The resting state functional connectivity (FC) analysis was performed to assess connectivity between each patient and healthy controls within the default mode network (DMN) and the dorsal attention network (DAN). In addition, dynamic causal modeling was used to compute effective connectivity (EC). Finally, a statistical analysis was performed to evaluate our findings.ResultsThe FC analysis revealed significant connectivity changes in patients giving 64.3% (18/28) and 78.6% (22/28) for DMN and DAN, respectively. Statistical analysis of FC was significant between the medial prefrontal cortex, posterior cingulate cortex, and bilateral inferior parietal cortex for DMN. For DAN, it was significant between the left and the right intraparietal sulcus and the frontal eye field. For the DMN, the patient group showed significant EC connectivity in the right inferior parietal cortex and the medial prefrontal cortex for the DMN. There was also bilateral connectivity between the medial prefrontal cortex and the posterior cingulate cortex, as well as between the left and right inferior parietal cortex. For DAN, patients showed significant connectivity in the right frontal eye field and the right intraparietal sulcus. Bilateral connectivity was also found between the left frontal eye field and the left intraparietal sulcus, as well as between the right frontal eye field and the right intraparietal sulcus. The statistical analysis of the EC revealed a significant result in the medial prefrontal cortex and the right intraparietal cortex for the DMN. The DAN was found significant in the left frontal eye field, as well as the left and right intraparietal sulcus.ConclusionOur results provide preliminary evidence to support that the combination of functional and effective connectivity analysis of rs-fMRI can aid in diagnosing epilepsy in the DMN and DAN networks

Does a Polycentric Spatial Structure Help to Reduce Industry Emissions?

City planners are increasingly drawn to ways of transforming urban spatial structure as an important strategy for reducing pollutant emissions. As its main contribution, this paper uses firm-level emissions data to quantify impact mechanisms related to factor flow, firm size, and division of labour. We examine the effects of spatial polycentricity on firm-level industrial emissions, using a pooled cross-sectional model, based on emissions data from individual firms in China. We show that, all else being equal, polycentric spatial structures help to reduce the emissions of industrial firms. This finding is not affected by index measures, changes in industrial structure, or city-sample selection. A mechanism analysis shows that polycentric structures not only enhance the emission-reduction effects of factor flow and firm size, but also reduce firm-level emissions by strengthening the urban division of labour. Our findings support the emission-reduction performance of polycentric spatial structures, promoting the integration of city planning and industrial policies that jointly contribute to reducing firm-level emissions and preventing and controlling air pollution

Impact of Population Density on PM_2.5 Concentrations: A Case Study in Shanghai, China

We examine the effects of the urban built environment on PM2.5 (fine particulate matter with diameters equal or smaller than 2.5 μm) concentrations by using an improved region-wide database, a spatial econometric model, and five built environment attributes: Density, design, diversity, distance to transit, and destination accessibility (the 5Ds). Our study uses Shanghai as a relevant case study and focuses on the role of density at the jiedao scale, the smallest administrative unit in China. The results suggest that population density is positively associated with PM2.5 concentrations, pointing to pollution centralization and congestion effects dominating the mitigating effects of mode-shifting associated with density. Other built environment variables, such as the proportion of road intersections, degree of mixed land use, and density of bus stops, are all positively associated with PM2.5 concentrations while distance to nearest primary or sub-center is negatively associated. Regional heterogeneity shows that suburban jiedao have lower PM2.5 concentrations when a subway station is present

How Can a Firm Innovate When Embedded in a Cluster?—Evidence from the Automobile Industrial Cluster in China

In the era of the knowledge economy, knowledge management is increasingly important. Knowledge management ability is one of the core factors influencing enterprise competitiveness, affecting innovation performance and sustainable development. To test the impact mechanism of the knowledge management of enterprises on innovation performance, a multilevel structural equation model was established using data from the automobile industry in China, with “knowledge management„ (KM) as the independent variable, the three dimensions of absorptive capacity as the mediating variables, and “innovation performance„ (IP) as the dependent variable at the firm level. At the cluster level, the innovation milieu of the cluster was introduced into the model. The results show that the three dimensions of absorptive capacity all significantly mediate the relationship between knowledge management and innovation performance. The innovation milieu of the cluster had a direct cross-level effect on the innovation performance of enterprises and a positive cross-level moderated effect on the relationship between explorative learning and innovation performance. These results support the promotion of enterprise innovation ability and the creation of an innovation milieu in the automobile industry in China

Does Urban Agglomeration Discourage Entrepreneurship in China? Micro-Empirical Evidence from China

As the net effect of agglomeration on entrepreneurship depends on the trade-off between positive and negative effects, urban agglomeration can either promote or discourage entrepreneurial activity in theory. However, there is an unexpected shortage of empirical confirmations on this potential cause-and-effect relationship. Our study strives to fill this empirical gap by providing credible evidence whether agglomeration, measured by the urban density or population, increases the probability of individuals being self-employed. Based on the China Labor-Force Dynamic Survey of 2012, 2014, and 2016, we find that big cities fail to facilitate individuals to start or run their own businesses. Further analyses illustrate that the entrepreneurs in large cities can be easily tempted by a wider range of salaried opportunities and are generally exposed to high fixed costs and intense competition. In contrast, entrepreneurship in large cities is of high reward. These results serve as direct evidence of the co-existence of agglomeration diseconomies and economies. This also suggests the direction of government policy in large cities, which is to alleviate, as much as possible, the negative impact on entrepreneurs

Construction of “double random one public” information system for coal mine safety supervision

In order to improve equity and efficiency of the existing coal mine safety supervision system, this paper applies “double random one public” mechanism to coal mine safety supervision system. This study establishes four logical architecture platform which contents the basic data layer, application layer, presentation layer and access layer for the coal mine safety supervision, which improves the function architecture of the expert management, coal mine management, key supervision projects selection and information publication, and proposes three core mechanisms of system operation including the key content of supervision, the problem tracking and expert rating. The results show that the “double random one public” information system of coal mine safety supervision can realize the “double blind” of safety inspection, the dynamic adjustment of supervision experts and the pertinence of the content of coal mine safety inspection, which provides a reference for the promotion and application of the “double random one public” system of coal mine safety supervision