This paper examines carbon dioxide (CO 2) emissions from the perspective of energy consumption, detailing an empirical investigation into the spatiotemporal variations and impact factors of energy-related CO 2 emissions in China. The study, which is based on a provincial panel data set for the period 1995-2011, used an extended STIRPAT model, which was in turn examined using System-Generalized Method of Moments (Sys-GMM) regression. Results indicate that while per capita CO 2 emissions in China were characterized by conspicuous regional imbalances during the period studied, regional inequality and spatial autocorrelation (agglomeration) both decreased gradually between 1995 and 2011, and the pattern evolutions of emissions evidenced a clear path dependency effect. The urbanization level was found to be the most important driving impact factor of CO 2 emissions, followed by economic level and industry proportion. Conversely, tertiary industry proportion constituted the main inhibiting factor among the negative influencing factors, which also included technology level, energy consumption structure, energy intensity, and tertiary industry proportion. Importantly, the study revealed that the CO 2 Kuznets Curve (CKC), which describes the relation between CO 2 emissions and economic growth, in fact took the form of N-shape in the medium-and long-term, rather than the classical inverted-U shape of the environmental Kuznets Curve (EKC). Specifically, an additional inflection appeared after the U-shape relationship between economic growth and CO 2 emissions, indicating the emergence of a relink phase between the two variables. The findings of this study have important implications for policy makers and urban planners: alongside steps to improve the technology level, accelerate the development of tertiary industry, and boost recycling and renewable energies, the optimization of a country's energy structure that can in fact reduce reliance on fossil energy resources and constitute an effective measure to reduce CO 2 emissions.

Solution-processed SnO2 colloidal quantum dots (CQDs) have emerged as an important new class of gas-sensing materials due to their potential for low-cost and high-throughput fabrication. Here we employed the design strategy based on the synergetic effect from highly sensitive SnO2 CQDs and excellent conductive properties of multiwalled carbon nanotubes (MWCNTs) to overcome the transport barrier in CQD gas sensors. The attachment and coverage of SnO2 CQDs on the MWCNT surfaces were achieved by simply mixing the presynthesized SnO2 CQDs and MWCNTs at room temperature. Compared to the pristine SnO2 CQDs, the sensor based on SnO2 quantum dot/MWCNT nanocomposites exhibited a higher response upon exposure to H2S, and the response toward 50 ppm of H2S at 70 °C was 108 with the response and recovery time being 23 and 44 s. Because of the favorable energy band alignment, the MWCNTs can serve as the acceptor of the electrons that are injected from H2S into SnO2 quantum dots in addition to the charge transport highway to direct the electron flow to the electrode, thereby enhancing the sensor response. Our research results open an easy pathway for developing highly sensitive and low-cost gas sensors.

Software-defined networking (SDN) achieves flexible and efficient network management by decoupling control plane from the data plane, where the controller with a global network view is responsible for planning routing for packets. However, the centralized design makes the controller become a potential bottleneck, and adversaries can exploit this vulnerability to launch distributed denial-of-service (DDoS) attacks to the controller. Existing solutions are fundamentally based forged traffic analysis, increasing computational cost and being prone to produce false positives. This paper proposes a safeguard scheme (SGS) for protecting control plane against DDoS attacks, and the main characteristic of SGS is deploying multicontroller in control plane through the controller's clustering. SGS procedures are organized in two modules: anomaly traffic detection and controller dynamic defense. Anomaly traffic detection focuses on switches in data plane to distinguish forged flows from legitimate ones by innovatively adopting four-tuple feature vector. Controller dynamic defense mitigates DDoS attacks' effects on control plane by remapping controller and sending the access control message to switches. The simulation results demonstrate the efficiency of our proposed SGS with real-time DDoS attack defense and high detection accuracy, as well as high-efficiency network resource utilization. INDEX TERMS Software-defined networking, multi-controller, DDoS, network security, anomaly traffic detection.

We proposed and experimental demonstrated all-optical two line-four line encoder and two bit-wise comparator of RZ data streams at 40Gb/s based on cross gain modulation (XGM) and four wave mixing (FWM) in three parallel SOAs. Five logic functions for digital encoder and comparator between two signals A and B: AB, AB, AB, AB and AOmicronB, were achieved simultaneously. The first three optical logics are realized based on XGM in SOAs, the fourth is realized with FWM, and the fifth is the mixing result of the first and the fourth. A detuning filter is employed to improve the output performance. The output extinction ratio (ER) for the XGM operation is above 10dB, and the ER for FWM operation is around 8 dB. Wide and clear eye patterns for the five logic outputs can be observed.

Identifying the microRNA (miRNA) expression level can provide critical information for early diagnosis of cancers or monitoring the cancer therapeutic efficacy. This paper focused on a kind of gold-nanoparticle-coated polystyrene microbeads (PS@Au microspheres)-based DNA probe as miRNA capture and duplex-specific nuclease (DSN) signal amplification platform based on an RGB value readout for detection of miRNAs. In virtue of the outstanding selectivity and simple experimental operation, 5'-fluorochrome-labeled molecular beacons (MBs) were immobilized on PS@Au microspheres via their 3'-thiol, in the wake of the fluorescence quenching by nanoparticle surface energy transfer (NSET). Target miRNAs were captured by the PS@Au microspheres-based DNA probe through DNA/RNA hybridization. DSN enzyme subsequently selectively cleaved the DNA to recycle the target miRNA and release of fluorophores, thereby triggering the signal amplification with more free fluorophores. The RGB value measurement enabled a detection limit of 50 fM, almost 4 orders of magnitude lower than PS@Au microspheres-based DNA probe detection without DSN. Meanwhile, by different encoding of dyes, miRNA-21 and miRNA-10b were simultaneously detected in the same sample. Considering the ability for quantitation, high sensitivity, and convenient merits, the PS@Au microspheres-based DNA probe and DSN signal amplification platform supplied valuable information for early diagnosis of cancers.

Carbon emissions research based on regional perspective is necessary and helpful for China to achieve its reduction targets. This research aims at analyzing the energy-related carbon emissions and finding out the most important driving forces for the carbon emissions increments in Guangdong province. LMDI (Logarithmic Mean Divisia Index) method based on the extended Kaya identity has been used to explore the main driving factors for energy-related carbon emissions in Guangdong province annually between 1990 and 2014. Research results show that the impacts and influences of various factors on carbon emissions are different in the different development stages. Economic growth effect and population size effect are the two most important driving factors for the increased carbon emissions. Energy intensity effect played the dominant role in curbing carbon emissions. Energy structure effect and technical progress effect had different but relatively minor effects on carbon emissions during the five different development stages.

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Laser speckle contrast imaging is a full-field imaging technique for measuring blood flow by mapping the speckle contrast with high spatial and temporal resolution. However, the statically scattered light from stationary tissues seriously degrades the accuracy of flow speed estimation. In this Letter, we present a simple calibration approach to calculate the proportions of dynamically scattered light and correct the effect of static scattering with single exposure time. Both the phantom and animal experimental results suggest that this calibration approach has the ability to improve the estimation of the relative blood flow in the presence of static scattering.