Anomalous thermoelectric transport of Dirac particles in graphene

We report a thermoelectric study of graphene in both zero and applied magnetic fields. As a direct consequence of the linear dispersion of massless particles, we find that the Seebeck coefficient Sxx diverges with 1 /, where n2D is the carrier density. We observe a very large Nernst signal Sxy (~ 50 uV/K at 8 T) at the Dirac point, and an oscillatory dependence of both Sxx and Sxy on n2D at low temperatures. Our results underscore the anomalous thermoelectric transport in graphene, which may be used as a highly sensitive probe for impurity bands near the Dirac point

Spatiotemporal subpixel mapping of time-series images

Land cover/land use (LCLU) information extraction from multitemporal sequences of remote sensing imagery is becoming increasingly important. Mixed pixels are a common problem in Landsat and MODIS images that are used widely for LCLU monitoring. Recently developed subpixel mapping (SPM) techniques can extract LCLU information at the subpixel level by dividing mixed pixels into subpixels to which hard classes are then allocated. However, SPM has rarely been studied for time-series images (TSIs). In this paper, a spatiotemporal SPM approach was proposed for SPM of TSIs. In contrast to conventional spatial dependence-based SPM methods, the proposed approach considers simultaneously spatial and temporal dependences, with the former considering the correlation of subpixel classes within each image and the latter considering the correlation of subpixel classes between images in a temporal sequence. The proposed approach was developed assuming the availability of one fine spatial resolution map which exists among the TSIs. The SPM of TSIs is formulated as a constrained optimization problem. Under the coherence constraint imposed by the coarse LCLU proportions, the objective is to maximize the spatiotemporal dependence, which is defined by blending both spatial and temporal dependences. Experiments on three data sets showed that the proposed approach can provide more accurate subpixel resolution TSIs than conventional SPM methods. The SPM results obtained from the TSIs provide an excellent opportunity for LCLU dynamic monitoring and change detection at a finer spatial resolution than the available coarse spatial resolution TSIs

Anomalous thermoelectric transport of Dirac particles in graphene

We report a thermoelectric study of graphene in both zero and applied magnetic fields. As a direct consequence of the linear dispersion of massless particles, we find that the Seebeck coefficient Sxx diverges with 1 /, where n2D is the carrier density. We observe a very large Nernst signal Sxy (~ 50 uV/K at 8 T) at the Dirac point, and an oscillatory dependence of both Sxx and Sxy on n2D at low temperatures. Our results underscore the anomalous thermoelectric transport in graphene, which may be used as a highly sensitive probe for impurity bands near the Dirac point

Prospective for urban informatics

The specialization of different urban sectors, theories, and technologies and their confluence in city development have led to a greatly accelerated growth in urban informatics, the transdisciplinary field for understanding and developing the city through new information technologies. While this young and highly promising field has attracted multiple reviews of its advances and outlook for its future, it would be instructive to probe further into the research initiatives of this rapidly evolving field, to provide reference to the development of not only urban informatics, but moreover the future of cities as a whole. This article thus presents a collection of research initiatives for urban informatics, based on the reviews of the state of the art in this field. The initiatives cover three levels, namely the future of urban science; core enabling technologies including geospatial artificial intelligence, high-definition mapping, quantum computing, artificial intelligence and the internet of things (AIoT), digital twins, explainable artificial intelligence, distributed machine learning, privacy-preserving deep learning, and applications in urban design and planning, transport, location-based services, and the metaverse, together with a discussion of algorithmic and data-driven approaches. The article concludes with hopes for the future development of urban informatics and focusses on the balance between our ever-increasing reliance on technology and important societal concerns

Enhancing spectral unmixing by considering the point spread function effect

The point spread function (PSF) effect exists ubiquitously in real remotely sensed data and such that the observed pixel signal is not only determined by the land cover within its own spatial coverage but also by that within neighboring pixels. The PSF, thus, imposes a fundamental limit on the amount of information captured in remotely sensed images and it introduces great uncertainty in the widely applied, inverse goal of spectral unmxing. Until now, spectral unmixing has erroneously been performed by assuming that the pixel signal is affected only by the land cover within the pixel, that is, ignoring the PSF. In this paper, a new method is proposed to account for the PSF effect within spectral unmxing to produce more accurate predictions of land cover proportions. Based on the mechanism of the PSF effect, the mathematical relation between the coarse proportion and sub-pixel proportions in a local window was deduced. Area-to-point kriging (ATPK) was then proposed to find a solution for the inverse prediction problem of estimating the sub-pixel proportions from the original coarse proportions. The sub-pixel proportions were finally upscaled using an ideal square wave response to produce the enhanced proportions. The effectiveness of the proposed method was demonstrated using two datasets. The proposed method has great potential for wide application since spectral unmixing is an extremely common approach in remote sensing

Solid lipid nanoparticle suspension enhanced the therapeutic efficacy of praziquantel against tapeworm

Hydatid disease caused by tapeworm is an increasing public health and socioeconomic concern. In order to enhance the therapeutic efficacy of praziquantel (PZQ) against tapeworm, PZQ-loaded hydrogenated castor oil solid lipid nanoparticle (PZQ-HCO-SLN) suspension was prepared by a hot homogenization and ultrasonication method. The stability of the suspension at 4°C and room temperature was evaluated by the physicochemical characteristics of the nanoparticles and in-vitro release pattern of the suspension. Pharmacokinetics was studied after subcutaneous administration of the suspension in dogs. The therapeutic effect of the novel formulation was evaluated in dogs naturally infected with Echinococcus granulosus. The results showed that the drug recovery of the suspension was 97.59% ± 7.56%. Nanoparticle diameter, polydispersivity index, and zeta potential were 263.00 ± 11.15 nm, 0.34 ± 0.06, and −11.57 ± 1.12 mV, respectively and showed no significant changes after 4 months of storage at both 4°C and room temperature. The stored suspensions displayed similar in-vitro release patterns as that of the newly prepared one. SLNs increased the bioavailability of PZQ 5.67-fold and extended the mean residence time of the drug from 56.71 to 280.38 hours. Single subcutaneous administration of PZQ-HCO-SLN suspension obtained enhanced therapeutic efficacy against tapeworm in infected dogs. At the dose of 5 mg/kg, the stool-ova reduction and negative conversion rates and tapeworm removal rate of the suspension were 100%, while the native PZQ were 91.55%, 87.5%, and 66.7%. When the dose reduced to 0.5 mg/kg, the native drug showed no effect, but the suspension still got the same therapeutic efficacy as that of the 5 mg/kg native PZQ. These results demonstrate that the PZQ-HCO-SLN suspension is a promising formulation to enhance the therapeutic efficacy of PZQ

Fusion of Sentinel-2 images

Sentinel-2 is a very new programme of the European Space Agency (ESA) that is designed for fine spatial resolution global monitoring. Sentinel-2 images provide four 10 m bands and six 20 m bands. To provide more explicit spatial information, this paper aims to downscale the six 20 m bands to 10 m spatial resolution using the four directly observed 10 m bands. The outcome of this fusion task is the production of 10 Sentinel-2 bands with 10 m spatial resolution. This new fusion problem involves four fine spatial resolution bands, which is different to, and more complex than, the common pan-sharpening fusion problem which involves only one fine band. To address this, we extend the existing two main families of image fusion approaches (i.e., component substitution, CS, and multiresolution analysis, MRA) with two different schemes, a band synthesis scheme and a band selection scheme. Moreover, the recently developed area-to-point regression kriging (ATPRK) approach was also developed and applied for the Sentinel-2 fusion task. Using two Sentinel-2 datasets released online, the three types of approaches (eight CS and MRA-based approaches, and ATPRK) were compared comprehensively in terms of their accuracies to provide recommendations for the task of fusion of Sentinel-2 images. The downscaled ten-band 10 m Sentinel-2 datasets represent important and promising products for a wide range of applications in remote sensing. They also have potential for blending with the upcoming Sentinel-3 data for fine spatio-temporal resolution monitoring at the global scale

Cooperative simultaneous inversion of satellite-based real-time PM2.5 and ozone levels using an improved deep learning model with attention mechanism

Ground-level fine particulate matter (PM2.5) and ozone (O3) are air pollutants that can pose severe health risks. Surface PM2.5 and O3 concentrations can be monitored from satellites, but most retrieval methods retrieve PM2.5 or O3 separately and disregard the shared information between the two air pollutants, for example due to common emission sources. Using surface observations across China spanning 2014–2021, we found a strong relationship between PM2.5 and O3 with distinct spatiotemporal characteristics. Thus, in this study, we propose a new deep learning model called the Simultaneous Ozone and PM2.5 inversion deep neural Network (SOPiNet), which allows for daily real-time monitoring and full coverage of PM2.5 and O3 simultaneously at a spatial resolution of 5 km. SOPiNet employs the multi-head attention mechanism to better capture the temporal variations in PM2.5 and O3 based on previous days’ conditions. Applying SOPiNet to MODIS data over China in 2022, using 2019–2021 to construct the network, we found that simultaneous retrievals of PM2.5 and O3 improved the performance compared with retrieving them independently: the temporal R2 increased from 0.66 to 0.72 for PM2.5, and from 0.79 to 0.82 for O3. The results suggest that near-real time satellite-based air quality monitoring can be improved by simultaneous retrieval of different but related pollutants. The codes of SOPiNet and its user guide are freely available online at https://github.com/RegiusQuant/ESIDLM

Suspension and Measurement of Graphene and Bi2Se3 Atomic Membranes

Coupling high quality, suspended atomic membranes to specialized electrodes enables investigation of many novel phenomena, such as spin or Cooper pair transport in these two dimensional systems. However, many electrode materials are not stable in acids that are used to dissolve underlying substrates. Here we present a versatile and powerful multi-level lithographical technique to suspend atomic membranes, which can be applied to the vast majority of substrate, membrane and electrode materials. Using this technique, we fabricated suspended graphene devices with Al electrodes and mobility of 5500 cm^2/Vs. We also demonstrate, for the first time, fabrication and measurement of a free-standing thin Bi2Se3 membrane, which has low contact resistance to electrodes and a mobility of >~500 cm^2/Vs