Agriculture applications rely on accurate land monitoring, especially paddy areas, for timely food security control and support actions. However, traditional monitoring requires field works or ...surveys performed by experts, which is costly, slow, and sparse. Agriculture monitoring systems are looking for sustainable land use monitoring solutions, starting with remote sensing on satellite data for cheap and timely paddy mapping. The aim of this study is to develop an autonomous and intelligent system built on top of imagery data streams, which is available from low-Earth orbiting satellites, to differentiate crop areas from non-crop areas. However, such agriculture mapping framework poses unique challenges for satellite image processing, including the seasonal nature of crop, the complexity of spectral channels, and adversarial conditions such as cloud and solar radiance. In this paper, we propose a novel multi-temporal high-spatial resolution classification method with an advanced spatio-temporal–spectral deep neural network to locate paddy fields at the pixel level for a whole year long and for each temporal instance. Our method is built and tested on the case study of Landsat 8 data due to its high spatial resolution. Empirical evaluations on real imagery datasets of different landscapes from 2016 to 2018 show the superior of our mapping model against the baselines with over 0.93 F1-score, the importance of each model design, the robustness against seasonal effects, and the visual mapping results.
•Our method leverages spatio-temporal–spectral information simultaneously.•Our agriculture mapping approach is adaptive to multiple temporal resolution.•Our system leverages the high spatial resolution of Landsat 8 satellite images.•Our model outperforms base methods with over 0.93 weighted F1-score.•Our performance is robust to multiple cropping types of paddy areas.
Cancer risk can be associated with exposure to polycyclic aromatic hydrocarbons (PAHs) in playground dust and soil. This study investigated the profiles and sources of PAHs from poured ...rubber-surfaced playground dust and uncovered playground surface soil, by applying an ex-situ equilibrium passive sampling technique. Surface dust and soil samples were collected from 15 different playgrounds in Seoul, Republic of Korea. The total 16 EPA PAHs concentrations in surface dust and soil varied from 198 to 919 μg kg−1 dw and 68–169 μg kg−1 dw, respectively. 4- to 6-ring PAHs were dominant, accounting for approximately 53.8%–94.5% of the total PAHs in surface dust and soil. The diagnostic ratios and principal component analysis suggested that a mixed coal combustion and vehicular emission was likely the main source of PAHs in the surface dust and soil. The higher total organic carbon content can explain the higher PAH accumulation and lower fugacities of PAHs. The fugacity comparison of phenanthrene and pyrene in dust, soil, air, and playground surface material indicated that atmospheric deposition is the main source of PAHs in the dust and soil on rubber-surfaced and uncovered surfaced playgrounds. This study contributes to the understanding of PAHs sources in dust and soil samples in children's playground and helps policymaker determine the right contamination sources for risk management.
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•Total concentration of Σ16 PAH in dust was approximately 5 times higher than soil.•Bioavailable fraction of PAHs was evaluated using passive samplers.•Fugacity analysis indicated that PAHs in dust/soil are mainly from atmospheric deposition.•Source apportionment suggests the main source of PAHs from petrogenic combustion.•PAHs in dust/soil showed weak correlations with TOC.
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Herein, the fast and simple formation of triangular silver nanoparticles (T-Ag NPs) was systematically studied by a chemical reduction process with the assistance of light ...irradiation. The effects of sodium borohydride (NaBH4) concentration, pH medium, and light source irradiation are investigated via the localized surface plasmon resonances (LSPRs) detection of silver nanoparticles (Ag NPs). Results indicated that pH and light source are the two primary factors for the morphology of Ag NPs while NaBH4 plays an important role to control the formation rate of T-Ag NPs. Moreover, the photoreduction time for synthesis of T-Ag NPs is 180 min, and this method can be easily synthesized in any laboratory.
Turn-taking has played an essential role in structuring the regulation of a conversation. The task of identifying the main speaker (who is properly taking his/her turn of speaking) and the ...interrupters (who are interrupting or reacting to the main speaker's utterances) remains a challenging task. Although some prior methods have partially addressed this task, there still remain some limitations. Firstly, a direct association of Audio and Visual features may limit the correlations to be extracted due to different modalities. Secondly, the relationship across temporal segments helping to maintain the consistency of localization, separation and conversation contexts is not effectively exploited. Finally, the interactions between speakers that usually contain the tracking and anticipatory decisions about transition to a new speaker is usually ignored. Therefore, this work introduces a new Audio-Visual Transformer approach to the problem of localization and highlighting the main speaker in both audio and visual channels of a multi-speaker conversation video in the wild. The proposed method exploits different types of correlations presented in both visual and audio signals. The temporal audio-visual relationships across spatial-temporal space are anticipated and optimized via the self-attention mechanism in a Transformer structure. Moreover, a newly collected dataset is introduced for the main speaker detection. To the best of our knowledge, it is one of the first studies that is able to automatically localize and highlight the main speaker in both visual and audio channels in multi-speaker conversation videos.
Photocatalysis has been studied and considered as a green and practical approach in addressing environmental pollution. However, factors that affect photocatalytic performance have not been ...systematically studied. In this work, we have presented a comprehensive roadmap for characterizing, interpreting, and reporting semiconductors’ electrical and optical properties through routinely used techniques such as diffuse reflectance spectroscopy, electrochemical techniques (Mott–Schottky plots), photoluminescence, X-ray photoelectron spectroscopy, and ultraviolet photoelectron spectroscopy in the context of photocatalysis. Having precisely studied the band structure of three representative photocatalysts, we have presented and highlighted the essential information and details, which are critical and beneficial for studies of (1) band alignments, (2) redox potentials, and (3) defects. Further works with a comprehensive understanding of the band structure are desirable and hold great promise.
Human exposure to microplastics contained in food has become a significant concern owing to the increasing accumulation of microplastics in the environment. In this paper, we summarize the presence ...of microplastics in food and the analytical methods used for isolation and identification of microplastics. Although a large number of studies on seafood such as fish and shellfish exist, estimating the overall human exposure to microplastics via food consumption is difficult owing to the lack of studies on other food items. Analytical methods still need to be optimized for appropriate recovery of microplastics in various food matrices, rendering a quantitative comparison of different studies challenging. In addition, microplastics could be added or removed from ingredients during processing or cooking. Thus, research on processed food is crucial to estimate the contribution of food to overall human microplastic consumption and to mitigate this exposure in the future.
NOx emissions cause many negative impacts on the living environment. The photocatalysis of semiconductors is superior for nitric oxide (NO) degradation due to their low redox potential. In this ...report, we combine SnO2-x/g-C3N4 heterojunction photocatalyst toward the high selectivity into green products under visible light illumination. Results show that SnO2-x/g-C3N4 heterojunction degraded 40.8% of NO, which is 1.6 times higher than that of g-C3N4. In addition, the selectivity coefficient of SnO2-x/g-C3N4 is higher 3 times than both pure SnO2-x and g-C3N4. Furthermore, SnO2-x/g-C3N4 expresses a superior stability for NO photocatalytic-degradation after five cycles. The scavenger trapping test results, and electron spin resonance (ESR) analysis also provide more understanding of the charge transfer mechanism of materials. SnO2-x/g-C3N4 heterojunction shows a high removal efficiency of NO gas, making it an up-and-coming environmental treatment candidate.
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•The first time applying SnO2-x/g-C3N4 heterojunction to towards the high selectivity into green products under visible light•Oxygen vacancy defects enhance photocatalytic performance of SnO2-x/g-C3N4 under visible light•SnO2-x/g-C3N4 expresses a superior stability for NO photocatalytic-degradation after five cycles•Understanding of the charge transfer mechanism into SnO2-x/g-C3N4
Due to the increase in video streaming traffic over the Internet, more innovative methods are in demand for improving both Quality of Experience (QoE) of users and Quality of Service (QoS) of ...providers. In recent years, HTTP Adaptive Streaming (HAS) has received significant attention from both industry and academia based on its impacts on the enhancement of media streaming services. However, HAS-alone cannot guarantee a seamless viewing experience, since this highly relies on the Network Operators’ infrastructure and evolving network conditions. Along with the development of future Internet infrastructure, Software-Defined Networking (SDN) has been researched and newly implemented as a promising solution in improving services of different Internet layers. In this paper, we present a novel architecture incorporating bitrate adaptation and dynamic route allocation. At the client side, adaptation logic of VBR videos streaming is built based on the MPEG-DASH standard. On the network side, an SDN controller is implemented with several routing strategies on top of the OpenFlow protocol. Our experimental results show that the proposed solution enhances at least 38% up to 185% in term of average bitrate in comparison with some existing solutions as well as achieves better viewing experience than the traditional Internet.
Targeted therapy with tyrosine kinase inhibitors (TKI) provides survival benefits to a majority of patients with non-small cell lung cancer (NSCLC). However, resistance to TKI almost always develops ...after treatment. Although genetic and epigenetic alterations have each been shown to drive resistance to TKI in cell line models, clinical evidence for their contribution in the acquisition of resistance remains limited. Here, we employed liquid biopsy for simultaneous analysis of genetic and epigenetic changes in 122 Vietnamese NSCLC patients undergoing TKI therapy and displaying acquired resistance. We detected multiple profiles of resistance mutations in 51 patients (41.8%). Of those, genetic alterations in EGFR, particularly EGFR amplification (n = 6), showed pronounced genome instability and genome-wide hypomethylation. Interestingly, the level of hypomethylation was associated with the duration of response to TKI treatment. We also detected hypermethylation in regulatory regions of Homeobox genes which are known to be involved in tumor differentiation. In contrast, such changes were not observed in cases with MET (n = 4) and HER2 (n = 4) amplification. Thus, our study showed that liquid biopsy could provide important insights into the heterogeneity of TKI resistance mechanisms in NSCLC patients, providing essential information for prediction of resistance and selection of subsequent treatment.
Deep learning methods predicated on convolutional neural networks and graph neural networks have enabled significant improvement in node classification and prediction when applied to graph ...representation with learning node embedding to effectively represent the hierarchical properties of graphs. An interesting approach (DiffPool) utilises a differentiable graph pooling technique which learns 'differentiable soft cluster assignment' for nodes at each layer of a deep graph neural network with nodes mapped on sets of clusters. However, effective control of the learning process is difficult given the inherent complexity in an 'end-to-end' model with the potential for a large number parameters (including the potential for redundant parameters). In this paper, we propose an approach termed FPool, which is a development of the basic method adopted in DiffPool (where pooling is applied directly to node representations). Techniques designed to enhance data classification have been created and evaluated using a number of popular and publicly available sensor datasets. Experimental results for FPool demonstrate improved classification and prediction performance when compared to alternative methods considered. Moreover, FPool shows a significant reduction in the training time over the basic DiffPool framework.
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