Explicit Haze & Cloud Removal for Global Land Cover Classification

Haze and clouds in Earth's atmosphere obstruct a seamless monitoring of our planet via optical satellites. Prior work shows that models can learn to adapt and perform remote sensing downstream tasks even in the presence of such sensor noise. So what are the auxiliary benefits of incorporating an explicit cloud removal task, and what is its relation to other tasks in the remote sensing pipeline? We address these questions and show that explicit cloud removal makes models for land cover classification furthermore robust to haze and clouds. Finally, we explore the relation to a self-supervised pre-text task (including abundant cloudy data) and demonstrate how to further ease the need for costly annotations on the land cover classification task

Dynamic prediction of traffic incident duration on urban expressways: a deep learning approach based on LSTM and MLP

Purpose – Efficient traffic incident management is needed to alleviate the negative impact of traffic incidents. Accurate and reliable estimation of traffic incident duration is of great importance for traffic incident management. Previous studies have proposed models for traffic incident duration prediction; however, most of these studies focus on the total duration and could not update prediction results in real-time. From a traveler’s perspective, the relevant factor is the residual duration of the impact of the traffic incident. Besides, few (if any) studies have used dynamic traffic flow parameters in the prediction models. This paper aims to propose a framework to fill these gaps. Design/methodology/approach – This paper proposes a framework based on the multi-layer perception (MLP) and long short-term memory (LSTM) model. The proposed methodology integrates traffic incident-related factors and real-time traffic flow parameters to predict the residual traffic incident duration. To validate the effectiveness of the framework, traffic incident data and traffic flow data from Shanghai Zhonghuan Expressway are used for modeling training and testing. Findings – Results show that the model with 30-min time window and taking both traffic volume and speed as inputs performed best. The area under the curve values exceed 0.85 and the prediction accuracies exceed 0.75. These indicators demonstrated that the model is appropriate for this study context. The model provides new insights into traffic incident duration prediction. Research limitations/implications – The incident samples applied by this study might not be enough and the variables are not abundant. The number of injuries and casualties, more detailed description of the incident location and other variables are expected to be used to characterize the traffic incident comprehensively. The framework needs to be further validated through a sufficiently large number of variables and locations. Practical implications – The framework can help reduce the impacts of incidents on the safety of efficiency of road traffic once implemented in intelligent transport system and traffic management systems in future practical applications. Originality/value – This study uses two artificial neural network methods, MLP and LSTM, to establish a framework aiming at providing accurate and time-efficient information on traffic incident duration in the future for transportation operators and travelers. This study will contribute to the deployment of emergency management and urban traffic navigation planning

Recognizing topological attributes and spatiotemporal patterns in spotted seals (Phoca largha) trophic networks based on eDNA metabarcoding

Spotted seals, a protected species, face multifaceted threats to their habitat, which in turn impact the closely associated trophic networks. These threats will lead to irreversible structural variations within the ecosystem. Therefore, investigating the topological variability of trophic networks in spotted seals is important. Applying environmental DNA methods, field sample collection was conducted in 2021 during both the sea fishing moratorium period and the fishing period to decode fish diversity. Assessing the current status of fish resources by using the multivariate statistics approach. Applying dietary information establishes the spotted seals’ trophic network. Selecting 12 network indexes to analyze the spatiotemporal patterns of network topological attributes. As a result, about 51 families, and 76 genera species were identified. During the sea fishing moratorium and the fishing period, there are 12 and 18 different food resources available for spotted seals, respectively. The diversity index revealed that the FP had greater species richness and diversity than the SP. Comparatively, the Fishing period exhibited higher species richness and biodiversity, likely influenced by habitat heterogeneity and anthropogenic activities. Additionally, the topological features of networks reflected the high clustering coefficients (CC=0.35) and the proportion of omnivorous species (O≈60%), indicating that the network structure in this region tends to form higher trophic-level clustering patterns, which facilitate the formation of weaker interactions between clusters, enhancing the robustness of the network. The higher connectivity complexity index during the fishing period (SC=12.3) supported that the spotted seal’s trophic network was relatively more stable in this period. Thus, during the fishing period, it is crucial to pay more attention to the intensity of human fishing on mid-to-high trophic-level omnivorous fish resources to ensure the sustainability of these potential food resources for spotted seals. This comprehensive study achieved three key objectives: (a) utilizing eDNA to characterize fish diversity during distinct periods, (b) establishing trophic networks of spotted seals, and (c) discerning topological attributes and spatiotemporal patterns within the ecological network. Overall, this study can provide technical and data support for integrated ecological network management and propose suggestions for protecting and recovering spotted seals

Determination of Malachite Green in Aquaculture Water by Adsorptive Stripping Voltammetry

© 2016, Copyright © Taylor & Francis Group, LLC. An adsorptive stripping voltammetric method for the determination of malachite green in aquaculture water has been developed. Initial studies were made using the cyclic voltammetry of malachite green at a glassy carbon electrode in 0.1M phosphate buffer from pH 2 to 10. The redox behavior observed for malachite green was verified by the characterization of malachite green and its reduction product, leucomalachite green. Furthermore, leucomalachite green was found not to interfere with the determination of malachite green at pH 7.4, the optimum pH for malachite green determination. As a result, further studies were performed using adsorptive stripping voltammetry for the determination of malachite green in aquaculture water. The voltammetric waveform, accumulation potential, and accumulation time were optimized. The calibration plot was linear from 0.2µM to 1.2µM for malachite green using differential pulse voltammetry with a sensitivity of 0.8311µA/µM. Using the method of multiple standard addition, aquaculture water fortified with 0.5µM and 0.75µM malachite green provided mean recoveries of 78.79% and 87.20% with coefficients of variation of 2.07% and 1.45%. Therefore, analytical figures of merit suggest that this method provides rapid, simple, economical, and precise determination of malachite green in aquaculture water