Contribution of leaf specular reflection to canopy reflectance under black soil case using stochastic radiative transfer model

Numerous canopy radiative transfer models have been proposed based on the assumption of “ideal bi-Lambertian leaves” with the aim of simplifying the interactions between photons and vegetation canopies. This assumption may cause discrepancy between the simulated and measured canopy bidirectional reflectance factor (BRF). Few studies have been devoted to evaluate the impacts of such assumption on simulation of canopy BRF at a high-to-medium spatial resolution (∼30 m). This paper focuses on quantifying the contribution of leaf specular reflection on the estimation of canopy BRF under a black soil case using one of the most efficient radiative transfer models, the stochastic radiative transfer model. Analyses of field and satellite data collected over the boreal Hyytiälä forest in Finland show that leaf specular reflection may lead to errors of up to 33.1% at 550 nm and 32.8% at 650 nm in terms of relative root mean square error. The results suggest that, in order to minimize these errors, leaf specular reflection should be accounted for in modeling BRF.This research was supported by the Fundamental Research Funds for the Central Universities under Grant No. 531107051063 and Guangxi Natural Science Foundation under Grant No. 2016JJD110017. We would like to thank Dr. Rautiainen Miina and Mottus Matti for sharing the field data and the USGS for making the EO-1 Hyperion hyperspectral data publically available. (531107051063 - Fundamental Research Funds for the Central Universities; 2016JJD110017 - Guangxi Natural Science Foundation)Accepted manuscrip

Supporting Transportation System Management and Operations Using Internet of Things Technology

Low power wide-area network (LPWAN) technology aims to provide long range and low power wireless communication. It can serve as an alternative technology for data transmissions in many application scenarios (e.g., parking monitoring and remote flood sensing). In order to explore its feasibility in transportation systems, this project conducted a review of relevant literature to understand the current status of LPWAN applications. An online survey that targeted professionals concerned with transportation was also developed to elicit input about their experiences in using LPWAN technology for their projects. The literature review and survey results showed that LPWAN’s application in the U.S. is still in an early stage. Many agencies were not familiar with LPWAN technology, and only a few off-the-shelf LPWAN products are currently available that may be directly used for transportation systems. To conceptually explore data transmission, a set of lab tests, using a primary LPWAN technology, namely LoRa, were performed on a university campus area as well as in a rural area. The lab tests showed that several key factors, such as the mounting heights of devices, distance between the gateway and sensor nodes, and brands of devices affected the LPWAN’s performance. Building upon these efforts, the research team proposed a high-level field test plan for facilitating a potential Phase 2 study that will address primary technical issues concerning the feasibility of transmitting data of different sizes, data transmission frequency, and transmission rate, deployment requirements, etc

Fusion of Landsat and Worldview Images

Pansharpened Landsat images have 15 m spatial resolution with 16-day revisit periods. On the other hand, Worldview images have 0.5 m resolution after pansharpening but the revisit times are uncertain. We present some preliminary results for a challenging image fusion problem that fuses Landsat and Worldview (WV) images to yield a high temporal resolution image sequence at the same spatial resolution of WV images. Since the spatial resolution between Landsat and Worldview is 30 to 1, our preliminary results are mixed in that the objective performance metrics such as peak signal-to-noise ratio (PSNR), correlation coefficient (CC), etc. sometimes showed good fusion performance, but at other times showed poor results. This indicates that more fusion research is still needed in this niche application

Tackling Diverse Minorities in Imbalanced Classification

Imbalanced datasets are commonly observed in various real-world applications, presenting significant challenges in training classifiers. When working with large datasets, the imbalanced issue can be further exacerbated, making it exceptionally difficult to train classifiers effectively. To address the problem, over-sampling techniques have been developed to linearly interpolating data instances between minorities and their neighbors. However, in many real-world scenarios such as anomaly detection, minority instances are often dispersed diversely in the feature space rather than clustered together. Inspired by domain-agnostic data mix-up, we propose generating synthetic samples iteratively by mixing data samples from both minority and majority classes. It is non-trivial to develop such a framework, the challenges include source sample selection, mix-up strategy selection, and the coordination between the underlying model and mix-up strategies. To tackle these challenges, we formulate the problem of iterative data mix-up as a Markov decision process (MDP) that maps data attributes onto an augmentation strategy. To solve the MDP, we employ an actor-critic framework to adapt the discrete-continuous decision space. This framework is utilized to train a data augmentation policy and design a reward signal that explores classifier uncertainty and encourages performance improvement, irrespective of the classifier's convergence. We demonstrate the effectiveness of our proposed framework through extensive experiments conducted on seven publicly available benchmark datasets using three different types of classifiers. The results of these experiments showcase the potential and promise of our framework in addressing imbalanced datasets with diverse minorities

A novel FCTF evaluation and prediction model for food efficacy based on association rule mining

IntroductionFood-components-target-function (FCTF) is an evaluation and prediction model based on association rule mining (ARM) and network interaction analysis, which is an innovative exploration of interdisciplinary integration in the food field.MethodsUsing the components as the basis, the targets and functions are comprehensively explored in various databases and platforms under the guidance of the ARM concept. The focused active components, key targets and preferred efficacy are then analyzed by different interaction calculations. The FCTF model is particularly suitable for preliminary studies of medicinal plants in remote and poor areas.ResultsThe FCTF model of the local medicinal food Laoxianghuang focuses on the efficacy of digestive system cancers and neurological diseases, with key targets ACE, PTGS2, CYP2C19 and corresponding active components citronellal, trans-nerolidol, linalool, geraniol, α-terpineol, cadinene and α-pinene.DiscussionCenturies of traditional experience point to the efficacy of Laoxianghuang in alleviating digestive disorders, and our established FCTF model of Laoxianghuang not only demonstrates this but also extends to its possible adjunctive efficacy in neurological diseases, which deserves later exploration. The FCTF model is based on the main line of components to target and efficacy and optimizes the research level from different dimensions and aspects of interaction analysis, hoping to make some contribution to the future development of the food discipline

A study on joint modeling and data augmentation of multi-modalities for audio-visual scene classification

In this paper, we propose two techniques, namely joint modeling and data augmentation, to improve system performances for audio-visual scene classification (AVSC). We employ pre-trained networks trained only on image data sets to extract video embedding; whereas for audio embedding models, we decide to train them from scratch. We explore different neural network architectures for joint modeling to effectively combine the video and audio modalities. Moreover, data augmentation strategies are investigated to increase audio-visual training set size. For the video modality the effectiveness of several operations in RandAugment is verified. An audio-video joint mixup scheme is proposed to further improve AVSC performances. Evaluated on the development set of TAU Urban Audio Visual Scenes 2021, our final system can achieve the best accuracy of 94.2% among all single AVSC systems submitted to DCASE 2021 Task 1b.Comment: 5 pages, 1 figure, submitted to INTERSPEECH 202

The FilZ protein contains a single PilZ domain and facilitates the swarming motility of pseudoalteromonas sp. SM9913

Swarming regulation is complicated in flagellated bacteria, especially those possessing dual flagellar systems. It remains unclear whether and how the movement of the constitutive polar flagellum is regulated during swarming motility of these bacteria. Here, we report the downregulation of polar flagellar motility by the c-di-GMP effector FilZ in the marine sedimentary bacterium Pseudoalteromonas sp. SM9913. Strain SM9913 possesses two flagellar systems, and filZ is located in the lateral flagellar gene cluster. The function of FilZ is negatively controlled by intracellular c-di-GMP. Swarming in strain SM9913 consists of three periods. Deletion and overexpression of filZ revealed that, during the period when strain SM9913 expands quickly, FilZ facilitates swarming. In vitro pull-down and bacterial two-hybrid assays suggested that, in the absence of c-di-GMP, FilZ interacts with the CheW homolog A2230, which may be involved in the chemotactic signal transduction pathway to the polar flagellar motor protein FliMp, to interfere with polar flagellar motility. When bound to c-di-GMP, FilZ loses its ability to interact with A2230. Bioinformatic investigation indicated that filZ-like genes are present in many bacteria with dual flagellar systems. Our findings demonstrate a novel mode of regulation of bacterial swarming motility