SRE-YOLOv8: An Improved UAV Object Detection Model Utilizing Swin Transformer and RE-FPN

To tackle the intricate challenges associated with the low detection accuracy of images taken by unmanned aerial vehicles (UAVs), arising from the diverse sizes and types of objects coupled with limited feature information, we present the SRE-YOLOv8 as an advanced method. Our method enhances the YOLOv8 object detection algorithm by leveraging the Swin Transformer and a lightweight residual feature pyramid network (RE-FPN) structure. Firstly, we introduce an optimized Swin Transformer module into the backbone network to preserve ample global contextual information during feature extraction and to extract a broader spectrum of features using self-attention mechanisms. Subsequently, we integrate a Residual Feature Augmentation (RFA) module and a lightweight attention mechanism named ECA, thereby transforming the original FPN structure to RE-FPN, intensifying the network\u27s emphasis on critical features. Additionally, an SOD (small object detection) layer is incorporated to enhance the network\u27s ability to recognize the spatial information of the model, thus augmenting accuracy in detecting small objects. Finally, we employ a Dynamic Head equipped with multiple attention mechanisms in the object detection head to enhance its performance in identifying low-resolution targets amidst complex backgrounds. Experimental evaluation conducted on the VisDrone2021 dataset reveals a significant advancement, showcasing an impressive 9.2% enhancement over the original YOLOv8 algorithm

EFWI\mathbf{\mathbb{E}^{FWI}}: Multi-parameter Benchmark Datasets for Elastic Full Waveform Inversion of Geophysical Properties

Elastic geophysical properties (such as P- and S-wave velocities) are of great importance to various subsurface applications like CO$_2$ sequestration and energy exploration (e.g., hydrogen and geothermal). Elastic full waveform inversion (FWI) is widely applied for characterizing reservoir properties. In this paper, we introduce $\mathbf{\mathbb{E}^{FWI}}$, a comprehensive benchmark dataset that is specifically designed for elastic FWI. $\mathbf{\mathbb{E}^{FWI}}$ encompasses 8 distinct datasets that cover diverse subsurface geologic structures (flat, curve, faults, etc). The benchmark results produced by three different deep learning methods are provided. In contrast to our previously presented dataset (pressure recordings) for acoustic FWI (referred to as OpenFWI), the seismic dataset in $\mathbf{\mathbb{E}^{FWI}}$ has both vertical and horizontal components. Moreover, the velocity maps in $\mathbf{\mathbb{E}^{FWI}}$ incorporate both P- and S-wave velocities. While the multicomponent data and the added S-wave velocity make the data more realistic, more challenges are introduced regarding the convergence and computational cost of the inversion. We conduct comprehensive numerical experiments to explore the relationship between P-wave and S-wave velocities in seismic data. The relation between P- and S-wave velocities provides crucial insights into the subsurface properties such as lithology, porosity, fluid content, etc. We anticipate that $\mathbf{\mathbb{E}^{FWI}}$ will facilitate future research on multiparameter inversions and stimulate endeavors in several critical research topics of carbon-zero and new energy exploration. All datasets, codes and relevant information can be accessed through our website at https://efwi-lanl.github.io/Comment: 20 pages, 11 figure

Pricing Currency Option Based on the Extension Principle and Defuzzification via Weighting Parameter Identification

We present a fuzzy version of the Garman-Kohlhagen (FG-K) formula for pricing European currency option based on the extension principle. In order to keep consistent with the real market, we assume that the interest rate, the spot exchange rate, and the volatility are fuzzy numbers in the FG-K formula. The conditions of a basic proposition about the fuzzy-valued functions of fuzzy subsets are modified. Based on the modified conditions and the extension principle, we prove that the fuzzy price obtained from the FG-K formula for European currency option is a fuzzy number. To simplify the trade, the weighted possibilistic mean (WPM) value with a weighting function is adopted to defuzzify the fuzzy price to a crisp price. The numerical example shows our method makes the α-level set of fuzzy price smaller, which decreases the fuzziness. The example also indicates that the WPM value has different approximation effects to real market price by taking different values of weighting parameter in the weighting function. Inspired by this example, we provide a method, which can identify the optimal parameter

Optimal Sizing and Pricing of Renewable Power to Ammonia Systems Considering the Limited Flexibility of Ammonia Synthesis

Converting renewable energy into ammonia has been recognized as a promising way to realize ``green hydrogen substitution" in the chemical industry. However, renewable power to ammonia (RePtA) requires an essential investment in facilities to provide a buffer against the strong volatility of renewable energy and the limited flexibility of ammonia synthesis, which involves the three main stakeholders, namely, power, hydrogen, and ammonia. Therefore, the sizing and pricing of RePtA play a core role in balancing the interest demands of investors. This paper proposes an optimal sizing and pricing method for RePtA system planning. First, power to ammonia (P2A) is modeled as a flexible load, especially considering the limited flexibility of ammonia synthesis, which has been verified using real dynamic regulation data. Second, the multi-investor economic (MIE) model is established considering both external and internal trading modes. Then, a two-stage decomposed sizing and pricing method is proposed to solve the problem caused by the strong coupling of planning, operation, and trading, and information gap decision theory (IGDT) method is utilized to handle the uncertainty of renewable generation. Finally, real data from a real-life system in Inner Mongolia are utilized to verify the proposed approach. The results show that the system proposed has a yield of 8.15%.Comment: 10 pages, 10 figure

Type-I-IFN-Stimulated Gene TRIM5γ Inhibits HBV Replication by Promoting HBx Degradation

To understand the molecular mechanisms that mediate the anti-hepatitis B virus (HBV) effect of interferon (IFN) therapy, we conduct highthroughput bimolecular fluorescence complementation screening to identify potential physical interactions between the HBx protein and 145 IFNstimulated genes (ISGs). Seven HBx-interacting ISGs have consistent and significant inhibitory effects on HBV replication, among which TRIM5g suppresses HBV replication by promoting K48-linked ubiquitination and degradation of the HBx protein on the K95 ubiquitin site. The B-Box domain of TRIM5g under overexpression conditions is sufficient to trigger HBx degradation and is responsible both for interacting with HBx and recruiting TRIM31, which is an ubiquitin ligase that triggers HBx ubiquitination. High expression levels of TRIM5g in IFN-a-treated HBV patients might indicate a better therapeutic effect. Thus, our studies identify a crucial role for TRIM5g and TRIM31 in promoting HBx degradation, which may facilitate the development of therapeutic agents for the treatment of patients with IFN-resistant HBV infection

Evaluating the risk of phosphorus loss with a distributed watershed model featuring zero-order mobilization and first-order delivery

Many semi-distributed models that simulate pollutants' losses from watersheds do not handle well detailed spatially distributed and temporal data with which to identify accurate and cost-effective strategies for controlling pollutants issuing from non-point sources. Such models commonly overlook the flow pathways of pollutants across the landscape. This work aims at closing such knowledge gap by developing a Spatially and Temporally Distributed Empirical model for Phosphorus Management (STEM-P) that simulates the daily phosphorus loss from source areas to receiving waters on a spatially-distributed grid-cell basis. STEM-P bypasses the use of complex mechanistic algorithms by representing the phosphorus mobilization and delivery processes with zero-order mobilization and first-order delivery, respectively. STEM-P was applied to a 217km2 watershed with mixed forest and agricultural land uses situated in southwestern China. The STEM-P simulation of phosphorus concentration at the watershed outlet approximated the observed data closely: the percent bias (Pbias) was -7.1%, with a Nash-Sutcliffe coefficient (ENS) of 0.80 on a monthly scale for the calibration period. The Pbias was 18.1%, with a monthly ENS equal to 0.72 for validation. The simulation results showed that 76% of the phosphorus load was transported with surface runoff, 25.2% of which came from 3.4% of the watershed area (classified as standard A critical source areas), and 55.3% of which originated from 17.1% of the watershed area (classified as standard B critical source areas). The standard A critical source areas were composed of 51% residences, 27% orchards, 18% dry fields, and 4% paddy fields. The standard B critical source areas were mainly paddy fields (81%). The calculated spatial and temporal patterns of phosphorus loss and recorded flow pathways identified with the STEM-P simulations revealed the field-scale critical source areas and guides the design and placement of effective practices for non-point source pollution control and water quality conservation

Comparative Proteomic Analysis Provides New Insights Into Low Nitrogen-Promoted Primary Root Growth in Hexaploid Wheat

Nitrogen deficient environments can promote wheat primary root growth (PRG) that allows for nitrogen uptake in deep soil. However, the mechanisms of low nitrogen-promoted root growth remain largely unknown. Here, an integrated comparative proteome study using iTRAQ analysis on the roots of two wheat varieties and their descendants with contrasting response to low nitrogen (LN) stress was performed under control (CK) and LN conditions. In total, 84 differentially abundant proteins (DAPs) specifically involved in the process of LN-promoted PRG were identified and 11 pathways were significantly enriched. The Glutathione metabolism, endocytosis, lipid metabolism, and phenylpropanoid biosynthesis pathways may play crucial roles in the regulation of LN-promoted PRG. We also identified 59 DAPs involved in the common response to LN stress in different genetic backgrounds. The common responsive DAPs to LN stress were mainly involved in nitrogen uptake, transportation and remobilization, and LN stress tolerance. Taken together, our results provide new insights into the metabolic and molecular changes taking place in contrasting varieties under LN conditions, which provide useful information for the genetic improvement of root traits and nitrogen use efficiency in wheat

Dnmt3a regulates emotional behavior and spine plasticity in the nucleus accumbens.

Despite abundant expression of DNA methyltransferases (Dnmts) in brain, the regulation and behavioral role of DNA methylation remain poorly understood. We found that Dnmt3a expression was regulated in mouse nucleus accumbens (NAc) by chronic cocaine use and chronic social defeat stress. Moreover, NAc-specific manipulations that block DNA methylation potentiated cocaine reward and exerted antidepressant-like effects, whereas NAc-specific Dnmt3a overexpression attenuated cocaine reward and was pro-depressant. On a cellular level, we found that chronic cocaine use selectively increased thin dendritic spines on NAc neurons and that DNA methylation was both necessary and sufficient to mediate these effects. These data establish the importance of Dnmt3a in the NAc in regulating cellular and behavioral plasticity to emotional stimuli