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As a variant of Convolutional Neural Networks (CNNs) in Deep Learning, the Fully Convolutional Network (FCN) model achieved state-of-the-art performance for natural image semantic segmentation. In this paper, an accurate classification approach for high resolution remote sensing imagery based on the improved FCN model is proposed. Firstly, we improve the density of output class maps by introducing Atrous convolution, and secondly, we design a multi-scale network architecture by adding a skip-layer structure to make it capable for multi-resolution image classification. Finally, we further refine the output class map using Conditional Random Fields (CRFs) post-processing. Our classification model is trained on 70 GF-2 true color images, and tested on the other 4 GF-2 images and 3 IKONOS true color images. We also employ object-oriented classification, patch-based CNN classification, and the FCN-8s approach on the same images for comparison. The experiments show that compared with the existing approaches, our approach has an obvious improvement in accuracy. The average precision, recall, and Kappa coefficient of our approach are 0.81, 0.78, and 0.83, respectively. The experiments also prove that our approach has strong applicability for multi-resolution image classification.

This paper reviews microwave sensors and their applications in permittivity measurement. The detection, diagnosis, classification, and monitoring without contact and invasion have been the subject of numerous studies based on permittivity characteristics tracking. This review illustrates many new types of research in recent years. Firstly, the application background is briefly introduced, and several main measurement methods are presented. An overview of measurement technology in various applications is compiled and summarized based on numerous typical examples. Exciting applications are compared and presented separately, combining resonator sensors with strong electric fields. Furthermore, differential signals represent trends for future applications with strong environmental immunity, an alternative option to expensive measuring equipment. With the alternation of metamaterials, microfluidics technologies, cross-technology, algorithms, and so on, sensors play an exceptionally prominent role in practical and low-cost applications.

This article employs a plate with V-shape ribs inside a tube as turbulator to augment the heat transfer rate. The utilized vortex generators are double-winglets arranged in a V-shape placed on both sides of the plate. The proposed system’s suggested working fluids are water-based hybrid nanofluids, including Al 2 O 3 –Cu/water, Cu–CuO/water, and Cu–TiO 2 /water. This work involves a numerical evaluation of the effects of the type and volume concentration of the examined hybrid nanofluids on the enhancement of heat transfer. The experimental results are used to validate the numerical model. It is worth mentioning that all the obtained numerical results are compared with the simple tube, without any turbulator (vortex generator) and in the presence of water instead of the hybrid nanofluids. Based on the numerical results, it can be concluded that all employed hybrid nanofluids showed improved thermal performance compared to pure water. Furthermore, the differences between the models are more substantial for higher Reynolds numbers than for lower Reynolds numbers. In Re = 30,000, the Cu–TiO 2 /water exhibits the lowest thermal performance improvement (augmentation of about 0.3%), while the Cu–CuO/water at Re = 50,000 exhibits the largest thermal performance improvement (augmentation of approximately 5.7%), in the case of ∅ 1  = ∅ 2  = 0.5%. For ∅ 1  = ∅ 2  = 1%, the Cu–TiO 2 /water at Re = 30,000 has the lowest thermal performance improvement (augmentation of around 1.1%), while the Cu–CuO/water at Re = 50,000 has the most thermal performance improvement (augmentation of roughly 8.7%). According to the augmentation of around 2.8% at Re = 30,000 for Cu–TiO 2 /water and approximately 10.8% at Re = 50,000 for Cu–CuO/water, the thermal performance increase in the scenario of ∅ 1  = ∅ 2  = 1.5% is the lowest. In Conclusion, the Cu–CuO/water hybrid nanofluid with a volume concentration of ∅ 1  = ∅ 2  = 1.5% has the greatest thermal performance value of all the hybrid nanofluids studied.

In industrial production, high-power microwaves are commonly used for heating and drying processes; however, their application in measurement is relatively limited. This paper presents a power measurement system to enhance the use of microwave measurements in industry and improve the efficiency of microwave drying for PET particles. Operating at 2.45 GHz, the system integrates four-port power measurements based on the multilayer perceptron (MLP). By introducing residual connectivity, the residual network is determined to detect the height of PET particles. Experimental results show that this system can perform rapid measurements without needing a vector network analyzer (VNA), significantly improving the efficiency of microwave energy utilization in the early drying stages. Furthermore, the system offers practical and cost-efficient predictions for low-loss particulate materials. This power measurement strategy holds promising application potential in future industrial production.

In this paper, an oblique aperture ridge waveguide operating at 2450 MHz is proposed, and, using the ridge waveguide, a permittivity measurement system is constructed which can measure the permittivity of materials during microwave heating. The system calculates the amplitudes of the scattering parameters by using the forward, reflected and transmitted powers of the power meters, and it reconstructs the permittivity of the material by combining the scattering parameters with an artificial neural network. The system is used to measure the complex permittivity of mixed solutions of methanol and ethanol with different ratios at room temperature, and the permittivity of methanol and ethanol with increasing temperature, from room temperature to 50 °C. The measured results are in good agreement with the reference data. The system allows simultaneous measurement of the permittivity with microwave heating and provides real-time, rapid changes in the permittivity during heating, avoiding thermal runaway and providing a reference for applications of microwave energy in the chemical industry.

The cellular DNA sensor cGMP-AMP synthase (cGAS) detects cytosolic viral DNA via the stimulator of interferon genes (STING) to initiate innate antiviral response. Herpesviruses are known to target key immune signaling pathways to persist in an immune-competent host. Marek's disease virus (MDV), a highly pathogenic and oncogenic herpesvirus of chickens, can antagonize host innate immune responses to achieve persistent infection. With a functional screen, we identified five MDV proteins that blocked beta interferon (IFN-β) induction downstream of the cGAS-STING pathway. Specifically, the MDV major oncoprotein Meq impeded the recruitment of TANK-binding kinase 1 and IFN regulatory factor 7 (IRF7) to the STING complex, thereby inhibiting IRF7 activation and IFN-β induction. Meq overexpression markedly reduced antiviral responses stimulated by cytosolic DNA, whereas knockdown of Meq heightened MDV-triggered induction of IFN-β and downstream antiviral genes. Moreover, Meq-deficient MDV induced more IFN-β production than wild-type MDV. Meq-deficient MDV also triggered a more robust CD8+ T cell response than wild-type MDV. As such, the Meq-deficient MDV was highly attenuated in replication and lymphoma induction compared to wild-type MDV. Taken together, these results revealed that MDV evades the cGAS-STING DNA sensing pathway, which underpins the efficient replication and oncogenesis. These findings improve our understanding of the virus-host interaction in MDV-induced lymphoma and may contribute to the development of novel vaccines against MDV infection.

Spatiotemporal heterogeneities in climatic, physiographic, and socio‐economic environments cause complex and varied formation mechanisms in flash flood disasters. However, previous studies were usually conducted at event or catchment scale in specific environments. Investigation on disaster formation mechanisms in climatic, physiographic, and socio‐economic environments with different combinations and quantities at large scale is not available, which further affects the decision‐making of mitigation measures. Our study develops a type‐based analytical framework of flash flood disasters and their causes from disaster‐prone environments using ten‐fold multivariate analysis including cluster analysis, analysis of similarities, and ordination analysis. Application of this framework to environment factors and losses of 37,332 disaster events across China revealed three disaster‐prone environment types, contributing 55.5% ± 0.3%, 55.9% ± 0.3%, and 50.9% ± 0.2% to variations in disaster attributes, respectively. The events with low disaster intensities (24.6%) in undeveloped northwestern China were governed by short rainfall, low retention capacity, and low prevention investments, and their mitigation focused on afforestation and construction of rainfall and flash flood monitoring systems. Those with high disaster intensities (38.5%) in developed and disturbed central and southeastern China were interpreted by frequent intense rainfall and good flood prevention infrastructures, and their mitigation prioritized development of flash flood forecasting warning models, and grain for green, etc. Those with intermediate disaster intensities (36.9%) in undeveloped southwestern and central China were shaped by frequent short intense rainfall and steep rivers, and their mitigation required satellites or radars in alpine regions, multi‐disaster prevention technology development, and dam construction. Plain Language Summary Flash flood disasters are one of the most dangerous natural disasters, and their formation mechanisms are influenced by the spatio‐temporal heterogeneities of climatic, physiographic, and socio‐economic environments. We develop a type‐based analytical framework of flash flood disasters and their causes from disaster‐prone environments. The framework and its robustness are applied and examined across China using massive flash flood disaster events and their environment factors. We discern three flash flood disaster‐prone environment types where 24.6%, 38.5%, and 36.9% of total flash flood disaster events have occurred during 1949–2019. We determine main causal factors and their contributions to shaping the attribute variability of flash flood disaster events for individual environment types, and further propose type‐specific measures to mitigate the occurrence and damage of flash flood disasters. This study provides a new insight for understanding flash flood disaster formation mechanisms, and provides supports for developing effective disaster management strategies. Key Points A type‐based analytical framework of flash flood disasters and their causes is proposed from disaster‐prone environments Three disaster formation types are derived through mining disaster‐prone environment factors and losses of 37,332 disaster events in China The variabilities of 50%∼56% in flash flood disasters are explained by the combinations of climate, physiography, and socio‐economy

Infectious bursal disease virus (IBDV), a double-stranded RNA virus, causes immunosuppression and high mortality in 3–6-week-old chickens. Innate immune defense is a physical barrier to restrict viral replication. After viral infection, the host shows crucial defense responses, such as stimulation of antiviral effectors to restrict viral replication. Here, we conducted RNA-seq in avian cells infected by IBDV and identified TRIM25 as a host restriction factor. Specifically, TRIM25 deficiency dramatically increased viral yields, whereas overexpression of TRIM25 significantly inhibited IBDV replication. Immunoprecipitation assays indicated that TRIM25 only interacted with VP3 among all viral proteins, mediating its K27-linked polyubiquitination and subsequent proteasomal degradation. Moreover, the Lys854 residue of VP3 was identified as the key target site for the ubiquitination catalyzed by TRIM25. The ubiquitination site destroyed enhanced the replication ability of IBDV in vitro and in vivo . These findings demonstrated that TRIM25 inhibited IBDV replication by specifically ubiquitinating and degrading the structural protein VP3.

Background Marek’s disease virus (MDV) is an oncogenic herpesvirus that can cause T-cell lymphomas in chicken. Long noncoding RNA (lncRNA) is strongly associated with various cancers and many other diseases. In chickens, lncRNAs have not been comprehensively identified. Here, we profiled mRNA and lncRNA repertoires in three groups of spleens from MDV-infected and non-infected chickens, including seven tumorous spleens (TS) from MDV-infected chickens, five spleens from the survivors (SS) without lesions after MDV infection, and five spleens from noninfected chickens (NS), to explore the underlying mechanism of host resistance in Marek’s disease (MD). Results By using a precise lncRNA identification pipeline, we identified 1315 putative lncRNAs and 1166 known lncRNAs in spleen tissue. Genomic features of putative lncRNAs were characterized. Differentially expressed (DE) mRNAs, putative lncRNAs, and known lncRNAs were profiled among three groups. We found that several specific intergroup differentially expressed genes were involved in important biological processes and pathways, including B cell activation and the Wnt signaling pathway; some of these genes were also found to be the hub genes in the co-expression network analyzed by WGCNA. Network analysis depicted both intergenic correlation and correlation between genes and MD traits. Five DE lncRNAs including MSTRG.360.1, MSTRG.6725.1, MSTRG.6754.1, MSTRG.15539.1, and MSTRG.7747.5 strongly correlated with MD-resistant candidate genes, such as IGF-I, CTLA4, HDAC9, SWAP70, CD72, JCHAIN, CXCL12, and CD8B, suggesting that lncRNAs may affect MD resistance and tumorigenesis in chicken spleens through their target genes. Conclusions Our results provide both transcriptomic and epigenetic insights on MD resistance and its pathological mechanism. The comprehensive lncRNA and mRNA transcriptomes in MDV-infected chicken spleens were profiled. Co-expression analysis identified integrated lncRNA-mRNA and gene-gene interaction networks, implying that hub genes or lncRNAs exert critical influence on MD resistance and tumorigenesis.

Accurate rainfall forecasting in watersheds is of indispensable importance for predicting streamflow and flash floods. This paper investigates the accuracy of several forecasting technologies based on Wavelet Packet Decomposition (WPD) in monthly rainfall forecasting. First, WPD decomposes the observed monthly rainfall data into several subcomponents. Then, three data-based models, namely Back-propagation Neural Network (BPNN) model, group method of data handing (GMDH) model, and autoregressive integrated moving average (ARIMA) model, are utilized to complete the prediction of the decomposed monthly rainfall series, respectively. Finally, the ensemble prediction result of the model is formulated by summing the outputs of all submodules. Meanwhile, these six models are employed for benchmark comparison to study the prediction performance of these conjunction methods, which are BPNN, WPD-BPNN, GMDH, WPD-GMDH, ARIMA, and WPD-ARIMA models. The paper takes monthly data from Luoning and Zuoyu stations in Luoyang city of China as the case study. The performance of these conjunction methods is tested by four quantitative indexes. Results show that WPD can efficiently improve the forecasting accuracy and the proposed WPD-BPNN model can achieve better prediction results. It is concluded that the hybrid forecast model is a very efficient tool to improve the accuracy of mid- and long-term rainfall forecasting.