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A rock-filled concrete (RFC) dam is an original dam construction technology invented in China nearly 20 years ago. The technology has been continuously improved and innovated upon, and the accumulated rich practical experience gradually formed a complete dam design and construction technology. Seismic design is a key design area for RFC dams that still requires more investigation; therefore, this article attempts to address some questions in this area. In the article, the seismic design for a curved gravity dam, currently under construction, is compared for RFC and conventional vibrating concrete (CVC) dam alternatives based on American design documents. The conclusions drawn from investigations include the following: The displacement and stress distributions in both the CVC and RFC alternatives are similar, but the maximum computed values for the RFC dam model are slightly smaller than those for the CVC one, while the sliding resistance of both dam alternatives can meet the requirements of the specifications. Regarding the nonlinear seismic analysis results, the extent of damage in the RFC dam model is significantly reduced when compared with the CVC model, which can be explained by the higher cracking resistance of RFC. In general, the seismic performance of the investigated dam made of RFC appears to be better than that of CVC. [ABSTRACT FROM AUTHOR]

In this study, we explored the relationship between the platelet endothelial aggregation receptor 1 (PEAR1) polymorphisms, platelet reactivity, and clinical outcomes in patients with minor stroke or transient ischemic attack (TIA). Randomized controlled trial subgroups were assessed, wherein patients received dual antiplatelet therapy for at least 21 days. Platelet reactivity was measured at different time intervals. Genotypes were categorized as wild-type, mutant heterozygous, and mutant homozygous. Clinical outcomes were evaluated after 90 days. The rs12041331 polymorphism predominantly influenced adenosine diphosphate channel platelet activity, with the AA genotype displaying significantly lower residual platelet activity to the P2Y12 response unit (p < 0.01). This effect was more evident after 7 days of dual antiplatelet treatment (p = 0.016). Mutant A allele carriers had decreased rates of recurrent stroke and complex endpoint events but were more prone to bleeding (p = 0.015). The rs2768759 polymorphism majorly impacted arachidonic acid (AA) channel platelet activity, which was particularly noticeable in the C allele carriers. Our regression analysis demonstrated that rs12041331 AA + GA and rs2768759 CA predicted 90-day post-stroke bleeding. In conclusion, the PEAR1 polymorphisms rs12041331 and rs2768759 interfere with platelet aggregation and the performance of antiplatelet drugs. These genetic variations may contribute to bleeding events associated with minor stroke and TIA. [ABSTRACT FROM AUTHOR]

Deep neural networks (DNNs) have been widely utilized in automatic visual navigation and recognition on modern unmanned aerial vehicles (UAVs), achieving state-of-the-art performances. However, DNN-based visual recognition systems on UAVs show serious vulnerability to adversarial camouflage patterns on targets and well-designed imperceptible perturbations in real-time images, which poses a threat to safety-related applications. Considering a scenario in which a UAV is suffering from adversarial attack, in this paper, we investigate and construct two ensemble approaches with CNN and transformer for both proactive (i.e., generate robust models) and reactive (i.e., adversarial detection) adversarial defense. They are expected to be secure under attack and adapt to the resource-limited environment on UAVs. Specifically, the probability distributions of output layers from base DNN models in the ensemble are combined in the proactive defense, which mainly exploits the weak adversarial transferability between the CNN and transformer. For the reactive defense, we integrate the scoring functions of several adversarial detectors with the hidden features and average the output confidence scores from ResNets and ViTs as a second integration. To verify their effectiveness in the recognition task of remote sensing images, we conduct experiments on both optical and synthetic aperture radar (SAR) datasets. We find that the ensemble model in proactive defense performs as well as three popular counterparts, and both of the ensemble approaches can achieve much more satisfactory results than a single base model/detector, which effectively alleviates adversarial vulnerability without extra re-training. In addition, we establish a one-stop platform for conveniently evaluating adversarial robustness and performing defense on recognition models called AREP-RSIs, which is beneficial for the future research of the remote sensing field. [ABSTRACT FROM AUTHOR]

Bahrain is a cluster of islands in the mid-section of the Arabian Gulf that serves as an important wintering and stop-over ground for many migratory shorebirds in the Central Asian Flyway (CAF). However, natural and anthropogenic factors have had a significant impact on these ecosystems over the last few decades. Long-term, systematic studies based on standardized survey observations are needed to understand the population dynamics and diversity changes of shorebirds in these critical sites. We systematically surveyed the shorebird population and community in Bahrain between 2010 January to 2021 December. This is the first comprehensive study from the entire Kingdom of Bahrain, and covered 13 sites over 12 years to establish the results. A total of 39 species were encountered during the study period from all 13 sites in Bahrain, of which 27 species were common and regular migrants to all the study sites; these were selected to analyze the population trend. Five species represented 77% or more of the total wintering shorebird population. All the shorebird species assessed exhibited significant declining trends over the years, and majority of them had over 1% relative abundance. Shorebirds in Bahrain were severely threatened at these sites, indicating that their population trend in the area could be crucially affected. Further conservation efforts are needed, aided by an understanding of the mechanisms driving the decline and diversity changes of shorebirds in the most stressed coastal regions of Bahrain. Further studies organized throughout the country's coasts may aid in establishing improved conservation measures to protect the shorebirds of the CAF in Bahrain. [ABSTRACT FROM AUTHOR]

The beam pattern of frequency diversity array (FDA) radar has a range–angle two-dimensional degree of freedom, which makes it possible to distinguish different targets from the same angle and brings a new approach to anti-jamming of radars. However, the beam pattern of conventional linearly frequency-biased FDA radar is range–angle-coupled and time-varying. The method of adding nonlinear frequency bias among the array elements of the FDA array has been shown to eliminate this coupling property while still allowing for better beam performance of the emitted beam. In this paper, we obtain a decoupled and time-invariant beam direction map using the FDA-multi-input–multi-output (FDA-MIMO) radar scheme and then obtain a sharp pencil-shaped main sphere beam pattern with range–angle dependence using a linear frequency offset scheme weighted by a Chebyshev window. Finally, the anti-interference performance of the proposed method is verified in an anti-interference experiment. [ABSTRACT FROM AUTHOR]

Simple Summary: In this study, consumer preferences for pork produced using animal-welfare-enhancing farming strategies were assessed. In China, the demand for animal-friendly products is increasing, but so far, studies on consumer preferences for animal welfare farming attributes are limited. The objective of this study was to analyze consumer preferences for pork based on four animal welfare farming attributes, namely feed nutrition, living environment, health care, and activity space. The study employed a choice experiment approach. The survey covered 1274 pork consumers in Guangdong province, China. Our empirical results suggest that Chinese consumers were willing to pay an additional 2.359–10.477 CNY/500 g (5.27–23.39%) for animal welfare pork. Furthermore, there was significant heterogeneity in consumer preferences. China is the world's largest producer and consumer of pork. Since the outbreaks of African swine fever in 2018, China's pork imports have been constantly on the rise. The results can contribute to the optimization of pork production structures and marketing plans for stakeholders and can assist with the timely development of international competition strategies for animal-derived trade products. Consumption demands for pork produced by farms that employ strategies to improve animal welfare ("animal welfare pork") will be an important indicator for predicting domestic pig feeding standards and pork industry development. This paper analyzes consumer preferences for animal welfare pork based on the choice experiment data of 1274 pork consumers in Guangdong province, China. The results show that consumers had a significant preference for animal welfare pork and that they were willing to pay a premium of 2.359–10.477 CNY/500 g (5.27–23.39%) on average. There is heterogeneity in consumer preferences regarding age, education level, and income. Producers of animal-derived products can not only adjust the mix of production conditions to improve pig welfare and innovate contractual arrangements for industry chain stakeholder groups, but they can also develop differentiated marketing strategies for animal welfare products to meet consumer demands for animal welfare. [ABSTRACT FROM AUTHOR]

Migratory shorebirds that move across continents along their flyways are undergoing a drastic decline globally. A greater proportion of them that regularly winter along the Indian coasts within the Central Asian Flyway (CAF) are also undergoing severe declines. However, the mechanisms underlying the population trends in these areas remain little understood. This study investigated the diversity, abundance, population dynamics and distribution patterns of shorebirds along the Indian coasts based on the available literature. The west coast of India is relatively less studied than the east coast in the CAF. Further, we observed that the diversity, abundance, population dynamics and distribution pattern of the shorebirds follow different trends on the west coast compared to the east coast. These variations are in accordance with the differences in topography and biotic and abiotic factors between the coasts. Anthropogenic activities have far-reaching effects on the survival and persistence of shorebirds along the coasts. The west coast is evidently more productive than the east coast at every trophic level and thus the west coast is expected to account for more abundance and diversity of shorebirds. Paradoxically, we found that the east coast supports a greater abundance and diversity of shorebirds than the west coast. The west coast, therefore, requires further investigations to obtain a better understanding of the causes of apparent differences in abundance and diversity as well as the observed declines in shorebirds, compared to the east coast of India. [ABSTRACT FROM AUTHOR]

SAR-optical images from different sensors can provide consistent information for scene classification. However, the utilization of unlabeled SAR-optical images in deep learning-based remote sensing image interpretation remains an open issue. In recent years, contrastive self-supervised learning (CSSL) methods have shown great potential for obtaining meaningful feature representations from massive amounts of unlabeled data. This paper investigates the effectiveness of CSSL-based pretraining models for SAR-optical remote-sensing classification. Firstly, we analyze the contrastive strategies of single-source and multi-source SAR-optical data augmentation under different CSSL architectures. We find that the CSSL framework without explicit negative sample selection naturally fits the multi-source learning problem. Secondly, we find that the registered SAR-optical images can guide the Siamese self-supervised network without negative samples to learn shared features, which is also the reason why the CSSL framework outperforms the CSSL framework with negative samples. Finally, we apply the CSSL pretrained network without negative samples that can learn the shared features of SAR-optical images to the downstream domain adaptation task of optical transfer to SAR images. We find that the choice of a pretrained network is important for downstream tasks. [ABSTRACT FROM AUTHOR]

Many populations of birds depend on networks of sites to survive. Sufficient connectivity that allows movement between the sites throughout the year is a critical requirement. We found that existing international frameworks and policies for identifying sites important for bird conservation focus more at the level of the individual site than on the site network and its connectivity. Only 21% of site criteria acknowledge the importance of movement networks for birds, and such network criteria were mostly (67%) qualitative. We suggest a three-step quantitative approach for informing conservation about the connectivity of bird movements (especially when migrating) from a network perspective, by reviewing current scientific knowledge. The first step is to construct a bird movement network by identifying sites frequently used by birds as 'nodes', and then define 'edges' from the probability of non-stop flight between each pair of nodes. The second step is to quantify network connectivity, i.e., the extent to which the site network facilitates bird movements. The last step is to assess the importance of each site from its contribution to network connectivity. This approach can serve as a tool for comprehensive and dynamic monitoring of the robustness of site networks during global change. [ABSTRACT FROM AUTHOR]

Community detection is a fundamental topic in network science, with a variety of applications. However, there are still fundamental questions about how to detect more realistic network community structures. To address this problem and considering the structure of a network, we propose an agglomerative community detection algorithm, which is based on node influence and the similarity of nodes. The proposed algorithm consists of three essential steps: identifying the central node based on node influence, selecting a candidate neighbor to expand the community based on the similarity of nodes, and merging the small community based on the similarity of communities. The performance and effectiveness of the proposed algorithm were tested on real and synthetic networks, and they were further evaluated through modularity and NMI anlaysis. The experimental results show that the proposed algorithm is effective in community detection and it is quite comparable to existing classic methods. [ABSTRACT FROM AUTHOR]

Simple Summary: The Wolong National Nature Reserve in Sichuan province covers a unique mountainous ecosystem located on the eastern border of the Tibetan Plateau in China. We applied a popular non-invasive observational method, i.e., infrared-triggered camera trapping, to gain thousands of photographs of wildlife to monitor biodiversity over three years. Combined with data on the local abiotic factors, our integrative statistical analysis identified the key environmental drivers, i.e., temperature and vegetation, affecting the distribution and abundance of mammals and birds in the reserve. All species were classified into three main types by their tolerance of or fondness for different environmental conditions. The detectability of each species by camera trapping was quantified and ranked to provide insights on each species' relative abundance in the area. The high-altitude ecosystem of the Tibetan Plateau in China is a biodiversity hotspot that provides unique habitats for endemic and relict species along an altitudinal gradient at the eastern edge. Acquiring biodiversity information in this area, where the average altitude is over 4000 m, has been difficult but has been aided by recent developments in non-invasive technology, including infrared-triggered camera trapping. We used camera trapping to acquire a substantial number of photographic wildlife records in Wolong National Nature Reserve, Sichuan, China, from 2013 to 2016. We collected information of the habitat surrounding the observation sites, resulting in a dataset covering 37 species and 12 environmental factors. We performed a multivariate statistical analysis to discern the dominant environmental factors and cluster the mammals and birds of the ecosystem in order to examine environmental factors contributing to the species' relative abundance. Species were generalized into three main types, i.e., cold-resistant, phyllophilic, and thermophilic, according to the identified key environmental drivers (i.e., temperature and vegetation) for their abundances. The mammal species with the highest relative abundance were bharal (Pseudois nayaur), Moupin pika (Ochotona thibetana), and Himalayan marmot (Marmota himalayana). The bird species with highest relative abundance were snow partridge (Lerwa lerwa), plain mountain finch (Leucosticte nemoricola), Chinese monal (Lophophorus lhuysii), and alpine accentor (Prunella collaris). [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) can perform observations at all times and has been widely used in the military field. Deep neural network (DNN)-based SAR target recognition models have achieved great success in recent years. Yet, the adversarial robustness of these models has received far less academic attention in the remote sensing community. In this article, we first present a comprehensive adversarial robustness evaluation framework for DNN-based SAR target recognition. Both data-oriented metrics and model-oriented metrics have been used to fully assess the recognition performance under adversarial scenarios. Adversarial training is currently one of the most successful methods to improve the adversarial robustness of DNN models. However, it requires class labels to generate adversarial attacks and suffers significant accuracy dropping on testing data. To address these problems, we introduced adversarial self-supervised learning into SAR target recognition for the first time and proposed a novel unsupervised adversarial contrastive learning-based defense method. Specifically, we utilize a contrastive learning framework to train a robust DNN with unlabeled data, which aims to maximize the similarity of representations between a random augmentation of a SAR image and its unsupervised adversarial example. Extensive experiments on two SAR image datasets demonstrate that defenses based on adversarial self-supervised learning can obtain comparable robust accuracy over state-of-the-art supervised adversarial learning methods. [ABSTRACT FROM AUTHOR]

Identifying influential edges in a complex network is a fundamental topic with a variety of applications. Considering the topological structure of networks, we propose an edge ranking algorithm DID (Dissimilarity Influence Distribution), which is based on node influence distribution and dissimilarity strategy. The effectiveness of the proposed method is evaluated by the network robustness R and the dynamic size of the giant component and compared with well-known existing metrics such as Edge Betweenness index, Degree Product index, Diffusion Intensity and Topological Overlap index in nine real networks and twelve BA networks. Experimental results show the superiority of DID in identifying influential edges. In addition, it is verified through experimental results that the effectiveness of Degree Product and Diffusion Intensity algorithm combined with node dissimilarity strategy has been effectively improved. [ABSTRACT FROM AUTHOR]

Urbanization changes the land surface environment, which alters the regional climate system. In this study, we took the Haihe River Basin in China as a case study area, as it is highly populated and experienced rapid urbanization from 2000–2015. We investigated how land use and cover change (LUCC) was driven by urban land development affects land-climate dynamics. From 2000–2015, we collected data from the land use and cover database, the remote sensing database of the Moderate Resolution Imaging Spectroradiometer (MODIS) series, and the meteorological database to process and generate regional datasets for LUCC maps. We organized data by years aligned with the selected indicators of land surface, normalized difference vegetation index (NDVI), albedo, and land surface temperature (LST), as well as of regional climate, cloud water content (CWC), and precipitation (P). The assembled datasets were processed to perform statistical analysis and conduct structural equation modelling (SEM). Based on eco-climatology principles and the biophysical process in the land-climate dynamics, we made assumptions on how the indicators connected to each other. Moreover, we testified and quantified them in SEM. LUCC results found that from 2000–2015 the urban area proportion increased by 214% (2.20–6.91%), while the agricultural land decreased by 7.2% (53.05–49.25%) and the forest increased by 4.3% (10.02–10.45%), respectively. This demonstrated how cropland intensification and afforestation happened in the urbanizing basin. SEM results showed that the forest had both positive and negative effects on the regional hydrological cycle. The agricultural land, grassland, and shrub had indirect effects on the P via different biophysical functions of LST. The overall effects of urbanization on regional precipitation was positive (pathway correlation coefficient = 0.25). The interpretation of how urbanization drives LUCC and alters regional climate were herein discussed in different aspects of socioeconomic development, biophysical processes, and urbanization-related atmospheric effects. We provided suggestions for further possible research on monitoring and assessment, putting forth recommendations to advance sustainability via land planning and management, including agricultural land conservation, paying more attention to the quality growth of forest rather than the merely area expansion, integrating the interdisciplinary approach, and assessing climatic risk for extreme precipitation and urban flooding. [ABSTRACT FROM AUTHOR]

Petal senescence involves numerous programmed changes in biological and biochemical processes. Ubiquitination plays a critical role in protein degradation, a hallmark of organ senescence. Therefore, we investigated changes in the proteome and ubiquitome of senescing rose (Rosa hybrida) petals to better understand their involvement in petal senescence. Of 3859 proteins quantified in senescing petals, 1198 were upregulated, and 726 were downregulated during senescence. We identified 2208 ubiquitinated sites, including 384 with increased ubiquitination in 298 proteins and 1035 with decreased ubiquitination in 674 proteins. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses revealed that proteins related to peptidases in proteolysis and autophagy pathways were enriched in the proteome, suggesting that protein degradation and autophagy play important roles in petal senescence. In addition, many transporter proteins accumulated in senescing petals, and several transport processes were enriched in the ubiquitome, indicating that transport of substances is associated with petal senescence and regulated by ubiquitination. Moreover, several components of the brassinosteroid (BR) biosynthesis and signaling pathways were significantly altered at the protein and ubiquitination levels, implying that BR plays an important role in petal senescence. Our data provide a comprehensive view of rose petal senescence at the posttranslational level. [ABSTRACT FROM AUTHOR]

Understanding risk factors for the spread of infectious diseases over time and across the landscape is critical for managing disease risk. While habitat connectivity and characteristics of local and neighboring animal (i.e., host) assemblages are known to influence the spread of diseases, the interactions among these factors remain poorly understood. In this study, we conducted a county-level analysis to test the effects of forest connectivity, together with the suitability of local assemblage (measured by the similarity of local host assemblage with neighboring assemblages) and the infection intensity of neighboring counties on the spatial expansion of Lyme disease in the United States. Our results suggested that both the similarity of local host assemblage and the infection intensity of neighboring counties were positively correlated with the probability of disease spread. Moreover, we found that increasing forest connectivity could facilitate the positive effect of neighbor infection intensity. In contrast, the effect size of the host assemblage similarity decreased with increasing connectivity, suggesting that host assemblage similarity was less effective in well-connected habitats. Our results thus indicate that habitat connectivity can indirectly influence disease spread by mediating the effects of other risk factors. [ABSTRACT FROM AUTHOR]

Mortar using seawater and sand was the material studied here. The mortar specimens, in particular, were cured in natural seawater. The foci development in the mortar was the principal interest in this study. The on-line damage detection experiment art, including dynamically global MSHCT (Multi-Slices Helical Computer Tomography) scan and the local detection of EDS (Energy Dispersive Spectrometer), SEM (Scanning Electron Microscopy), and XRD (X-ray Diffraction) was designed to research the foci development in the mortar specimen. The mortar specimens with 70-day age were produced and investigated by the on-line damage detection experiments. The experiment results indicated that the mortar using seawater and sand offered appreciable strength at the early age, at least, although some saline minerals were generated during the preparation. The residual strength of the mortar was above 13 MPa, which helped to prevent the sharp damage of engineering bodies. The micro-interfacial behavior and the parental foci development controlled the damage evolution in the mortar using seawater and sand, the performance of which was still the adjustable one by composition optimization. [ABSTRACT FROM AUTHOR]

Underwater acoustic (UWA) sensor networks demand high-rate communications with high reliability between sensor nodes for massive data transmission. Filtered multitone (FMT) is an attractive multicarrier technique used in high-rate UWA communications, and can obviously shorten the span of intersymbol interference (ISI) with high spectral efficiency and low frequency offset sensitivity by dividing the communication band into several separated wide sub-bands without guard bands. The joint receive diversity and adaptive equalization scheme is often used as a general ISI suppression technique in FMT-UWA communications, but large receive array for high diversity gain has an adverse effect on the miniaturization of UWA sensor nodes. A time-reversal space-time block coding (TR-STBC) technique specially designed for frequency-selective fading channels can replace receive diversity with transmit diversity for high diversity gain, and therefore is helpful for ISI suppression with simple receive configuration. Moreover, the spatio-temporal matched filtering (MF) in TR-STBC decoding can mitigate ISI obviously, and therefore is of benefit to lessen the complexion of adaptive equalization for post-processing. In this paper, joint TR-STBC and adaptive equalization FMT-UWA communication method is proposed based on the merit of TR-STBC. The proposed method is analyzed in theory, and its performance is assessed using simulation analysis and real experimental data collected from an indoor pool communication trial. The validity of the proposed method is proved through comparing the proposed method with the joint single-input-single-output (SISO) and adaptive equalization method and the joint single-input-multiple-output (SIMO) and adaptive equalization method. The results show that the proposed method can achieve better communication performance than the joint SISO and adaptive equalization method, and can achieve similar performance with more simpler receive configuration as the joint SIMO and adaptive equalization method., Competing Interests: The authors declare no conflict of interest.

Hexadepsipeptide 2, the precursor of Hirsutellide A (1), was synthesized in an overall yield of 45% from N-Boc-Me-Gly via three coupling reactions using dicyclohexylcarbodiimide (DCC), O-(7-azabenzotriazol-1-yl)-N,N,N',N'-tetramethyl- uronium hexafluorophosphate (HATU) and bis(2-oxo-3-oxazolidinyl)phosphinic chloride (BOP-Cl), respectively.