Spatial dynamic patterns of saltmarsh vegetation in southern Hangzhou Bay:Exotic and native species

A saltmarsh has developed rapidly on the mudflat of Andong Shoal,in southern Hangzhou Bay,over the last decade since embankment.The saltmarsh vegetation changes are driven by both sediment dynamic conditions and the competition between the exotic species Spartina alterniflora(S.alterniflora) and the native species Scirpus mariqueter(S.mariqueter).This study attempted to investigate large-scale spatial variations in the exotic and native species,by analyzing and interpreting a time series(2016-2018) of high-resolution(less than 1 m) remote sensing images.The total area of the saltmarsh increased at a rate of 1.07 km~2/year,due to the accretion of the whole tidal flat.The spatial patterns revealed a new bimodal pattern for S.alterniflora invasion.S.alterniflora expanded over the upper to middle saltmarsh at a rate of1.68 km2/year.However,the S.alterniflora patches at the seaward edge expanded at a negative rate of-0.005 km~2/year,indicating a different pattern in competition:S.alterniflora had more advantages in the upper saltmarsh,while S.mariqueter had more advantages in the pioneer zone with low elevation.Consequently,S.alterniflora mainly established new habitats by invading S.mariqueter in the middle saltmarsh,but S.mariqueter occupied the bare mudflat for tradeoff.Our results aindicated that the interspecific competition result between S.alterniflora and S.mariqueter depended on elevation,and low elevation might create a favorable environment for S.mariqueter to win the competition with exotic species.This finding could be applied to future invasive species control and saltmarsh management.     

国土空间规划体系中 景观设计学科与行业的困惑及机遇

A true understanding of Ecological Civilization Construction is key to comprehend the connotation and goals of China’s territorial spatial planning, which echoes the ideological keynote of the country’s upgrade of socio-economic development mode. Missioned as a means to public policy making, territorial spatial planning develops roadmaps for Ecological Civilization Construction by addressing macro spatial governance problems such as the shortage of resources, severe environmental pollution, and ecosystem degradation. Furthermore, the author interprets the relationship between ecological restoration and territorial spatial planning system, and argues that the territorial spatial planning system should employ a macro-medium-micro hierarchy in scale while focusing on life-circle ecological restoration planning and implementation. In addition to the necessity of developing crossadministrative border plans, the author emphasizes the importance of multi-disciplinary cooperation. Then, he points out that "The Two Assessment Standards" should value regional characteristics and avoid to indiscriminately apply planning and design patterns in different cases. Finally, the author puts forward suggestions to practitioners in Landscape Architecture and other allied professions to prepare themselves by selfretraining with new concepts, methodologies, and technologies to be more competent for the contemporary needs of territorial spatial planning.     

Exploring and visualizing spatial-temporal evolution of patent collaboration networks: A case of China's intelligent manufacturing equipment industry

With the integration of intelligent manufacturing (IM) technology and the manufacturing industry, the intelligent manufacturing equipment industry (IMEI) has become the focus of a new round of industrial revolution. This integration has led to a new revolution in the manufacturing industry and is key to transforming China into a manufacturing country with international competitiveness. However, China's IMEI is in the development stage, thus presenting a major challenge to it becoming a competitive core industry. Therefore, there is an urgent need to explore the IMEI patent collaboration network and analyze its evolutionary characteristics, which can improve not only the innovation ability of network organizations but also the overall competitiveness of the IMEI. This study employs social network analysis (SNA) to study the evolution of the patent collaboration network of China's IMEI. The results show that the number of co-patents for the IMEI field in China has obvious stage characteristics, the collaborative innovation patterns adopted by different provinces vary, and the State Grid has a strong influence on the network. The scale of the patent collaboration network is gradually expanding, exhibiting a “small-world” structure. Jiangsu mainly innovates through intra-provincial collaboration. Provinces such as Beijing, Guangdong and Shandong are more inclined to inter-provincial collaboration.     

DLSE-Net: A robust weakly supervised network for fabric defect detection

The feasibility of deep convolutional neural network for fabric defect detection has been proven, but the detection performance often depends on large-scale labeled datasets. However, it is troublesome to collect large amounts of fabric defects with pixel-level labeling in industrial production. Although the weakly supervised detection methods can reduce the labeling workload, fabric defect detection is still a challenging task due to the slight difference between defects and complex texture backgrounds, and the diversity of defect types. To alleviate this issue, this paper proposes an effective weakly supervised shallow network, called DLSE-Net, with Link-SE (L-SE) module and Dilation Up-Weight CAM (DUW-CAM) for fabric defect detection. Firstly, the network regards a residual connection as a new branch to alleviate the semantic gap generated by the connection of different layers. Secondly, L-SE module forces the weights to be associated with the overall network in a global optimization manner instead of only within a single layer. Finally, a novel DUW-CAM with an attention mechanism is proposed to improve the adaptability of the network by combining dilated convolution and attention mechanism. Moreover, DUW-CAM can effectively suppress the background and highlight defect regions, even on complex fabric textures. Experimental results demonstrate that our proposed approach can localize the defects with high accuracy, and outperforms the state-of-the-art methods on two distinctive fabric datasets with different textures.     

A spatial-channel progressive fusion ResNet for remote sensing classification

In recent years, the panchromatic (PAN) and the multispectral (MS) remote sensing images classification has become a research hotspot. In this paper, we propose a spatial-channel progressive fusion residual network (SCPF-ResNet) for multi-resolution remote sensing classification. Firstly, for the inputs of the proposed network, the interactive data fusion strategy (IDFS) combines generalized-intensity-hue-saturation (GIHS), and discrete wavelet transform (DWT) to interfuse patch pairs of the PAN and the MS images, so as to increase the similarity between them, thus reduce the difference in information between them. Secondly, for the branches of feature extraction, we design an adaptive spatial attention module (ASA-Module) and an adaptive channel attention module (ACA-Module) to strengthen spatial features from both larger-sized with smaller-sized targets and spectral features among channels. Finally, we insert the ASA-Module and ACA-Module into the residual modules to form a triple-branch network and use the common spatial-channel features extracted by the Fusion_Branch to gradually enhance the pure independent features extracted by the PAN_Branch and the MS_Branch, respectively. The experimental results indicate that SCPF-ResNet can achieve competitive performance.     

Placemaking Based on Site Experience—Landscape Design of Parkhill Commons in Shenzhen

Placemaking of public open spaces, such as community parks and streetscapes that are foundational to residents’ daily social and recreational life, can significantly influence their site experience. Scholars have explored the influence of such kind of experience on users’ behavioral, physical, and mental conditions from multiple perspectives including attention restoration and stress reduction. However, the findings are seldom applied in practice to convey certain design concepts. This article introduces the landscape design project of Parkhill Commons in Shenzhen. The project team reviewed relevant research findings, and characterized the health-promoting restorative environment and social-capital-friendly communities. Accordingly, strategies for activity zones, planting design, service facilities, and slow-traffic neighborhoods were proposed to enhance site experience and serve nearby residents by creating vital and pleasant community-level public open spaces, and to maximize the social benefits and reinforce community cohesion. After completion, the project team has investigated the site usage and the public’s evaluation, in order to stimulate reflections on design strategies. Results from the questionnaire survey and field observation show the effect of placemaking strategies on users’ site experience. This article provides references for placemaking practice of community-level public open spaces and is expected to help bridge the gap between theoretical research and design practice.     

Activating nano-bulk interplays for sustainable ammonia electrosynthesis

Small changes in a catalyst’s composition, modification, and/or integration into a reactor can have significant yet often poorly understood effects on (electro)catalysis. Here we demonstrate the careful tailoring of Ru/La_0.25Ce_0.75O_2−x catalysts through the post-synthesized hydrothermal treatment together with control over the Ru loadings to create hydroxyl groups and electronic restructuring for ammonia electrosynthesis. When integrated into a protonic ceramic electrolyzer, the in situ formed Ce³⁺−OH/Ru sites facilitate both the NN decoupling and NH formation at 400 °C and 1 bar of N₂, boosting the ammonia production rate (2.92 mol h⁻¹ m⁻²) up to 100-fold higher than the current state-of-the-art electrolyzers. Moreover, such catalysts and electrolyzer design concepts can be readily tuned to more complex applications such as coproducing ammonia and other chemicals with hydrocarbons as direct hydrogen sources. The creation of coordinated saturated support –OH/metal sites in the advanced electrolyzer offers an attractive approach for future clean-energy and green-chemical industries.     

Spatial patterns of residents’ daily activity space and its influencing factors based on the CatBoost model: A case study of Nanjing,China

The complexity and fragmentation of people's activity space are challenging to planners. However, the relevant studies are mostly concerned on the relationship between the social attributes and the activity space of residents in a single or several communities, or the spatiotemporal laws of activity space on a macro scale. The research on the spatial characteristics of residents' activity space still needs to be strengthened. The present study analyses the spatial patterns of residents' activity space based on mobile phone signaling data to fill the gap of previous studies that assessed residents' activity space across small geographic areas. First, according to the spatial scope and direction of an activity space and residents' activity coverage rate, spatial patterns can be divided into three types: compact, extended, and directional extension patterns. The CatBoost method is then used to statistically analyze the influencing variables of spatial patterns, and the order of importance of the following influencing factors is determined: the built environment is more influential than social and economic situations. This study aims to strengthen the understanding of residents' activity space at the spatial level and provide a basis for the optimization of communities with different spatial patterns.     

Simulating the influence of terror management strategies on the voter ideological distance using agent-based modeling

This paper simulates the effect of the strategies implemented by politicians after the terrorists attacks in Madrid on 11 March 2004 on the ideological distance between voters and political parties. The attacks took place three days before the elections and changed the campaign’s agenda, which centered around the issue of who was responsible for the attack: ETA or Al Qaeda. It also altered the agenda of the mass media, which focused its informative activity on broadcasting news related to this issue. We did an exhaustive selection process of all the news broadcast on television, radio, and newspapers that made reference to the authorship of the attack. Using these messages we developed an agent-based model for explaining how the political strategies implemented by political parties influenced the ideological distance. The proposed model is based on the ideological proximity model by Downs (1957). After calibrating and validating the model with real data, we simulated the effect of three political strategies from the theory of terror management on the ideological distance between voters and political parties: the rally around the flag, the opinion leadership, and the priming of public opinion and media coverage. The results show that these strategies have a significant and stable impact on the ideological distance. In particular, the rally around the flag can have a lasting effect, capable of changing the ideological distance in the short term after a terrorist attack.     

A fusion spatial attention approach for few-shot learning

Few-shot learning is a challenging problem in computer vision that aims to learn a new visual concept from very limited data. A core issue is that there is a large amount of uncertainty introduced by the small training set. For example, the few images may include cluttered backgrounds or different scales of objects. Existing approaches mostly address this problem from either the original image space or the embedding space by using meta-learning. To the best of our knowledge, none of them tackle this problem from both spaces jointly. To this end, we propose a fusion spatial attention approach that performs spatial attention in both image and embedding spaces. In the image space, we employ a Saliency Object Detection (SOD) module to extract the saliency map of an image and provide it to the network as an additional channel. In the embedding space, we propose an Adaptive Pooling (Ada-P) module tailored to few-shot learning that introduces a meta-learner to adaptively fuse local features of the feature maps for each individual embedding. The fusion process assigns different pooling weights to the features at different spatial locations. Then, weighted pooling can be conducted over an embedding to fuse local information, which can avoid losing useful information by considering the spatial importance of the features. The SOD and Ada-P modules can be used within a plug-and-play module and incorporated into various existing few-shot learning approaches. We empirically demonstrate that designing spatial attention methods for few-shot learning is a nontrivial task and our method has proven effective for it. We evaluate our method using both shallow and deeper networks on three widely used few-shot learning benchmarks, miniImageNet, tieredImageNet and CUB, and demonstrate very competitive performance.