SuperpixelGraph: Semi-automatic generation of building footprint through semantic-sensitive superpixel and neural graph networks

Most urban applications necessitate building footprints in the form of concise vector graphics with sharp boundaries rather than pixel-wise raster images. This need contrasts with the majority of existing methods, which typically generate over-smoothed footprint polygons. Editing these automatically produced polygons can be inefficient, if not more time-consuming than manual digitization. This paper introduces a semi-automatic approach for building footprint extraction through semantically-sensitive superpixels and neural graph networks. Drawing inspiration from object-based classification techniques, we first learn to generate superpixels that are not only boundary-preserving but also semantically-sensitive. The superpixels respond exclusively to building boundaries rather than other natural objects, while simultaneously producing semantic segmentation of the buildings. These intermediate superpixel representations can be naturally considered as nodes within a graph. Consequently, graph neural networks are employed to model the global interactions among all superpixels and enhance the representativeness of node features for building segmentation. Classical approaches are utilized to extract and regularize boundaries for the vectorized building footprints. Utilizing minimal clicks and straightforward strokes, we efficiently accomplish accurate segmentation outcomes, eliminating the necessity for editing polygon vertices. Our proposed approach demonstrates superior precision and efficacy, as validated by experimental assessments on various public benchmark datasets. A significant improvement of 8% in AP50 was observed in vector graphics evaluation, surpassing established techniques. Additionally, we have devised an optimized and sophisticated pipeline for interactive editing, poised to further augment the overall quality of the results

Deglacial Subantarctic CO2 outgassing driven by a weakened solubility pump

The Subantarctic Southern Ocean has long been thought to be an important contributor to increases in atmospheric carbon dioxide partial pressure (pCO2) during glacial-interglacial transitions. Extensive studies suggest that a weakened biological pump, a process associated with nutrient utilization efficiency, drove up surface-water pCO2 in this region during deglaciations. By contrast, regional influences of the solubility pump, a process mainly linked to temperature variations, have been largely overlooked. Here, we evaluate relative roles of the biological and solubility pumps in determining surface-water pCO2 variabilities in the Subantarctic Southern Ocean during the last deglaciation, based on paired reconstructions of surface-water pCO2, temperature, and nutrient utilization efficiency. We show that compared to the biological pump, the solubility pump imposed a strong impact on deglacial Subantarctic surface-water pCO2 variabilities. Our findings therefore reveal a previously underappreciated role of the solubility pump in modulating deglacial Subantarctic CO2 release and possibly past atmospheric pCO2 fluctuations

Blue carbon cooperation in the Maritime Silk Road with network game model and simulation

The blue carbon cooperation is a joint effort of the countries along the Maritime Silk Road (MSR) to utilize marine activities and organisms to absorb and store carbon dioxide in the atmosphere, an initiative that has great strategic value for coping with the most important environmental problems in the 21st century and promoting the building of a community with shared aspirations for mankind's future. This research combines the decision-making structure model with the reality of the blue carbon cooperation game of the MSR to make conditional assumptions and carry out model construction. It uses the simulation method to test the influencing factors such as decision-maker type, initial input cost, continuous input maintenance cost, rate of return, carbon tax rate and others. The results suggest that initial and continuous input costs, returns, and neighbor subsidies have positive impacts on blue carbon cooperation, while carbon tax rates and income discount rates have negative impacts on blue carbon cooperation. To promote blue carbon cooperation along the MSR, emphasis should be placed on the design of incentive and subsidy mechanisms, together with the appropriate punishment mechanisms.</p

Multinational companies’ coordination mechanism for extending corporate social responsibility to Chinese suppliers

From the global supply chain perspective, this research explores how multinational companies (MNCs) can extend corporate social responsibility (CSR) practices to emerging countries such as China. A two-stage supply chain game model consisting of a Chinese supplier and a MNC is constructed. The study finds that an increase in the level of the Chinese supplier's CSR increases the product demand and the stakeholders' economic profits, but reduces the supplier's economic return; the product demand and the stakeholders' benefits increase along with the product green degree improvement, but the changes in the Chinese supplier's economic profits are jointly affected by the level of CSR and green production efficiency. The supply chain coordination can be achieved based on a revenue sharing contract. Finally, the effects of revenue sharing fraction and supplier's CRS level on product green degree, supplier's revenue and MNC's earnings are discussed by numerical simulation

Mineral Characteristics and the Mineralization of Leptynite-Type Nb–Ta Ore Deposit in the Western Qilian Orogenic Belt

A large Nb–Ta ore deposit was found in the Yushishan leptynite in the west Qilian Orogenic Belt (QOB). Based on a field geological survey and using a Mineral Liberation Analyser (MLA, including scanning electron microscopy (SEM) and energy-dispersive spectrometer (EDS)) methods, eight Nb minerals (fergusonite, polycrase, columbite, Nb-rutile, aeschynite, pyrochlore, microlite, and ilmenorutile) were found to occur in the leptynite. This accounted for approximately 69% of Nb, with fergusonite, polycrase, and columbite being the dominant phases. The other 17.90% Nb as a minor element was dispersed in titanium magnetite–maghemite, and another 13.00% Nb was dispersed in gangue minerals. Nb minerals are formed mainly by two metallogenesis stages. The first stage is magmatic genesis to form four Nb minerals, euhedral-subhedral fergusonite, polycrase, pyrochlore, and microlite, which are crystallized within or between primary minerals, such as quartz and feldspar. Late alteration phenomena are locally observed. The second stage is the hydrothermal genesis of columbite, anhedral fergusonite, Nb-rutile, and aeschynite, which are dispersed in the fissures of the wall rocks as irregular veins and lump assemblages. Meanwhile, they are closely associated with metasomatic chlorite, albite, and secondary quartz. Furthermore, direct metasomatism among different Nb minerals is also found at the local scale. The Nb percentage of these two Nb mineral mineralization types is approximately equal, which reflects two main mineralizing periods. The first stage of mineralization occurred in the Neoproterozoic Era (834–790 Ma). Magmatism of this period produced early niobium and formed fergusonite, polycrase, pyrochlore, microlite, and zircon. The initial enrichment of Nb, Ta, and other rare metals occurred during this stage. The second stage of mineralization occurred in the Caledonian period (490–455 Ma). Large-scale and intense tectonic–magmatic thermal events occurred in the western part of the QOB due to the plate subduction and convergence (510–450 Ma). Hydrothermal activity in this period formed columbite, fergusonite, Nb-rutile, and aeschynite. Moreover, rare metal elements in the Nb-bearing rocks activated and migrated at short distances, forming in situ Nb–Ta-rich ore deposits

Recent Progress in the Application of Metal Organic Frameworks in Surface-Enhanced Raman Scattering Detection

Metal–organic framework (MOF) compounds are centered on metal ions or metal ion clusters, forming lattices with a highly ordered periodic porous network structure by connecting organic ligands. As MOFs have the advantages of high porosity, large specific surface area, controllable pore size, etc., they are widely used in gas storage, catalysis, adsorption, separation and other fields. SERS substrate based on MOFs can not only improve the sensitivity of SERS analysis but also solve the problem of easy aggregation of substrate nanoparticles. By combining MOFs with SERS, SERS performance is further improved, and tremendous research progress has been made in recent years. In this review, three methods of preparing MOF-based SERS substrates are introduced, and the latest applications of MOF-based SERS substrates in biosensors, the environment, gases and medical treatments are discussed. Finally, the current status and prospects of MOF-based SERS analysis are summarized

Reinforcement learning-based distant supervision relation extraction for fault diagnosis knowledge graph construction under industry 4.0

Fault diagnosis is the key concern in the operation and maintenance of industrial assets. A fault diagnosis knowledge graph (KG) can provide decision support to the engineers to efficiently conduct maintenance tasks. However, as a type of domain KG, it would be time-consuming to manually label the corpus collected from the multi-source including the maintenance log, handbook and article. Meanwhile, the existence of the noisy sentence in the multi-source corpus jeopardises the performance of relation extraction modelling. In order to address this issue, this paper proposes a distant supervision relation extraction (DSRE)-based approach to construct a fault diagnosis KG. In this approach, the ontology of the fault diagnosis KG is firstly designed. Subsequently, a DSRE algorithm named relation-aware-based sentence-level attention enhanced piecewise convolutional neural network with reinforcement learning strategy (PCNN-ATTRA-RL) is proposed. The algorithm can effectively lower the impact of noisy sentences and accurately label the relation of different entities when the labelled data is insufficient. In this algorithm, PCNN-ATTRA is designed as the DSRE classifier to effectively extract the relation between entity pairs. RL is conducted to remove the noisy sentence so as to further improve the performance. An experimental study based on the multi-source corpus collected from the real world reveals that the proposed approach shows merits in comparison with the state-of-the-art algorithms. Meanwhile, a fault diagnosis KG, which can greatly support the decision-making of the engineers in the fault diagnosis, is established via the proposed approac