This paper presents an overview of the causes and consequences of the carbon corrosion mechanism and summarizes the corresponding mitigation strategies for various operating conditions. The high ...potential at the cathode caused by the anode hydrogen-oxygen interface leads to the occurrence of carbon corrosion reactions during the startup and shutdown process. System strategies, including gas purge and auxiliary load, have been developed to alleviate the performance decay during startup and shutdown processes. Improper water management will cause local flooding to accelerate the carbon corrosion. Many novel flow fields have been proposed to solve water management problems, such as an optimized 3D flow field, baffle flow field and porous media flow field. The carbon corrosion under normal operation is not as serious as that under start-up and flooding, but it is serious enough to affect the later performance of the PEMFC after long-term operation. Varieties of new supports, such as mesoporous carbon, graphitized carbon, carbon nanotubes (CNTs), carbon nanofibers (CNF), and metal oxides, have been developed to improve the durability of the catalyst support. This review aims to provide a clear understanding of the carbon corrosion mechanisms, thereby helping researchers to prolong the lifetime of PEMFCs.
Carbon corrosion in three operating conditions. Display omitted
•An overview of carbon corrosion mechanism under three conditions was addressed.•Corresponding mitigation strategies for carbon corrosion were presented.•Future goals of mitigation strategies about carbon corrosion were suggested.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Fine-grained image classification is to recognize hundreds of subcategories belonging to the same basic-level category, such as 200 subcategories belonging to the bird, which is highly challenging ...due to large variance in the same subcategory and small variance among different subcategories. Existing methods generally first locate the objects or parts and then discriminate which subcategory the image belongs to. However, they mainly have two limitations: 1) relying on object or part annotations which are heavily labor consuming; and 2) ignoring the spatial relationships between the object and its parts as well as among these parts, both of which are significantly helpful for finding discriminative parts. Therefore, this paper proposes the object-part attention model (OPAM) for weakly supervised fine-grained image classification and the main novelties are: 1) object-part attention model integrates two level attentions: object-level attention localizes objects of images, and part-level attention selects discriminative parts of object. Both are jointly employed to learn multi-view and multi-scale features to enhance their mutual promotion; and 2) Object-part spatial constraint model combines two spatial constraints: object spatial constraint ensures selected parts highly representative and part spatial constraint eliminates redundancy and enhances discrimination of selected parts. Both are jointly employed to exploit the subtle and local differences for distinguishing the subcategories. Importantly, neither object nor part annotations are used in our proposed approach, which avoids the heavy labor consumption of labeling. Compared with more than ten state-of-the-art methods on four widely-used datasets, our OPAM approach achieves the best performance.
IL-17, a potent proinflammatory cytokine, has been shown to intimately contribute to the formation, growth, and metastasis of a wide range of malignancies. Recent studies implicate IL-17 as a link ...among inflammation, wound healing, and cancer. While IL-17-mediated production of inflammatory mediators mobilizes immune-suppressive and angiogenic myeloid cells, emerging studies reveal that IL-17 can directly act on tissue stem cells to promote tissue repair and tumorigenesis. Here, we review the pleotropic impacts of IL-17 on cancer biology, focusing how IL-17-mediated inflammatory response and mitogenic signaling are exploited to equip its cancer-promoting function and discussing the implications in therapies.
Under cap-and-trade regulation, this paper investigates the decision and coordination in the dual-channel supply chain arising out of low-carbon preference and channel substitution. From the game ...theoretical perspective, we develop the decision-making models of the centralized and decentralized supply chain, which consist of one manufacturer and one retailer. We design an improved revenue-sharing contract to effectively coordinate the manufacturer and retailer. The results suggestthe government make cap-and-trade regulation to reduce carbon emission efficiently andrealize coordinated development betweenthe economy and environment. In addition, the supply chaincan obtain greater profits based on the consumer's low-carbon preference. Meanwhile, the improved revenue-sharing contract leads to a Paretoimprovement of the efficiency between the manufacturer and retailer, as well as the optimal profits are related to bargaining powers.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
ABSTRACT
The Advanced LIGO and Virgo detectors opened a new era to study black holes (BHs) in our Universe. A population of stellar-mass binary black holes (BBHs) are discovered to be heavier than ...previously expected. These heavy BBHs provide us an opportunity to achieve multiband observation with ground-based and space-based gravitational-wave (GW) detectors. In this work, we use BBHs discovered by the LIGO/Virgo Collaboration as indubitable examples, and study in great detail the prospects for multiband observation with GW detectors in the near future. We apply the Fisher matrix to spinning, non-precessing inspiral-merger-ringdown waveforms, while taking the motion of space-based GW detectors fully into account. Our analysis shows that, detectors with decihertz sensitivity are expected to log stellar-mass BBH signals with very large signal-to-noise ratio and provide accurate parameter estimation, including the sky location and time to coalescence. Furthermore, the combination of multiple detectors will achieve unprecedented measurement of BBH properties. As an explicit example, we present the multiband sensitivity to the generic dipole radiation for BHs, which is vastly important for the equivalence principle in the foundation of gravitation, in particular for those theories that predict curvature-induced scalarization of BHs.
IL-17 is a highly versatile pro-inflammatory cytokine crucial for a variety of processes, including host defense, tissue repair, the pathogenesis of inflammatory disease and the progression of ...cancer. In contrast to its profound impact in vivo, IL-17 exhibits surprisingly moderate activity in cell-culture models, which presents a major knowledge gap about the molecular mechanisms of IL-17 signaling. Emerging studies are revealing a new dimension of complexity in the IL-17 pathway that may help explain its potent and diverse in vivo functions. Discoveries of new mRNA stabilizers and receptor-directed mRNA metabolism have provided insights into the means by which IL-17 cooperates functionally with other stimuli in driving inflammation, whether beneficial or destructive. The integration of IL-17 with growth-receptor signaling in specific cell types offers new understanding of the mitogenic effect of IL-17 on tissue repair and cancer. This Review summarizes new developments in IL-17 signaling and their pathophysiological implications.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Fine-grained visual categorization (FGVC) aims to discriminate similar subcategories that belong to the same superclass. Since the distinctions among similar subcategories are quite subtle and local, ...it is highly challenging to distinguish them from each other even for humans. So the localization of distinctions is essential for fine-grained visual categorization, and there are two pivotal problems: (1)
Which
regions are discriminative and representative to distinguish from other subcategories? (2)
How many
discriminative regions are necessary to achieve the best categorization performance? It is still difficult to address these two problems
adaptively
and
intelligently
. Artificial prior and experimental validation are widely used in existing mainstream methods to discover
which
and
how many
regions to gaze. However, their applications extremely restrict the
usability
and
scalability
of the methods. To address the above two problems, this paper proposes a
multi-scale and multi-granularity deep reinforcement learning approach (M2DRL)
, which learns multi-granularity discriminative region attention and multi-scale region-based feature representation. Its main contributions are as follows: (1)
Multi-granularity discriminative localization
is proposed to localize the distinctions via a two-stage deep reinforcement learning approach, which discovers the discriminative regions with multiple granularities in a hierarchical manner (“which problem”), and determines the number of discriminative regions in an automatic and adaptive manner (“how many problem”). (2)
Multi-scale representation learning
helps to localize regions in different scales as well as encode images in different scales, boosting the fine-grained visual categorization performance. (3)
Semantic reward function
is proposed to drive M2DRL to fully capture the salient and conceptual visual information, via jointly considering attention and category information in the reward function. It allows the deep reinforcement learning to localize the distinctions in a weakly supervised manner or even an unsupervised manner. (4)
Unsupervised discriminative localization
is further explored to avoid the heavy labor consumption of annotating, and extremely strengthen the
usability
and
scalability
of our M2DRL approach. Compared with state-of-the-art methods on two widely-used fine-grained visual categorization datasets, our M2DRL approach achieves the best categorization accuracy.
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CEKLJ, DOBA, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, IZUM, KILJ, KISLJ, MFDPS, NLZOH, NUK, OBVAL, OILJ, PILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, SIK, UILJ, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
NETs are chromatin-derived webs extruded from neutrophils as a result of either infection or sterile stimulation using chemicals, cytokines, or microbes. In addition to the classical role that NETs ...play in innate immunity against infection and injuries, NETs have been implicated extensively in cancer progression, metastatic dissemination, and therapy resistance. The purpose of this review is to describe recent investigations into NETs and the roles they play in tumor biology and to explore their potential as therapeutic targets in cancer treatment.
Inflammation is an important contributor to the pathogenesis of obesity-related type 2 diabetes (T2D). Adipose tissue-resident immune cells have been observed, and the potential contribution of these ...cells to metabolic dysfunction has been appreciated in recent years. This review focused on adipose tissue-resident immune cells that are dysregulated in the context of obesity and T2D. We comprehensively overviewed emerging knowledge regarding the phenotypic and functional properties of these cells and local factors that control their development. We discussed their function in controlling the immune response cascade and disease progression. We also characterized the metabolic profiles of these cells to explain the functional consequences in obese adipose tissues. Finally, we discussed the potential therapeutic targeting of adipose tissue-resident immune cells with the aim of addressing novel therapeutic approaches for the treatment of this disease.
Fine-grained image classification is to recognize hundreds of subcategories in each basic-level category. Existing methods employ discriminative localization to find the key distinctions between ...similar subcategories. However, they generally have two limitations: 1) discriminative localization relies on region proposal methods to hypothesize the locations of discriminative regions, which are time-consuming and the bottleneck of improving classification speed and 2) the training of discriminative localization depends on object or part annotations which are heavily labor-consuming and the obstacle of marching toward practical application. It is highly challenging to address the two limitations simultaneously , while existing methods only focus on one of them. Therefore, we propose a weakly supervised discriminative localization approach (WSDL) for fast fine-grained image classification to address the two limitations at the same time, and its main advantages are: 1) multi-level attention guided localization learning is proposed to localize discriminative regions with different focuses automatically, without using object and part annotations, avoiding the labor consumption. Different level attentions focus on different characteristics of the image, which are complementary and boost classification accuracy and 2) <inline-formula> <tex-math notation="LaTeX">n </tex-math></inline-formula>-pathway end-to-end discriminative localization network is proposed to improve classification speed, which simultaneously localizes multiple different discriminative regions for one image to boost classification accuracy, and shares full-image convolutional features generated by a region proposal network to accelerate the process of generating region proposals as well as reduce the computation of convolutional operation. Both are jointly employed to simultaneously improve classification speed and eliminate dependence on object and part annotations. Comparing with state-of-the-art methods on two widely used fine-grained image classification data sets, our WSDL approach achieves the best accuracy and the efficiency of classification.
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