Critical Role of Phosphorus in Hollow Structures Cobalt-Based Phosphides as Bifunctional Catalysts for Water Splitting

Cobalt phosphides electrocatalysts have great potential for water splitting, but the unclear active sides hinder the further development of cobalt phosphides. Wherein, three different cobalt phosphides with the same hollow structure morphology (CoP-HS, CoP-HS, CoP-HS) based on the same sacrificial template of ZIF-67 are prepared. Surprisingly, these cobalt phosphides exhibit similar OER performances but quite different HER performances. The identical OER performance of these CoP-HS in alkaline solution is attributed to the similar surface reconstruction to CoOOH. CoP-HS exhibits the best catalytic activity for HER among these CoP-HS in both acidic and alkaline media, originating from the adjusted electronic density of phosphorus to affect absorption–desorption process on H. Moreover, the calculated ΔG based on P-sites of CoP-HS follows a quite similar trend with the normalized overpotential and Tafel slope, indicating the important role of P-sites for the HER process. Moreover, CoP-HS displays good performance (cell voltage of 1.67 V at a current density of 50 mA cm) and high stability in 1 M KOH. For the first time, this work detailly presents the critical role of phosphorus in cobalt-based phosphides for water splitting, which provides the guidance for future investigations on transition metal phosphides from material design to mechanism understanding.W.Z. and N.H. contributed equally to this work. X.Z. and J.F. are grateful for the Research Foundation-Flanders (FWO) project (12ZV320N). Funding from National Natural Science Foundation of China (project No.: 22005250, 21776120, and 51901161) is appreciated. M.X. is grateful to the National Natural Science Foundation of China (project No.: 22179109). W.Z. is grateful to the China Scholarship Council (NO. 201808310068). W.G. is grateful to the China Scholarship Council (NO. 201806030189). S.X. is grateful to the China Scholarship Council. K.W. is grateful to the Oversea Study Program of Guangzhou Elite Project. Funding from the Research Foundation–Flanders (FWO) (project No.: G0B3218N) and Natural Science Foundation of Fujian Province, China (No.: 2018J01433) is acknowledged. ICN2 acknowledges funding from Generalitat de Catalunya 2017 SGR 327 and the Spanish MINECO project ECOCAT and subproject NANOGEN. ICN2 is supported by the Severo Ochoa program from Spanish MINECO (Grant No. SEV-2017-0706) and is funded by the CERCA Programme/Generalitat de Catalunya. Part of the present work has been performed in the framework of Universitat Autònoma de Barcelona Materials Science Ph.D. program. This work has received funding from the European Union's Horizon 2020 Research and Innovation Programme under grant agreement No. 654360 NFFA-Europe. X.H. thanks China Scholarship Council for scholarship support (201804910551)

A Recombinant Vaccine of H5N1 HA1 Fused with Foldon and Human IgG Fc Induced Complete Cross-Clade Protection against Divergent H5N1 Viruses

Development of effective vaccines to prevent influenza, particularly highly pathogenic avian influenza (HPAI) caused by influenza A virus (IAV) subtype H5N1, is a challenging goal. In this study, we designed and constructed two recombinant influenza vaccine candidates by fusing hemagglutinin 1 (HA1) fragment of A/Anhui/1/2005(H5N1) to either Fc of human IgG (HA1-Fc) or foldon plus Fc (HA1-Fdc), and evaluated their immune responses and cross-protection against divergent strains of H5N1 virus. Results showed that these two recombinant vaccines induced strong immune responses in the vaccinated mice, which specifically reacted with HA1 proteins and an inactivated heterologous H5N1 virus. Both proteins were able to cross-neutralize infections by one homologous strain (clade 2.3) and four heterologous strains belonging to clades 0, 1, and 2.2 of H5N1 pseudoviruses as well as three heterologous strains (clades 0, 1, and 2.3.4) of H5N1 live virus. Importantly, immunization with these two vaccine candidates, especially HA1-Fdc, provided complete cross-clade protection against high-dose lethal challenge of different strains of H5N1 virus covering clade 0, 1, and 2.3.4 in the tested mouse model. This study suggests that the recombinant fusion proteins, particularly HA1-Fdc, could be developed into an efficacious universal H5N1 influenza vaccine, providing cross-protection against infections by divergent strains of highly pathogenic H5N1 virus

Seizing the window of opportunity to mitigate the impact of climate change on the health of Chinese residents

The health threats posed by climate change in China are increasing rapidly. Each province faces different health risks. Without a timely and adequate response, climate change will impact lives and livelihoods at an accelerated rate and even prevent the achievement of the Healthy and Beautiful China initiatives. The 2021 China Report of the Lancet Countdown on Health and Climate Change is the first annual update of China’s Report of the Lancet Countdown. It comprehensively assesses the impact of climate change on the health of Chinese households and the measures China has taken. Invited by the Lancet committee, Tsinghua University led the writing of the report and cooperated with 25 relevant institutions in and outside of China. The report includes 25 indicators within five major areas (climate change impacts, exposures, and vulnerability; adaptation, planning, and resilience for health; mitigation actions and health co-benefits; economics and finance; and public and political engagement) and a policy brief. This 2021 China policy brief contains the most urgent and relevant indicators focusing on provincial data: The increasing health risks of climate change in China; mixed progress in responding to climate change. In 2020, the heatwave exposures per person in China increased by 4.51 d compared with the 1986–2005 average, resulting in an estimated 92% increase in heatwave-related deaths. The resulting economic cost of the estimated 14500 heatwave-related deaths in 2020 is US$176 million. Increased temperatures also caused a potential 31.5 billion h in lost work time in 2020, which is equivalent to 1.3% of the work hours of the total national workforce, with resulting economic losses estimated at 1.4% of China’s annual gross domestic product. For adaptation efforts, there has been steady progress in local adaptation planning and assessment in 2020, urban green space growth in 2020, and health emergency management in 2019. 12 of 30 provinces reported that they have completed, or were developing, provincial health adaptation plans. Urban green space, which is an important heat adaptation measure, has increased in 18 of 31 provinces in the past decade, and the capacity of China’s health emergency management increased in almost all provinces from 2018 to 2019. As a result of China’s persistent efforts to clean its energy structure and control air pollution, the premature deaths due to exposure to ambient particulate matter of 2.5 μm or less (PM2.5) and the resulting costs continue to decline. However, 98% of China’s cities still have annual average PM2.5 concentrations that are more than the WHO guideline standard of 10 μg/m3. It provides policymakers and the public with up-to-date information on China’s response to climate change and improvements in health outcomes and makes the following policy recommendations. (1) Promote systematic thinking in the related departments and strengthen multi-departmental cooperation. Sectors related to climate and development in China should incorporate health perspectives into their policymaking and actions, demonstrating WHO’s and President Xi Jinping’s so-called health-in-all-policies principle. (2) Include clear goals and timelines for climate-related health impact assessments and health adaptation plans at both the national and the regional levels in the National Climate Change Adaptation Strategy for 2035. (3) Strengthen China’s climate mitigation actions and ensure that health is included in China’s pathway to carbon neutrality. By promoting investments in zero-carbon technologies and reducing fossil fuel subsidies, the current rebounding trend in carbon emissions will be reversed and lead to a healthy, low-carbon future. (4) Increase awareness of the linkages between climate change and health at all levels. Health professionals, the academic community, and traditional and new media should raise the awareness of the public and policymakers on the important linkages between climate change and health.</p

“Scrambling”: Logic of horizontal competition between local governments based on three cases of interprovincial disaster counterpart support

Interprovincial counterpart support is a cooperative system used by local governments to achieve horizontal flow of resources based on cross-regional cooperation. Existing research has mainly focused on governance efficiency, institutional advantages, and ranking incentives while ignoring the scrambling behavior and operational mechanisms of local governments formed by ranking incentives and territorial responsibilities. This study selected the Wenchuan earthquake, Yushu earthquake, and COVID-19 as three typical cases. We constructed a theoretical framework for competition among provincial local governments and found that competition in interprovincial disaster counterpart support followed a dual behavioral logic of “striving to be first” and “fear of being last”. Specifically, local governments will choose striving to be first under the logic of time coercion, content games, and territorial responsibility; they will choose fear of being last under the logic of responsibility avoidance and moral pressure. This type of scrambling-based horizontal competition reflects the logic of local government competition tournaments. This study further revealed the specific processes, mechanisms, and results of horizontal local government competition, which can provide inspiration for cross-regional and provincial cooperation

Monthly Runoff Interval Prediction Based on Fuzzy Information Granulation and Improved Neural Network

High-precision monthly runoff prediction results are of great significance to regional water resource management. However, with the changes in human activity, climate, and underlying surface conditions, the runoff sequence presents highly nonlinear and random characteristics. In order to improve the accuracy of runoff prediction, this study proposed a runoff prediction model based on fuzzy information granulation (FIG) and back propagation neural network (BPNN) improved with genetic algorithm (FIG-GA-BP). First, FIG was used to process the original runoff data to generate three sequences of minimum, average, and maximum that can reflect the rule of runoff changes. Then, genetic algorithms (GA) were used to obtain the optimal initial weights and thresholds of the BPNN through selection, crossover, and mutation. Finally, BPNN was used to predict the generated three sequences separately to obtain the prediction interval. The proposed model was applied to the monthly runoff interval prediction of Linjiacun and Weijiabu hydrological stations in the main stream of the Wei River and Zhangjiashan hydrological station on Jing River, a tributary of the Wei River. Compared with the interval prediction model FIG-BP, FIG-WNN, and traditional BP model. The results show that the FIG-GA-BP interval prediction model had a good prediction effect, with higher prediction accuracy and a narrower range of prediction intervals. Therefore, this model has superiority and practicability in monthly runoff interval prediction

Monthly Runoff Interval Prediction Based on Fuzzy Information Granulation and Improved Neural Network

High-precision monthly runoff prediction results are of great significance to regional water resource management. However, with the changes in human activity, climate, and underlying surface conditions, the runoff sequence presents highly nonlinear and random characteristics. In order to improve the accuracy of runoff prediction, this study proposed a runoff prediction model based on fuzzy information granulation (FIG) and back propagation neural network (BPNN) improved with genetic algorithm (FIG-GA-BP). First, FIG was used to process the original runoff data to generate three sequences of minimum, average, and maximum that can reflect the rule of runoff changes. Then, genetic algorithms (GA) were used to obtain the optimal initial weights and thresholds of the BPNN through selection, crossover, and mutation. Finally, BPNN was used to predict the generated three sequences separately to obtain the prediction interval. The proposed model was applied to the monthly runoff interval prediction of Linjiacun and Weijiabu hydrological stations in the main stream of the Wei River and Zhangjiashan hydrological station on Jing River, a tributary of the Wei River. Compared with the interval prediction model FIG-BP, FIG-WNN, and traditional BP model. The results show that the FIG-GA-BP interval prediction model had a good prediction effect, with higher prediction accuracy and a narrower range of prediction intervals. Therefore, this model has superiority and practicability in monthly runoff interval prediction

Large-Scale Place Recognition Based on Camera-LiDAR Fused Descriptor

In the field of autonomous driving, carriers are equipped with a variety of sensors, including cameras and LiDARs. However, the camera suffers from problems of illumination and occlusion, and the LiDAR encounters motion distortion, degenerate environment and limited ranging distance. Therefore, fusing the information from these two sensors deserves to be explored. In this paper, we propose a fusion network which robustly captures both the image and point cloud descriptors to solve the place recognition problem. Our contribution can be summarized as: (1) applying the trimmed strategy in the point cloud global feature aggregation to improve the recognition performance, (2) building a compact fusion framework which captures both the robust representation of the image and 3D point cloud, and (3) learning a proper metric to describe the similarity of our fused global feature. The experiments on KITTI and KAIST datasets show that the proposed fused descriptor is more robust and discriminative than the single sensor descriptor