摘要 :
The use of boron dipyrromethene (BODIPY) in biomedicine is reviewed. To
open, its synthesis and regulatory strategies are summarized, and inspiring
cutting-edge work in post-functionalization strategies is highlighted. A brief
...
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The use of boron dipyrromethene (BODIPY) in biomedicine is reviewed. To
open, its synthesis and regulatory strategies are summarized, and inspiring
cutting-edge work in post-functionalization strategies is highlighted. A brief
overview of assembly model of BODIPY is then provided: BODIPY is introduced
as a promising building block for the formation of single- and multicomponent
self-assembled systems, including nanostructures suitable for
aqueous environments, thereby showing the great development potential of
supramolecular assembly in biomedicine applications. The frontier progress
of BODIPY in biomedical application is thereafter described, supported by
examples of the frontiers of biomedical applications of BODIPY-containing
smart materials: it mainly involves the application of materials based on
BODIPY building blocks and their assemblies in fluorescence bioimaging,
photoacoustic imaging, disease treatment including photodynamic therapy,
photothermal therapy, and immunotherapy. Lastly, not only the current status
of the BODIPY family in the biomedical field but also the challenges worth
considering are summarized. At the same time, insights into the future development
prospects of biomedically applicable BODIPY are provided.
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Geological hazard is an adverse geological condition that can cause loss of life and property. Accurate prediction and analysis of geological hazards is an important and challenging task. In the past decade, there has been a great...
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Geological hazard is an adverse geological condition that can cause loss of life and property. Accurate prediction and analysis of geological hazards is an important and challenging task. In the past decade, there has been a great expansion of geohazard detection data and advancement in data-driven simulation techniques. In particular, great efforts have been made in applying deep learning to predict geohazards. To understand the recent progress in this field, this paper provides an overview of the commonly used data sources and deep neural networks in the prediction of a variety of geological hazards.
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Ni-rich layered oxides (NRLOs) and Li-rich layered oxides (LRLOs) have been considered as promising next-generation cathode materials for lithium ion batteries (LIBs) due to their high energy density, low cost, and environmental f...
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Ni-rich layered oxides (NRLOs) and Li-rich layered oxides (LRLOs) have been considered as promising next-generation cathode materials for lithium ion batteries (LIBs) due to their high energy density, low cost, and environmental friendliness. However, these two layered oxides suffer from similar problems like capacity fading and different obstacles such as thermal runaway for NRLOs and voltage decay for LRLOs. Understanding the similarities and differences of their challenges and strategies at multiple scales plays a paramount role in the cathode development of advanced LIBs. Herein, we provide a comprehensive review of state-of-the-art progress made in NRLOs and LRLOs based on multi-scale insights into electrons/ions, crystals, particles, electrodes and cells. For NRLOs, issues like structure disorder, cracks, interracial degradation and thermal runaway are elaborately discussed. Superex-change interaction and magnetic frustration are blamed for structure disorder while strains induced by universal structural collapse result in issues like cracks. For LRLOs, we present an overview of the origin of high capacity followed by local crystal structure and the root of voltage hysteresis/decay, which are ascribed to reduced valence of transition metal ions, phase transformation, strains, and microstructure degradation. We then discuss failure mechanism in full cells with NRLO cathode and commercial challenges of LRLOs. Moreover, strategies to improve the performance of NRLOs and LRLOs from different scales such as ion-doping, microstructure designs, particle modifications, and electrode/electrolyte interface engineering are summarized. Dopants like Na, Mg and Zr, delicate gradient concentration design, coatings like spinel LiNi_(0.5)Mn_(1.5)O_4 or Li_3PO_4 and novel electrolyte formulas are highly desired. Developing single crystals for NRLOs and new crystallographic structure or heterostructure for LRLOs are also emphasized. Finally, remaining challenges and perspectives are outlined for the development of NRLOs and LRLOs. This review offers fundamental understanding and future perspectives towards high-performance cathodes for next-generation LIBs.
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摘要 :
Using the daily temperature data of the national meteorological
stations, we measure the high-temperature
exposure risk of Chinese A-share
listed enterprises, investigate
the impact of high-temperature
exposure risk
on corpo...
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Using the daily temperature data of the national meteorological
stations, we measure the high-temperature
exposure risk of Chinese A-share
listed enterprises, investigate
the impact of high-temperature
exposure risk
on corporate prime operating revenue and performance,
and further discuss securities analysts' forecasts for this
risk. We find that increased exposure to high temperature
reduces corporate prime operating revenue, and the response
of enterprises to high-temperature
risk will lead
to a rise in management expenses and the deterioration
of business performance. Further evidence suggests that
securities analysts generally underestimate or ignore the
impact of high-temperature
exposure risk, and our results
are robust to different measures and samples.
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Cinnamomum cassia Presl is a tropical aromatic evergreen tree of the Lauraceae family, commonly used in traditional Chinese medicine. It is also a traditional spice, widely used around the world. This paper summarizes the achievem...
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Cinnamomum cassia Presl is a tropical aromatic evergreen tree of the Lauraceae family, commonly used in traditional Chinese medicine. It is also a traditional spice, widely used around the world. This paper summarizes the achievements of modern research on C. cassia, including the traditional uses, phytochemistry, pharmacology and toxicology. In addition, this review also discusses some significant issues and the potential direction of future C. cassia research. More than 160 chemicals have been separated and identified from C. cassia. The main constituents of C. cassia are terpenoids, phenylpropanoids, glycosides, etc. Modern studies have confirmed that C. cassia has a wide range of pharmacological effects, including antitumour, anti-inflammatory and analgesic, anti-diabetic and anti-obesity, antibacterial and antiviral, cardiovascular protective, cytoprotective, neuroprotective, immunoregulatory effects, anti-tyrosinase activity and other effects. However, the modern studies of C. cassia are still not complete and more in-depth investigations need to be conducted in alimentotherapy, health product, toxicity and side effects, and more bioactive components and potential pharmacological effects need to be explored in the future.
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Home service robots prioritize cost-effectiveness and convenience over the precision required for industrial tasks like autonomous driving, making their task execution more easily. Meanwhile, path planning tasks using Deep Reinfor...
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Home service robots prioritize cost-effectiveness and convenience over the precision required for industrial tasks like autonomous driving, making their task execution more easily. Meanwhile, path planning tasks using Deep Reinforcement Learning(DRL) are commonly sparse reward problems with limited data utilization, posing challenges in obtaining meaningful rewards during training, consequently resulting in slow or challenging training. In response to these challenges, our paper introduces a lightweight end-to-end path planning algorithm employing with hindsight experience replay(HER). Initially, we optimize the reinforcement learning training process from scratch and map the complex high-dimensional action space and state space to the representative low-dimensional action space. At the same time, we improve the network structure to decouple the model navigation and obstacle avoidance module to meet the requirements of lightweight. Subsequently, we integrate HER and curriculum learning (CL) to tackle issues related to inefficient training. Additionally, we propose a multi-step hindsight experience replay (MS-HER) specifically for the path planning task, markedly enhancing both training efficiency and model generalization across diverse environments. To substantiate the enhanced training efficiency of the refined algorithm, we conducted tests within diverse Gazebo simulation environments. Results of the experiments reveal noteworthy enhancements in critical metrics, including success rate and training efficiency. To further ascertain the enhanced algorithm's generalization capability, we evaluate its performance in some "never-before-seen" simulation environment. Ultimately, we deploy the trained model onto a real lightweight robot for validation. The experimental outcomes indicate the model's competence in successfully executing the path planning task, even on a small robot with constrained computational resources.
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摘要 :
The flourishing development of multifunctional flexible electronics cannot leave the beneficial role of nature, which provides continuous inspiration in their material, structural, and functional designs. During the evolution of f...
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The flourishing development of multifunctional flexible electronics cannot leave the beneficial role of nature, which provides continuous inspiration in their material, structural, and functional designs. During the evolution of flexible electronics, some originated from nature, some were?even beyond nature, and others were?implantable or biodegradable eventually to nature. Therefore, the relationship between flexible electronics and nature is undoubtedly vital since harmony between nature and technology evolution would promote the sustainable development. Herein, materials selection and functionality design for flexible electronics that are mostly inspired from nature are first introduced with certain functionality even beyond nature. Then, frontier advances on flexible electronics including the main individual components (i.e., energy (the power source) and the sensor (the electric load)) are presented from nature, beyond nature, and to nature with the aim of enlightening the harmonious relationship between the modern electronics technology and nature. Finally, critical issues in next‐generation flexible electronics are discussed to provide possible solutions and new insights in prospective exploration directions.
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Climate change communication is an important behavioral manifestation of the public's understanding, expression, and participation in addressing climate change. Social media play an important role in the climate change knowledge c...
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Climate change communication is an important behavioral manifestation of the public's understanding, expression, and participation in addressing climate change. Social media play an important role in the climate change knowledge communication. Does social media promote climate change communication behavior in the Chinese context? Is its effect stronger than other types of media? Combined with the research context, we divide media into central media, local media, and social media and construct the influence mechanism model of media use on climate change communication behavior. In this study, a questionnaire survey was conducted among the public in China, and 1062 valid questionnaires were empirically tested by methods of hierarchical regression and bootstrapping. According to the findings of the study, different media use has a positive effect on climate change communication behavior. While social media is more likely to be used by the public to obtain climate change-related information than central and local media (with a mean value of 3.84 for social media compared to 3.51 for central media and 3.19 for local media), it is actually the central media that have the greatest effect on climate change communication behavior. This is evident in the total effect value, where the central media have a value of 0.21, which is higher than social media's value of 0.20 and local media's value of 0.12. Risk perception and environmental values play an important mediating role in the influence of media use on climate change communication behavior, among which environmental values have the largest mediating effect. (Specifically, the mediating effects of environmental values were 26.83%, 31.28%, and 38.57% for central media, local media, and social media, respectively.) In addition, risk perception can also positively affect environmental values, thus forming a chain mediating effect between media use and climate change communication behavior (the confidence intervals for the chain mediating effect also exclude the numbers 0).
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Abstract For addressing the low efficiency of structural reliability analysis under the random‐interval mixed uncertainties (RIMU), this paper establishes the line sampling method (LS) under the RIMU. The proposed LS divides the ...
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Abstract For addressing the low efficiency of structural reliability analysis under the random‐interval mixed uncertainties (RIMU), this paper establishes the line sampling method (LS) under the RIMU. The proposed LS divides the reliability analysis under RIMU into two stages. The Markov chain simulation is used to efficiently search the design point under RIMU in the first stage, then the upper and lower bounds of failure probability are estimated by LS in the second stage. To improve the computational efficiency of the proposed LS under RIMU, the Kriging model is employed to reduce the model evaluation numbers in the two stages. For efficiently searching the design point, the Kriging model is constructed and adaptively updated in the first stage to accurately recognize the Markov chain candidate state, and then it is sequentially updated by the improved U learning function in the second stage to accurately estimate the failure probability bounds. The proposed LS under RIMU with Kriging model can not only reduce the model evaluation numbers but also decrease the candidate sample pool size for constructing the Kriging model in two stages. The presented examples demonstrate the superior computational efficiency and accuracy of the proposed method by comparison with some existing methods.
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Abstract Baihe Dihuang decoction is a commonly used herbal formula to treat depression and insomnia in traditional Chinese medicine. This study established a liquid chromatography–mass spectrometry method to investigate the poten...
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Abstract Baihe Dihuang decoction is a commonly used herbal formula to treat depression and insomnia in traditional Chinese medicine. This study established a liquid chromatography–mass spectrometry method to investigate the potential active ingredients and the components absorbed in the blood and brain tissue of mice. Using a new data processing method, 94 chemical components were identified, 33 and 9 of which were absorbed in the blood and brain. More interestingly, we analyzed the substance changes during co‐decoction and the characteristics of the compounds absorbed in the blood and brain. The results show that 71 newly generated chemical components were discovered from co‐decoction: 38 with fragment information and five absorbed in the blood. Ultimately, the results of molecular docking show that these components have excellent performance in proteins of γ‐aminobutyric acid, serotonin and melatonin receptors. The docking results of emodin with Monoamine Oxidase A and Melatonin Receptor 1A, and luteolin with Solute Carrier Family 6 Member 4, Glyoxalase I, Monoamine Oxidase B and Melatonin Receptor 1A, may explain the mechanism of action of Baihe Dihuang decoction in treating insomnia and depression. Overall, our research results may provide novel perspectives for further understanding of the effective substances in Baihe Dihuang decoction.
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