| 1. |
- Li, Shuting, et al.
(författare)
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Direct structure determination of vemurafenib polymorphism from compact spherulites using 3D electron diffraction
- 2023
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Ingår i: Communications Chemistry. - : Springer Science and Business Media LLC. - 2399-3669. ; 6
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Tidskriftsartikel (refereegranskat)abstract
- The spherulitic morphology is considered to be the most common morphology of crystalline materials and is particularly apparent in melt-crystallized products. Yet, historically, the polycrystalline nature of spherulites has hindered successful crystal structure determination. Here, we report the direct structure determination of a clinical drug, vemurafenib (VMN), in compact spherulite form using 3D electron diffraction (3D ED). VMN has four known polymorphs. We first solved the crystal structures of α-, β-, and γ-VMN from compact spherulites using 3D ED, and the resulting structures were highly consistent with those obtained by single-crystal X-ray diffraction. We then determined the crystal structure of δ-VMN—the least stable polymorph which cannot be cultivated as a single crystal—directly from the compact spherulite sample. We unexpectedly discovered a new polymorph during our studies, denoted as ε-VMN. Single crystals of ε-VMN are extremely thin and not suitable for study by X-ray diffraction. Again, we determined the structure of ε-VMN in a compact spherulite form. This successful structure elucidation of all five VMN polymorphs demonstrates the possibility of directly determining structures from melt-grown compact spherulite samples. Thereby, this discovery will improve the efficiency and broaden the scope of polymorphism research, especially within the field of melt crystallization.
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| 2. |
- Lightowler, Molly, et al.
(författare)
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Phase identification and discovery of an elusive polymorph of drug-polymer inclusion complex using automated 3D electron diffraction
- 2024
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Ingår i: Angewandte Chemie International Edition. - 1433-7851 .- 1521-3773. ; 63:16
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Tidskriftsartikel (refereegranskat)abstract
- 3D electron diffraction (3D ED) has shown great potential in crystal structure determination in materials, small organic molecules, and macromolecules. In this work, an automated, low-dose and low-bias 3D ED protocol has been implemented to identify six phases from a multiple-phase melt-crystallisation product of an active pharmaceutical ingredient, griseofulvin (GSF). Batch data collection under low-dose conditions using a widely available commercial software was combined with automated data analysis to collect and process over 230 datasets in three days. Accurate unit cell parameters obtained from 3D ED data allowed direct phase identification of GSF Forms III, I and the known GSF inclusion complex (IC) with polyethylene glycol (PEG) (GSF-PEG IC-I), as well as three minor phases, namely GSF Forms II, V and an elusive new phase, GSF-PEG IC-II. Their structures were then directly determined by 3D ED. Furthermore, we reveal how the stabilities of the two GSF-PEG IC polymorphs are closely related to their crystal structures. These results demonstrate the power of automated 3D ED for accurate phase identification and direct structure determination of complex, beam-sensitive crystallisation products, which is significant for drug development where solid form screening is crucial for the overall efficacy of the drug product.
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| 3. |
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| 4. |
- Lightowler, Molly, et al.
(författare)
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Indomethacin Polymorph δ Revealed To Be Two Plastically Bendable Crystal Forms by 3D Electron Diffraction : Correcting a 47-Year-Old Misunderstanding
- 2022
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Ingår i: Angewandte Chemie International Edition. - : Wiley. - 1433-7851 .- 1521-3773. ; 61:7
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Tidskriftsartikel (refereegranskat)abstract
- Indomethacin is a clinically classical non-steroidal anti-inflammatory drug that has been marketed since 1965. The third polymorph, Form δ, was discovered by both melt and solution crystallization in 1974. δ-indomethacin cannot be cultivated as large single crystals suitable for X-ray crystallography and, therefore, its crystal structure has not yet been determined. Here, we report the structure elucidation of δ-indomethacin by 3D electron diffraction and reveal the truth that melt-crystallized and solution-crystallized δ-indomethacin are in fact two polymorphs with different crystal structures. We propose to keep the solution-crystallized polymorph as Form δ and name the melt-crystallized polymorph as Form θ. Intriguingly, both structures display plastic flexibility based on a slippage mechanism, making indomethacin the first drug to have two plastic polymorphs. This discovery and correction of a 47-year-old misunderstanding signify that 3D electron diffraction has become a powerful tool for polymorphic structural studies.
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| 5. |
- Lin, Hongyi, et al.
(författare)
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How generative adversarial networks promote the development of intelligent transportation systems: A survey
- 2023
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Ingår i: IEEE/CAA Journal of Automatica Sinica. - 2329-9274 .- 2329-9266. ; 10:9, s. 1781-1796
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Tidskriftsartikel (refereegranskat)abstract
- In current years, the improvement of deep learning has brought about tremendous changes: As a type of unsupervised deep learning algorithm, generative adversarial networks (GANs) have been widely employed in various fields including transportation. This paper reviews the development of GANs and their applications in the transportation domain. Specifically, many adopted GAN variants for autonomous driving are classified and demonstrated according to data generation, video trajectory prediction, and security of detection. To introduce GANs to traffic research, this review summarizes the related techniques for spatio-temporal, sparse data completion, and time-series data evaluation. GAN-based traffic anomaly inspections such as infrastructure detection and status monitoring are also assessed. Moreover, to promote further development of GANs in intelligent transportation systems (ITSs), challenges and noteworthy research directions on this topic are provided. In general, this survey summarizes 130 GAN-related references and provides comprehensive knowledge for scholars who desire to adopt GANs in their scientific works, especially transportation-related tasks.
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| 6. |
- Lin, Hongyi, et al.
(författare)
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Insights into Travel Pattern Analysis and Demand Prediction: A Data-Driven Approach in Bike-Sharing Systems
- 2024
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Ingår i: Journal of Transportation Engineering Part A: Systems. - 2473-2893 .- 2473-2907. ; 150:2
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Tidskriftsartikel (refereegranskat)abstract
- With the advent of the Internet of Things, bike-sharing systems have seen widespread adoption globally, whereas they often grapple with an uneven spatiotemporal distribution of vehicles. This issue is particularly acute in the wake of electronic fences, with some areas often faced with the predicament of inadequate supply. To tackle this challenge, accurate prediction of borrowing and returning demands at different parking spots and varying times is necessary. In this study, we used a comprehensive data set from Yancheng, Jiangsu, China, covering shared bicycle usage across 394 parking spots. These data enabled us to delve deep into urban travel patterns and discern the various factors influencing these behaviors. To enhance the prediction accuracy, we propose the time-series weighted regression (TSWR) model, a long-term multistep forecasting method, which adeptly addresses issues associated with sparse statistical data and long-term prediction inaccuracies, outperforming other machine learning models in our experiments. Further recognizing the considerable impact of geographical location and weather conditions on shared bicycle demand, we incorporated the rule-based adjustment optimization (RAO) method into our approach, which refines nonlinear components by accounting for various factors. The implementation of RAO resulted in a 10.34% increase in accuracy compared to TSWR alone and an improvement of over 35% in comparison to other approaches. Overall, this study illuminates the underlying influences on urban travel patterns and offers valuable suggestions for bike dispatching to those enterprises, contributing significantly to the research in this field.
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| 7. |
- McMillan, Hilary, et al.
(författare)
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Panta Rhei 2013-2015 : global perspectives on hydrology, society and change
- 2016
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Ingår i: Hydrological Sciences Journal. - : Taylor & Francis Group. - 0262-6667 .- 2150-3435. ; 61:7, s. 1174-1191
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Tidskriftsartikel (refereegranskat)abstract
- In 2013, the International Association of Hydrological Sciences (IAHS) launched the hydrological decade 2013-2022 with the theme "Panta Rhei: Change in Hydrology and Society". The decade recognizes the urgency of hydrological research to understand and predict the interactions of society and water, to support sustainable water resource use under changing climatic and environmental conditions. This paper reports on the first Panta Rhei biennium 2013-2015, providing a comprehensive resource that describes the scope and direction of Panta Rhei. We bring together the knowledge of all the Panta Rhei working groups, to summarize the most pressing research questions and how the hydrological community is progressing towards those goals. We draw out interconnections between different strands of research, and reflect on the need to take a global view on hydrology in the current era of human impacts and environmental change. Finally, we look back to the six driving science questions identified at the outset of Panta Rhei, to quantify progress towards those aims.
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| 8. |
- Sun, Pengliang, et al.
(författare)
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Round-the-clock bifunctional honeycomb-like nitrogen-doped carbon-decorated Co2P/Mo2C-heterojunction electrocatalyst for direct water splitting with 18.1% STH efficiency
- 2022
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Ingår i: Applied Catalysis B. - : Elsevier. - 0926-3373 .- 1873-3883. ; 310
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Tidskriftsartikel (refereegranskat)abstract
- Hydrogen production via solar and electrochemical water splitting is a promising approach for storing solar energy and achieving a carbon-neutral economy. However, hydrogen production by photoelectric coupling remains a challenge. Here, by the cooperative coupling of heteroatoms and a heterojunction interface engineering strategy in a limited space, a honeycomb porous Co2P/Mo2C@NC catalyst was obtained for the first time. In contrast most traditional chemical syntheses, this method maintains excellent electrical interconnections among the nanoparticles and results in large surface areas and many catalytically active sites. Theoretical calculations reveal that the construction of a heterostructure can effectively lower the hydrogen evolution reaction and oxygen evolution reaction barriers as well as improve the electrical conductivity, consequently enhancing the electrochemical performance. Significantly, the overall water-splitting hydrolytic tank assembled using AsGa solar cells enabled the system to achieve a stable solar hydrogen conversion efficiency of 18.1%, which provides a new approach for facilitating large-scale hydrogen production via portable water hydrolysis driven by solar cells.
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| 9. |
- Tan, Fang, et al.
(författare)
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Improving the hydrogen evolution reaction activity of molybdenum-based heterojunction nanocluster capsules via electronic modulation by erbium–nitrogen–phosphorus ternary doping
- 2023
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Ingår i: Chemical Engineering Journal. - : Elsevier. - 1385-8947 .- 1873-3212. ; 454:Part 1
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Tidskriftsartikel (refereegranskat)abstract
- The realization of a hydrogen-based economy with robust hydrogen evolution reaction catalysts remains a challenge. In this study, we prepared MoO2/Mo2N3 heterostructure nanoclusters co-doped with nitrogen, phosphorus, and erbium for the first time. The introduction of the nitrogen and phosphorus atoms into the transition metal increases the d-electron density and contracts the d-band, which leads to a rearranged electronic structure of the MoO2/Mo2N3 heterojunction. The coupling of the rare earth erbium dopant with the valence band of the heterojunction leads to the redistribution of the electron density in the catalyst and promotes covalent interaction with the adsorbed intermediates, thereby optimizing the Gibbs free energy of intermediate adsorption and improving the catalytic activity for the hydrogen evolution reaction. Not only is an efficient and economical catalyst for electrolytic aquatic hydrogen production provided in this work, but a new synthesis scheme is also proposed for the rational synthesis of homologous core–shell polymetallic nanostructures with broad application prospects.
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| 10. |
- Xiong, Ailun, et al.
(författare)
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Determinants of Social Networks in Rural China : Does Transportation Have a Role to Play?
- 2019
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Ingår i: Social Science Quarterly. - : Wiley. - 0038-4941 .- 1540-6237. ; 100:5, s. 1709-1725
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Tidskriftsartikel (refereegranskat)abstract
- Objectives: In recent years, the origins and sources of social networks and social capital have been extensively studied. Previous studies have primarily focused on social demographic factors. To enrich our understanding of the determinants of social networks, this article explores the role of mobility in rural China. Methods: Drawing upon a data set from the Chinese General Social Survey, this article first uses clustered effect logit models and then adopts a propensity score matching (PSM) model for a robustness check. Results: The results demonstrate that citizens who have access to more advanced transportation modes and spend less time on traveling are more likely to establish weak ties, especially with nonagricultural citizens in prestigious job positions. The results also indicate that strong family ties are not the consequence of mobility. By disaggregating the full sample, this article further reveals that the young, rich, and female citizens reap more benefit from mobility. Conclusions: Given the great importance of automobiles for strengthening social networks, this article suggests that car sharing/pooling/lifting programs might be a viable solution to social network deficits in rural areas.
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| 11. |
- Xiong, Ailun, et al.
(författare)
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Queen Bees : How Is Female Managers' Happiness Determined?
- 2022
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Ingår i: Frontiers in Psychology. - : Frontiers Media SA. - 1664-1078. ; 13
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Tidskriftsartikel (refereegranskat)abstract
- This paper aims to study the determinants of subjective happiness among working females with a focus on female managers. Drawn on a large social survey data set (N = 10470) in China, this paper constructs gender development index at sub-national levels to study how institutional settings are related to female managers' happiness. We find that female managers report higher levels of happiness than non-managerial employees. However, the promoting effect is contingent on individual characteristics and social-economic settings. The full sample regression suggests that female managers behaving in a masculine way generally report a high level of happiness. Meanwhile, female managers who refuse to support gender equality report low happiness levels. Sub-sample analysis reveals that these causalities are conditioned on regional culture. Masculine behavior and gender role orientation significantly predict subjective happiness only in gender-egalitarian regions. This study is one of the first to consider both internal (individual traits) and external (social-economic environment) factors when investigating how female managers' happiness is impacted. Also, this study challenges the traditional wisdom on the relationship between female managers' job satisfaction and work-home conflict. This study extends the literature by investigating the impacts of female managers' masculine behavior on their happiness. This study is useful for promoting female managers' leadership effectiveness and happiness.
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| 12. |
- Xiong, Ailun, et al.
(författare)
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Social Capital and Total Factor Productivity : Evidence from Chinese Provinces
- 2017
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Ingår i: China & World Economy. - : Wiley-Blackwell. - 1671-2234 .- 1749-124X. ; 25:4, s. 22-43
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Tidskriftsartikel (refereegranskat)abstract
- The impact of social capital on economic development has been broadly studied by scholars. However, research in the Chinese context is relatively rare. Drawing upon data from the China General Social Survey, our results suggest that the enhancing effect of social capital on total factor productivity is very limited in the case of China. The network dimension of social capital is significant only in pooled OLS estimations, and trust as well as the participation dimension of social capital exert no impact across all estimations. Our interpretation is that this is partly due to the fact that trust, values and norms formed in civil society are inherently difficult to transmit to the market sector. Besides, the impact of social capital on economic performance is undermined when physical capital plays a significant role in production. We therefore propose that the effect of social capital on economic performance is contingent on localized social and economic conditions.
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| 13. |
- Zhang, Hongyi, 1996, et al.
(författare)
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Autonomous Navigation and Configuration of Integrated Access Backhauling for UAV Base Station Using Reinforcement Learning
- 2022
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Ingår i: Proceedings - 2022 IEEE Future Networks World Forum, FNWF 2022. - : IEEE. ; , s. 184-189, s. 184-189
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Konferensbidrag (refereegranskat)abstract
- Fast and reliable connectivity is essential to enhance situational awareness and operational efficiency for public safety mission-critical (MC) users. In emergency or disaster circumstances, where existing cellular network coverage and capacity may not be available to meet MC communication demands, deployable-network-based solutions such as cells-on-wheels/wings can be utilized swiftly to ensure reliable connection for MC users. In this paper, we consider a scenario where a macro base station (BS) is destroyed due to a natural disaster and an unmanned aerial vehicle carrying BS (UAV-BS) is set up to provide temporary coverage for users in the disaster area. The UAV-BS is integrated into the mobile network using the 5G integrated access and backhaul (IAB) technology. We propose a framework and signalling procedure for applying machine learning to this use case. A deep reinforcement learning algorithm is designed to jointly optimize the access and backhaul antenna tilt as well as the three-dimensional location of the UAV-BS in order to best serve the on-ground MC users while maintaining a good backhaul connection. Our result shows that the proposed algorithm can autonomously navigate and configure the UAV-BS to improve the throughput and reduce the drop rate of MC users.
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| 14. |
- Zhang, Hongyi, 1996, et al.
(författare)
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Deep Reinforcement Learning for Multiple Agents in a Decentralized Architecture: A Case Study in the Telecommunication Domain
- 2023
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Ingår i: Proceedings - IEEE 20th International Conference on Software Architecture Companion, ICSA-C 2023. - : IEEE COMPUTER SOC. ; , s. 183-186
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Konferensbidrag (refereegranskat)abstract
- Deep reinforcement learning has made significant development in recent years, and it is currently applied not only in simulators and games but also in embedded systems. However, when implemented in a real-world context, reinforcement learning is frequently shown to be unstable and incapable of adapting to realistic situations, particularly when directing a large number of agents. In this paper, we develop a decentralized architecture for reinforcement learning to allow multiple agents to learn optimal control policies on their own devices of the same kind but in varied environments. For such multiple agents, the traditional centralized learning algorithm usually requires a costly or time-consuming effort to develop the best-regulating policy and is incapable of scaling to a large-scale system. To address this issue, we propose a decentralized reinforcement learning algorithm (DecRL) and information exchange scheme for each individual device, in which each agent shares the individual learning experience and information with other agents based on local model training. We incorporate the algorithm into each agent in the proposed collaborative architecture and validate it in the telecommunication domain under emergency conditions, in which a macro base station (BS) is broken due to a natural disaster, and three unmanned aerial vehicles carrying BSs (UAV-BSs) are deployed to provide temporary coverage for mission-critical (MC) users in the disaster area. Based on the findings, we show that the proposed decentralized reinforcement learning algorithm can successfully support multi-agent learning, while the learning speed and service quality can be further enhanced.
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| 15. |
- Zhang, Hongyi, et al.
(författare)
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Deep Reinforcement Learning in a Dynamic Environment : A Case Study in the Telecommunication Industry
- 2022
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Ingår i: 2022 48th Euromicro Conference on Software Engineering and Advanced Applications (SEAA). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781665461528 - 9781665461535
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Konferensbidrag (refereegranskat)abstract
- Reinforcement learning, particularly deep reinforcement learning, has made remarkable progress in recent years and is now used not only in simulators and games but is also making its way into embedded systems as another software-intensive domain. However, when implemented in a real-world context, reinforcement learning is typically shown to be fragile and incapable of adapting to dynamic environments. In this paper, we provide a novel dynamic reinforcement learning algorithm for adapting to complex industrial situations. We apply and validate our approach using a telecommunications use case. The proposed algorithm can dynamically adjust the position and antenna tilt of a drone-based base station to maintain reliable wireless connectivity for mission-critical users. When compared to traditional reinforcement learning approaches, the dynamic reinforcement learning algorithm improves the overall service performance of a drone-based base station by roughly 20%. Our results demonstrate that the algorithm can quickly evolve and continuously adapt to the complex dynamic industrial environment.
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| 16. |
- Zhang, Hongyi, 1996, et al.
(författare)
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Deep Reinforcement Learning in a Dynamic Environment: A Case Study in the Telecommunication Industry
- 2022
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Ingår i: Proceedings - 48th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2022. ; , s. 68-75
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Konferensbidrag (refereegranskat)abstract
- Reinforcement learning, particularly deep reinforcement learning, has made remarkable progress in recent years and is now used not only in simulators and games but is also making its way into embedded systems as another software-intensive domain. However, when implemented in a real-world context, reinforcement learning is typically shown to be fragile and incapable of adapting to dynamic environments. In this paper, we provide a novel dynamic reinforcement learning algorithm for adapting to complex industrial situations. We apply and validate our approach using a telecommunications use case. The proposed algorithm can dynamically adjust the position and antenna tilt of a drone-based base station to maintain reliable wireless connectivity for mission-critical users. When compared to traditional reinforcement learning approaches, the dynamic reinforcement learning algorithm improves the overall service performance of a drone-based base station by roughly 20%. Our results demonstrate that the algorithm can quickly evolve and continuously adapt to the complex dynamic industrial environment.
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| 17. |
- Zhang, Hongyi, 1996, et al.
(författare)
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Multi-Agent Reinforcement Learning in Dynamic Industrial Context
- 2023
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Ingår i: Proceedings - International Computer Software and Applications Conference. - 0730-3157. ; 2023-June, s. 448-457
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Konferensbidrag (refereegranskat)abstract
- Deep reinforcement learning has advanced signifi-cantly in recent years, and it is now used in embedded systems in addition to simulators and games. Reinforcement Learning (RL) algorithms are currently being used to enhance device operation so that they can learn on their own and offer clients better services. It has recently been studied in a variety of industrial applications. However, reinforcement learning, especially when controlling a large number of agents in an industrial environment, has been demonstrated to be unstable and unable to adapt to realistic situations when used in a real-world setting. To address this problem, the goal of this study is to enable multiple reinforcement learning agents to independently learn control policies on their own in dynamic industrial contexts. In order to solve the problem, we propose a dynamic multi-agent reinforcement learning (dynamic multi-RL) method along with adaptive exploration (AE) and vector-based action selection (VAS) techniques for accelerating model convergence and adapting to a complex industrial environment. The proposed algorithm is tested for validation in emergency situations within the telecommunications industry. In such circumstances, three unmanned aerial vehicles (UAV-BSs) are used to provide temporary coverage to mission-critical (MC) customers in disaster zones when the original serving base station (BS) is destroyed by natural disasters. The algorithm directs the participating agents automatically to enhance service quality. Our findings demonstrate that the proposed dynamic multi-RL algorithm can proficiently manage the learning of multiple agents and adjust to dynamic industrial environments. Additionally, it enhances learning speed and improves the quality of service.
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| 18. |
- Zhang, Hongyi, et al.
(författare)
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Multi-Agent Reinforcement Learning in Dynamic Industrial Context
- 2023
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Ingår i: 2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC). - : Institute of Electrical and Electronics Engineers (IEEE). - 9798350326970 - 9798350326987
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Konferensbidrag (refereegranskat)abstract
- Deep reinforcement learning has advanced signifi-cantly in recent years, and it is now used in embedded systems in addition to simulators and games. Reinforcement Learning (RL) algorithms are currently being used to enhance device operation so that they can learn on their own and offer clients better services. It has recently been studied in a variety of industrial applications. However, reinforcement learning, especially when controlling a large number of agents in an industrial environment, has been demonstrated to be unstable and unable to adapt to realistic situations when used in a real-world setting. To address this problem, the goal of this study is to enable multiple reinforcement learning agents to independently learn control policies on their own in dynamic industrial contexts. In order to solve the problem, we propose a dynamic multi-agent reinforcement learning (dynamic multi-RL) method along with adaptive exploration (AE) and vector-based action selection (VAS) techniques for accelerating model convergence and adapting to a complex industrial environment. The proposed algorithm is tested for validation in emergency situations within the telecommunications industry. In such circumstances, three unmanned aerial vehicles (UAV-BSs) are used to provide temporary coverage to mission-critical (MC) customers in disaster zones when the original serving base station (BS) is destroyed by natural disasters. The algorithm directs the participating agents automatically to enhance service quality. Our findings demonstrate that the proposed dynamic multi-RL algorithm can proficiently manage the learning of multiple agents and adjust to dynamic industrial environments. Additionally, it enhances learning speed and improves the quality of service.
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| 19. |
- Zhao, Xue, et al.
(författare)
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Efficient degradation of Health-threatening organic pollutants in water by atomically dispersed Cobalt-Activated peroxymonosulfate
- 2022
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Ingår i: Chemical Engineering Journal. - : Elsevier. - 1385-8947 .- 1873-3212. ; 450
-
Tidskriftsartikel (refereegranskat)abstract
- Degrading health-threatening organic pollutants (HTOPs) in water systems through advanced oxidation processes (AOPs) is an effective way to treat environmental wastewater; however, such processes require advanced catalysts. This study combined complexation effects and structural confinement strategies to rapidly prepare Co2+-isolated metal–organic framework polymers and utilized a thermal treatment process to achieve the efficient anchoring of atom-dispersed Co in a boron–carbon-nitrogen matrix (denoted as SACoN/BCN), which can improve the utilization of Co catalytic sites. SACoN/BCN effectively activated peroxymonosulfate (PMS), with the ratio and mineralization rate of sulfamethazine (SMT) removed by degradation within 40 min reached 95.2 % and 70.0 %, respectively. Radical inhibition experiments and electron paramagnetic resonance (EPR) tests showed that 1O2 generated from SACoN/BCN-activated PMS was the key reactive oxygen species that promoted HTOP degradation. Density functional theory calculations revealed that, following the introduction of electron-deficient B heteroatoms, electrons in PMS will be injected into SACoN/BCN, thereby realizing strong adsorption and further activation of PMS. The cytotoxicity of SACoN/BCN is almost negligible because of the chemical bonding (or entrapment) of Co atoms in the inorganic boron–carbon-nitrogen matrix, thereby preventing Co from forming mobile CoII ions in the aqueous system. This research provides information for advanced catalysts for the removal of HTOPs and experimental and theoretical inspiration for the preparation of single-atom catalysts for advanced oxidation processes and the mechanism of PMS activation.
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