In the international shipping industry, digital intelligence transformation has become essential, with both governments and enterprises actively working to integrate diverse datasets. The domain of maritime and shippi...In the international shipping industry, digital intelligence transformation has become essential, with both governments and enterprises actively working to integrate diverse datasets. The domain of maritime and shipping is characterized by a vast array of document types, filled with complex, large-scale, and often chaotic knowledge and relationships. Effectively managing these documents is crucial for developing a Large Language Model (LLM) in the maritime domain, enabling practitioners to access and leverage valuable information. A Knowledge Graph (KG) offers a state-of-the-art solution for enhancing knowledge retrieval, providing more accurate responses and enabling context-aware reasoning. This paper presents a framework for utilizing maritime and shipping documents to construct a knowledge graph using GraphRAG, a hybrid tool combining graph-based retrieval and generation capabilities. The extraction of entities and relationships from these documents and the KG construction process are detailed. Furthermore, the KG is integrated with an LLM to develop a Q&A system, demonstrating that the system significantly improves answer accuracy compared to traditional LLMs. Additionally, the KG construction process is up to 50% faster than conventional LLM-based approaches, underscoring the efficiency of our method. This study provides a promising approach to digital intelligence in shipping, advancing knowledge accessibility and decision-making.展开更多
Anion exchange membrane fuel cell(AEMFC)technology is attracting intensive attention,due to its great potential by using non-precious-metal catalysts(NPMCs)in the cathode and cheap bipolar plate materials in alkaline ...Anion exchange membrane fuel cell(AEMFC)technology is attracting intensive attention,due to its great potential by using non-precious-metal catalysts(NPMCs)in the cathode and cheap bipolar plate materials in alkaline media.However,in such case,the kinetics of hydrogen oxidation reaction(HOR)in the anode is two orders of magnitude sluggish than that of acidic electrolytes,which is recognized as the grand challenge in this field.Herein,we report the rationally designed Ni nanoparticles encapsulated by N-doped graphene layers(Ni@NG)using a facile pyrolysis strategy.Based on the density functional theory calculations and electrochemical performance analysis,it is witnessed that the rich Pyridinic-N within the graphene shell optimizes the binding energy of the intermediates,thus enabling the fundamentally enhanced activity for HOR with robust stability.As a proof of concept,the resultant Ni@NG sample as the anode with a low loading(1.8 mg cm^(-2))in AEMFCs delivers a high peak power density of 500 mW cm^(-2),outperforming all of those of NPMC-based analogs ever reported.展开更多
BACKGROUND： Structural and functional synaptic changes, as well as blood-brain barrier （BBB） changes, affect the micro-environment of nervous tissue and excitation, both of which play an important role in epilepsy....BACKGROUND： Structural and functional synaptic changes, as well as blood-brain barrier （BBB） changes, affect the micro-environment of nervous tissue and excitation, both of which play an important role in epilepsy. OBJECTIVE： To observe synaptic and BBB ultrastructural changes in the motor cortex of a rat epilepsy model induced by coriaria lacton, and to investigate the synaptic and BBB effects on the mechanism of epilepsy. DESIGN： A randomized controlled animal experiment. SETTING： Department of Histology and Embryology, Luzhou Medical College; and Electron Microscopy Laboratory, Luzhou Medical College. MATERIALS： Twenty healthy male Sprague Dawley rats, aged 8 weeks, were chosen for this study. The rats weighed （280 ± 50） g and were supplied by the Experimental Animal Center of Luzhou Medical College. Experimentation was performed in accordance with the ethical guidelines for the use and care of animals. The animals were randomly divided into a control group and an epilepsy group, with 10 rats in each group. METHODS： This study was performed at the Department of Histology and Embryology, and Electron Microscopy Laboratory, Luzhou Medical College between February and December 2006. According to the protocol, the epilepsy group was injected with 10 μ L/100 g coriaria lacton into the lateral ventricles to establish an epileptic model. The control group rats were not administered anything. Eight days after the model was established, all rats were anesthetized with ether. The motor cortex was removed and sectioned into ultrathin sections. Synaptic and BBB ultrastructural changes were observed by electron microscopy. MAIN OUTCOME MEASURES： （1）Structural changes of three different parts of the synapses, synaptic cleft width, postsynaptic density thickness, proportion of perforation synapses, curvature of synaptic interface, and length of active zones. （2）Capillary and BBB changes （endothelium, basement membrane, pericyte, and the astrocyte endfeet）. RESULTS： （1）Curvature of synaptic interface, length of active zones, thickness of postsynaptic density, and percentage of perforation synapses increased significantly. （2）There was significant edema in the endothelium, basement membrane, and the pericyte of the epilepsy group; the electron density of the basement membrane was reduced. CONCLUSION： （1） The coriaria lacton treatment altered synaptic ultrastructure, as well as BBB characteristics, in the epileptic rat model, and also improved synaptic transmission efficiency, as well as BBB permeability; （2）Synaptic and BBB ultrastructural changes might play an important role in the mechanism of epilepsy.展开更多
AIM: To investigate the effect of Girdin knockdown on the chemosensitivity of colorectal cancer cells to oxaliplatin and the possible mechanisms involved.
中子辐射俘获反应在反应堆运行、核装置设计及核天体物理研究中起重要的作用.4πBaF_(2)探测装置有着高时间分辨能力、低中子灵敏度、高探测效率等优点,适合开展中子辐射俘获反应截面数据的测量.中国原子能科学研究院核数据重点实验室...中子辐射俘获反应在反应堆运行、核装置设计及核天体物理研究中起重要的作用.4πBaF_(2)探测装置有着高时间分辨能力、低中子灵敏度、高探测效率等优点,适合开展中子辐射俘获反应截面数据的测量.中国原子能科学研究院核数据重点实验室建立了伽马全吸收装置(Gamma total absorption facility,GTAF),该装置用28块六棱BaF_(2)晶体和12块五棱BaF_(2)晶体构成了外径25 cm,内径10 cm的球壳,覆盖了95.2%的立体角.利用GTAF在中国散裂中子源Back-n束线上,测量了197Au(n,γ)的反应截面数据.测量数据通过能量筛选、PSD方法、晶体多重性筛选进行了初步本底扣除,随后结合对^(nat)C及空样品的测量数据对本底进行了分析及扣除,获得了197Au俘获反应的产额,利用SAMMY程序拟合得到了^(197)Au在1—100 e V的共振能量、中子共振宽度和伽马共振宽度参数.实验测量结果与ENDF/B-VIII.0数据库符合良好,其共振参数存在一定差异,分析原因可能与GTAF能量分辨率、Back-n的中子能谱测量精度、以及实验本底扣除方法相关,这也是下一步工作的重点.展开更多
As global warming caused by greenhouse gases grows (GHGs) into a global environmental threat, carbon dioxide emissions are drawing increasing attention in these years. Among all emission sources, transportation is a m...As global warming caused by greenhouse gases grows (GHGs) into a global environmental threat, carbon dioxide emissions are drawing increasing attention in these years. Among all emission sources, transportation is a major contributor to climate change because of its high dependence on fossil fuels. The International Maritime Organization (IMO) has therefore been promoting the reduction of fuel usage and carbon emissions for container ships by such measures as improving shipping route selection, shipping speed optimization, and constructing clean energy propulsion systems. In this paper, a review of the impact of carbon dioxide emissions on climate change is presented;the current situations of carbon dioxide emissions, decarbonizing methods, IMO regulations, and possible future directions of decarbonizing in the maritime transportation industry are also discussed. Based on the result, it is found that in the case that non intelligent ships still occupy the vast majority of operating ships, the use of new energy as the main propulsion fuel has the defects of high renewal cost and long effective period. It is more likely to achieve energy conservation and emission reduction in the shipping industry in a short period of time by using intelligent means and artificial intelligence to assist ship operation. .展开更多
Ship energy consumption and emission prediction are the main concern of the shipping industry for ship energy efficiency management and pollution gas emission control. And they are attracting more global attention and...Ship energy consumption and emission prediction are the main concern of the shipping industry for ship energy efficiency management and pollution gas emission control. And they are attracting more global attention and research interests because of the increase in global shipping trade volume. As the core of maritime transportation, a large volume of data is collected around ships such as voyage data. Due to the rapid development of computational power and the widely equipped AIS device on ships, the use of maritime big data for improving and monitoring ship’s energy efficiency is becoming possible. In this paper, a fuel consumption and carbon emission model using the artificial neural network (ANN) framework is proposed by using AIS, ship machinery, and weather data. The proposed work is a complete framework including data collection, data cleaning, data clustering and model-building methodology. To obtain the suitable parameters of the model, the number of neurons, data inputs and activate functions were tested on both AIS-based data and MRV-based data for comparison. The results show that the proposed method can provide a solid prediction of ship’s fuel consumption and carbon emissions under varying weather conditions.展开更多
文摘In the international shipping industry, digital intelligence transformation has become essential, with both governments and enterprises actively working to integrate diverse datasets. The domain of maritime and shipping is characterized by a vast array of document types, filled with complex, large-scale, and often chaotic knowledge and relationships. Effectively managing these documents is crucial for developing a Large Language Model (LLM) in the maritime domain, enabling practitioners to access and leverage valuable information. A Knowledge Graph (KG) offers a state-of-the-art solution for enhancing knowledge retrieval, providing more accurate responses and enabling context-aware reasoning. This paper presents a framework for utilizing maritime and shipping documents to construct a knowledge graph using GraphRAG, a hybrid tool combining graph-based retrieval and generation capabilities. The extraction of entities and relationships from these documents and the KG construction process are detailed. Furthermore, the KG is integrated with an LLM to develop a Q&A system, demonstrating that the system significantly improves answer accuracy compared to traditional LLMs. Additionally, the KG construction process is up to 50% faster than conventional LLM-based approaches, underscoring the efficiency of our method. This study provides a promising approach to digital intelligence in shipping, advancing knowledge accessibility and decision-making.
基金financially funded by the Natural Science Foundation of Ningbo(No.2022J139)the Ningbo Yongjiang Talent Introduction Programme(No.2022A-227-G)+5 种基金the National Natural Science Foundation of China(No.51972178)the financial support from Scientific and Technological Bases and Talents of Guangxi(Guike AD21075051)the National Natural Science Foundation of China(12174075)the special fund for“Guangxi Bagui Scholars”support by ERC-CZ program(project LL2101)from the Ministry of Education Youth and Sports(MEYS)by the project Advanced Functional Nanorobots(reg.No.CZ.02.1.01/0.0/0.0/15_003/0000444 financed by the EFRR)
文摘Anion exchange membrane fuel cell(AEMFC)technology is attracting intensive attention,due to its great potential by using non-precious-metal catalysts(NPMCs)in the cathode and cheap bipolar plate materials in alkaline media.However,in such case,the kinetics of hydrogen oxidation reaction(HOR)in the anode is two orders of magnitude sluggish than that of acidic electrolytes,which is recognized as the grand challenge in this field.Herein,we report the rationally designed Ni nanoparticles encapsulated by N-doped graphene layers(Ni@NG)using a facile pyrolysis strategy.Based on the density functional theory calculations and electrochemical performance analysis,it is witnessed that the rich Pyridinic-N within the graphene shell optimizes the binding energy of the intermediates,thus enabling the fundamentally enhanced activity for HOR with robust stability.As a proof of concept,the resultant Ni@NG sample as the anode with a low loading(1.8 mg cm^(-2))in AEMFCs delivers a high peak power density of 500 mW cm^(-2),outperforming all of those of NPMC-based analogs ever reported.
基金the Natural Science Foundation of Sichuan Educational Bureau,No.(2001)149-01LA40the Natural Science Foundation of Sichuan Bureau of Science and Technology,No.(2003) 14-05JY029-103
文摘BACKGROUND： Structural and functional synaptic changes, as well as blood-brain barrier （BBB） changes, affect the micro-environment of nervous tissue and excitation, both of which play an important role in epilepsy. OBJECTIVE： To observe synaptic and BBB ultrastructural changes in the motor cortex of a rat epilepsy model induced by coriaria lacton, and to investigate the synaptic and BBB effects on the mechanism of epilepsy. DESIGN： A randomized controlled animal experiment. SETTING： Department of Histology and Embryology, Luzhou Medical College; and Electron Microscopy Laboratory, Luzhou Medical College. MATERIALS： Twenty healthy male Sprague Dawley rats, aged 8 weeks, were chosen for this study. The rats weighed （280 ± 50） g and were supplied by the Experimental Animal Center of Luzhou Medical College. Experimentation was performed in accordance with the ethical guidelines for the use and care of animals. The animals were randomly divided into a control group and an epilepsy group, with 10 rats in each group. METHODS： This study was performed at the Department of Histology and Embryology, and Electron Microscopy Laboratory, Luzhou Medical College between February and December 2006. According to the protocol, the epilepsy group was injected with 10 μ L/100 g coriaria lacton into the lateral ventricles to establish an epileptic model. The control group rats were not administered anything. Eight days after the model was established, all rats were anesthetized with ether. The motor cortex was removed and sectioned into ultrathin sections. Synaptic and BBB ultrastructural changes were observed by electron microscopy. MAIN OUTCOME MEASURES： （1）Structural changes of three different parts of the synapses, synaptic cleft width, postsynaptic density thickness, proportion of perforation synapses, curvature of synaptic interface, and length of active zones. （2）Capillary and BBB changes （endothelium, basement membrane, pericyte, and the astrocyte endfeet）. RESULTS： （1）Curvature of synaptic interface, length of active zones, thickness of postsynaptic density, and percentage of perforation synapses increased significantly. （2）There was significant edema in the endothelium, basement membrane, and the pericyte of the epilepsy group; the electron density of the basement membrane was reduced. CONCLUSION： （1） The coriaria lacton treatment altered synaptic ultrastructure, as well as BBB characteristics, in the epileptic rat model, and also improved synaptic transmission efficiency, as well as BBB permeability; （2）Synaptic and BBB ultrastructural changes might play an important role in the mechanism of epilepsy.
基金Supported by National Natural Science Foundation of China,No.81272480/H1609
文摘AIM: To investigate the effect of Girdin knockdown on the chemosensitivity of colorectal cancer cells to oxaliplatin and the possible mechanisms involved.
文摘中子辐射俘获反应在反应堆运行、核装置设计及核天体物理研究中起重要的作用.4πBaF_(2)探测装置有着高时间分辨能力、低中子灵敏度、高探测效率等优点,适合开展中子辐射俘获反应截面数据的测量.中国原子能科学研究院核数据重点实验室建立了伽马全吸收装置(Gamma total absorption facility,GTAF),该装置用28块六棱BaF_(2)晶体和12块五棱BaF_(2)晶体构成了外径25 cm,内径10 cm的球壳,覆盖了95.2%的立体角.利用GTAF在中国散裂中子源Back-n束线上,测量了197Au(n,γ)的反应截面数据.测量数据通过能量筛选、PSD方法、晶体多重性筛选进行了初步本底扣除,随后结合对^(nat)C及空样品的测量数据对本底进行了分析及扣除,获得了197Au俘获反应的产额,利用SAMMY程序拟合得到了^(197)Au在1—100 e V的共振能量、中子共振宽度和伽马共振宽度参数.实验测量结果与ENDF/B-VIII.0数据库符合良好,其共振参数存在一定差异,分析原因可能与GTAF能量分辨率、Back-n的中子能谱测量精度、以及实验本底扣除方法相关,这也是下一步工作的重点.
文摘As global warming caused by greenhouse gases grows (GHGs) into a global environmental threat, carbon dioxide emissions are drawing increasing attention in these years. Among all emission sources, transportation is a major contributor to climate change because of its high dependence on fossil fuels. The International Maritime Organization (IMO) has therefore been promoting the reduction of fuel usage and carbon emissions for container ships by such measures as improving shipping route selection, shipping speed optimization, and constructing clean energy propulsion systems. In this paper, a review of the impact of carbon dioxide emissions on climate change is presented;the current situations of carbon dioxide emissions, decarbonizing methods, IMO regulations, and possible future directions of decarbonizing in the maritime transportation industry are also discussed. Based on the result, it is found that in the case that non intelligent ships still occupy the vast majority of operating ships, the use of new energy as the main propulsion fuel has the defects of high renewal cost and long effective period. It is more likely to achieve energy conservation and emission reduction in the shipping industry in a short period of time by using intelligent means and artificial intelligence to assist ship operation. .
文摘Ship energy consumption and emission prediction are the main concern of the shipping industry for ship energy efficiency management and pollution gas emission control. And they are attracting more global attention and research interests because of the increase in global shipping trade volume. As the core of maritime transportation, a large volume of data is collected around ships such as voyage data. Due to the rapid development of computational power and the widely equipped AIS device on ships, the use of maritime big data for improving and monitoring ship’s energy efficiency is becoming possible. In this paper, a fuel consumption and carbon emission model using the artificial neural network (ANN) framework is proposed by using AIS, ship machinery, and weather data. The proposed work is a complete framework including data collection, data cleaning, data clustering and model-building methodology. To obtain the suitable parameters of the model, the number of neurons, data inputs and activate functions were tested on both AIS-based data and MRV-based data for comparison. The results show that the proposed method can provide a solid prediction of ship’s fuel consumption and carbon emissions under varying weather conditions.
