Scientific research is a journey into an uncharted territory.Researchers need to have the big picture for navigation and at the same time be detail-oriented,as details make a difference.Here I offer a few tips for con...Scientific research is a journey into an uncharted territory.Researchers need to have the big picture for navigation and at the same time be detail-oriented,as details make a difference.Here I offer a few tips for conducting research that I summarized based on my 30+years of research experience.展开更多
https://www.sciencedirect.com/journal/energy-and-buildings/vol/328/suppl/C Volume 328,1 February 2025[OA](1)Best practices of techno-economic methods for solar photovoltaic coupled heat pump analysis in cold climates ...https://www.sciencedirect.com/journal/energy-and-buildings/vol/328/suppl/C Volume 328,1 February 2025[OA](1)Best practices of techno-economic methods for solar photovoltaic coupled heat pump analysis in cold climates by Shafquat Rana,Nelson Sommerfeldt,Joshua M.Pearce,Article 115196 Abstract:One of the most promising methods of decarbonizing the global building heating and cooling load is with solar photovoltaic(PV)powered heat pumps(HP).The complex nature of these systems and the interdependent interactions between each technology and the energy markets involve various sophisticated models to simulate accurately.This often leaves model descriptions lacking,particularly when qualitative discussion is required.This article reviews the models that exist and provides best practices for designing and simulating PV+HP systems of various complexities.The key performance indicators for electricity generation and total life cycle cost are summarized.This article then provides a detailed and comprehensive method for the techno-economic analysis of heat pumps powered with PV using an example of North American cold climates.For each component of the system。展开更多
Liver transplantation,as an effective therapy for patients with liver cancer,plays an important role in improving the quality of life of patients.However,the com-plexity and trauma of liver transplantation can easily ...Liver transplantation,as an effective therapy for patients with liver cancer,plays an important role in improving the quality of life of patients.However,the com-plexity and trauma of liver transplantation can easily lead to the occurrence of malnutrition in patients,and then increase the risk of postoperative complica-tions,which has aroused widespread clinical attention.Reasonable nutritional support can not only maintain the stability of the body’s internal environment,reduce the occurrence of complications,but also promote the recovery of liver and other organ functions.In recent years,with the in-depth understanding of nut-ritional metabolism after liver transplantation,the application of enteral nutrition and parenteral nutrition in nutritional support after liver transplantation has been increasingly extensive and achieved remarkable results.This paper discusses the effect of early postoperative nutritional intervention on patients with liver cancer and liver transplantation,and combined with its mechanism of action,can better understand the effectiveness of intervention,and provide reference for the deve-lopment of scientific and reasonable nutritional support programs in clinical pra-ctice.展开更多
The rise of social media platforms has revolutionized communication, enabling the exchange of vast amounts of data through text, audio, images, and videos. These platforms have become critical for sharing opinions and...The rise of social media platforms has revolutionized communication, enabling the exchange of vast amounts of data through text, audio, images, and videos. These platforms have become critical for sharing opinions and insights, influencing daily habits, and driving business, political, and economic decisions. Text posts are particularly significant, and natural language processing (NLP) has emerged as a powerful tool for analyzing such data. While traditional NLP methods have been effective for structured media, social media content poses unique challenges due to its informal and diverse nature. This has spurred the development of new techniques tailored for processing and extracting insights from unstructured user-generated text. One key application of NLP is the summarization of user comments to manage overwhelming content volumes. Abstractive summarization has proven highly effective in generating concise, human-like summaries, offering clear overviews of key themes and sentiments. This enhances understanding and engagement while reducing cognitive effort for users. For businesses, summarization provides actionable insights into customer preferences and feedback, enabling faster trend analysis, improved responsiveness, and strategic adaptability. By distilling complex data into manageable insights, summarization plays a vital role in improving user experiences and empowering informed decision-making in a data-driven landscape. This paper proposes a new implementation framework by fine-tuning and parameterizing Transformer Large Language Models to manage and maintain linguistic and semantic components in abstractive summary generation. The system excels in transforming large volumes of data into meaningful summaries, as evidenced by its strong performance across metrics like fluency, consistency, readability, and semantic coherence.展开更多
We demonstrate a multi-method approach towards discovering and structuring sustainability transition knowl edge in marginalized mountain regions.By employing reflective thinking,artificial intelligence(AI)-powered tex...We demonstrate a multi-method approach towards discovering and structuring sustainability transition knowl edge in marginalized mountain regions.By employing reflective thinking,artificial intelligence(AI)-powered text summarization and text mining,we synthesize experts’narratives on sustainable development challenges and solutions in Kardüz Upland,Türkiye.We then analyze their alignment with the UN Sustainable Development Goals(SDGs)using document embedding.Investment in infrastructure,education,and resilient socio-ecological systems emerged as priority sectors to combat poor infrastructure,geographic isolation,climate change,poverty,depopulation,unemployment,low education levels,and inadequate social services.The narratives were closest in substance to SDG 1,3,and 11.Social dimensions of sustainability were more pronounced than environmental dimensions.The presented approach supports policymakers in organizing loosely structured sustainability tran sition knowledge and fragmented data corpora,while also advancing AI applications for designing and planning sustainable development policies at the regional level.展开更多
We analyze the suitability of existing pre-trained transformer-based language models(PLMs)for abstractive text summarization on German technical healthcare texts.The study focuses on the multilingual capabilities of t...We analyze the suitability of existing pre-trained transformer-based language models(PLMs)for abstractive text summarization on German technical healthcare texts.The study focuses on the multilingual capabilities of these models and their ability to perform the task of abstractive text summarization in the healthcare field.The research hypothesis was that large language models could perform high-quality abstractive text summarization on German technical healthcare texts,even if the model is not specifically trained in that language.Through experiments,the research questions explore the performance of transformer language models in dealing with complex syntax constructs,the difference in performance between models trained in English and German,and the impact of translating the source text to English before conducting the summarization.We conducted an evaluation of four PLMs(GPT-3,a translation-based approach also utilizing GPT-3,a German language Model,and a domain-specific bio-medical model approach).The evaluation considered the informativeness using 3 types of metrics based on Recall-Oriented Understudy for Gisting Evaluation(ROUGE)and the quality of results which is manually evaluated considering 5 aspects.The results show that text summarization models could be used in the German healthcare domain and that domain-independent language models achieved the best results.The study proves that text summarization models can simplify the search for pre-existing German knowledge in various domains.展开更多
INSPIRED by the insight from American political scientist Lasswell, who summarized the environmental role in societal surveillance [1], Schramm coined the term “social radar” [2] as it resembles the activities of ra...INSPIRED by the insight from American political scientist Lasswell, who summarized the environmental role in societal surveillance [1], Schramm coined the term “social radar” [2] as it resembles the activities of radar in collecting and processing information, playing a crucial role in helping humans perceive changes in the internal and external environment and promptly adjusting adaptive behaviors.展开更多
A long history has passed since electromyography(EMG)signals have been explored in human-centered robots for intuitive interaction.However,it still has a gap between scientific research and real-life applications.Prev...A long history has passed since electromyography(EMG)signals have been explored in human-centered robots for intuitive interaction.However,it still has a gap between scientific research and real-life applications.Previous studies mainly focused on EMG decoding algorithms,leaving a dynamic relationship between the human,robot,and uncertain environment in real-life scenarios seldomly concerned.To fill this gap,this paper presents a comprehensive review of EMG-based techniques in human-robot-environment interaction(HREI)systems.The general processing framework is summarized,and three interaction paradigms,including direct control,sensory feedback,and partial autonomous control,are introduced.EMG-based intention decoding is treated as a module of the proposed paradigms.Five key issues involving precision,stability,user attention,compliance,and environmental awareness in this field are discussed.Several important directions,including EMG decomposition,robust algorithms,HREI dataset,proprioception feedback,reinforcement learning,and embodied intelligence,are proposed to pave the way for future research.To the best of what we know,this is the first time that a review of EMG-based methods in the HREI system is summarized.It provides a novel and broader perspective to improve the practicability of current myoelectric interaction systems,in which factors in human-robot interaction,robot-environment interaction,and state perception by human sensations are considered,which has never been done by previous studies.展开更多
Summary:The reviews of this special column are summarized as follows:Time-dependent diffusion magnetic resonance imaging:measurement,modeling,and applications Ba RC,Kang LY,Wu D,2024.25(10):765-787.htps://doi.org/10.1...Summary:The reviews of this special column are summarized as follows:Time-dependent diffusion magnetic resonance imaging:measurement,modeling,and applications Ba RC,Kang LY,Wu D,2024.25(10):765-787.htps://doi.org/10.1631/jzus A2400139 Dan WU and her colleagues from the College of Biomedical Engineering&Instrument Science,Zhejiang University(ZJU)studied the measurement,modeling,and applications of time-dependent diffusion magnetic resonance imaging.This method reveals time-related changes in the diffusional behavior of water molecules in biological tissues,thereby enabling us to probe related microstructure events.展开更多
China has pledged to the world to achieve carbon peak in 10 years and carbon neutrality in 30 years.This is an extremely arduous task,as it faces numerous challenges,including high energy consumption,heavy reliance on...China has pledged to the world to achieve carbon peak in 10 years and carbon neutrality in 30 years.This is an extremely arduous task,as it faces numerous challenges,including high energy consumption,heavy reliance on coal within its energy mix,and a large base of carbon emissions that must be controlled.To this end,it is necessary to advance the new energy security strategy of“Four Revolutions,One Cooperation”to a deeper level.According to interpretations from various parties,the new energy system is preliminarily summarized to have six features:new energy structure,new system form,new industrial system,new governance system,new system and mechanism,and new regulatory method.Considering building a new energy system comprehensively,“Ten Commitments”have been proposed to help achieve the dual-carbon goals.The specific measures include:ensuring the security and stability of energy supply,accelerating the transformation to green and low-carbon energy,giving priority to energy conservation and efficiency improvement,promoting multi-energy complementation and synergistic and integrated development,enhancing the digital intelligence level in the energy industry,developing centralized and distributed energy,advancing the rural energy revolution,developing critical and core technological equipment and the comprehensive energy service industry,and promoting high-quality development of the Belt and Road Initiative.展开更多
Oirat dialect is a unique Mongolian dialect,having its own writing system.This paper analyzed the grammatical research of the Oirat dialect,and summarized the achievements and existing problems of previous studies,and...Oirat dialect is a unique Mongolian dialect,having its own writing system.This paper analyzed the grammatical research of the Oirat dialect,and summarized the achievements and existing problems of previous studies,and provided suggestions about strengthening comprehensive in-depth study of morphology,syntax and word formation in this Mongolian dialect.展开更多
Identifying rare patterns for medical diagnosis is a challenging task due to heterogeneity and the volume of data.Data summarization can create a concise version of the original data that can be used for effective dia...Identifying rare patterns for medical diagnosis is a challenging task due to heterogeneity and the volume of data.Data summarization can create a concise version of the original data that can be used for effective diagnosis.In this paper,we propose an ensemble summarization method that combines clustering and sampling to create a summary of the original data to ensure the inclusion of rare patterns.To the best of our knowledge,there has been no such technique available to augment the performance of anomaly detection techniques and simultaneously increase the efficiency of medical diagnosis.The performance of popular anomaly detection algorithms increases significantly in terms of accuracy and computational complexity when the summaries are used.Therefore,the medical diagnosis becomes more effective,and our experimental results reflect that the combination of the proposed summarization scheme and all underlying algorithms used in this paper outperforms the most popular anomaly detection techniques.展开更多
Video summarization aims to select key frames or key shots to create summaries for fast retrieval,compression,and efficient browsing of videos.Graph neural networks efficiently capture information about graph nodes an...Video summarization aims to select key frames or key shots to create summaries for fast retrieval,compression,and efficient browsing of videos.Graph neural networks efficiently capture information about graph nodes and their neighbors,but ignore the dynamic dependencies between nodes.To address this challenge,we propose an innovative Adaptive Graph Convolutional Adjacency Matrix Network(TAMGCN),leveraging the attention mechanism to dynamically adjust dependencies between graph nodes.Specifically,we first segment shots and extract features of each frame,then compute the representative features of each shot.Subsequently,we utilize the attention mechanism to dynamically adjust the adjacency matrix of the graph convolutional network to better capture the dynamic dependencies between graph nodes.Finally,we fuse temporal features extracted by Bi-directional Long Short-Term Memory network with structural features extracted by the graph convolutional network to generate high-quality summaries.Extensive experiments are conducted on two benchmark datasets,TVSum and SumMe,yielding F1-scores of 60.8%and 53.2%,respectively.Experimental results demonstrate that our method outperforms most state-of-the-art video summarization techniques.展开更多
The rapid expansion of online content and big data has precipitated an urgent need for efficient summarization techniques to swiftly comprehend vast textual documents without compromising their original integrity.Curr...The rapid expansion of online content and big data has precipitated an urgent need for efficient summarization techniques to swiftly comprehend vast textual documents without compromising their original integrity.Current approaches in Extractive Text Summarization(ETS)leverage the modeling of inter-sentence relationships,a task of paramount importance in producing coherent summaries.This study introduces an innovative model that integrates Graph Attention Networks(GATs)with Transformer-based Bidirectional Encoder Representa-tions from Transformers(BERT)and Latent Dirichlet Allocation(LDA),further enhanced by Term Frequency-Inverse Document Frequency(TF-IDF)values,to improve sentence selection by capturing comprehensive topical information.Our approach constructs a graph with nodes representing sentences,words,and topics,thereby elevating the interconnectivity and enabling a more refined understanding of text structures.This model is stretched to Multi-Document Summarization(MDS)from Single-Document Summarization,offering significant improvements over existing models such as THGS-GMM and Topic-GraphSum,as demonstrated by empirical evaluations on benchmark news datasets like Cable News Network(CNN)/Daily Mail(DM)and Multi-News.The results consistently demonstrate superior performance,showcasing the model’s robustness in handling complex summarization tasks across single and multi-document contexts.This research not only advances the integration of BERT and LDA within a GATs but also emphasizes our model’s capacity to effectively manage global information and adapt to diverse summarization challenges.展开更多
With the help of pre-trained language models,the accuracy of the entity linking task has made great strides in recent years.However,most models with excellent performance require fine-tuning on a large amount of train...With the help of pre-trained language models,the accuracy of the entity linking task has made great strides in recent years.However,most models with excellent performance require fine-tuning on a large amount of training data using large pre-trained language models,which is a hardware threshold to accomplish this task.Some researchers have achieved competitive results with less training data through ingenious methods,such as utilizing information provided by the named entity recognition model.This paper presents a novel semantic-enhancement-based entity linking approach,named semantically enhanced hardware-friendly entity linking(SHEL),which is designed to be hardware friendly and efficient while maintaining good performance.Specifically,SHEL's semantic enhancement approach consists of three aspects:(1)semantic compression of entity descriptions using a text summarization model;(2)maximizing the capture of mention contexts using asymmetric heuristics;(3)calculating a fixed size mention representation through pooling operations.These series of semantic enhancement methods effectively improve the model's ability to capture semantic information while taking into account the hardware constraints,and significantly improve the model's convergence speed by more than 50%compared with the strong baseline model proposed in this paper.In terms of performance,SHEL is comparable to the previous method,with superior performance on six well-established datasets,even though SHEL is trained using a smaller pre-trained language model as the encoder.展开更多
Text representation is a key aspect in determining the success of various text summarizing techniques.Summarization using pretrained transformer models has produced encouraging results.Yet the scope of applying these ...Text representation is a key aspect in determining the success of various text summarizing techniques.Summarization using pretrained transformer models has produced encouraging results.Yet the scope of applying these models in medical and drug discovery is not examined to a proper extent.To address this issue,this article aims to perform extractive summarization based on fine-tuned transformers pertaining to drug and medical domain.This research also aims to enhance sentence representation.Exploring the extractive text summarization aspects of medical and drug discovery is a challenging task as the datasets are limited.Hence,this research concentrates on the collection of abstracts collected from PubMed for various domains of medical and drug discovery such as drug and COVID,with a total capacity of 1,370 abstracts.A detailed experimentation using BART(Bidirectional Autoregressive Transformer),T5(Text-to-Text Transfer Transformer),LexRank,and TexRank for the analysis of the dataset is carried out in this research to perform extractive text summarization.展开更多
Retrieving information from evolving digital data collection using a user’s query is always essential and needs efficient retrieval mechanisms that help reduce the required time from such massive collections.Large-sc...Retrieving information from evolving digital data collection using a user’s query is always essential and needs efficient retrieval mechanisms that help reduce the required time from such massive collections.Large-scale time consumption is certain to scan and analyze to retrieve the most relevant textual data item from all the documents required a sophisticated technique for a query against the document collection.It is always challenging to retrieve a more accurate and fast retrieval from a large collection.Text summarization is a dominant research field in information retrieval and text processing to locate the most appropriate data object as single or multiple documents from the collection.Machine learning and knowledge-based techniques are the two query-based extractive text summarization techniques in Natural Language Processing(NLP)which can be used for precise retrieval and are considered to be the best option.NLP uses machine learning approaches for both supervised and unsupervised learning for calculating probabilistic features.The study aims to propose a hybrid approach for query-based extractive text summarization in the research study.Text-Rank Algorithm is used as a core algorithm for the flow of an implementation of the approach to gain the required goals.Query-based text summarization of multiple documents using a hybrid approach,combining the K-Means clustering technique with Latent Dirichlet Allocation(LDA)as topic modeling technique produces 0.288,0.631,and 0.328 for precision,recall,and F-score,respectively.The results show that the proposed hybrid approach performs better than the graph-based independent approach and the sentences and word frequency-based approach.展开更多
Irritable bowel syndrome(IBS-D)with diarrhea is a common gastrointestinal functional disease in clinical practice,which seriously affects the quality of life of patients.Cur‐rently,Western medicine has poor therapeut...Irritable bowel syndrome(IBS-D)with diarrhea is a common gastrointestinal functional disease in clinical practice,which seriously affects the quality of life of patients.Cur‐rently,Western medicine has poor therapeutic effects,while traditional Chinese medi‐cine has unique advantages in relieving IBS-D symptoms and preventing recurrence.In recent years,especially with external treatment of traditional Chinese medicine,it has become a new treatment direction in clinical practice and has achieved good therapeutic effects.This article will provide a review of recent research on the treatment of IBS-D using traditional Chinese medicine external treatment methods.展开更多
The special issue deals with the Cretaceous-Tertiary deserts in southwestern China and relevant sedimentological problems about deserts. The study area is located between 101°10＇- 107°00＇E and 28°00＇...The special issue deals with the Cretaceous-Tertiary deserts in southwestern China and relevant sedimentological problems about deserts. The study area is located between 101°10＇- 107°00＇E and 28°00＇-30°40′ N. The ancient desert study is so little in China that there has not been a systematic report about it up to now. Based on the study in many ways on Cretaceous-Tertiary deserts in southwestern China, plentiful data have been obtained. Though it is still a bit rough, the study is a good beginning of the ancient desert study in China. The main ideas and conclusions are summarized as follows.展开更多
The following six aspects in the utilization of lake resources and progress of limnological research in China are described : 1 . Expeditions for comprehensive investigation of lakes ; 2 . Physical limnology; 3 . Lacu...The following six aspects in the utilization of lake resources and progress of limnological research in China are described : 1 . Expeditions for comprehensive investigation of lakes ; 2 . Physical limnology; 3 . Lacustrine sedimentology and paleolimnology ; 4 . Hydrobiology and ecology ; 5 . Hydrochemistry and environmental protection; 6 . Development and utilization of lake resources.展开更多
文摘Scientific research is a journey into an uncharted territory.Researchers need to have the big picture for navigation and at the same time be detail-oriented,as details make a difference.Here I offer a few tips for conducting research that I summarized based on my 30+years of research experience.
文摘https://www.sciencedirect.com/journal/energy-and-buildings/vol/328/suppl/C Volume 328,1 February 2025[OA](1)Best practices of techno-economic methods for solar photovoltaic coupled heat pump analysis in cold climates by Shafquat Rana,Nelson Sommerfeldt,Joshua M.Pearce,Article 115196 Abstract:One of the most promising methods of decarbonizing the global building heating and cooling load is with solar photovoltaic(PV)powered heat pumps(HP).The complex nature of these systems and the interdependent interactions between each technology and the energy markets involve various sophisticated models to simulate accurately.This often leaves model descriptions lacking,particularly when qualitative discussion is required.This article reviews the models that exist and provides best practices for designing and simulating PV+HP systems of various complexities.The key performance indicators for electricity generation and total life cycle cost are summarized.This article then provides a detailed and comprehensive method for the techno-economic analysis of heat pumps powered with PV using an example of North American cold climates.For each component of the system。
文摘Liver transplantation,as an effective therapy for patients with liver cancer,plays an important role in improving the quality of life of patients.However,the com-plexity and trauma of liver transplantation can easily lead to the occurrence of malnutrition in patients,and then increase the risk of postoperative complica-tions,which has aroused widespread clinical attention.Reasonable nutritional support can not only maintain the stability of the body’s internal environment,reduce the occurrence of complications,but also promote the recovery of liver and other organ functions.In recent years,with the in-depth understanding of nut-ritional metabolism after liver transplantation,the application of enteral nutrition and parenteral nutrition in nutritional support after liver transplantation has been increasingly extensive and achieved remarkable results.This paper discusses the effect of early postoperative nutritional intervention on patients with liver cancer and liver transplantation,and combined with its mechanism of action,can better understand the effectiveness of intervention,and provide reference for the deve-lopment of scientific and reasonable nutritional support programs in clinical pra-ctice.
文摘The rise of social media platforms has revolutionized communication, enabling the exchange of vast amounts of data through text, audio, images, and videos. These platforms have become critical for sharing opinions and insights, influencing daily habits, and driving business, political, and economic decisions. Text posts are particularly significant, and natural language processing (NLP) has emerged as a powerful tool for analyzing such data. While traditional NLP methods have been effective for structured media, social media content poses unique challenges due to its informal and diverse nature. This has spurred the development of new techniques tailored for processing and extracting insights from unstructured user-generated text. One key application of NLP is the summarization of user comments to manage overwhelming content volumes. Abstractive summarization has proven highly effective in generating concise, human-like summaries, offering clear overviews of key themes and sentiments. This enhances understanding and engagement while reducing cognitive effort for users. For businesses, summarization provides actionable insights into customer preferences and feedback, enabling faster trend analysis, improved responsiveness, and strategic adaptability. By distilling complex data into manageable insights, summarization plays a vital role in improving user experiences and empowering informed decision-making in a data-driven landscape. This paper proposes a new implementation framework by fine-tuning and parameterizing Transformer Large Language Models to manage and maintain linguistic and semantic components in abstractive summary generation. The system excels in transforming large volumes of data into meaningful summaries, as evidenced by its strong performance across metrics like fluency, consistency, readability, and semantic coherence.
基金work conducted under COST Action CA21125-a European forum for revitalisation of marginalised moun-tain areas(MARGISTAR)supported by COST(European Cooperation in Science and Technology)gratefully acknowledges the support received for the research from the University of Ljubljana’s research program Forest,forestry and renewable forest resources(P4-0059).
文摘We demonstrate a multi-method approach towards discovering and structuring sustainability transition knowl edge in marginalized mountain regions.By employing reflective thinking,artificial intelligence(AI)-powered text summarization and text mining,we synthesize experts’narratives on sustainable development challenges and solutions in Kardüz Upland,Türkiye.We then analyze their alignment with the UN Sustainable Development Goals(SDGs)using document embedding.Investment in infrastructure,education,and resilient socio-ecological systems emerged as priority sectors to combat poor infrastructure,geographic isolation,climate change,poverty,depopulation,unemployment,low education levels,and inadequate social services.The narratives were closest in substance to SDG 1,3,and 11.Social dimensions of sustainability were more pronounced than environmental dimensions.The presented approach supports policymakers in organizing loosely structured sustainability tran sition knowledge and fragmented data corpora,while also advancing AI applications for designing and planning sustainable development policies at the regional level.
文摘We analyze the suitability of existing pre-trained transformer-based language models(PLMs)for abstractive text summarization on German technical healthcare texts.The study focuses on the multilingual capabilities of these models and their ability to perform the task of abstractive text summarization in the healthcare field.The research hypothesis was that large language models could perform high-quality abstractive text summarization on German technical healthcare texts,even if the model is not specifically trained in that language.Through experiments,the research questions explore the performance of transformer language models in dealing with complex syntax constructs,the difference in performance between models trained in English and German,and the impact of translating the source text to English before conducting the summarization.We conducted an evaluation of four PLMs(GPT-3,a translation-based approach also utilizing GPT-3,a German language Model,and a domain-specific bio-medical model approach).The evaluation considered the informativeness using 3 types of metrics based on Recall-Oriented Understudy for Gisting Evaluation(ROUGE)and the quality of results which is manually evaluated considering 5 aspects.The results show that text summarization models could be used in the German healthcare domain and that domain-independent language models achieved the best results.The study proves that text summarization models can simplify the search for pre-existing German knowledge in various domains.
基金partially supported by the National Key Research and Development Program of China (2023YFB3209800)China Postdoctoral Science Foundation (2023M740264)。
文摘INSPIRED by the insight from American political scientist Lasswell, who summarized the environmental role in societal surveillance [1], Schramm coined the term “social radar” [2] as it resembles the activities of radar in collecting and processing information, playing a crucial role in helping humans perceive changes in the internal and external environment and promptly adjusting adaptive behaviors.
基金supported by the National Key Research and Development Program of China(2022YFF1202500,2022YFF1202502,2022YFB4703200,2023YFB4704700,2023YFB4704702)the National Natural Science Foundation of China(U22A2067,U20A20197,61773369,61903360,92048302,62203430)+1 种基金the Self-Planned Project of the State Key Laboratory of Robotics(2023-Z05)China Postdoctoral Science Foundation funded project(2022M723312)。
文摘A long history has passed since electromyography(EMG)signals have been explored in human-centered robots for intuitive interaction.However,it still has a gap between scientific research and real-life applications.Previous studies mainly focused on EMG decoding algorithms,leaving a dynamic relationship between the human,robot,and uncertain environment in real-life scenarios seldomly concerned.To fill this gap,this paper presents a comprehensive review of EMG-based techniques in human-robot-environment interaction(HREI)systems.The general processing framework is summarized,and three interaction paradigms,including direct control,sensory feedback,and partial autonomous control,are introduced.EMG-based intention decoding is treated as a module of the proposed paradigms.Five key issues involving precision,stability,user attention,compliance,and environmental awareness in this field are discussed.Several important directions,including EMG decomposition,robust algorithms,HREI dataset,proprioception feedback,reinforcement learning,and embodied intelligence,are proposed to pave the way for future research.To the best of what we know,this is the first time that a review of EMG-based methods in the HREI system is summarized.It provides a novel and broader perspective to improve the practicability of current myoelectric interaction systems,in which factors in human-robot interaction,robot-environment interaction,and state perception by human sensations are considered,which has never been done by previous studies.
文摘Summary:The reviews of this special column are summarized as follows:Time-dependent diffusion magnetic resonance imaging:measurement,modeling,and applications Ba RC,Kang LY,Wu D,2024.25(10):765-787.htps://doi.org/10.1631/jzus A2400139 Dan WU and her colleagues from the College of Biomedical Engineering&Instrument Science,Zhejiang University(ZJU)studied the measurement,modeling,and applications of time-dependent diffusion magnetic resonance imaging.This method reveals time-related changes in the diffusional behavior of water molecules in biological tissues,thereby enabling us to probe related microstructure events.
文摘China has pledged to the world to achieve carbon peak in 10 years and carbon neutrality in 30 years.This is an extremely arduous task,as it faces numerous challenges,including high energy consumption,heavy reliance on coal within its energy mix,and a large base of carbon emissions that must be controlled.To this end,it is necessary to advance the new energy security strategy of“Four Revolutions,One Cooperation”to a deeper level.According to interpretations from various parties,the new energy system is preliminarily summarized to have six features:new energy structure,new system form,new industrial system,new governance system,new system and mechanism,and new regulatory method.Considering building a new energy system comprehensively,“Ten Commitments”have been proposed to help achieve the dual-carbon goals.The specific measures include:ensuring the security and stability of energy supply,accelerating the transformation to green and low-carbon energy,giving priority to energy conservation and efficiency improvement,promoting multi-energy complementation and synergistic and integrated development,enhancing the digital intelligence level in the energy industry,developing centralized and distributed energy,advancing the rural energy revolution,developing critical and core technological equipment and the comprehensive energy service industry,and promoting high-quality development of the Belt and Road Initiative.
文摘Oirat dialect is a unique Mongolian dialect,having its own writing system.This paper analyzed the grammatical research of the Oirat dialect,and summarized the achievements and existing problems of previous studies,and provided suggestions about strengthening comprehensive in-depth study of morphology,syntax and word formation in this Mongolian dialect.
文摘Identifying rare patterns for medical diagnosis is a challenging task due to heterogeneity and the volume of data.Data summarization can create a concise version of the original data that can be used for effective diagnosis.In this paper,we propose an ensemble summarization method that combines clustering and sampling to create a summary of the original data to ensure the inclusion of rare patterns.To the best of our knowledge,there has been no such technique available to augment the performance of anomaly detection techniques and simultaneously increase the efficiency of medical diagnosis.The performance of popular anomaly detection algorithms increases significantly in terms of accuracy and computational complexity when the summaries are used.Therefore,the medical diagnosis becomes more effective,and our experimental results reflect that the combination of the proposed summarization scheme and all underlying algorithms used in this paper outperforms the most popular anomaly detection techniques.
基金This work was supported by Natural Science Foundation of Gansu Province under Grant Nos.21JR7RA570,20JR10RA334Basic Research Program of Gansu Province No.22JR11RA106,Gansu University of Political Science and Law Major Scientific Research and Innovation Projects under Grant No.GZF2020XZDA03+1 种基金the Young Doctoral Fund Project of Higher Education Institutions in Gansu Province in 2022 under Grant No.2022QB-123,Gansu Province Higher Education Innovation Fund Project under Grant No.2022A-097the University-Level Research Funding Project under Grant No.GZFXQNLW022 and University-Level Innovative Research Team of Gansu University of Political Science and Law.
文摘Video summarization aims to select key frames or key shots to create summaries for fast retrieval,compression,and efficient browsing of videos.Graph neural networks efficiently capture information about graph nodes and their neighbors,but ignore the dynamic dependencies between nodes.To address this challenge,we propose an innovative Adaptive Graph Convolutional Adjacency Matrix Network(TAMGCN),leveraging the attention mechanism to dynamically adjust dependencies between graph nodes.Specifically,we first segment shots and extract features of each frame,then compute the representative features of each shot.Subsequently,we utilize the attention mechanism to dynamically adjust the adjacency matrix of the graph convolutional network to better capture the dynamic dependencies between graph nodes.Finally,we fuse temporal features extracted by Bi-directional Long Short-Term Memory network with structural features extracted by the graph convolutional network to generate high-quality summaries.Extensive experiments are conducted on two benchmark datasets,TVSum and SumMe,yielding F1-scores of 60.8%and 53.2%,respectively.Experimental results demonstrate that our method outperforms most state-of-the-art video summarization techniques.
文摘The rapid expansion of online content and big data has precipitated an urgent need for efficient summarization techniques to swiftly comprehend vast textual documents without compromising their original integrity.Current approaches in Extractive Text Summarization(ETS)leverage the modeling of inter-sentence relationships,a task of paramount importance in producing coherent summaries.This study introduces an innovative model that integrates Graph Attention Networks(GATs)with Transformer-based Bidirectional Encoder Representa-tions from Transformers(BERT)and Latent Dirichlet Allocation(LDA),further enhanced by Term Frequency-Inverse Document Frequency(TF-IDF)values,to improve sentence selection by capturing comprehensive topical information.Our approach constructs a graph with nodes representing sentences,words,and topics,thereby elevating the interconnectivity and enabling a more refined understanding of text structures.This model is stretched to Multi-Document Summarization(MDS)from Single-Document Summarization,offering significant improvements over existing models such as THGS-GMM and Topic-GraphSum,as demonstrated by empirical evaluations on benchmark news datasets like Cable News Network(CNN)/Daily Mail(DM)and Multi-News.The results consistently demonstrate superior performance,showcasing the model’s robustness in handling complex summarization tasks across single and multi-document contexts.This research not only advances the integration of BERT and LDA within a GATs but also emphasizes our model’s capacity to effectively manage global information and adapt to diverse summarization challenges.
基金the Beijing Municipal Science and Technology Program(Z231100001323004)。
文摘With the help of pre-trained language models,the accuracy of the entity linking task has made great strides in recent years.However,most models with excellent performance require fine-tuning on a large amount of training data using large pre-trained language models,which is a hardware threshold to accomplish this task.Some researchers have achieved competitive results with less training data through ingenious methods,such as utilizing information provided by the named entity recognition model.This paper presents a novel semantic-enhancement-based entity linking approach,named semantically enhanced hardware-friendly entity linking(SHEL),which is designed to be hardware friendly and efficient while maintaining good performance.Specifically,SHEL's semantic enhancement approach consists of three aspects:(1)semantic compression of entity descriptions using a text summarization model;(2)maximizing the capture of mention contexts using asymmetric heuristics;(3)calculating a fixed size mention representation through pooling operations.These series of semantic enhancement methods effectively improve the model's ability to capture semantic information while taking into account the hardware constraints,and significantly improve the model's convergence speed by more than 50%compared with the strong baseline model proposed in this paper.In terms of performance,SHEL is comparable to the previous method,with superior performance on six well-established datasets,even though SHEL is trained using a smaller pre-trained language model as the encoder.
文摘Text representation is a key aspect in determining the success of various text summarizing techniques.Summarization using pretrained transformer models has produced encouraging results.Yet the scope of applying these models in medical and drug discovery is not examined to a proper extent.To address this issue,this article aims to perform extractive summarization based on fine-tuned transformers pertaining to drug and medical domain.This research also aims to enhance sentence representation.Exploring the extractive text summarization aspects of medical and drug discovery is a challenging task as the datasets are limited.Hence,this research concentrates on the collection of abstracts collected from PubMed for various domains of medical and drug discovery such as drug and COVID,with a total capacity of 1,370 abstracts.A detailed experimentation using BART(Bidirectional Autoregressive Transformer),T5(Text-to-Text Transfer Transformer),LexRank,and TexRank for the analysis of the dataset is carried out in this research to perform extractive text summarization.
文摘Retrieving information from evolving digital data collection using a user’s query is always essential and needs efficient retrieval mechanisms that help reduce the required time from such massive collections.Large-scale time consumption is certain to scan and analyze to retrieve the most relevant textual data item from all the documents required a sophisticated technique for a query against the document collection.It is always challenging to retrieve a more accurate and fast retrieval from a large collection.Text summarization is a dominant research field in information retrieval and text processing to locate the most appropriate data object as single or multiple documents from the collection.Machine learning and knowledge-based techniques are the two query-based extractive text summarization techniques in Natural Language Processing(NLP)which can be used for precise retrieval and are considered to be the best option.NLP uses machine learning approaches for both supervised and unsupervised learning for calculating probabilistic features.The study aims to propose a hybrid approach for query-based extractive text summarization in the research study.Text-Rank Algorithm is used as a core algorithm for the flow of an implementation of the approach to gain the required goals.Query-based text summarization of multiple documents using a hybrid approach,combining the K-Means clustering technique with Latent Dirichlet Allocation(LDA)as topic modeling technique produces 0.288,0.631,and 0.328 for precision,recall,and F-score,respectively.The results show that the proposed hybrid approach performs better than the graph-based independent approach and the sentences and word frequency-based approach.
文摘Irritable bowel syndrome(IBS-D)with diarrhea is a common gastrointestinal functional disease in clinical practice,which seriously affects the quality of life of patients.Cur‐rently,Western medicine has poor therapeutic effects,while traditional Chinese medi‐cine has unique advantages in relieving IBS-D symptoms and preventing recurrence.In recent years,especially with external treatment of traditional Chinese medicine,it has become a new treatment direction in clinical practice and has achieved good therapeutic effects.This article will provide a review of recent research on the treatment of IBS-D using traditional Chinese medicine external treatment methods.
文摘The special issue deals with the Cretaceous-Tertiary deserts in southwestern China and relevant sedimentological problems about deserts. The study area is located between 101°10＇- 107°00＇E and 28°00＇-30°40′ N. The ancient desert study is so little in China that there has not been a systematic report about it up to now. Based on the study in many ways on Cretaceous-Tertiary deserts in southwestern China, plentiful data have been obtained. Though it is still a bit rough, the study is a good beginning of the ancient desert study in China. The main ideas and conclusions are summarized as follows.
文摘The following six aspects in the utilization of lake resources and progress of limnological research in China are described : 1 . Expeditions for comprehensive investigation of lakes ; 2 . Physical limnology; 3 . Lacustrine sedimentology and paleolimnology ; 4 . Hydrobiology and ecology ; 5 . Hydrochemistry and environmental protection; 6 . Development and utilization of lake resources.
