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Single-cell pan-omics, environmental neurology, and artificial intelligence:the time for holistic brain health research
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作者 Paolo Abondio Francesco Bruno 《Neural Regeneration Research》 SCIE CAS 2025年第6期1703-1704,共2页
The brain,with its trillions of neural connections,different cellular types,and molecular complexities,presents a formidable challenge for researchers aiming to comprehend the multifaceted nature of neural health.As t... The brain,with its trillions of neural connections,different cellular types,and molecular complexities,presents a formidable challenge for researchers aiming to comprehend the multifaceted nature of neural health.As traditional methods have provided valuable insights,emerging technologies offer unprecedented opportunities to delve deeper into the underpinnings of brain function.In the everevolving landscape of neuroscience,the quest to unravel the mysteries of the human brain is bound to take a leap forward thanks to new technological improvements and bold interpretative frameworks. 展开更多
关键词 function artificial LANDSCAPE
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Revolutionizing diabetic retinopathy screening and management:The role of artificial intelligence and machine learning
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作者 Mona Mohamed Ibrahim Abdalla Jaiprakash Mohanraj 《World Journal of Clinical Cases》 SCIE 2025年第5期1-12,共12页
Diabetic retinopathy(DR)remains a leading cause of vision impairment and blindness among individuals with diabetes,necessitating innovative approaches to screening and management.This editorial explores the transforma... Diabetic retinopathy(DR)remains a leading cause of vision impairment and blindness among individuals with diabetes,necessitating innovative approaches to screening and management.This editorial explores the transformative potential of artificial intelligence(AI)and machine learning(ML)in revolutionizing DR care.AI and ML technologies have demonstrated remarkable advancements in enhancing the accuracy,efficiency,and accessibility of DR screening,helping to overcome barriers to early detection.These technologies leverage vast datasets to identify patterns and predict disease progression with unprecedented precision,enabling clinicians to make more informed decisions.Furthermore,AI-driven solutions hold promise in personalizing management strategies for DR,incorpo-rating predictive analytics to tailor interventions and optimize treatment path-ways.By automating routine tasks,AI can reduce the burden on healthcare providers,allowing for a more focused allocation of resources towards complex patient care.This review aims to evaluate the current advancements and applic-ations of AI and ML in DR screening,and to discuss the potential of these techno-logies in developing personalized management strategies,ultimately aiming to improve patient outcomes and reduce the global burden of DR.The integration of AI and ML in DR care represents a paradigm shift,offering a glimpse into the future of ophthalmic healthcare. 展开更多
关键词 Diabetic retinopathy artificial intelligence Machine learning SCREENING MANAGEMENT Predictive analytics Personalized medicine
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Recognition and quality mapping of traditional herbal drugs:way forward towards artificial intelligence
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作者 Sanyam Sharma Subh Naman Ashish Baldi 《Traditional Medicine Research》 2025年第1期12-26,共15页
The use of traditional herbal drugs derived from natural sources is on the rise due to their minimal side effects and numerous health benefits.However,a major limitation is the lack of standardized knowledge for ident... The use of traditional herbal drugs derived from natural sources is on the rise due to their minimal side effects and numerous health benefits.However,a major limitation is the lack of standardized knowledge for identifying and mapping the quality of these herbal medicines.This article aims to provide practical insights into the application of artificial intelligence for quality-based commercialization of raw herbal drugs.It focuses on feature extraction methods,image processing techniques,and the preparation of herbal images for compatibility with machine learning models.The article discusses commonly used image processing tools such as normalization,slicing,cropping,and augmentation to prepare images for artificial intelligence-based models.It also provides an overview of global herbal image databases and the models employed for herbal plant/drug identification.Readers will gain a comprehensive understanding of the potential application of various machine learning models,including artificial neural networks and convolutional neural networks.The article delves into suitable validation parameters like true positive rates,accuracy,precision,and more for the development of artificial intelligence-based identification and authentication techniques for herbal drugs.This article offers valuable insights and a conclusive platform for the further exploration of artificial intelligence in the field of herbal drugs,paving the way for smarter identification and authentication methods. 展开更多
关键词 artificial intelligence AYURVEDA machine learning models herbal drugs image pre-processing medicinal plants
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Gallbladder carcinoma in the era of artificial intelligence: Early diagnosis for better treatment
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作者 Ismail AS Burud Sherreen Elhariri Nabil Eid 《World Journal of Gastrointestinal Oncology》 SCIE 2025年第1期256-259,共4页
Gallbladder carcinoma(GBC)is the most common malignant tumor of biliary tract,with poor prognosis due to its aggressive nature and limited therapeutic options.Early detection of GBC is a major challenge,with most GBCs... Gallbladder carcinoma(GBC)is the most common malignant tumor of biliary tract,with poor prognosis due to its aggressive nature and limited therapeutic options.Early detection of GBC is a major challenge,with most GBCs being detected accidentally during cholecystectomy procedures for gallbladder stones.This letter comments on the recent article by Deqing et al in the World Journal of Gastrointestinal Oncology,which summarized the various current methods used in early diagnosis of GBC,including endoscopic ultrasound(EUS)examination of the gallbladder for high-risk GBC patients,and the use of EUS-guided elasto-graphy,contrast-enhanced EUS,trans-papillary biopsy,natural orifice translu-minal endoscopic surgery,magnifying endoscopy,choledochoscopy,and confocal laser endomicroscopy when necessary for early diagnosis of GBC.However,there is a need for novel methods for early GBC diagnosis,such as the use of artificial intelligence and non-coding RNA biomarkers for improved screening protocols.Additionally,the use of in vitro and animal models may provide critical insights for advancing early detection and treatment strategies of this aggressive tumor. 展开更多
关键词 Gallbladder carcinoma Endoscopic ultrasound BIOPSY ELASTOGRAPHY Cho-ledochoscopy artificial intelligence Non-coding RNAs Screening Animal models In vitro studies
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Harnessing artificial intelligence for identifying conflicts of interest in research
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作者 Abdulqadir J Nashwan 《World Journal of Methodology》 2025年第1期6-8,共3页
This editorial explores the transformative potential of artificial intelligence(AI)in identifying conflicts of interest(COIs)within academic and scientific research.By harnessing advanced data analysis,pattern recogni... This editorial explores the transformative potential of artificial intelligence(AI)in identifying conflicts of interest(COIs)within academic and scientific research.By harnessing advanced data analysis,pattern recognition,and natural language processing techniques,AI offers innovative solutions for enhancing transparency and integrity in research.This editorial discusses how AI can automatically detect COIs,integrate data from various sources,and streamline reporting processes,thereby maintaining the credibility of scientific findings. 展开更多
关键词 artificial intelligence Conflicts of interest TRANSPARENCY Research integrity Natural language processing
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Diabetes mellitus and glymphatic dysfunction:Roles for oxidative stress,mitochondria,circadian rhythm,artificial intelligence,and imaging
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作者 Kenneth Maiese 《World Journal of Diabetes》 SCIE 2025年第1期39-48,共10页
Diabetes mellitus(DM)is a debilitating disorder that impacts all systems of the body and has been increasing in prevalence throughout the globe.DM represents a significant clinical challenge to care for individuals an... Diabetes mellitus(DM)is a debilitating disorder that impacts all systems of the body and has been increasing in prevalence throughout the globe.DM represents a significant clinical challenge to care for individuals and prevent the onset of chronic disability and ultimately death.Underlying cellular mechanisms for the onset and development of DM are multi-factorial in origin and involve pathways associated with the production of reactive oxygen species and the generation of oxidative stress as well as the dysfunction of mitochondrial cellular organelles,programmed cell death,and circadian rhythm impairments.These pathways can ultimately involve failure in the glymphatic pathway of the brain that is linked to circadian rhythms disorders during the loss of metabolic homeostasis.New studies incorporate a number of promising techniques to examine patients with metabolic disorders that can include machine learning and artificial intelligence pathways to potentially predict the onset of metabolic dysfunction. 展开更多
关键词 artificial intelligence Circadian rhythm Clock genes Diabetes mellitus magnetic resonance imaging Glymphatic pathway MITOCHONDRIA Oxidative stress Programmed cell death Sleep fragmentation
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Toward a Learnable Climate Model in the Artificial Intelligence Era 被引量:3
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作者 Gang HUANG Ya WANG +3 位作者 Yoo-Geun HAM Bin MU Weichen TAO Chaoyang XIE 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2024年第7期1281-1288,共8页
Artificial intelligence(AI)models have significantly impacted various areas of the atmospheric sciences,reshaping our approach to climate-related challenges.Amid this AI-driven transformation,the foundational role of ... Artificial intelligence(AI)models have significantly impacted various areas of the atmospheric sciences,reshaping our approach to climate-related challenges.Amid this AI-driven transformation,the foundational role of physics in climate science has occasionally been overlooked.Our perspective suggests that the future of climate modeling involves a synergistic partnership between AI and physics,rather than an“either/or”scenario.Scrutinizing controversies around current physical inconsistencies in large AI models,we stress the critical need for detailed dynamic diagnostics and physical constraints.Furthermore,we provide illustrative examples to guide future assessments and constraints for AI models.Regarding AI integration with numerical models,we argue that offline AI parameterization schemes may fall short of achieving global optimality,emphasizing the importance of constructing online schemes.Additionally,we highlight the significance of fostering a community culture and propose the OCR(Open,Comparable,Reproducible)principles.Through a better community culture and a deep integration of physics and AI,we contend that developing a learnable climate model,balancing AI and physics,is an achievable goal. 展开更多
关键词 artificial intelligence deep learning learnable climate model
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Artificial intelligence-assisted repair of peripheral nerve injury: a new research hotspot and associated challenges 被引量:2
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作者 Yang Guo Liying Sun +3 位作者 Wenyao Zhong Nan Zhang Zongxuan Zhao Wen Tian 《Neural Regeneration Research》 SCIE CAS CSCD 2024年第3期663-670,共8页
Artificial intelligence can be indirectly applied to the repair of peripheral nerve injury.Specifically,it can be used to analyze and process data regarding peripheral nerve injury and repair,while study findings on p... Artificial intelligence can be indirectly applied to the repair of peripheral nerve injury.Specifically,it can be used to analyze and process data regarding peripheral nerve injury and repair,while study findings on peripheral nerve injury and repair can provide valuable data to enrich artificial intelligence algorithms.To investigate advances in the use of artificial intelligence in the diagnosis,rehabilitation,and scientific examination of peripheral nerve injury,we used CiteSpace and VOSviewer software to analyze the relevant literature included in the Web of Science from 1994–2023.We identified the following research hotspots in peripheral nerve injury and repair:(1)diagnosis,classification,and prognostic assessment of peripheral nerve injury using neuroimaging and artificial intelligence techniques,such as corneal confocal microscopy and coherent anti-Stokes Raman spectroscopy;(2)motion control and rehabilitation following peripheral nerve injury using artificial neural networks and machine learning algorithms,such as wearable devices and assisted wheelchair systems;(3)improving the accuracy and effectiveness of peripheral nerve electrical stimulation therapy using artificial intelligence techniques combined with deep learning,such as implantable peripheral nerve interfaces;(4)the application of artificial intelligence technology to brain-machine interfaces for disabled patients and those with reduced mobility,enabling them to control devices such as networked hand prostheses;(5)artificial intelligence robots that can replace doctors in certain procedures during surgery or rehabilitation,thereby reducing surgical risk and complications,and facilitating postoperative recovery.Although artificial intelligence has shown many benefits and potential applications in peripheral nerve injury and repair,there are some limitations to this technology,such as the consequences of missing or imbalanced data,low data accuracy and reproducibility,and ethical issues(e.g.,privacy,data security,research transparency).Future research should address the issue of data collection,as large-scale,high-quality clinical datasets are required to establish effective artificial intelligence models.Multimodal data processing is also necessary,along with interdisciplinary collaboration,medical-industrial integration,and multicenter,large-sample clinical studies. 展开更多
关键词 artificial intelligence artificial prosthesis medical-industrial integration brain-machine interface deep learning machine learning networked hand prosthesis neural interface neural network neural regeneration peripheral nerve
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Concept of Artificial Intelligence (AI) and Its Use in Orthopaedic Practice: Applications and Pitfalls: A Narrative Review 被引量:1
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作者 Mir Sadat-Ali 《Open Journal of Orthopedics》 2024年第1期32-40,共9页
Background: The growth and use of Artificial Intelligence (AI) in the medical field is rapidly rising. AI is exhibiting a practical tool in the healthcare industry in patient care. The objective of this current review... Background: The growth and use of Artificial Intelligence (AI) in the medical field is rapidly rising. AI is exhibiting a practical tool in the healthcare industry in patient care. The objective of this current review is to assess and analyze the use of AI and its use in orthopedic practice, as well as its applications, limitations, and pitfalls. Methods: A review of all relevant databases such as EMBASE, Cochrane Database of Systematic Reviews, MEDLINE, Science Citation Index, Scopus, and Web of Science with keywords of AI, orthopedic surgery, applications, and drawbacks. All related articles on AI and orthopaedic practice were reviewed. A total of 3210 articles were included in the review. Results: The data from 351 studies were analyzed where in orthopedic surgery. AI is being used for diagnostic procedures, radiological diagnosis, models of clinical care, and utilization of hospital and bed resources. AI has also taken a chunk of share in assisted robotic orthopaedic surgery. Conclusions: AI has now become part of the orthopedic practice and will further increase its stake in the healthcare industry. Nonetheless, clinicians should remain aware of AI’s serious limitations and pitfalls and consider the drawbacks and errors in its use. 展开更多
关键词 artificial Intelligence Healthcare PITFALLS Drawbacks
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Status of clinical applications of artificial intelligence in echocardiography 被引量:1
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作者 MA Chunyan 《中国医学影像技术》 CSCD 北大核心 2024年第8期1121-1123,共3页
Artificial intelligence(AI)technology has been increasingly used in medical field with its rapid developments.Echocardiography is one of the best imaging methods for clinical diagnosis of heart diseases,and combining ... Artificial intelligence(AI)technology has been increasingly used in medical field with its rapid developments.Echocardiography is one of the best imaging methods for clinical diagnosis of heart diseases,and combining with AI could further improve its diagnostic efficiency.Though the applications of AI in echocardiography remained at a relatively early stage,a variety of automated quantitative and analytical techniques were rapidly emerging and initially entered clinical practice.The status of clinical applications of AI in echocardiography were reviewed in this article. 展开更多
关键词 ECHOCARDIOGRAPHY artificial intelligence
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Artificial Intelligence and Computer Vision during Surgery: Discussing Laparoscopic Images with ChatGPT4—Preliminary Results 被引量:1
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作者 Savvas Hirides Petros Hirides +1 位作者 Kouloufakou Kalliopi Constantinos Hirides 《Surgical Science》 2024年第3期169-181,共13页
Introduction: Ultrafast latest developments in artificial intelligence (ΑΙ) have recently multiplied concerns regarding the future of robotic autonomy in surgery. However, the literature on the topic is still scarce... Introduction: Ultrafast latest developments in artificial intelligence (ΑΙ) have recently multiplied concerns regarding the future of robotic autonomy in surgery. However, the literature on the topic is still scarce. Aim: To test a novel AI commercially available tool for image analysis on a series of laparoscopic scenes. Methods: The research tools included OPENAI CHATGPT 4.0 with its corresponding image recognition plugin which was fed with a list of 100 laparoscopic selected snapshots from common surgical procedures. In order to score reliability of received responses from image-recognition bot, two corresponding scales were developed ranging from 0 - 5. The set of images was divided into two groups: unlabeled (Group A) and labeled (Group B), and according to the type of surgical procedure or image resolution. Results: AI was able to recognize correctly the context of surgical-related images in 97% of its reports. For the labeled surgical pictures, the image-processing bot scored 3.95/5 (79%), whilst for the unlabeled, it scored 2.905/5 (58.1%). Phases of the procedure were commented in detail, after all successful interpretations. With rates 4 - 5/5, the chatbot was able to talk in detail about the indications, contraindications, stages, instrumentation, complications and outcome rates of the operation discussed. Conclusion: Interaction between surgeon and chatbot appears to be an interesting frontend for further research by clinicians in parallel with evolution of its complex underlying infrastructure. In this early phase of using artificial intelligence for image recognition in surgery, no safe conclusions can be drawn by small cohorts with commercially available software. Further development of medically-oriented AI software and clinical world awareness are expected to bring fruitful information on the topic in the years to come. 展开更多
关键词 artificial Intelligence SURGERY Image Recognition Autonomous Surgery
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Application of artificial hibernation technology in acute brain injury 被引量:1
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作者 Xiaoni Wang Shulian Chen +5 位作者 Xiaoyu Wang Zhen Song Ziqi Wang Xiaofei Niu Xiaochu Chen Xuyi Chen 《Neural Regeneration Research》 SCIE CAS CSCD 2024年第9期1940-1946,共7页
Controlling intracranial pressure,nerve cell regeneration,and microenvironment regulation are the key issues in reducing mortality and disability in acute brain injury.There is currently a lack of effective treatment ... Controlling intracranial pressure,nerve cell regeneration,and microenvironment regulation are the key issues in reducing mortality and disability in acute brain injury.There is currently a lack of effective treatment methods.Hibernation has the characteristics of low temperature,low metabolism,and hibernation rhythm,as well as protective effects on the nervous,cardiovascular,and motor systems.Artificial hibernation technology is a new technology that can effectively treat acute brain injury by altering the body’s metabolism,lowering the body’s core temperature,and allowing the body to enter a state similar to hibernation.This review introduces artificial hibernation technology,including mild hypothermia treatment technology,central nervous system regulation technology,and artificial hibernation-inducer technology.Upon summarizing the relevant research on artificial hibernation technology in acute brain injury,the research results show that artificial hibernation technology has neuroprotective,anti-inflammatory,and oxidative stress-resistance effects,indicating that it has therapeutic significance in acute brain injury.Furthermore,artificial hibernation technology can alleviate the damage of ischemic stroke,traumatic brain injury,cerebral hemorrhage,cerebral infarction,and other diseases,providing new strategies for treating acute brain injury.However,artificial hibernation technology is currently in its infancy and has some complications,such as electrolyte imbalance and coagulation disorders,which limit its use.Further research is needed for its clinical application. 展开更多
关键词 cute brain injury artificial hibernation HYPOTHERMIA low metabolism mild hypothermia
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Application of artificial intelligence in the diagnosis and treatment of Kawasaki disease 被引量:1
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作者 Yan Pan Fu-Yong Jiao 《World Journal of Clinical Cases》 SCIE 2024年第23期5304-5307,共4页
This editorial provides commentary on an article titled"Potential and limitationsof ChatGPT and generative artificial intelligence(AI)in medical safety education"recently published in the World Journal of Cl... This editorial provides commentary on an article titled"Potential and limitationsof ChatGPT and generative artificial intelligence(AI)in medical safety education"recently published in the World Journal of Clinical Cases.AI has enormous potentialfor various applications in the field of Kawasaki disease(KD).One is machinelearning(ML)to assist in the diagnosis of KD,and clinical prediction models havebeen constructed worldwide using ML;the second is using a gene signalcalculation toolbox to identify KD,which can be used to monitor key clinicalfeatures and laboratory parameters of disease severity;and the third is using deeplearning(DL)to assist in cardiac ultrasound detection.The performance of the DLalgorithm is similar to that of experienced cardiac experts in detecting coronaryartery lesions to promoting the diagnosis of KD.To effectively utilize AI in thediagnosis and treatment process of KD,it is crucial to improve the accuracy of AIdecision-making using more medical data,while addressing issues related topatient personal information protection and AI decision-making responsibility.AIprogress is expected to provide patients with accurate and effective medicalservices that will positively impact the diagnosis and treatment of KD in thefuture. 展开更多
关键词 artificial intelligence Kawasaki disease DIAGNOSIS PREDICTION IMAGE
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Advanced Design of Soft Robots with Artificial Intelligence 被引量:1
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作者 Ying Cao Bingang Xu +1 位作者 Bin Li Hong Fu 《Nano-Micro Letters》 SCIE EI CAS CSCD 2024年第10期474-521,共48页
In recent years,breakthrough has been made in the field of artificial intelligence(AI),which has also revolutionized the industry of robotics.Soft robots featured with high-level safety,less weight,lower power consump... In recent years,breakthrough has been made in the field of artificial intelligence(AI),which has also revolutionized the industry of robotics.Soft robots featured with high-level safety,less weight,lower power consumption have always been one of the research hotspots.Recently,multifunctional sensors for perception of soft robotics have been rapidly developed,while more algorithms and models of machine learning with high accuracy have been optimized and proposed.Designs of soft robots with AI have also been advanced ranging from multimodal sensing,human-machine interaction to effective actuation in robotic systems.Nonethe-less,comprehensive reviews concerning the new developments and strategies for the ingenious design of the soft robotic systems equipped with AI are rare.Here,the new development is systematically reviewed in the field of soft robots with AI.First,background and mechanisms of soft robotic systems are briefed,after which development focused on how to endow the soft robots with AI,including the aspects of feeling,thought and reaction,is illustrated.Next,applications of soft robots with AI are systematically summarized and discussed together with advanced strategies proposed for performance enhancement.Design thoughts for future intelligent soft robotics are pointed out.Finally,some perspectives are put forward. 展开更多
关键词 Soft robotic systems artificial intelligence Design tactics Review and perspective
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Advances in memristor based artificial neuron fabrication-materials,models,and applications 被引量:1
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作者 Jingyao Bian Zhiyong Liu +5 位作者 Ye Tao Zhongqiang Wang Xiaoning Zhao Ya Lin Haiyang Xu Yichun Liu 《International Journal of Extreme Manufacturing》 SCIE EI CAS CSCD 2024年第1期27-50,共24页
Spiking neural network(SNN),widely known as the third-generation neural network,has been frequently investigated due to its excellent spatiotemporal information processing capability,high biological plausibility,and l... Spiking neural network(SNN),widely known as the third-generation neural network,has been frequently investigated due to its excellent spatiotemporal information processing capability,high biological plausibility,and low energy consumption characteristics.Analogous to the working mechanism of human brain,the SNN system transmits information through the spiking action of neurons.Therefore,artificial neurons are critical building blocks for constructing SNN in hardware.Memristors are drawing growing attention due to low consumption,high speed,and nonlinearity characteristics,which are recently introduced to mimic the functions of biological neurons.Researchers have proposed multifarious memristive materials including organic materials,inorganic materials,or even two-dimensional materials.Taking advantage of the unique electrical behavior of these materials,several neuron models are successfully implemented,such as Hodgkin–Huxley model,leaky integrate-and-fire model and integrate-and-fire model.In this review,the recent reports of artificial neurons based on memristive devices are discussed.In addition,we highlight the models and applications through combining artificial neuronal devices with sensors or other electronic devices.Finally,the future challenges and outlooks of memristor-based artificial neurons are discussed,and the development of hardware implementation of brain-like intelligence system based on SNN is also prospected. 展开更多
关键词 artificial neuron MEMRISTOR memristive materials neuron model micro-nano manufacturing spiking neural network
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A data-driven model of drop size prediction based on artificial neural networks using small-scale data sets 被引量:1
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作者 Bo Wang Han Zhou +3 位作者 Shan Jing Qiang Zheng Wenjie Lan Shaowei Li 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2024年第2期71-83,共13页
An artificial neural network(ANN)method is introduced to predict drop size in two kinds of pulsed columns with small-scale data sets.After training,the deviation between calculate and experimental results are 3.8%and ... An artificial neural network(ANN)method is introduced to predict drop size in two kinds of pulsed columns with small-scale data sets.After training,the deviation between calculate and experimental results are 3.8%and 9.3%,respectively.Through ANN model,the influence of interfacial tension and pulsation intensity on the droplet diameter has been developed.Droplet size gradually increases with the increase of interfacial tension,and decreases with the increase of pulse intensity.It can be seen that the accuracy of ANN model in predicting droplet size outside the training set range is reach the same level as the accuracy of correlation obtained based on experiments within this range.For two kinds of columns,the drop size prediction deviations of ANN model are 9.6%and 18.5%and the deviations in correlations are 11%and 15%. 展开更多
关键词 artificial neural network Drop size Solvent extraction Pulsed column Two-phase flow HYDRODYNAMICS
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Research on simulation of gun muzzle flow field empowered by artificial intelligence 被引量:1
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作者 Mengdi Zhou Linfang Qian +3 位作者 Congyong Cao Guangsong Chen Jin Kong Ming-hao Tong 《Defence Technology(防务技术)》 SCIE EI CAS CSCD 2024年第2期196-208,共13页
Artificial intelligence technology is introduced into the simulation of muzzle flow field to improve its simulation efficiency in this paper.A data-physical fusion driven framework is proposed.First,the known flow fie... Artificial intelligence technology is introduced into the simulation of muzzle flow field to improve its simulation efficiency in this paper.A data-physical fusion driven framework is proposed.First,the known flow field data is used to initialize the model parameters,so that the parameters to be trained are close to the optimal value.Then physical prior knowledge is introduced into the training process so that the prediction results not only meet the known flow field information but also meet the physical conservation laws.Through two examples,it is proved that the model under the fusion driven framework can solve the strongly nonlinear flow field problems,and has stronger generalization and expansion.The proposed model is used to solve a muzzle flow field,and the safety clearance behind the barrel side is divided.It is pointed out that the shape of the safety clearance under different launch speeds is roughly the same,and the pressure disturbance in the area within 9.2 m behind the muzzle section exceeds the safety threshold,which is a dangerous area.Comparison with the CFD results shows that the calculation efficiency of the proposed model is greatly improved under the condition of the same calculation accuracy.The proposed model can quickly and accurately simulate the muzzle flow field under various launch conditions. 展开更多
关键词 Muzzle flow field artificial intelligence Deep learning Data-physical fusion driven Shock wave
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Self-supervised learning artificial intelligence noise reduction technology based on the nearest adjacent layer in ultra-low dose CT of urinary calculi 被引量:1
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作者 ZHOU Cheng LIU Yang +4 位作者 QIU Yingwei HE Daijun YAN Yu LUO Min LEI Youyuan 《中国医学影像技术》 CSCD 北大核心 2024年第8期1249-1253,共5页
Objective To observe the value of self-supervised deep learning artificial intelligence(AI)noise reduction technology based on the nearest adjacent layer applicated in ultra-low dose CT(ULDCT)for urinary calculi.Metho... Objective To observe the value of self-supervised deep learning artificial intelligence(AI)noise reduction technology based on the nearest adjacent layer applicated in ultra-low dose CT(ULDCT)for urinary calculi.Methods Eighty-eight urinary calculi patients were prospectively enrolled.Low dose CT(LDCT)and ULDCT scanning were performed,and the effective dose(ED)of each scanning protocol were calculated.The patients were then randomly divided into training set(n=75)and test set(n=13),and a self-supervised deep learning AI noise reduction system based on the nearest adjacent layer constructed with ULDCT images in training set was used for reducing noise of ULDCT images in test set.In test set,the quality of ULDCT images before and after AI noise reduction were compared with LDCT images,i.e.Blind/Referenceless Image Spatial Quality Evaluator(BRISQUE)scores,image noise(SD ROI)and signal-to-noise ratio(SNR).Results The tube current,the volume CT dose index and the dose length product of abdominal ULDCT scanning protocol were all lower compared with those of LDCT scanning protocol(all P<0.05),with a decrease of ED for approximately 82.66%.For 13 patients with urinary calculi in test set,BRISQUE score showed that the quality level of ULDCT images before AI noise reduction reached 54.42%level but raised to 95.76%level of LDCT images after AI noise reduction.Both ULDCT images after AI noise reduction and LDCT images had lower SD ROI and higher SNR than ULDCT images before AI noise reduction(all adjusted P<0.05),whereas no significant difference was found between the former two(both adjusted P>0.05).Conclusion Self-supervised learning AI noise reduction technology based on the nearest adjacent layer could effectively reduce noise and improve image quality of urinary calculi ULDCT images,being conducive for clinical application of ULDCT. 展开更多
关键词 urinary calculi tomography X-ray computed artificial intelligence prospective studies
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A review of artificial intelligence applications in high-speed railway systems 被引量:2
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作者 Xuehan Li Minghao Zhu +3 位作者 Boyang Zhang Xiaoxuan Wang Zha Liu Liang Han 《High-Speed Railway》 2024年第1期11-16,共6页
In recent years,the global surge of High-speed Railway(HSR)revolutionized ground transportation,providing secure,comfortable,and punctual services.The next-gen HSR,fueled by emerging services like video surveillance,e... In recent years,the global surge of High-speed Railway(HSR)revolutionized ground transportation,providing secure,comfortable,and punctual services.The next-gen HSR,fueled by emerging services like video surveillance,emergency communication,and real-time scheduling,demands advanced capabilities in real-time perception,automated driving,and digitized services,which accelerate the integration and application of Artificial Intelligence(AI)in the HSR system.This paper first provides a brief overview of AI,covering its origin,evolution,and breakthrough applications.A comprehensive review is then given regarding the most advanced AI technologies and applications in three macro application domains of the HSR system:mechanical manufacturing and electrical control,communication and signal control,and transportation management.The literature is categorized and compared across nine application directions labeled as intelligent manufacturing of trains and key components,forecast of railroad maintenance,optimization of energy consumption in railroads and trains,communication security,communication dependability,channel modeling and estimation,passenger scheduling,traffic flow forecasting,high-speed railway smart platform.Finally,challenges associated with the application of AI are discussed,offering insights for future research directions. 展开更多
关键词 High-speed railway artificial intelligence Intelligent distribution Intelligent control Intelligent scheduling
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Integrating artificial intelligence and high-throughput phenotyping for crop improvement 被引量:1
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作者 Mansoor Sheikh Farooq Iqra +3 位作者 Hamadani Ambreen Kumar A Pravin Manzoor Ikra Yong Suk Chung 《Journal of Integrative Agriculture》 SCIE CAS CSCD 2024年第6期1787-1802,共16页
Crop improvement is crucial for addressing the global challenges of food security and sustainable agriculture.Recent advancements in high-throughput phenotyping(HTP)technologies and artificial intelligence(AI)have rev... Crop improvement is crucial for addressing the global challenges of food security and sustainable agriculture.Recent advancements in high-throughput phenotyping(HTP)technologies and artificial intelligence(AI)have revolutionized the field,enabling rapid and accurate assessment of crop traits on a large scale.The integration of AI and machine learning algorithms with HTP data has unlocked new opportunities for crop improvement.AI algorithms can analyze and interpret large datasets,and extract meaningful patterns and correlations between phenotypic traits and genetic factors.These technologies have the potential to revolutionize plant breeding programs by providing breeders with efficient and accurate tools for trait selection,thereby reducing the time and cost required for variety development.However,further research and collaboration are needed to overcome the existing challenges and fully unlock the power of HTP and AI in crop improvement.By leveraging AI algorithms,researchers can efficiently analyze phenotypic data,uncover complex patterns,and establish predictive models that enable precise trait selection and crop breeding.The aim of this review is to explore the transformative potential of integrating HTP and AI in crop improvement.This review will encompass an in-depth analysis of recent advances and applications,highlighting the numerous benefits and challenges associated with HTP and AI. 展开更多
关键词 artificial intelligence crop improvement data analysis high-throughput phenotyping machine learning precision agriculture trait selection
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