A growing global population and the increasing prevalence of diet-related health issues such as“hidden hunger”,obesity,hypertension,and diabetes necessitate a fundamental rethinking of crop design and breeding.Synth...A growing global population and the increasing prevalence of diet-related health issues such as“hidden hunger”,obesity,hypertension,and diabetes necessitate a fundamental rethinking of crop design and breeding.Synthetic metabolic engineering offers a method to modify and redesign metabolic pathways to increase the nutritional value of crops.We summarize recent advances in the biofortification of key nutrients including provitamin A,vitamin C,vitamin B9,iron,zinc,anthocyanins,flavonoids,and unsaturated fatty acids.We discuss the potential of multi-gene stacking,gene editing,enzyme engineering,and artificial intelligence in synthetic metabolic engineering.We propose future research directions and potential solutions centered on leveraging AI-driven systems biology,precision gene editing,enzyme engineering,agrobacterium-mediated genotype-independent transformation,and modular metabolic engineering strategies to develop next-generation nutritionally enhanced super crops and transform global food systems.展开更多
Blood-brain barrier disruption and the neuroinflammatory response are significant pathological features that critically influence disease progression and treatment outcomes.This review systematically analyzes the curr...Blood-brain barrier disruption and the neuroinflammatory response are significant pathological features that critically influence disease progression and treatment outcomes.This review systematically analyzes the current understanding of the bidirectional relationship between blood-brain barrier disruption and neuroinflammation in traumatic brain injury,along with emerging combination therapeutic strategies.Literature review indicates that blood-brain barrier disruption and neuroinflammatory responses are key pathological features following traumatic brain injury.In the acute phase after traumatic brain injury,the pathological characteristics include primary blood-brain barrier disruption and the activation of inflammatory cascades.In the subacute phase,the pathological features are characterized by repair mechanisms and inflammatory modulation.In the chronic phase,the pathological features show persistent low-grade inflammation and incomplete recovery of the blood-brain barrier.Various physiological changes,such as structural alterations of the blood-brain barrier,inflammatory cascades,and extracellular matrix remodeling,interact with each other and are influenced by genetic,age,sex,and environmental factors.The dynamic balance between blood-brain barrier permeability and neuroinflammation is regulated by hormones,particularly sex hormones and stress-related hormones.Additionally,the role of gastrointestinal hormones is receiving increasing attention.Current treatment strategies for traumatic brain injury include various methods such as conventional drug combinations,multimodality neuromonitoring,hyperbaric oxygen therapy,and non-invasive brain stimulation.Artificial intelligence also shows potential in treatment decision-making and personalized therapy.Emerging sequential combination strategies and precision medicine approaches can help improve treatment outcomes;however,challenges remain,such as inadequate research on the mechanisms of the chronic phase traumatic brain injury and difficulties with technology integration.Future research on traumatic brain injury should focus on personalized treatment strategies,the standardization of techniques,costeffectiveness evaluations,and addressing the needs of patients with comorbidities.A multidisciplinary approach should be used to enhance treatment and improve patient outcomes.展开更多
This article presents findings from an extensive experimental program conducted by the French Agency for the Management of Radioactive Waste(ANDRA)at its Underground Research Laboratory.The focus of the study is the N...This article presents findings from an extensive experimental program conducted by the French Agency for the Management of Radioactive Waste(ANDRA)at its Underground Research Laboratory.The focus of the study is the NSC(Noyau de SCellement)experiment,designed to assess the performance of a sealing core composed of a mixture of 40%bentonite and 60%sand.The objective is to evaluate in situ hydraulic permeability,swelling behavior,and gas performance of the seal,which are essential components for the safe disposal of Intermediate-Level(IL)and High-Level Radioactive Waste(HLW)in deep geological repositories.The experimental setup,construction details of the NSC experiment,and the timeline of the project,from excavation,construction of the seal,and artificial hydration to hydraulic permeability tests,are comprehensively described.Andra has acquired valuable expertise in constructing an engineered barrier system,encompassing concrete plugs,and efficiently assembling a 5-m-long seal.Over the past decade,a controlled water injection process has been employed to hydrate the seal.The operational efficiency of sensors has been evaluated,with pore pressure sensors exhibiting moderate performance.In contrast,others,including total pressure,capacitive humidity,psychrometric humidity,and saturation sensors,demonstrate excellent performance up to their operational limits.The high-quality data obtained from this experiment will facilitate the modeling of the resaturation and hydraulic-gas performance of the seal.展开更多
Standard bacterial suspensions play a crucial role in microbiological diagnosis.Traditional prepar-ation methods,which rely heavily on manual operations,face challenges such as poor reproducibility,low ef-ficiency,and...Standard bacterial suspensions play a crucial role in microbiological diagnosis.Traditional prepar-ation methods,which rely heavily on manual operations,face challenges such as poor reproducibility,low ef-ficiency,and biosafety concerns.In this study,we propose a high-precision automated colony extraction and separation system that combines large-field imaging and artificial intelligence(AI)to facilitate intelligent screening and localization of colonies.Firstly,a large-field imaging system was developed to capture high-resolution images of 90 mm Petri dishes,achieving a physical resolution of 13.2μm and an imaging speed of 13 frames per second.Subsequently,AI technology was employed for the automatic recognition and localiza-tion of colonies,enabling the selection of target colonies with diameters ranging from 1.9 to 2.3 mm.Next,a three-axis motion control platform was designed,accompanied by a path planning algorithm for the efficient extraction of colonies.An electronic pipette was employed for accurate colony collection.Additionally,a bacterial suspension concentration measurement module was developed,incorporating a 650 nm laser diode as the light source,achieving a measurement accuracy of 0.01 McFarland concentration(MCF).Finally,the system’s performance was validated through the preparation of an Esckerichia coli(E.coli)suspension.After 17 hours of cultivation,E.coli was extracted four times,achieving the target concentration set by the system.This work is expected to enable rapid and accurate microbial sample preparation,significantly reducing de-tection cycles and alleviating the workload of healthcare personnel.展开更多
Tomasz Mizerkiewicz is a professor at the Faculty of Polish and Classical Philology at Adam Mickiewicz University and the editor-in-chief of Forum of Poetics.He has published extensively in the field of 20th-century a...Tomasz Mizerkiewicz is a professor at the Faculty of Polish and Classical Philology at Adam Mickiewicz University and the editor-in-chief of Forum of Poetics.He has published extensively in the field of 20th-century and 21st-century Polish literature and literary theory.This interview addresses topics including key concepts,theoretical terminologies,and interdisciplinary research in Ethical Literary Criticism,as well as its future development and practical significance.Using different texts,including Polish literary works,Mizerkiewicz illustrates the application of core terms in Ethical Literary Criticism,analyzes ethical expressions and ethical values embedded in literary works,and offers insights into the relevance of Ethical Literary Criticism in the era of artificial intelligence,as well as its connections with ESG(Environmental,Social,and Governance)and sustainability.The interview underscores his emphasis on the vibrant academic value and practical significance of Ethical Literary Criticism in exploring ethical thoughts in literature,fostering interdisciplinary dialogue,and contributing to the advancement of modern society.展开更多
With the increase of semiconductor integration density,in order to cope with the increase of wafer defect complexity and types,especially the low recognition accuracy of overlapping mixed defects and unknown wafer def...With the increase of semiconductor integration density,in order to cope with the increase of wafer defect complexity and types,especially the low recognition accuracy of overlapping mixed defects and unknown wafer defects,this study proposes a lightweight model for wafer defect detection called LightWMNet.First,using a hierarchical attention Encoder-Decoder architecture,the features of wafer defect pattern(WDP)are channel recalibrated to generate high-resolution fine-grained features and low-resolution coarse-grained features.Secondly,the backbone network incorporates two novel attention modules—feedforward spatial attention(FFSa)and feedforward channel attention(FFCa)—to amplify responses in critical defect regions and suppress noise from stochastic discrete pixels.These mechanisms synergistically enhance feature discriminability without introducing significant parametric overhead.Finally,the Dice loss function and the cross entropy loss function are combined to jointly evaluate the segmentation and classification accuracy of the model.Experimental results on the public mixed wafer defect dataset MixedWM38 show that the pixel accuracy(PA),intersection over union(IoU)and Dice coefficient of the proposed network reach 98.26%,94.83%and 97.22%,respectively.Without significantly increasing the computational complexity and size of the model,compared with the existing state-of-the-art(SOTA)model,the classification accuracy of lightWMNet in single defect,three mixed defects and four mixed defects is improved by 0.5%,0.25%and 0.89%respectively.Furthermore,we used transfer learning for the first time to evaluate the model's generalisation ability for unseen defect categories.The results showed that LightWMNet still has a certain recognition ability even in untrained wafer defects.展开更多
“By successfully integrating artificial intelligence(AI)into research workflows,researchers could substantially increase scientific productivity”[1].In biofabrication,AI is dr iving a paradigm shift from empiricism ...“By successfully integrating artificial intelligence(AI)into research workflows,researchers could substantially increase scientific productivity”[1].In biofabrication,AI is dr iving a paradigm shift from empiricism toward intelligen t,data centric manufacturing[2].By integrating computation,automation,and biology,AI gives rise to self-evolving,adaptive systems that learn from data,predict complex behaviors,and autonomously optimize fabrication outcomes.Such systems translate experimental insights into patient-specific and clinically relevant solutions,bridging laboratory research and regenerative therapies[3].This emerging frontier is rapidly advancing from concept to application.This Special Column highlights how AI-driven advanc es in materials,design,and manufacturing are reshaping biof abrication for regenerative medicine and clinical translation.展开更多
This review comprehensively summarized the potential of artificial intelligence(AI)in the management of esophageal cancer.It highlighted the significance of AI-assisted endoscopy in Japan where endoscopy is central to...This review comprehensively summarized the potential of artificial intelligence(AI)in the management of esophageal cancer.It highlighted the significance of AI-assisted endoscopy in Japan where endoscopy is central to both screening and diagnosis.For the clinical adaptation of AI,several challenges remain for its effective translation.The establishment of high-quality clinical databases,such as the National Clinical Database and Japan Endoscopy Database in Japan,which covers almost all cases of esophageal cancer,is essential for validating multimodal AI models.This requires rigorous external validation using diverse datasets,including those from different endoscope manufacturers and image qualities.Furthermore,endoscopists’skills significantly affect diagnostic accuracy,suggesting that AI should serve as a supportive tool rather than a replacement.Addressing these challenges,along with country-specific legal and ethical considerations,will facilitate the successful integration of multimodal AI into the management of esophageal cancer,particularly in endoscopic diagnosis,and contribute to improved patient outcomes.Although this review focused on Japan as a case study,the challenges and solutions described are broadly applicable to other high-incidence regions.展开更多
The year 2025 marked the inaugural volume of Artificial Intelligence Science and Engineering(AISE).Withthe strong support of the global academic community in artificial intelligence and control science,and through the...The year 2025 marked the inaugural volume of Artificial Intelligence Science and Engineering(AISE).Withthe strong support of the global academic community in artificial intelligence and control science,and through thededicated efforts of our editorial board members,authors,and reviewers,all four issues were successfullypublished.These issues brought together significant original contributions and authoritative surveys from leadingscholars,laying a solid academic foundation for the long-term development of the journal.展开更多
Sponsored by Chongqing University of Technology(CQUT)and the Chinese Association for Artificial Intelligence(CAAI),CAAI Transactions on Intelligence Technology is a gold Open Access journal that publishes articles on ...Sponsored by Chongqing University of Technology(CQUT)and the Chinese Association for Artificial Intelligence(CAAI),CAAI Transactions on Intelligence Technology is a gold Open Access journal that publishes articles on the theoretical and experimental aspects of artificial intelligence technology.This journal is a high-quality international platform for the exchange of the latest academic developments from the global artificial intelligence community.展开更多
Industrial Cyber-Physical Systems(ICPSs)play a vital role in modern industries by providing an intellectual foundation for automated operations.With the increasing integration of information-driven processes,ensuring ...Industrial Cyber-Physical Systems(ICPSs)play a vital role in modern industries by providing an intellectual foundation for automated operations.With the increasing integration of information-driven processes,ensuring the security of Industrial Control Production Systems(ICPSs)has become a critical challenge.These systems are highly vulnerable to attacks such as denial-of-service(DoS),eclipse,and Sybil attacks,which can significantly disrupt industrial operations.This work proposes an effective protection strategy using an Artificial Intelligence(AI)-enabled Smart Contract(SC)framework combined with the Heterogeneous Barzilai-Borwein Support Vector(HBBSV)method for industrial-based CPS environments.The approach reduces run time and minimizes the probability of attacks.Initially,secured ICPSs are achieved through a comprehensive exchange of views on production plant strategies for condition monitoring using SC and blockchain(BC)integrated within a BC network.The SC executes the HBBSV strategy to verify the security consensus.The Barzilai-Borwein Support Vectorized algorithm computes abnormal attack occurrence probabilities to ensure that components operate within acceptable production line conditions.When a component remains within these conditions,no security breach occurs.Conversely,if a component does not satisfy the condition boundaries,a security lapse is detected,and those components are isolated.The HBBSV method thus strengthens protection against DoS,eclipse,and Sybil attacks.Experimental results demonstrate that the proposed HBBSV approach significantly improves security by enhancing authentication accuracy while reducing run time and authentication time compared to existing techniques.展开更多
As 2026 unfolds,the image of an autonomous port in Ningbo loading a U.S.-bound ship encapsulates the transformative power of China’s surge in artificial intelligence(AI).This automation marvel not only symbolizes Ch...As 2026 unfolds,the image of an autonomous port in Ningbo loading a U.S.-bound ship encapsulates the transformative power of China’s surge in artificial intelligence(AI).This automation marvel not only symbolizes China’s economic power but also demonstrates how deeply integrated AI has become into its global competition strategy.Once largely confined to academic research and pilot programs,AI is now embedded in China’s national strategy.The goal:drive productivity,modernize industries,and strengthen its competitive position in global technology markets.展开更多
Background:Artificial intelligence medical diagnostic devices(AIMDDs)show strong potential but face barriers to clinical use,emphasizing the need for rigorous clinical research.Objective:We assessed current AIMDD rese...Background:Artificial intelligence medical diagnostic devices(AIMDDs)show strong potential but face barriers to clinical use,emphasizing the need for rigorous clinical research.Objective:We assessed current AIMDD research,key challenges,and future directions.Methods:A scoping review followed Arksey and O'Malley's methodological framework and the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews guidelines.PubMed,Web of Science Core Collection,and the Cochrane Database of Systematic Reviews(January 2020-December 2024)were searched on AIMDD design,implementation,and evaluation.Two independent researchers screened and extracted data from the literature using predefined criteria.Results:Ninety-seven articles met the inclusion criteria.Machine learning and deep learning approaches dominated across diverse disease fields,with oncology being the most frequent(41%).The key challenges identified include insufficient quantity,quality,representativeness,and diversity of data;research designs that do not adequately address clinical needs;poor patient selection;poorly defined gold standards;lack of external and prospective validation;and a disconnect between validation strategies and clinical practice.Additionally,issues such as the“black box”phenomenon,overfitting,and data privacy concerns hinder clinical translation.Completeness and standardization of reporting were also found to be lacking.Conclusions:Significant challenges remain in the development and clinical application of AIMDD.To facilitate their clinical translation,improvements are needed in dataset optimization,clinically driven research design,development of evaluation frameworks,enhanced interpretability,and standardized reporting and validation of algorithms.展开更多
Generative artificial intelligence(AI),specifically large language models,such as DeepSeek,has accelerated the digital transformation of healthcare systems in both developing and developed countries.The use of AI in d...Generative artificial intelligence(AI),specifically large language models,such as DeepSeek,has accelerated the digital transformation of healthcare systems in both developing and developed countries.The use of AI in diagnostics,image processing and interpretation,treatment personalization,clinical documentation,and drug discovery is an example of the implementation of AI in Western medicine.The need for evidence-based studies and a standardized approach to scientific medicine aligns well with these applications.AI can leave a lasting impact on the Chinese medicine(CM)landscape by increasing expectations and presenting new challenges.The analogy between the CM-specific diagnostic methods and pattern differentiation,which is holistic,pattern-oriented,patient-centered,and clinical data analysis,is significant at multiple levels.These qualities pose challenges for AI usage in CM,which heavily relies on structured data and pattern recognition.Despite these adversities,AI can still be used in CM through data standardization,prediction formulation,and treatment planning,provided that the integration of this tool considers the primary principles of CM and adheres to ethical and regulatory considerations.This review examines the dichotomous approach to health and medicine in the contexts of AI and CM,highlighting the evolving potential,inherent limitations,and ethical and regulatory issues associated with the application of AI to CM.It provides a foundation for developing technologically progressive yet culturally and philosophically sensitive strategies that are in harmony with traditional clinical values.展开更多
Fluorescent RNA aptamers offer promising opportunities for next-generation biosensing but are often limited by low signal-to-background ratios and unstable folding kinetics.In this work,a label-free Förster reson...Fluorescent RNA aptamers offer promising opportunities for next-generation biosensing but are often limited by low signal-to-background ratios and unstable folding kinetics.In this work,a label-free Förster resonance energy transfer(FRET)-enhanced fluorescent artificial RNA condensate(F-FARCON)is developed for small-molecule sensing,leveraging neutral molecular crowders(e.g.,polyethylene glycol 8K),and RNA structural motifs to induce multivalent interactions and drive dynamic self-assembly.As a demonstration,a label-free FRET system is constructed by integrating a histamine-responsive RNA aptamer with thioflavin T(ThT)as the fluorescence donor,which increases the signal-to-noise ratio while reducing sequence complexity and production costs.Molecular crowders optimize the thermodynamic environment of RNA-ligand and RNA-RNA multivalent interactions,thereby improving folding stability,signal amplitude(dynamic range of up to~970-fold),and target affinity.The platform exhibits fast kinetics(<15 min),an adjustable detection range(0.1200 and 51000μM),and high sensitivity(limit of detection,15.36 nM),with robust performance in complex biological matrices.The platform is further integrated into a freeze-dried paper-based portable device that enables dual-channel fluorescence readout for on-site rapid detection without sophisticated instrumentation.To further validate the modularity of F-FARCON beyond histamine,we reprogrammed the recognition module to target S-adenosyl-L-methionine(SAM),achieving nanomolar limits of detection.By linking crowding-guided assembly to hierarchical photophysical enhancement and analytical performance,the work delineates a generalizable aggregate-science route to versatile,low-cost,and field-deployable fluorescence sensing across food safety,environmental monitoring,and biomedical diagnostics.展开更多
The Internet of Things(IoT)and cloud computing have significantly contributed to the development of smart cities,enabling real-time monitoring,intelligent decision-making,and efficient resource management.These system...The Internet of Things(IoT)and cloud computing have significantly contributed to the development of smart cities,enabling real-time monitoring,intelligent decision-making,and efficient resource management.These systems,particularly in IoT networks,rely on numerous interconnected devices that handle time-sensitive data for critical applications.In related approaches,trusted communication and reliable device interaction have been overlooked,thereby lowering security when sharing sensitive IoT data.Moreover,it incurs additional energy consumption and overhead while addressing potential threats in the dynamic environment.In this research,an Artificial Intelligence(AI)recommended fault-tolerant framework is proposed that leverages blockchain technology,aiming to enhance device trustworthiness and ensure data privacy.In addition,the intelligence of the proposed framework enables more authentic and authorized device involvement in data routing,thereby enabling seamless transmission in smart cities integrated with lightweight computing.To evaluate dynamic network conditions,the proposed framework offers a timely decision-making system to ensure robust delivery of IoT-assisted services.Using simulations,the efficacy of the proposed framework is validated by comparing it with existing approaches across various network metrics,demonstrating remarkable performance while achieving energy efficiency and optimizing network resources.展开更多
Liver transplantation(LT)remains the optimal life-saving intervention for patients with end-stage liver disease.Despite the recent advances in LT several barriers,including organ allocation,donor-recipient matching,an...Liver transplantation(LT)remains the optimal life-saving intervention for patients with end-stage liver disease.Despite the recent advances in LT several barriers,including organ allocation,donor-recipient matching,and patient education,persist.With the growing progress of artificial intelligence,particularly large language models(LLMs)like ChatGPT,new applications have emerged in the field of LT.Current studies demonstrating usage of ChatGPT in LT include various areas of application,from clinical settings to research and education.ChatGPT usage can benefit both healthcare professionals,by decreasing the time spent on non-clinical work,but also LT recipients by providing accurate information.Future potential applications include the expanding usage of ChatGPT and other LLMs in the field of LT pathology and radiology as well as the automated creation of discharge summaries or other related paperwork.Additionally,the next models of ChatGPT might have the potential to provide more accurate patient education material with increased readability.Although ChatGPT usage presents promising applications,there are certain ethical and practical limitations.Key concerns include patient data privacy,information accuracy,misinformation possibility and lack of legal framework.Healthcare providers and policymakers should collaborate for the establishment of a controlled framework for the safe use of ChatGPT.The aim of this minireview is to summarize current literature on ChatGPT in LT,highlighting both opportunities and limitations,while also providing future possible applications.展开更多
The regulation of signal transmission speed is one of the most important capabilities of the biological nervous system.This study explores the mechanisms and methods for regulating signal transmission speed among nonm...The regulation of signal transmission speed is one of the most important capabilities of the biological nervous system.This study explores the mechanisms and methods for regulating signal transmission speed among nonmyelinated neurons within the same brain region,starting from spike-timing-dependent plasticity(STDP)of synapses.Building upon the Hodgkin-Huxley model,the dynamic behavior of synapses is incorporated,and the adaptive growth neuron(AGN)model is proposed.Artificial synaptic structures and neuronal physical nodes are also designed.The artificial synaptic structure exhibits unidirectionality,memory capacity,and STDP,enabling it to connect neuronal physical nodes through branching and merging structures.Furthermore,the artificial synapse can adjust signal transmission speed,regulate functional competition between different regions of the neuromorphic network,and promote information interaction.The findings of this study endow neuromorphic networks with the ability to regulate signal transmission speed over the long term,providing new insights into the development of neuromorphic networks.展开更多
Artificial intelligence(AI)is reshaping financial systems and services,as intelligent AI agents increasingly form the foundation of autonomous,goal-driven systems capable of reasoning,learning,and action.This review s...Artificial intelligence(AI)is reshaping financial systems and services,as intelligent AI agents increasingly form the foundation of autonomous,goal-driven systems capable of reasoning,learning,and action.This review synthesizes recent research and developments in the application of AI agents across core financial domains.Specifically,it covers the deployment of agent-based AI in algorithmic trading,fraud detection,credit risk assessment,roboadvisory,and regulatory compliance(RegTech).The review focuses on advanced agent-based methodologies,including reinforcement learning,multi-agent systems,and autonomous decision-making frameworks,particularly those leveraging large language models(LLMs),contrasting these with traditional AI or purely statistical models.Our primary goals are to consolidate current knowledge,identify significant trends and architectural approaches,review the practical efficiency and impact of current applications,and delineate key challenges and promising future research directions.The increasing sophistication of AI agents offers unprecedented opportunities for innovation in finance,yet presents complex technical,ethical,and regulatory challenges that demand careful consideration and proactive strategies.This review aims to provide a comprehensive understanding of this rapidly evolving landscape,highlighting the role of agent-based AI in the ongoing transformation of the financial industry,and is intended to serve financial institutions,regulators,investors,analysts,researchers,and other key stakeholders in the financial ecosystem.展开更多
基金supported by grants from the Guangxi Science and Technology Major Project(GKAA24206023)the Biological Breeding-National Science and Technology Major Project(2024ZD04077)+2 种基金the National Natural Science Foundation of China(32272120)the National Key Research and Development Program of China(2024YFF1000800)the Guangdong Basic Research Center of Excellence for Precise Breeding of Future Crops Major Project(FCBRCE-202502,FCBRCE-202504).
文摘A growing global population and the increasing prevalence of diet-related health issues such as“hidden hunger”,obesity,hypertension,and diabetes necessitate a fundamental rethinking of crop design and breeding.Synthetic metabolic engineering offers a method to modify and redesign metabolic pathways to increase the nutritional value of crops.We summarize recent advances in the biofortification of key nutrients including provitamin A,vitamin C,vitamin B9,iron,zinc,anthocyanins,flavonoids,and unsaturated fatty acids.We discuss the potential of multi-gene stacking,gene editing,enzyme engineering,and artificial intelligence in synthetic metabolic engineering.We propose future research directions and potential solutions centered on leveraging AI-driven systems biology,precision gene editing,enzyme engineering,agrobacterium-mediated genotype-independent transformation,and modular metabolic engineering strategies to develop next-generation nutritionally enhanced super crops and transform global food systems.
基金supported by Open Scientific Research Program of Military Logistics,No.BLB20J009(to YZhao).
文摘Blood-brain barrier disruption and the neuroinflammatory response are significant pathological features that critically influence disease progression and treatment outcomes.This review systematically analyzes the current understanding of the bidirectional relationship between blood-brain barrier disruption and neuroinflammation in traumatic brain injury,along with emerging combination therapeutic strategies.Literature review indicates that blood-brain barrier disruption and neuroinflammatory responses are key pathological features following traumatic brain injury.In the acute phase after traumatic brain injury,the pathological characteristics include primary blood-brain barrier disruption and the activation of inflammatory cascades.In the subacute phase,the pathological features are characterized by repair mechanisms and inflammatory modulation.In the chronic phase,the pathological features show persistent low-grade inflammation and incomplete recovery of the blood-brain barrier.Various physiological changes,such as structural alterations of the blood-brain barrier,inflammatory cascades,and extracellular matrix remodeling,interact with each other and are influenced by genetic,age,sex,and environmental factors.The dynamic balance between blood-brain barrier permeability and neuroinflammation is regulated by hormones,particularly sex hormones and stress-related hormones.Additionally,the role of gastrointestinal hormones is receiving increasing attention.Current treatment strategies for traumatic brain injury include various methods such as conventional drug combinations,multimodality neuromonitoring,hyperbaric oxygen therapy,and non-invasive brain stimulation.Artificial intelligence also shows potential in treatment decision-making and personalized therapy.Emerging sequential combination strategies and precision medicine approaches can help improve treatment outcomes;however,challenges remain,such as inadequate research on the mechanisms of the chronic phase traumatic brain injury and difficulties with technology integration.Future research on traumatic brain injury should focus on personalized treatment strategies,the standardization of techniques,costeffectiveness evaluations,and addressing the needs of patients with comorbidities.A multidisciplinary approach should be used to enhance treatment and improve patient outcomes.
文摘This article presents findings from an extensive experimental program conducted by the French Agency for the Management of Radioactive Waste(ANDRA)at its Underground Research Laboratory.The focus of the study is the NSC(Noyau de SCellement)experiment,designed to assess the performance of a sealing core composed of a mixture of 40%bentonite and 60%sand.The objective is to evaluate in situ hydraulic permeability,swelling behavior,and gas performance of the seal,which are essential components for the safe disposal of Intermediate-Level(IL)and High-Level Radioactive Waste(HLW)in deep geological repositories.The experimental setup,construction details of the NSC experiment,and the timeline of the project,from excavation,construction of the seal,and artificial hydration to hydraulic permeability tests,are comprehensively described.Andra has acquired valuable expertise in constructing an engineered barrier system,encompassing concrete plugs,and efficiently assembling a 5-m-long seal.Over the past decade,a controlled water injection process has been employed to hydrate the seal.The operational efficiency of sensors has been evaluated,with pore pressure sensors exhibiting moderate performance.In contrast,others,including total pressure,capacitive humidity,psychrometric humidity,and saturation sensors,demonstrate excellent performance up to their operational limits.The high-quality data obtained from this experiment will facilitate the modeling of the resaturation and hydraulic-gas performance of the seal.
文摘Standard bacterial suspensions play a crucial role in microbiological diagnosis.Traditional prepar-ation methods,which rely heavily on manual operations,face challenges such as poor reproducibility,low ef-ficiency,and biosafety concerns.In this study,we propose a high-precision automated colony extraction and separation system that combines large-field imaging and artificial intelligence(AI)to facilitate intelligent screening and localization of colonies.Firstly,a large-field imaging system was developed to capture high-resolution images of 90 mm Petri dishes,achieving a physical resolution of 13.2μm and an imaging speed of 13 frames per second.Subsequently,AI technology was employed for the automatic recognition and localiza-tion of colonies,enabling the selection of target colonies with diameters ranging from 1.9 to 2.3 mm.Next,a three-axis motion control platform was designed,accompanied by a path planning algorithm for the efficient extraction of colonies.An electronic pipette was employed for accurate colony collection.Additionally,a bacterial suspension concentration measurement module was developed,incorporating a 650 nm laser diode as the light source,achieving a measurement accuracy of 0.01 McFarland concentration(MCF).Finally,the system’s performance was validated through the preparation of an Esckerichia coli(E.coli)suspension.After 17 hours of cultivation,E.coli was extracted four times,achieving the target concentration set by the system.This work is expected to enable rapid and accurate microbial sample preparation,significantly reducing de-tection cycles and alleviating the workload of healthcare personnel.
文摘Tomasz Mizerkiewicz is a professor at the Faculty of Polish and Classical Philology at Adam Mickiewicz University and the editor-in-chief of Forum of Poetics.He has published extensively in the field of 20th-century and 21st-century Polish literature and literary theory.This interview addresses topics including key concepts,theoretical terminologies,and interdisciplinary research in Ethical Literary Criticism,as well as its future development and practical significance.Using different texts,including Polish literary works,Mizerkiewicz illustrates the application of core terms in Ethical Literary Criticism,analyzes ethical expressions and ethical values embedded in literary works,and offers insights into the relevance of Ethical Literary Criticism in the era of artificial intelligence,as well as its connections with ESG(Environmental,Social,and Governance)and sustainability.The interview underscores his emphasis on the vibrant academic value and practical significance of Ethical Literary Criticism in exploring ethical thoughts in literature,fostering interdisciplinary dialogue,and contributing to the advancement of modern society.
基金supported by the National Natural Science Foundation of China under Grant 61573183.
文摘With the increase of semiconductor integration density,in order to cope with the increase of wafer defect complexity and types,especially the low recognition accuracy of overlapping mixed defects and unknown wafer defects,this study proposes a lightweight model for wafer defect detection called LightWMNet.First,using a hierarchical attention Encoder-Decoder architecture,the features of wafer defect pattern(WDP)are channel recalibrated to generate high-resolution fine-grained features and low-resolution coarse-grained features.Secondly,the backbone network incorporates two novel attention modules—feedforward spatial attention(FFSa)and feedforward channel attention(FFCa)—to amplify responses in critical defect regions and suppress noise from stochastic discrete pixels.These mechanisms synergistically enhance feature discriminability without introducing significant parametric overhead.Finally,the Dice loss function and the cross entropy loss function are combined to jointly evaluate the segmentation and classification accuracy of the model.Experimental results on the public mixed wafer defect dataset MixedWM38 show that the pixel accuracy(PA),intersection over union(IoU)and Dice coefficient of the proposed network reach 98.26%,94.83%and 97.22%,respectively.Without significantly increasing the computational complexity and size of the model,compared with the existing state-of-the-art(SOTA)model,the classification accuracy of lightWMNet in single defect,three mixed defects and four mixed defects is improved by 0.5%,0.25%and 0.89%respectively.Furthermore,we used transfer learning for the first time to evaluate the model's generalisation ability for unseen defect categories.The results showed that LightWMNet still has a certain recognition ability even in untrained wafer defects.
文摘“By successfully integrating artificial intelligence(AI)into research workflows,researchers could substantially increase scientific productivity”[1].In biofabrication,AI is dr iving a paradigm shift from empiricism toward intelligen t,data centric manufacturing[2].By integrating computation,automation,and biology,AI gives rise to self-evolving,adaptive systems that learn from data,predict complex behaviors,and autonomously optimize fabrication outcomes.Such systems translate experimental insights into patient-specific and clinically relevant solutions,bridging laboratory research and regenerative therapies[3].This emerging frontier is rapidly advancing from concept to application.This Special Column highlights how AI-driven advanc es in materials,design,and manufacturing are reshaping biof abrication for regenerative medicine and clinical translation.
基金Supported by Japan Society for the Promotion of Science,No.24K11935.
文摘This review comprehensively summarized the potential of artificial intelligence(AI)in the management of esophageal cancer.It highlighted the significance of AI-assisted endoscopy in Japan where endoscopy is central to both screening and diagnosis.For the clinical adaptation of AI,several challenges remain for its effective translation.The establishment of high-quality clinical databases,such as the National Clinical Database and Japan Endoscopy Database in Japan,which covers almost all cases of esophageal cancer,is essential for validating multimodal AI models.This requires rigorous external validation using diverse datasets,including those from different endoscope manufacturers and image qualities.Furthermore,endoscopists’skills significantly affect diagnostic accuracy,suggesting that AI should serve as a supportive tool rather than a replacement.Addressing these challenges,along with country-specific legal and ethical considerations,will facilitate the successful integration of multimodal AI into the management of esophageal cancer,particularly in endoscopic diagnosis,and contribute to improved patient outcomes.Although this review focused on Japan as a case study,the challenges and solutions described are broadly applicable to other high-incidence regions.
文摘The year 2025 marked the inaugural volume of Artificial Intelligence Science and Engineering(AISE).Withthe strong support of the global academic community in artificial intelligence and control science,and through thededicated efforts of our editorial board members,authors,and reviewers,all four issues were successfullypublished.These issues brought together significant original contributions and authoritative surveys from leadingscholars,laying a solid academic foundation for the long-term development of the journal.
文摘Sponsored by Chongqing University of Technology(CQUT)and the Chinese Association for Artificial Intelligence(CAAI),CAAI Transactions on Intelligence Technology is a gold Open Access journal that publishes articles on the theoretical and experimental aspects of artificial intelligence technology.This journal is a high-quality international platform for the exchange of the latest academic developments from the global artificial intelligence community.
文摘Industrial Cyber-Physical Systems(ICPSs)play a vital role in modern industries by providing an intellectual foundation for automated operations.With the increasing integration of information-driven processes,ensuring the security of Industrial Control Production Systems(ICPSs)has become a critical challenge.These systems are highly vulnerable to attacks such as denial-of-service(DoS),eclipse,and Sybil attacks,which can significantly disrupt industrial operations.This work proposes an effective protection strategy using an Artificial Intelligence(AI)-enabled Smart Contract(SC)framework combined with the Heterogeneous Barzilai-Borwein Support Vector(HBBSV)method for industrial-based CPS environments.The approach reduces run time and minimizes the probability of attacks.Initially,secured ICPSs are achieved through a comprehensive exchange of views on production plant strategies for condition monitoring using SC and blockchain(BC)integrated within a BC network.The SC executes the HBBSV strategy to verify the security consensus.The Barzilai-Borwein Support Vectorized algorithm computes abnormal attack occurrence probabilities to ensure that components operate within acceptable production line conditions.When a component remains within these conditions,no security breach occurs.Conversely,if a component does not satisfy the condition boundaries,a security lapse is detected,and those components are isolated.The HBBSV method thus strengthens protection against DoS,eclipse,and Sybil attacks.Experimental results demonstrate that the proposed HBBSV approach significantly improves security by enhancing authentication accuracy while reducing run time and authentication time compared to existing techniques.
文摘As 2026 unfolds,the image of an autonomous port in Ningbo loading a U.S.-bound ship encapsulates the transformative power of China’s surge in artificial intelligence(AI).This automation marvel not only symbolizes China’s economic power but also demonstrates how deeply integrated AI has become into its global competition strategy.Once largely confined to academic research and pilot programs,AI is now embedded in China’s national strategy.The goal:drive productivity,modernize industries,and strengthen its competitive position in global technology markets.
基金supported by the National Key R&D Program of China(2022YFC3501000,2022YFC3502300)National Natural Science Foundation of China(82374627)+2 种基金Fundamental Research Funds for the Central public welfare research institutes(Z0876)Fundamental Research Funds for the Central Universities(2024-JYB-KYPT-01)Beijing Municipal Science and Technology Commission(Z241100007724010).
文摘Background:Artificial intelligence medical diagnostic devices(AIMDDs)show strong potential but face barriers to clinical use,emphasizing the need for rigorous clinical research.Objective:We assessed current AIMDD research,key challenges,and future directions.Methods:A scoping review followed Arksey and O'Malley's methodological framework and the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews guidelines.PubMed,Web of Science Core Collection,and the Cochrane Database of Systematic Reviews(January 2020-December 2024)were searched on AIMDD design,implementation,and evaluation.Two independent researchers screened and extracted data from the literature using predefined criteria.Results:Ninety-seven articles met the inclusion criteria.Machine learning and deep learning approaches dominated across diverse disease fields,with oncology being the most frequent(41%).The key challenges identified include insufficient quantity,quality,representativeness,and diversity of data;research designs that do not adequately address clinical needs;poor patient selection;poorly defined gold standards;lack of external and prospective validation;and a disconnect between validation strategies and clinical practice.Additionally,issues such as the“black box”phenomenon,overfitting,and data privacy concerns hinder clinical translation.Completeness and standardization of reporting were also found to be lacking.Conclusions:Significant challenges remain in the development and clinical application of AIMDD.To facilitate their clinical translation,improvements are needed in dataset optimization,clinically driven research design,development of evaluation frameworks,enhanced interpretability,and standardized reporting and validation of algorithms.
文摘Generative artificial intelligence(AI),specifically large language models,such as DeepSeek,has accelerated the digital transformation of healthcare systems in both developing and developed countries.The use of AI in diagnostics,image processing and interpretation,treatment personalization,clinical documentation,and drug discovery is an example of the implementation of AI in Western medicine.The need for evidence-based studies and a standardized approach to scientific medicine aligns well with these applications.AI can leave a lasting impact on the Chinese medicine(CM)landscape by increasing expectations and presenting new challenges.The analogy between the CM-specific diagnostic methods and pattern differentiation,which is holistic,pattern-oriented,patient-centered,and clinical data analysis,is significant at multiple levels.These qualities pose challenges for AI usage in CM,which heavily relies on structured data and pattern recognition.Despite these adversities,AI can still be used in CM through data standardization,prediction formulation,and treatment planning,provided that the integration of this tool considers the primary principles of CM and adheres to ethical and regulatory considerations.This review examines the dichotomous approach to health and medicine in the contexts of AI and CM,highlighting the evolving potential,inherent limitations,and ethical and regulatory issues associated with the application of AI to CM.It provides a foundation for developing technologically progressive yet culturally and philosophically sensitive strategies that are in harmony with traditional clinical values.
基金supported by the National Key Research and Development Program of China(2023YFD1301505)Beijing Innovation Consortium of Agriculture Research System(BAIC01)National Natural Science Foundation of China(32372437).
文摘Fluorescent RNA aptamers offer promising opportunities for next-generation biosensing but are often limited by low signal-to-background ratios and unstable folding kinetics.In this work,a label-free Förster resonance energy transfer(FRET)-enhanced fluorescent artificial RNA condensate(F-FARCON)is developed for small-molecule sensing,leveraging neutral molecular crowders(e.g.,polyethylene glycol 8K),and RNA structural motifs to induce multivalent interactions and drive dynamic self-assembly.As a demonstration,a label-free FRET system is constructed by integrating a histamine-responsive RNA aptamer with thioflavin T(ThT)as the fluorescence donor,which increases the signal-to-noise ratio while reducing sequence complexity and production costs.Molecular crowders optimize the thermodynamic environment of RNA-ligand and RNA-RNA multivalent interactions,thereby improving folding stability,signal amplitude(dynamic range of up to~970-fold),and target affinity.The platform exhibits fast kinetics(<15 min),an adjustable detection range(0.1200 and 51000μM),and high sensitivity(limit of detection,15.36 nM),with robust performance in complex biological matrices.The platform is further integrated into a freeze-dried paper-based portable device that enables dual-channel fluorescence readout for on-site rapid detection without sophisticated instrumentation.To further validate the modularity of F-FARCON beyond histamine,we reprogrammed the recognition module to target S-adenosyl-L-methionine(SAM),achieving nanomolar limits of detection.By linking crowding-guided assembly to hierarchical photophysical enhancement and analytical performance,the work delineates a generalizable aggregate-science route to versatile,low-cost,and field-deployable fluorescence sensing across food safety,environmental monitoring,and biomedical diagnostics.
基金funded by the Deanship of Graduate Studies and Scientific Research at Jouf University under grant No.(DGSSR-2024-02-02152).
文摘The Internet of Things(IoT)and cloud computing have significantly contributed to the development of smart cities,enabling real-time monitoring,intelligent decision-making,and efficient resource management.These systems,particularly in IoT networks,rely on numerous interconnected devices that handle time-sensitive data for critical applications.In related approaches,trusted communication and reliable device interaction have been overlooked,thereby lowering security when sharing sensitive IoT data.Moreover,it incurs additional energy consumption and overhead while addressing potential threats in the dynamic environment.In this research,an Artificial Intelligence(AI)recommended fault-tolerant framework is proposed that leverages blockchain technology,aiming to enhance device trustworthiness and ensure data privacy.In addition,the intelligence of the proposed framework enables more authentic and authorized device involvement in data routing,thereby enabling seamless transmission in smart cities integrated with lightweight computing.To evaluate dynamic network conditions,the proposed framework offers a timely decision-making system to ensure robust delivery of IoT-assisted services.Using simulations,the efficacy of the proposed framework is validated by comparing it with existing approaches across various network metrics,demonstrating remarkable performance while achieving energy efficiency and optimizing network resources.
文摘Liver transplantation(LT)remains the optimal life-saving intervention for patients with end-stage liver disease.Despite the recent advances in LT several barriers,including organ allocation,donor-recipient matching,and patient education,persist.With the growing progress of artificial intelligence,particularly large language models(LLMs)like ChatGPT,new applications have emerged in the field of LT.Current studies demonstrating usage of ChatGPT in LT include various areas of application,from clinical settings to research and education.ChatGPT usage can benefit both healthcare professionals,by decreasing the time spent on non-clinical work,but also LT recipients by providing accurate information.Future potential applications include the expanding usage of ChatGPT and other LLMs in the field of LT pathology and radiology as well as the automated creation of discharge summaries or other related paperwork.Additionally,the next models of ChatGPT might have the potential to provide more accurate patient education material with increased readability.Although ChatGPT usage presents promising applications,there are certain ethical and practical limitations.Key concerns include patient data privacy,information accuracy,misinformation possibility and lack of legal framework.Healthcare providers and policymakers should collaborate for the establishment of a controlled framework for the safe use of ChatGPT.The aim of this minireview is to summarize current literature on ChatGPT in LT,highlighting both opportunities and limitations,while also providing future possible applications.
基金supported by the National Natural Science Foundation of China(Grant No.62171182)the Natural Scienceof Hunan Province(Grant No.2025JJ50345)the Postgraduate Scientific Research Innovation Project of Hunan Province(Grant No.CX20240452)。
文摘The regulation of signal transmission speed is one of the most important capabilities of the biological nervous system.This study explores the mechanisms and methods for regulating signal transmission speed among nonmyelinated neurons within the same brain region,starting from spike-timing-dependent plasticity(STDP)of synapses.Building upon the Hodgkin-Huxley model,the dynamic behavior of synapses is incorporated,and the adaptive growth neuron(AGN)model is proposed.Artificial synaptic structures and neuronal physical nodes are also designed.The artificial synaptic structure exhibits unidirectionality,memory capacity,and STDP,enabling it to connect neuronal physical nodes through branching and merging structures.Furthermore,the artificial synapse can adjust signal transmission speed,regulate functional competition between different regions of the neuromorphic network,and promote information interaction.The findings of this study endow neuromorphic networks with the ability to regulate signal transmission speed over the long term,providing new insights into the development of neuromorphic networks.
基金supported by the Ministry of Education and Science of the Republic of North Macedonia through the project“Utilizing AI and National Large Language Models to Advance Macedonian Language Capabilties”。
文摘Artificial intelligence(AI)is reshaping financial systems and services,as intelligent AI agents increasingly form the foundation of autonomous,goal-driven systems capable of reasoning,learning,and action.This review synthesizes recent research and developments in the application of AI agents across core financial domains.Specifically,it covers the deployment of agent-based AI in algorithmic trading,fraud detection,credit risk assessment,roboadvisory,and regulatory compliance(RegTech).The review focuses on advanced agent-based methodologies,including reinforcement learning,multi-agent systems,and autonomous decision-making frameworks,particularly those leveraging large language models(LLMs),contrasting these with traditional AI or purely statistical models.Our primary goals are to consolidate current knowledge,identify significant trends and architectural approaches,review the practical efficiency and impact of current applications,and delineate key challenges and promising future research directions.The increasing sophistication of AI agents offers unprecedented opportunities for innovation in finance,yet presents complex technical,ethical,and regulatory challenges that demand careful consideration and proactive strategies.This review aims to provide a comprehensive understanding of this rapidly evolving landscape,highlighting the role of agent-based AI in the ongoing transformation of the financial industry,and is intended to serve financial institutions,regulators,investors,analysts,researchers,and other key stakeholders in the financial ecosystem.
