In this paper,we establish some strong laws of large numbers,which are for nonindependent random variables under the framework of sublinear expectations.One of our main results is for blockwise m-dependent random vari...In this paper,we establish some strong laws of large numbers,which are for nonindependent random variables under the framework of sublinear expectations.One of our main results is for blockwise m-dependent random variables,and another is for sub-orthogonal random variables.Both extend the strong law of large numbers for independent random variables under sublinear expectations to the non-independent case.展开更多
Commercial phosphor-converted white LEDs(pc-WLEDs)face two inherent limitations,namely blue light hazard and low color rendering index,due to the use of blue LEDs as excitation source.To address these challenges,viole...Commercial phosphor-converted white LEDs(pc-WLEDs)face two inherent limitations,namely blue light hazard and low color rendering index,due to the use of blue LEDs as excitation source.To address these challenges,violet LEDs are proposed as an alternative solution.Currently,phosphors that can be efficiently excited by violet light(with wavelengths from 400 to 420 nm)remain under development still.In this study,we utilize large language models to construct a comprehensive database of Eu^(2+)and Ce^(3+)doped phosphors for discovering novel violet-excited phosphors.A total of 822 phosphor data entries,including elemental compositions,crystal structures and excitation/emission wavelengths,have been extracted and validated from 9551 research papers.Compared with Ce^(3+)doped phosphors,the Eu^(2+)are in general more suited for violet-excited phosphors,as well as red-emitting phosphors.In particular,Eu^(2+)doped nitrides and sulfides are worth of exploration for violet-excited phosphors.This database is expected to be useful in the future development of phosphors for pc-WLEDs based on artificial intelligence methods.The datasets in this article are listed in Science Data Bank at http://doi.org/10.57760/sciencedb.34314.展开更多
Model evaluation using benchmark datasets is an important method to measure the capability of large language models(LLMs)in specific domains,and it is mainly used to assess the knowledge and reasoning abilities of LLM...Model evaluation using benchmark datasets is an important method to measure the capability of large language models(LLMs)in specific domains,and it is mainly used to assess the knowledge and reasoning abilities of LLMs.Therefore,in order to better assess the capability of LLMs in the agricultural domain,Agri-Eval was proposed as a benchmark for assessing the knowledge and reasoning ability of LLMs in agriculture.The assessment dataset used in Agri-Eval covered seven major disciplines in the agricultural domain:crop science,horticulture,plant protection,animal husbandry,forest science,aquaculture science,and grass science,and contained a total of 2283 questions.Among domestic general-purpose LLMs,DeepSeek R1 performed best with an accuracy rate of 75.49%.In the realm of international general-purpose LLMs,Gemini 2.0 pro exp 0205 standed out as the top performer,achieving an accuracy rate of 74.28%.As an LLMs in agriculture vertical,Shennong V2.0 outperformed all the LLMs in China,and the answer accuracy rate of agricultural knowledge exceeded that of all the existing general-purpose LLMs.The launch of Agri-Eval helped the LLM developers to comprehensively evaluate the model's capability in the field of agriculture through a variety of tasks and tests to promote the development of the LLMs in the field of agriculture.展开更多
To the Editor,Artificial intelligence(AI)usage has been increasing.Many fields have implemented the use of AI and Large LanguageModels(LLMs),especially in medicine.Furthermore,manypatients have increasingly been using...To the Editor,Artificial intelligence(AI)usage has been increasing.Many fields have implemented the use of AI and Large LanguageModels(LLMs),especially in medicine.Furthermore,manypatients have increasingly been using AI;often,they will prompt AI with questions before even stepping into a physi-cian's office.The question lies in whether the information produced by AI is reliable and if this information is concise and easy to read across all patient populations.展开更多
This study evaluated the accuracy,completeness,and comprehensibility of responses from mainstream large language models(LLMs)to hepatitis C virus(HCV)-related questions,aiming to assess their performance in addressing...This study evaluated the accuracy,completeness,and comprehensibility of responses from mainstream large language models(LLMs)to hepatitis C virus(HCV)-related questions,aiming to assess their performance in addressing patient queries about disease and lifestyle behaviors.The models selected were ChatGPT-4o,Gemini 2.0 Pro,Claude 3.5 Sonnet,and DeepSeek V3,with 12 questions chosen by two HCV experts from the domains of prevention,diagnosis,and treatment.展开更多
The emergence of large language models(LLMs)has brought about revolutionary social value.However,concerns have arisen regarding the generation of deceptive content by LLMs and their potential for misuse.Consequently,a...The emergence of large language models(LLMs)has brought about revolutionary social value.However,concerns have arisen regarding the generation of deceptive content by LLMs and their potential for misuse.Consequently,a crucial research question arises:How can we differentiate between AI-generated and human-authored text?Existing detectors face some challenges,such as operating as black boxes,relying on supervised training,and being vulnerable to manipulation and misinformation.To tackle these challenges,we propose an innovative unsupervised white-box detection method that utilizes a“dual-driven verification mechanism”to achieve high-performance detection,even in the presence of obfuscated attacks in the text content.To be more specific,we initially employ the SpaceInfi strategy to enhance the difficulty of detecting the text content.Subsequently,we randomly select vulnerable spots from the text and perturb them using another pre-trained language model(e.g.,T5).Finally,we apply a dual-driven defense mechanism(D3M)that validates text content with perturbations,whether generated by a model or authored by a human,based on the dimensions of Information TransmissionQuality and Information TransmissionDensity.Through experimental validation,our proposed novelmethod demonstrates state-of-the-art(SOTA)performancewhen exposed to equivalent levels of perturbation intensity across multiple benchmarks,thereby showcasing the effectiveness of our strategies.展开更多
It is known that correlation does not imply causality.Some relationships identified in the analysis of data are coincidental or unknown,and some are produced by real-world causality of the situation,which is problemat...It is known that correlation does not imply causality.Some relationships identified in the analysis of data are coincidental or unknown,and some are produced by real-world causality of the situation,which is problematic,since there is a need to differentiate between these two scenarios.Until recently,the proper−semantic−causality of the relationship could have been determined only by human experts from the area of expertise of the studied data.This has changed with the advance of large language models,which are often utilized as surrogates for such human experts,making the process automated and readily available to all data analysts.This motivates the main objective of this work,which is to introduce the design and implementation of a large language model-based semantic causality evaluator based on correlation analysis,together with its visual analysis model called Causal heatmap.After the implementation itself,the model is evaluated from the point of view of the quality of the visual model,from the point of view of the quality of causal evaluation based on large language models,and from the point of view of comparative analysis,while the results reached in the study highlight the usability of large language models in the task and the potential of the proposed approach in the analysis of unknown datasets.The results of the experimental evaluation demonstrate the usefulness of the Causal heatmap method,supported by the evident highlighting of interesting relationships,while suppressing irrelevant ones.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
Electrochromic smart windows(ESWs)can significantly reduce building energy consumption,but the high cost hinders large-scale production.The in situ growth of tungsten oxide(WO_(3))films is only by a simple immersion p...Electrochromic smart windows(ESWs)can significantly reduce building energy consumption,but the high cost hinders large-scale production.The in situ growth of tungsten oxide(WO_(3))films is only by a simple immersion process,the silver nanowires(AgNWs)undergo oxidation to Ag^(+)ions through electron loss,and the liberated electrons provide driving force for the deposition of WO_(4)^(2-).Enabled the fabrication of large-area WO_(3)films and ESWs were fabricated under minimal laboratory conditions,demonstrating the economic feasibility,efficient and reliable nature of industrial production.Structural characterization and density functional theory calculations were combined to confirm that AgNWs effectively regulate oxygen vacancies of WO_(3)films and promote the in situ growth process.The optimized WO_(3)exhibits a maximum transmittance modulation of 90.8%and excellent cycling stability of 20,000 cycles.The largescale WO_(3)-based ESWs can save building energy up to 140.0 MJ m^(-2)compared to traditional windows in tropical regions,as verified by simulations more than40 global cities.This research provides a new approach for improving the performance and industrial production of ESW,providing the full understanding and development direction to short the distance of the ESW commercial production.展开更多
Background:Assess ChatGPT and Bard's effectiveness in the initial identification of articles for Otolaryngology—Head and Neck Surgery systematic literature reviews.Methods:Three PRISMA-based systematic reviews(Ja...Background:Assess ChatGPT and Bard's effectiveness in the initial identification of articles for Otolaryngology—Head and Neck Surgery systematic literature reviews.Methods:Three PRISMA-based systematic reviews(Jabbour et al.2017,Wong et al.2018,and Wu et al.2021)were replicated using ChatGPTv3.5 and Bard.Outputs(author,title,publication year,and journal)were compared to the original references and cross-referenced with medical databases for authenticity and recall.Results:Several themes emerged when comparing Bard and ChatGPT across the three reviews.Bard generated more outputs and had greater recall in Wong et al.'s review,with a broader date range in Jabbour et al.'s review.In Wu et al.'s review,ChatGPT-2 had higher recall and identified more authentic outputs than Bard-2.Conclusion:Large language models(LLMs)failed to fully replicate peer-reviewed methodologies,producing outputs with inaccuracies but identifying relevant,especially recent,articles missed by the references.While human-led PRISMA-based reviews remain the gold standard,refining LLMs for literature reviews shows potential.展开更多
This study demonstrates a novel integration of large language models,machine learning,and multicriteria decision-making to investigate self-moderation in small online communities,a topic under-explored compared to use...This study demonstrates a novel integration of large language models,machine learning,and multicriteria decision-making to investigate self-moderation in small online communities,a topic under-explored compared to user behavior and platform-driven moderation on social media.The proposed methodological framework(1)utilizes large language models for social media post analysis and categorization,(2)employs k-means clustering for content characterization,and(3)incorporates the TODIM(Tomada de Decisão Interativa Multicritério)method to determine moderation strategies based on expert judgments.In general,the fully integrated framework leverages the strengths of these intelligent systems in a more systematic evaluation of large-scale decision problems.When applied in social media moderation,this approach promotes nuanced and context-sensitive self-moderation by taking into account factors such as cultural background and geographic location.The application of this framework is demonstrated within Facebook groups.Eight distinct content clusters encompassing safety,harassment,diversity,and misinformation are identified.Analysis revealed a preference for content removal across all clusters,suggesting a cautious approach towards potentially harmful content.However,the framework also highlights the use of other moderation actions,like account suspension,depending on the content category.These findings contribute to the growing body of research on self-moderation and offer valuable insights for creating safer and more inclusive online spaces within smaller communities.展开更多
Although Named Entity Recognition(NER)in cybersecurity has historically concentrated on threat intelligence,vital security data can be found in a variety of sources,such as open-source intelligence and unprocessed too...Although Named Entity Recognition(NER)in cybersecurity has historically concentrated on threat intelligence,vital security data can be found in a variety of sources,such as open-source intelligence and unprocessed tool outputs.When dealing with technical language,the coexistence of structured and unstructured data poses serious issues for traditional BERT-based techniques.We introduce a three-phase approach for improved NER inmulti-source cybersecurity data that makes use of large language models(LLMs).To ensure thorough entity coverage,our method starts with an identification module that uses dynamic prompting techniques.To lessen hallucinations,the extraction module uses confidence-based self-assessment and cross-checking using regex validation.The tagging module links to knowledge bases for contextual validation and uses SecureBERT in conjunction with conditional random fields to detect entity boundaries precisely.Our framework creates efficient natural language segments by utilizing decoderbased LLMs with 10B parameters.When compared to baseline SecureBERT implementations,evaluation across four cybersecurity data sources shows notable gains,with a 9.4%–25.21%greater recall and a 6.38%–17.3%better F1-score.Our refined model matches larger models and achieves 2.6%–4.9%better F1-score for technical phrase recognition than the state-of-the-art alternatives Claude 3.5 Sonnet,Llama3-8B,and Mixtral-7B.The three-stage architecture identification-extraction-tagging pipeline tackles important cybersecurity NER issues.Through effective architectures,these developments preserve deployability while setting a new standard for entity extraction in challenging security scenarios.The findings show how specific enhancements in hybrid recognition,validation procedures,and prompt engineering raise NER performance above monolithic LLM approaches in cybersecurity applications,especially for technical entity extraction fromheterogeneous sourceswhere conventional techniques fall short.Because of itsmodular nature,the framework can be upgraded at the component level as new methods are developed.展开更多
Background:Despite the promise shown by large language models(LLMs)for standardized tasks,their multidimensional performance in real-world oncology decision-making remains unevaluated.This study aims to introduce a fr...Background:Despite the promise shown by large language models(LLMs)for standardized tasks,their multidimensional performance in real-world oncology decision-making remains unevaluated.This study aims to introduce a framework for evaluating LLMs and physician decisions in challenging lung cancer cases.Methods:We curated 50 challenging lung cancer cases(25 local and 25 published)classified as complex,rare,or refractory.Blinded three-dimensional,five-point Likert evaluations(1–5 for comprehensiveness,specificity,and readability)compared standalone LLMs(DeepSeek R1,Claude 3.5,Gemini 1.5,and GPT-4o),physicians by experience level(junior,intermediate,and senior),and AI-assisted juniors;intergroup differences and augmentation effects were analyzed statistically.Results:Of 50 challenging cases(18 complex,17 rare,and 15 refractory)rated by three experts,DeepSeek R1 achieved scores of 3.95±0.33,3.71±0.53,and 4.26±0.18 for comprehensiveness,specificity,and readability,respectively,positioning it between intermediate(3.68,3.68,3.75)and senior(4.50,4.64,4.53)physicians.GPT-4o and Claude 3.5 reached intermediate physician–level comprehensiveness(3.76±0.39,3.60±0.39)but junior-to-intermediate physician–level specificity(3.39±0.39,3.39±0.49).All LLMs scored higher on rare cases than intermediate physicians but fell below junior physicians in refractory-case specificity.AIassisted junior physicians showed marked gains in rare cases,with comprehensiveness rising from 2.32 to 4.29(84.8%),specificity from 2.24 to 4.26(90.8%),and readability from 2.76 to 4.59(66.0%),while specificity declined by 3.2%(3.17 to 3.07)in refractory cases.Error analysis showed complementary strengths,with physicians demonstrating reasoning stability and LLMs excelling in knowledge updating and risk management.Conclusions:LLMs performed variably in clinical decision-making tasks depending on case type,performing better in rare cases and worse in refractory cases requiring longitudinal reasoning.Complementary strengths between LLMs and physicians support case-and task-tailored human–AI collaboration.展开更多
Polyfluoroalkyl substances(PFAS)have emerged as persistent environmental contaminants because of their chemical stability,degradation-resistance and bioaccumulation potential.However,current studies mainly focus on th...Polyfluoroalkyl substances(PFAS)have emerged as persistent environmental contaminants because of their chemical stability,degradation-resistance and bioaccumulation potential.However,current studies mainly focus on the toxicity of single PFAS such as perfluorooctanoic acid(PFOA)and perfluorobutanoic acid(PFBA),the knowledge of their combined effects is relatively limited.In this study,we explored the immune response of the gut in large yellow croaker(Larimichthys crocea)under the combined stress of PFOA and PFBA.Histologicalanalyses revealed that the combined effect induced intestinal vacuolization and decreased the length of intestinal villi.And it significantly activated pro-inflammatory pathways with marked upregulation of tnfα,il1β,il6 and myd88 expressions,particularly after 14 days of exposure.Gut microbiota analysis revealed substantial dysbiosis,including 1)reduced alpha diversity,2)increased abundance of potential pathogenic taxa(Proteobacteria and Spirochaetota),and 3)depletion of beneficial Firmicutes.PICRUSt-based functional prediction indicated temporal metabolic shifts,with upregulation of DNA repair pathways at day 3 and enhanced bacterial motility protein activity at days 7 and 14 of post-exposure.The Pearson correlation analysis further indicated that these immune genes had significant positive correlations with Vibrio and Brevinema,and negative correlations with Streptococcus.Our present study will provide novel insights into the microbiome-mediated immunomodulation in the larger yellow croaker exposed to combined PFAS,which will be helpful for healthy farming of economically important marine species.展开更多
The use of Unmanned Aerial Vehicles(UAVs)for defect detection on railway slopes is becoming increasingly widespread due to their ability to capture high-resolution images over large,inaccessible,and topographically co...The use of Unmanned Aerial Vehicles(UAVs)for defect detection on railway slopes is becoming increasingly widespread due to their ability to capture high-resolution images over large,inaccessible,and topographically complex areas.However,current UAV-based detection methods face several critical limitations,including constrained deployment frequency,limited availability of annotated defect data,and the lack of mature risk assessment frameworks.To address these challenges,this study introduces a novel approach that integrates diffusion models with Large Language Models(LLMs)to generate highquality synthetic defect images tailored to railway slope scenarios.Furthermore,an improved transformerbased architecture is proposed,incorporating attention mechanisms and LLM-guided diffusion-generated imagery to enhance defect recognition performance under complex environmental conditions.Experimental evaluations conducted on a dataset of 300 field-collected images from high-risk railway slopes demonstrate that the proposed method significantly outperforms existing baselines in terms of precision,recall,and robustness,indicating strong applicability for real-world railway infrastructure monitoring and disaster prevention.展开更多
Foundation models are reshaping artificial intelligence,yet their deployment in specialised domains such as agricultural question answering(AQA)still faces challenges including data scarcity and barriers to domainspec...Foundation models are reshaping artificial intelligence,yet their deployment in specialised domains such as agricultural question answering(AQA)still faces challenges including data scarcity and barriers to domainspecific knowledge.To systematically review recent progress in this area,this paper adopts a task–paradigmperspective and examines applications across three major AQA task families.For text-based QA,we analyse the strengths and limitations of retrieval-based,generative,and hybrid approaches built on large languagemodels,revealing a clear trend toward hybrid paradigms that balance precision and flexibility.For visual diagnosis,we discuss techniques such as crossmodal alignment and prompt-driven generation,which are pushing systems beyond simple pest and disease recognition toward deeper causal reasoning.Formultimodal reasoning,we show how the fusion of heterogeneous data—including text,images,speech,and sensor streams—enables comprehensive decision-making for diagnosis,monitoring,and yield prediction.To address the lack of unified benchmarks,we further propose a standardised evaluation protocol and a diagnostic taxonomy specifically designed to characterise agriculture-specific errors.Finally,we outline a concreteAQA roadmap that emphasises safety alignment,hallucination control,and lightweight deployment,aiming to guide future systems toward greater efficiency,trustworthiness,and sustainability.展开更多
This study presents an implicit multiphysics coupling method integrating Computational Fluid Dynamics(CFD),the Multiphase Particle-in-Cell(MPPIC)model,and the Finite Element Method(FEM),implemented with OpenFOAM,Calcu...This study presents an implicit multiphysics coupling method integrating Computational Fluid Dynamics(CFD),the Multiphase Particle-in-Cell(MPPIC)model,and the Finite Element Method(FEM),implemented with OpenFOAM,CalculiX,and preCICE to simulate fluid-particle-structure interactions with large deformations.Mesh motion in the fluid field is handled using the radial basis function(RBF)method.The particle phase is modeled by MPPIC,where fluid-particle interaction is described through momentum exchange,and inter-particle collisions are characterized by collision stress.The structural field is solved by nonlinear FEM to capture large deformations induced by geometric nonlinearity.Coupling among fields is realized through a partitioned,parallel,and non-intrusive iterative strategy,ensuring stable transfer and convergence of interface forces and displacements.Notably,the influence of particles on the structure is not direct but mediated by the fluid,while structural motion directly affects particle dynamics.The results demonstrate that the proposed approach effectively captures multiphysics interaction processes and provides a valuable reference for numerical modeling of coupled fluid-particle-structure systems.展开更多
This study investigates the turbulence-induced disturbances and stall precursor triggering mechanism in NACA65-18(10)cascade based on large eddy simulations.The results indicate that the disturbances exist under vario...This study investigates the turbulence-induced disturbances and stall precursor triggering mechanism in NACA65-18(10)cascade based on large eddy simulations.The results indicate that the disturbances exist under various operating conditions along the performance curve.The shear layer is the physical structure responsible for the generation,propagation,and dissipation of disturbances.When operating near stall,the separation on the suction surface intensifies,and strong unsteady backflow occurs at the trailing edge of the passage.Under the influence of inlet disturbances,unsteady behaviors between passages form specific phase differences,leading the entire system to oscillate in a first-order mode.As the flow develops from near-stall to stall,axial momentum decreases further,reducing the main flow’s ability to drive blockages downstream through convection.Consequently,the blockage accumulates during the circumferential propagation process until the stall onset.Based on the above mechanism,this study proposes factors describing the size of the backflow zone,shedding frequency,and convection velocity to characterize blockage dynamics,identifying critical values that represent the stall onset.展开更多
BACKGROUND:To provide a comprehensive analysis of the landscape of artifi cial intelligence(AI)applications in cardiac arrest(CA).METHODS:Comprehensive searches were conducted in PubMed,the Cochrane Library,Webof Scie...BACKGROUND:To provide a comprehensive analysis of the landscape of artifi cial intelligence(AI)applications in cardiac arrest(CA).METHODS:Comprehensive searches were conducted in PubMed,the Cochrane Library,Webof Science,and EMBASE from database inception through 10 June 2025.Studies that applied AI inboth in-hospital cardiac arrest(IHCA)and out-of-hospital cardiac arrest(OHCA)populations acrossthe following domains were included:prediction of cardiac arrest occurrence,prognostication ofCA outcomes,applications of large language models(LLMs),and evaluation of cardiopulmonaryresuscitation(CPR)and other AI-driven interventions related to CA.RESULTS:The scoping review included 114 studies,encompassing data from 9,574,462patients in total.AI was most commonly applied to the prediction of CA(overall,n=40;IHCA,n=30;OHCA,n=4;and both,n=6),CPR-related decision support during CA(n=16),and post-arrestprognosis and rehabilitation outcomes(overall,n=38;OHCA,n=21;IHCA,n=3;and both,n=14).Additional application areas included LLM-based applications(n=8),emergency call handling(n=4),wearable device-based detection(n=3),heart rhythm identification(n=2),education(n=2),and extracorporeal cardiopulmonary resuscitation(ECPR)candidate identifi cation(n=1).Across allapplication scenarios,the highest area under the receiver operating characteristic curve(AUROC)value for pre-arrest CA prediction in IHCA patients was 0.998 using a multilayer perceptron(MLP)model,whereas the optimal AUROC for pre-arrest CA prediction in OHCA patients was 0.950 usingextreme gradient boosting(XGBoost)or random forest(RF)models.For CPR-related decisionsupport during CA,the highest AUROC achieved was 0.990 with a convolutional neural network(CNN)model.In prognostic prediction,the optimal AUROC for IHCA patients was 0.960 usingXGBoost,while for OHCA patients it reached 0.976 using an MLP model.CONCLUSION:This review shows that AI is most commonly used for the prediction of CA andCPR-related support,as well as post-arrest and rehabilitation outcomes.Future research directions includedrug discovery,post-resuscitation management,neurorehabilitation,and clinical trial innovation.Furtherstudies should prioritize multicenter clinical trials to evaluate AI models in real-world settings and validatetheir eff ectiveness across diverse patient populations.Overall,AI has signifi cant potential to improve clinicalpractice,and its role in CA application is increasingly important.展开更多
Tree failure is an international problem,a major risk to public safety,and of growing concern because of extreme weather events.Tree biomechanics can inform the probability of tree failure,but empirical data from trop...Tree failure is an international problem,a major risk to public safety,and of growing concern because of extreme weather events.Tree biomechanics can inform the probability of tree failure,but empirical data from tropical settings are scarce.As a case study,we analyze the biomechanics(safety factor)of large heritage trees in public spaces in Indonesia.We examined critical buckling height using the Euler and Ylinen bending stress method.Tree morphometry(height,diameter at breast height,crown diameter),stability(modulus of elasticity),critical buckling height,and safety factor were quantified during this study.We found that large heritage trees in public spaces with buttresses have taller and larger morphometry and higher trunk and crown weights than small trees without buttresses.These trees are highly stable against external pressure.The presence of buttresses protects the target tree from rain and wind,resulting in a higher critical buckling height(H_(cr))of large(58.9 m)and buttressed target trees(58.8)than small(33.5 m)and unbuttressed trees(42.6 m),and a safety factor level of 68%safer.We make recommendations for selecting and managing trees in public spaces in a way that(i)can enhance wellbeing and biodiversity in urban planning,and(ii)is informed by risk to public safety.展开更多
文摘In this paper,we establish some strong laws of large numbers,which are for nonindependent random variables under the framework of sublinear expectations.One of our main results is for blockwise m-dependent random variables,and another is for sub-orthogonal random variables.Both extend the strong law of large numbers for independent random variables under sublinear expectations to the non-independent case.
基金National Key Research and Development Program of China(2021YFB3500501)。
文摘Commercial phosphor-converted white LEDs(pc-WLEDs)face two inherent limitations,namely blue light hazard and low color rendering index,due to the use of blue LEDs as excitation source.To address these challenges,violet LEDs are proposed as an alternative solution.Currently,phosphors that can be efficiently excited by violet light(with wavelengths from 400 to 420 nm)remain under development still.In this study,we utilize large language models to construct a comprehensive database of Eu^(2+)and Ce^(3+)doped phosphors for discovering novel violet-excited phosphors.A total of 822 phosphor data entries,including elemental compositions,crystal structures and excitation/emission wavelengths,have been extracted and validated from 9551 research papers.Compared with Ce^(3+)doped phosphors,the Eu^(2+)are in general more suited for violet-excited phosphors,as well as red-emitting phosphors.In particular,Eu^(2+)doped nitrides and sulfides are worth of exploration for violet-excited phosphors.This database is expected to be useful in the future development of phosphors for pc-WLEDs based on artificial intelligence methods.The datasets in this article are listed in Science Data Bank at http://doi.org/10.57760/sciencedb.34314.
文摘Model evaluation using benchmark datasets is an important method to measure the capability of large language models(LLMs)in specific domains,and it is mainly used to assess the knowledge and reasoning abilities of LLMs.Therefore,in order to better assess the capability of LLMs in the agricultural domain,Agri-Eval was proposed as a benchmark for assessing the knowledge and reasoning ability of LLMs in agriculture.The assessment dataset used in Agri-Eval covered seven major disciplines in the agricultural domain:crop science,horticulture,plant protection,animal husbandry,forest science,aquaculture science,and grass science,and contained a total of 2283 questions.Among domestic general-purpose LLMs,DeepSeek R1 performed best with an accuracy rate of 75.49%.In the realm of international general-purpose LLMs,Gemini 2.0 pro exp 0205 standed out as the top performer,achieving an accuracy rate of 74.28%.As an LLMs in agriculture vertical,Shennong V2.0 outperformed all the LLMs in China,and the answer accuracy rate of agricultural knowledge exceeded that of all the existing general-purpose LLMs.The launch of Agri-Eval helped the LLM developers to comprehensively evaluate the model's capability in the field of agriculture through a variety of tasks and tests to promote the development of the LLMs in the field of agriculture.
文摘To the Editor,Artificial intelligence(AI)usage has been increasing.Many fields have implemented the use of AI and Large LanguageModels(LLMs),especially in medicine.Furthermore,manypatients have increasingly been using AI;often,they will prompt AI with questions before even stepping into a physi-cian's office.The question lies in whether the information produced by AI is reliable and if this information is concise and easy to read across all patient populations.
基金funded by the National Key Research and Development Program of China(No.2021YFA1100500)the National Natural Science Foundation of China(No.82370662)the Key Research&Development Plan of Zhejiang Province(No.2024C03051).
文摘This study evaluated the accuracy,completeness,and comprehensibility of responses from mainstream large language models(LLMs)to hepatitis C virus(HCV)-related questions,aiming to assess their performance in addressing patient queries about disease and lifestyle behaviors.The models selected were ChatGPT-4o,Gemini 2.0 Pro,Claude 3.5 Sonnet,and DeepSeek V3,with 12 questions chosen by two HCV experts from the domains of prevention,diagnosis,and treatment.
文摘The emergence of large language models(LLMs)has brought about revolutionary social value.However,concerns have arisen regarding the generation of deceptive content by LLMs and their potential for misuse.Consequently,a crucial research question arises:How can we differentiate between AI-generated and human-authored text?Existing detectors face some challenges,such as operating as black boxes,relying on supervised training,and being vulnerable to manipulation and misinformation.To tackle these challenges,we propose an innovative unsupervised white-box detection method that utilizes a“dual-driven verification mechanism”to achieve high-performance detection,even in the presence of obfuscated attacks in the text content.To be more specific,we initially employ the SpaceInfi strategy to enhance the difficulty of detecting the text content.Subsequently,we randomly select vulnerable spots from the text and perturb them using another pre-trained language model(e.g.,T5).Finally,we apply a dual-driven defense mechanism(D3M)that validates text content with perturbations,whether generated by a model or authored by a human,based on the dimensions of Information TransmissionQuality and Information TransmissionDensity.Through experimental validation,our proposed novelmethod demonstrates state-of-the-art(SOTA)performancewhen exposed to equivalent levels of perturbation intensity across multiple benchmarks,thereby showcasing the effectiveness of our strategies.
基金supported by University Grant Agency of Matej Bel University in Banská Bystrica project number UGA-14-PDS-2025.
文摘It is known that correlation does not imply causality.Some relationships identified in the analysis of data are coincidental or unknown,and some are produced by real-world causality of the situation,which is problematic,since there is a need to differentiate between these two scenarios.Until recently,the proper−semantic−causality of the relationship could have been determined only by human experts from the area of expertise of the studied data.This has changed with the advance of large language models,which are often utilized as surrogates for such human experts,making the process automated and readily available to all data analysts.This motivates the main objective of this work,which is to introduce the design and implementation of a large language model-based semantic causality evaluator based on correlation analysis,together with its visual analysis model called Causal heatmap.After the implementation itself,the model is evaluated from the point of view of the quality of the visual model,from the point of view of the quality of causal evaluation based on large language models,and from the point of view of comparative analysis,while the results reached in the study highlight the usability of large language models in the task and the potential of the proposed approach in the analysis of unknown datasets.The results of the experimental evaluation demonstrate the usefulness of the Causal heatmap method,supported by the evident highlighting of interesting relationships,while suppressing irrelevant ones.
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the National Natural Science Foundation of China(grant No.52163022,62305076)Sichuan Science and Technology Program(2024ZYD0196)+1 种基金China Postdoctoral Science Foundation(2023M740505)Sichuan Postdoctoral Science Special Foundation(No.TB2023010)。
文摘Electrochromic smart windows(ESWs)can significantly reduce building energy consumption,but the high cost hinders large-scale production.The in situ growth of tungsten oxide(WO_(3))films is only by a simple immersion process,the silver nanowires(AgNWs)undergo oxidation to Ag^(+)ions through electron loss,and the liberated electrons provide driving force for the deposition of WO_(4)^(2-).Enabled the fabrication of large-area WO_(3)films and ESWs were fabricated under minimal laboratory conditions,demonstrating the economic feasibility,efficient and reliable nature of industrial production.Structural characterization and density functional theory calculations were combined to confirm that AgNWs effectively regulate oxygen vacancies of WO_(3)films and promote the in situ growth process.The optimized WO_(3)exhibits a maximum transmittance modulation of 90.8%and excellent cycling stability of 20,000 cycles.The largescale WO_(3)-based ESWs can save building energy up to 140.0 MJ m^(-2)compared to traditional windows in tropical regions,as verified by simulations more than40 global cities.This research provides a new approach for improving the performance and industrial production of ESW,providing the full understanding and development direction to short the distance of the ESW commercial production.
文摘Background:Assess ChatGPT and Bard's effectiveness in the initial identification of articles for Otolaryngology—Head and Neck Surgery systematic literature reviews.Methods:Three PRISMA-based systematic reviews(Jabbour et al.2017,Wong et al.2018,and Wu et al.2021)were replicated using ChatGPTv3.5 and Bard.Outputs(author,title,publication year,and journal)were compared to the original references and cross-referenced with medical databases for authenticity and recall.Results:Several themes emerged when comparing Bard and ChatGPT across the three reviews.Bard generated more outputs and had greater recall in Wong et al.'s review,with a broader date range in Jabbour et al.'s review.In Wu et al.'s review,ChatGPT-2 had higher recall and identified more authentic outputs than Bard-2.Conclusion:Large language models(LLMs)failed to fully replicate peer-reviewed methodologies,producing outputs with inaccuracies but identifying relevant,especially recent,articles missed by the references.While human-led PRISMA-based reviews remain the gold standard,refining LLMs for literature reviews shows potential.
基金funded by the Office of the Vice-President for Research and Development of Cebu Technological University.
文摘This study demonstrates a novel integration of large language models,machine learning,and multicriteria decision-making to investigate self-moderation in small online communities,a topic under-explored compared to user behavior and platform-driven moderation on social media.The proposed methodological framework(1)utilizes large language models for social media post analysis and categorization,(2)employs k-means clustering for content characterization,and(3)incorporates the TODIM(Tomada de Decisão Interativa Multicritério)method to determine moderation strategies based on expert judgments.In general,the fully integrated framework leverages the strengths of these intelligent systems in a more systematic evaluation of large-scale decision problems.When applied in social media moderation,this approach promotes nuanced and context-sensitive self-moderation by taking into account factors such as cultural background and geographic location.The application of this framework is demonstrated within Facebook groups.Eight distinct content clusters encompassing safety,harassment,diversity,and misinformation are identified.Analysis revealed a preference for content removal across all clusters,suggesting a cautious approach towards potentially harmful content.However,the framework also highlights the use of other moderation actions,like account suspension,depending on the content category.These findings contribute to the growing body of research on self-moderation and offer valuable insights for creating safer and more inclusive online spaces within smaller communities.
文摘Although Named Entity Recognition(NER)in cybersecurity has historically concentrated on threat intelligence,vital security data can be found in a variety of sources,such as open-source intelligence and unprocessed tool outputs.When dealing with technical language,the coexistence of structured and unstructured data poses serious issues for traditional BERT-based techniques.We introduce a three-phase approach for improved NER inmulti-source cybersecurity data that makes use of large language models(LLMs).To ensure thorough entity coverage,our method starts with an identification module that uses dynamic prompting techniques.To lessen hallucinations,the extraction module uses confidence-based self-assessment and cross-checking using regex validation.The tagging module links to knowledge bases for contextual validation and uses SecureBERT in conjunction with conditional random fields to detect entity boundaries precisely.Our framework creates efficient natural language segments by utilizing decoderbased LLMs with 10B parameters.When compared to baseline SecureBERT implementations,evaluation across four cybersecurity data sources shows notable gains,with a 9.4%–25.21%greater recall and a 6.38%–17.3%better F1-score.Our refined model matches larger models and achieves 2.6%–4.9%better F1-score for technical phrase recognition than the state-of-the-art alternatives Claude 3.5 Sonnet,Llama3-8B,and Mixtral-7B.The three-stage architecture identification-extraction-tagging pipeline tackles important cybersecurity NER issues.Through effective architectures,these developments preserve deployability while setting a new standard for entity extraction in challenging security scenarios.The findings show how specific enhancements in hybrid recognition,validation procedures,and prompt engineering raise NER performance above monolithic LLM approaches in cybersecurity applications,especially for technical entity extraction fromheterogeneous sourceswhere conventional techniques fall short.Because of itsmodular nature,the framework can be upgraded at the component level as new methods are developed.
文摘Background:Despite the promise shown by large language models(LLMs)for standardized tasks,their multidimensional performance in real-world oncology decision-making remains unevaluated.This study aims to introduce a framework for evaluating LLMs and physician decisions in challenging lung cancer cases.Methods:We curated 50 challenging lung cancer cases(25 local and 25 published)classified as complex,rare,or refractory.Blinded three-dimensional,five-point Likert evaluations(1–5 for comprehensiveness,specificity,and readability)compared standalone LLMs(DeepSeek R1,Claude 3.5,Gemini 1.5,and GPT-4o),physicians by experience level(junior,intermediate,and senior),and AI-assisted juniors;intergroup differences and augmentation effects were analyzed statistically.Results:Of 50 challenging cases(18 complex,17 rare,and 15 refractory)rated by three experts,DeepSeek R1 achieved scores of 3.95±0.33,3.71±0.53,and 4.26±0.18 for comprehensiveness,specificity,and readability,respectively,positioning it between intermediate(3.68,3.68,3.75)and senior(4.50,4.64,4.53)physicians.GPT-4o and Claude 3.5 reached intermediate physician–level comprehensiveness(3.76±0.39,3.60±0.39)but junior-to-intermediate physician–level specificity(3.39±0.39,3.39±0.49).All LLMs scored higher on rare cases than intermediate physicians but fell below junior physicians in refractory-case specificity.AIassisted junior physicians showed marked gains in rare cases,with comprehensiveness rising from 2.32 to 4.29(84.8%),specificity from 2.24 to 4.26(90.8%),and readability from 2.76 to 4.59(66.0%),while specificity declined by 3.2%(3.17 to 3.07)in refractory cases.Error analysis showed complementary strengths,with physicians demonstrating reasoning stability and LLMs excelling in knowledge updating and risk management.Conclusions:LLMs performed variably in clinical decision-making tasks depending on case type,performing better in rare cases and worse in refractory cases requiring longitudinal reasoning.Complementary strengths between LLMs and physicians support case-and task-tailored human–AI collaboration.
基金supported by the Ningbo Natural Science Foundation(Youth Foundation,No.2024J449)the Scientific Research Foundation for Introduced Talents of Ningbo University(Nos.ZX2022000602 and ZX2024000043)。
文摘Polyfluoroalkyl substances(PFAS)have emerged as persistent environmental contaminants because of their chemical stability,degradation-resistance and bioaccumulation potential.However,current studies mainly focus on the toxicity of single PFAS such as perfluorooctanoic acid(PFOA)and perfluorobutanoic acid(PFBA),the knowledge of their combined effects is relatively limited.In this study,we explored the immune response of the gut in large yellow croaker(Larimichthys crocea)under the combined stress of PFOA and PFBA.Histologicalanalyses revealed that the combined effect induced intestinal vacuolization and decreased the length of intestinal villi.And it significantly activated pro-inflammatory pathways with marked upregulation of tnfα,il1β,il6 and myd88 expressions,particularly after 14 days of exposure.Gut microbiota analysis revealed substantial dysbiosis,including 1)reduced alpha diversity,2)increased abundance of potential pathogenic taxa(Proteobacteria and Spirochaetota),and 3)depletion of beneficial Firmicutes.PICRUSt-based functional prediction indicated temporal metabolic shifts,with upregulation of DNA repair pathways at day 3 and enhanced bacterial motility protein activity at days 7 and 14 of post-exposure.The Pearson correlation analysis further indicated that these immune genes had significant positive correlations with Vibrio and Brevinema,and negative correlations with Streptococcus.Our present study will provide novel insights into the microbiome-mediated immunomodulation in the larger yellow croaker exposed to combined PFAS,which will be helpful for healthy farming of economically important marine species.
基金supported in part by the National Natural Science Foundation of China under Grant 52432012in part by the Shanghai Science and Technology Project with 25ZR1402508。
文摘The use of Unmanned Aerial Vehicles(UAVs)for defect detection on railway slopes is becoming increasingly widespread due to their ability to capture high-resolution images over large,inaccessible,and topographically complex areas.However,current UAV-based detection methods face several critical limitations,including constrained deployment frequency,limited availability of annotated defect data,and the lack of mature risk assessment frameworks.To address these challenges,this study introduces a novel approach that integrates diffusion models with Large Language Models(LLMs)to generate highquality synthetic defect images tailored to railway slope scenarios.Furthermore,an improved transformerbased architecture is proposed,incorporating attention mechanisms and LLM-guided diffusion-generated imagery to enhance defect recognition performance under complex environmental conditions.Experimental evaluations conducted on a dataset of 300 field-collected images from high-risk railway slopes demonstrate that the proposed method significantly outperforms existing baselines in terms of precision,recall,and robustness,indicating strong applicability for real-world railway infrastructure monitoring and disaster prevention.
基金supported by the Ningxia Natural Science Foundation(2025AAC050001)the Scientific Research Startup Project for Full-Time Introduced High-Level Talents in Ningxia(2024BEH04130)+2 种基金the National Natural Science Foundation of China(32460444)the Ningxia Hui Autonomous Region Key Research and Development Program(2024BBF0101302,2023BDE02001)Supported by the Special Fund for Basic Research Business of Central Universities of North Minzu University(2025BG234,2023ZRLG12).
文摘Foundation models are reshaping artificial intelligence,yet their deployment in specialised domains such as agricultural question answering(AQA)still faces challenges including data scarcity and barriers to domainspecific knowledge.To systematically review recent progress in this area,this paper adopts a task–paradigmperspective and examines applications across three major AQA task families.For text-based QA,we analyse the strengths and limitations of retrieval-based,generative,and hybrid approaches built on large languagemodels,revealing a clear trend toward hybrid paradigms that balance precision and flexibility.For visual diagnosis,we discuss techniques such as crossmodal alignment and prompt-driven generation,which are pushing systems beyond simple pest and disease recognition toward deeper causal reasoning.Formultimodal reasoning,we show how the fusion of heterogeneous data—including text,images,speech,and sensor streams—enables comprehensive decision-making for diagnosis,monitoring,and yield prediction.To address the lack of unified benchmarks,we further propose a standardised evaluation protocol and a diagnostic taxonomy specifically designed to characterise agriculture-specific errors.Finally,we outline a concreteAQA roadmap that emphasises safety alignment,hallucination control,and lightweight deployment,aiming to guide future systems toward greater efficiency,trustworthiness,and sustainability.
基金supported in part by the Mining Hydraulic Technology and Equipment Engineering Research Center,Liaoning Technical University,Fuxin,China(Grant No.MHTE23-R04)the Fundamental Research Funds for the Central Universities(ID N25BSS068).
文摘This study presents an implicit multiphysics coupling method integrating Computational Fluid Dynamics(CFD),the Multiphase Particle-in-Cell(MPPIC)model,and the Finite Element Method(FEM),implemented with OpenFOAM,CalculiX,and preCICE to simulate fluid-particle-structure interactions with large deformations.Mesh motion in the fluid field is handled using the radial basis function(RBF)method.The particle phase is modeled by MPPIC,where fluid-particle interaction is described through momentum exchange,and inter-particle collisions are characterized by collision stress.The structural field is solved by nonlinear FEM to capture large deformations induced by geometric nonlinearity.Coupling among fields is realized through a partitioned,parallel,and non-intrusive iterative strategy,ensuring stable transfer and convergence of interface forces and displacements.Notably,the influence of particles on the structure is not direct but mediated by the fluid,while structural motion directly affects particle dynamics.The results demonstrate that the proposed approach effectively captures multiphysics interaction processes and provides a valuable reference for numerical modeling of coupled fluid-particle-structure systems.
基金support of the National Natural Science Foundation of China(Nos.52322603 and U24A20141)the Science Center for Gas Turbine Project of China(No.P2023-B-Ⅱ-001-001)the Fundamental Research Funds for the Central Universities of China and the Beijing Nova Program of China(Nos.20220484074 and 20230484479)。
文摘This study investigates the turbulence-induced disturbances and stall precursor triggering mechanism in NACA65-18(10)cascade based on large eddy simulations.The results indicate that the disturbances exist under various operating conditions along the performance curve.The shear layer is the physical structure responsible for the generation,propagation,and dissipation of disturbances.When operating near stall,the separation on the suction surface intensifies,and strong unsteady backflow occurs at the trailing edge of the passage.Under the influence of inlet disturbances,unsteady behaviors between passages form specific phase differences,leading the entire system to oscillate in a first-order mode.As the flow develops from near-stall to stall,axial momentum decreases further,reducing the main flow’s ability to drive blockages downstream through convection.Consequently,the blockage accumulates during the circumferential propagation process until the stall onset.Based on the above mechanism,this study proposes factors describing the size of the backflow zone,shedding frequency,and convection velocity to characterize blockage dynamics,identifying critical values that represent the stall onset.
基金supported by grant from the National Natural Science Foundation of China(82372207).
文摘BACKGROUND:To provide a comprehensive analysis of the landscape of artifi cial intelligence(AI)applications in cardiac arrest(CA).METHODS:Comprehensive searches were conducted in PubMed,the Cochrane Library,Webof Science,and EMBASE from database inception through 10 June 2025.Studies that applied AI inboth in-hospital cardiac arrest(IHCA)and out-of-hospital cardiac arrest(OHCA)populations acrossthe following domains were included:prediction of cardiac arrest occurrence,prognostication ofCA outcomes,applications of large language models(LLMs),and evaluation of cardiopulmonaryresuscitation(CPR)and other AI-driven interventions related to CA.RESULTS:The scoping review included 114 studies,encompassing data from 9,574,462patients in total.AI was most commonly applied to the prediction of CA(overall,n=40;IHCA,n=30;OHCA,n=4;and both,n=6),CPR-related decision support during CA(n=16),and post-arrestprognosis and rehabilitation outcomes(overall,n=38;OHCA,n=21;IHCA,n=3;and both,n=14).Additional application areas included LLM-based applications(n=8),emergency call handling(n=4),wearable device-based detection(n=3),heart rhythm identification(n=2),education(n=2),and extracorporeal cardiopulmonary resuscitation(ECPR)candidate identifi cation(n=1).Across allapplication scenarios,the highest area under the receiver operating characteristic curve(AUROC)value for pre-arrest CA prediction in IHCA patients was 0.998 using a multilayer perceptron(MLP)model,whereas the optimal AUROC for pre-arrest CA prediction in OHCA patients was 0.950 usingextreme gradient boosting(XGBoost)or random forest(RF)models.For CPR-related decisionsupport during CA,the highest AUROC achieved was 0.990 with a convolutional neural network(CNN)model.In prognostic prediction,the optimal AUROC for IHCA patients was 0.960 usingXGBoost,while for OHCA patients it reached 0.976 using an MLP model.CONCLUSION:This review shows that AI is most commonly used for the prediction of CA andCPR-related support,as well as post-arrest and rehabilitation outcomes.Future research directions includedrug discovery,post-resuscitation management,neurorehabilitation,and clinical trial innovation.Furtherstudies should prioritize multicenter clinical trials to evaluate AI models in real-world settings and validatetheir eff ectiveness across diverse patient populations.Overall,AI has signifi cant potential to improve clinicalpractice,and its role in CA application is increasingly important.
文摘Tree failure is an international problem,a major risk to public safety,and of growing concern because of extreme weather events.Tree biomechanics can inform the probability of tree failure,but empirical data from tropical settings are scarce.As a case study,we analyze the biomechanics(safety factor)of large heritage trees in public spaces in Indonesia.We examined critical buckling height using the Euler and Ylinen bending stress method.Tree morphometry(height,diameter at breast height,crown diameter),stability(modulus of elasticity),critical buckling height,and safety factor were quantified during this study.We found that large heritage trees in public spaces with buttresses have taller and larger morphometry and higher trunk and crown weights than small trees without buttresses.These trees are highly stable against external pressure.The presence of buttresses protects the target tree from rain and wind,resulting in a higher critical buckling height(H_(cr))of large(58.9 m)and buttressed target trees(58.8)than small(33.5 m)and unbuttressed trees(42.6 m),and a safety factor level of 68%safer.We make recommendations for selecting and managing trees in public spaces in a way that(i)can enhance wellbeing and biodiversity in urban planning,and(ii)is informed by risk to public safety.
