Background:The prognostic significance of the chemokine receptor CCR7 in diffuse large B-cell lymphoma(DLBCL)has been reported previously.However,the detailed mechanisms of CCR7 in DLBCL,particularly regarding its int...Background:The prognostic significance of the chemokine receptor CCR7 in diffuse large B-cell lymphoma(DLBCL)has been reported previously.However,the detailed mechanisms of CCR7 in DLBCL,particularly regarding its interaction with lenalidomide treatment,are not fully understood.Methods:Our study utilized bioinformatics approaches to identify hub genes in SU-DHL-2 cell lines treated with lenalidomide compared to control groups.Immunohistochemical data and clinical information from 122 patients with DLBCL were analyzed to assess the correlation of CCR7 and p-ERK1/2 expression with the prognosis of DLBCL.Furthermore,in vitro and in vivo experiments were conducted to clarify the role of CCR7 in the response of DLBCL to lenalidomide treatment.Results:Our bioinformatics analysis pinpointed CCR7 as a hub gene in the context of lenalidomide treatment in DLBCL.Notably,31.14%and 36.0%(44/122)of DLBCL cases showed positive expression for CCR7 and ERK1/2 respectively,establishing them as independent prognostic factors for adverse outcomes in DLBCL via multivariate Cox regression analysis.Additionally,our studies demonstrated that the external application of the protein CCL21 promoted proliferation,migration,invasion,and activation of the ERK1/2 pathway in SU-DHL-2 and OCI-LY3 cell lines with high levels of CCR7 expression.This effect was mitigated by CCR7 silencing through siRNA,application of ERK inhibitors,or lenalidomide treatment.In vivo experiments reinforced the efficacy of lenalidomide,significantly reducing tumor growth rate,tumor mass,serum total LDH levels,and expression of CCR7 and p-ERK1/2 in a SUDHL-2 xenograft model in nude mice(p<0.05).Conclusion:Our study clarifies the potential role of the CCL21/CCR7/ERK1/2 axis in the therapeutic effects of lenalidomide in DLBCL treatment.展开更多
Significant advancements have been achieved in the field of Single Image Super-Resolution(SISR)through the utilization of Convolutional Neural Networks(CNNs)to attain state-of-the-art performance.Recent efforts have e...Significant advancements have been achieved in the field of Single Image Super-Resolution(SISR)through the utilization of Convolutional Neural Networks(CNNs)to attain state-of-the-art performance.Recent efforts have explored the incorporation of Transformers to augment network performance in SISR.However,the high computational cost of Transformers makes them less suitable for deployment on lightweight devices.Moreover,the majority of enhancements for CNNs rely predominantly on small spatial convolutions,thereby neglecting the potential advantages of large kernel convolution.In this paper,the authors propose a Multi-Perception Large Kernel convNet(MPLKN)which delves into the exploration of large kernel convolution.Specifically,the authors have architected a Multi-Perception Large Kernel(MPLK)module aimed at extracting multi-scale features and employ a stepwise feature fusion strategy to seamlessly integrate these features.In addition,to enhance the network's capacity for nonlinear spatial information processing,the authors have designed a Spatial-Channel Gated Feed-forward Network(SCGFN)that is capable of adapting to feature interactions across both spatial and channel dimensions.Experimental results demonstrate that MPLKN outperforms other lightweight image super-resolution models while maintaining a minimal number of parameters and FLOPs.展开更多
This article elucidates the concept of large model technology,summarizes the research status of large model technology both domestically and internationally,provides an overview of the application status of large mode...This article elucidates the concept of large model technology,summarizes the research status of large model technology both domestically and internationally,provides an overview of the application status of large models in vertical industries,outlines the challenges and issues confronted in applying large models in the oil and gas sector,and offers prospects for the application of large models in the oil and gas industry.The existing large models can be briefly divided into three categories:large language models,visual large models,and multimodal large models.The application of large models in the oil and gas industry is still in its infancy.Based on open-source large language models,some oil and gas enterprises have released large language model products using methods like fine-tuning and retrieval augmented generation.Scholars have attempted to develop scenario-specific models for oil and gas operations by using visual/multimodal foundation models.A few researchers have constructed pre-trained foundation models for seismic data processing and interpretation,as well as core analysis.The application of large models in the oil and gas industry faces challenges such as current data quantity and quality being difficult to support the training of large models,high research and development costs,and poor algorithm autonomy and control.The application of large models should be guided by the needs of oil and gas business,taking the application of large models as an opportunity to improve data lifecycle management,enhance data governance capabilities,promote the construction of computing power,strengthen the construction of“artificial intelligence+energy”composite teams,and boost the autonomy and control of large model technology.展开更多
Smart contracts on the Ethereum blockchain continue to revolutionize decentralized applications (dApps) by allowing for self-executing agreements. However, bad actors have continuously found ways to exploit smart cont...Smart contracts on the Ethereum blockchain continue to revolutionize decentralized applications (dApps) by allowing for self-executing agreements. However, bad actors have continuously found ways to exploit smart contracts for personal financial gain, which undermines the integrity of the Ethereum blockchain. This paper proposes a computer program called SADA (Static and Dynamic Analyzer), a novel approach to smart contract vulnerability detection using multiple Large Language Model (LLM) agents to analyze and flag suspicious Solidity code for Ethereum smart contracts. SADA not only improves upon existing vulnerability detection methods but also paves the way for more secure smart contract development practices in the rapidly evolving blockchain ecosystem.展开更多
Cardiac rehabilitation is a crucial multidisciplinary approach to improve patient outcomes.There is a growing body of evidence that suggests that these programs contribute towards reducing cardiovascular mortality and...Cardiac rehabilitation is a crucial multidisciplinary approach to improve patient outcomes.There is a growing body of evidence that suggests that these programs contribute towards reducing cardiovascular mortality and recurrence.Despite this,cardiac rehabilitation is underutilized and adherence to these programs has been a demonstrated barrier in achieving these outcomes.As a result,there is a growing focus on innovating these programs,especially from the standpoint of digital health and personalized medicine.This editorial discusses the possible roles of large language models,such as their role in ChatGPT,in further personalizing cardiac rehabilitation programs through simplifying medical jargon and employing motivational interviewing techniques,thus boosting patient engagement and adherence.However,these possibilities must be further investigated in the clinical literature.Likewise,the integration of large language models in cardiac rehabilitation will be challenging in its nascent stages to ensure accurate and ethical information delivery.展开更多
Microplastics(MPs)have garnered significant international scrutiny as an emerging environmental pollutant,constituting one of the four principal global environmental threats and posing potential health hazards to huma...Microplastics(MPs)have garnered significant international scrutiny as an emerging environmental pollutant,constituting one of the four principal global environmental threats and posing potential health hazards to humans.However,data on the impact of MPs on the early life of the commercially important fish remain limited.In this study,polystyrene microspheres(PS-MPs)(1 and 5μm)were used to investigate the effects of MPs on the growth,development,and metabolism in early life stages of large yellow croaker Pseudosciaena crocea.Results indicate that MPs were enriched in the gastrointestinal tract and gills of the fish.In addition,PS-MPs(1μm)exhibited no obvious effects on embryo hatching and heart rates,while increased the mortality rate(23.00%vs.control 14.99%)and decreased the body length(4098.61±447.03μm vs.control with 2827.04±254.75μm)of the larvae at the highest exposure concentration(5×10^(4)items/L).Metabolomics analysis revealed that PS-MPs(5μm)induced mild perturbations in phospholipid metabolism,specifically alterations in phosphatidylethanolamine(PE)levels.These changes influenced the cell membranes of juvenile fish,and consequently elicited inflammatory responses,disrupted lipid homeostasis,and affected other critical physiological processes.Ultimately,these effects may avoid the growth retardation and potential mortality.Therefore,PS-MPs could affect negatively the fish health in the early life stage,which has implications for aquatic ecosystems.展开更多
A survey conducted on the premature bolting of Huarong large leaf mustard from 2018 to 2024 revealed that Huarong large leaf mustard sown in middle August was associated with a higher propensity for premature bolting....A survey conducted on the premature bolting of Huarong large leaf mustard from 2018 to 2024 revealed that Huarong large leaf mustard sown in middle August was associated with a higher propensity for premature bolting. Furthermore, it was observed that the earlier being sown, the greater the rate of premature bolting when being sown prior to middle August. The rate of premature bolting observed in seedlings sown on August 8 was recorded at 35.6%. It was noted that as the age of the seedlings increased, the rate of premature bolting correspondingly increased. There were notable differences in the tolerance of various cultivars to elevated temperatures and prolonged sunlight exposure. For instance, cultivars such as Zhangjie 1 and Sichuan Shaguodi, which exhibit greater heat resistance, did not demonstrate premature bolting when sown in early August. The prolonged exposure to elevated temperatures, drought conditions, and extended periods of sunlight during the seedling stage of Huarong large leaf mustard, coupled with delayed irrigation and transplantation, contributed to the occurrence of premature bolting. The Huarong large leaf mustard, when been sown from late August to early September and transplanted at the appropriate time, exhibited normal growth and development, with no instances of premature bolting observed. It is advisable to select heat-resistant varieties, such as Zhangjie 1, prior to middle August. Huarong large leaf mustard should be sown in early to middle September. Additionally, it is essential to ensure centralized production and timely release of seeds, prompt transplantation and harvesting, and enhance the management of pests and diseases.展开更多
The integration of artificial intelligence(AI)technology,particularly large language models(LLMs),has become essential across various sectors due to their advanced language comprehension and generation capabilities.De...The integration of artificial intelligence(AI)technology,particularly large language models(LLMs),has become essential across various sectors due to their advanced language comprehension and generation capabilities.Despite their transformative impact in fields such as machine translation and intelligent dialogue systems,LLMs face significant challenges.These challenges include safety,security,and privacy concerns that undermine their trustworthiness and effectiveness,such as hallucinations,backdoor attacks,and privacy leakage.Previous works often conflated safety issues with security concerns.In contrast,our study provides clearer and more reasonable definitions for safety,security,and privacy within the context of LLMs.Building on these definitions,we provide a comprehensive overview of the vulnerabilities and defense mechanisms related to safety,security,and privacy in LLMs.Additionally,we explore the unique research challenges posed by LLMs and suggest potential avenues for future research,aiming to enhance the robustness and reliability of LLMs in the face of emerging threats.展开更多
The advent of large language models(LLMs)has made knowledge acquisition and content creation increasingly easier and cheaper,which in turn redefines learning and urges transformation in software engineering education....The advent of large language models(LLMs)has made knowledge acquisition and content creation increasingly easier and cheaper,which in turn redefines learning and urges transformation in software engineering education.To do so,there is a need to understand the impact of LLMs on software engineering education.In this paper,we conducted a preliminary case study on three software requirements engineering classes where students are allowed to use LLMs to assist in their projects.Based on the students’experience,performance,and feedback from a survey conducted at the end of the courses,we characterized the challenges and benefits of applying LLMs in software engineering education.This research contributes to the ongoing discourse on the integration of LLMs in education,emphasizing both their prominent potential and the need for balanced,mindful usage.展开更多
Rehabilitation training is believed to be an effectual strategy that canreduce the risk of dysfunction caused by spasticity.However,achieving visualizationrehabilitation training for patients remains clinically challe...Rehabilitation training is believed to be an effectual strategy that canreduce the risk of dysfunction caused by spasticity.However,achieving visualizationrehabilitation training for patients remains clinically challenging.Herein,wepropose visual rehabilitation training system including iontronic meta-fabrics withskin-friendly and large matrix features,as well as high-resolution image modules fordistribution of human muscle tension.Attributed to the dynamic connection and dissociationof the meta-fabric,the fabric exhibits outstanding tactile sensing properties,such as wide tactile sensing range(0~300 kPa)and high-resolution tactile perception(50 Pa or 0.058%).Meanwhile,thanks to the differential capillary effect,the meta-fabric exhibits a“hitting three birds with one stone”property(dryness wearing experience,long working time and cooling sensing).Based on this,the fabrics can be integrated with garmentsand advanced data analysis systems to manufacture a series of large matrix structure(40×40,1600 sensing units)training devices.Significantly,the tunability of piezo-ionic dynamics of the meta-fabric and the programmability of high-resolution imaging modules allowthis visualization training strategy extendable to various common disease monitoring.Therefore,we believe that our study overcomes theconstraint of standard spasticity rehabilitation training devices in terms of visual display and paves the way for future smart healthcare.展开更多
Taking the Pusa Collapse in Nayong County,Guizhou Province,China as a case study,this paper investigates the impact of multi-layer coal mining on karst mountains characterized by deep fissures.Based on field investiga...Taking the Pusa Collapse in Nayong County,Guizhou Province,China as a case study,this paper investigates the impact of multi-layer coal mining on karst mountains characterized by deep fissures.Based on field investigations and employing discrete element numerical simulations,the deformation and failure mechanisms of karst mountain containing deep and large fissures under multi-seam mining conditions was investigated.The influence of the direction of coal seam extraction and the sequence of extraction between multiple coal seams on the failure modes of karst mountain with deep and large fissures was studied.The results indicate that underground mining primarily manifests in the development of mininginduced fissures in the mountain body,subsidence and deformation of slope masses,and triggering the expansion of existing fissures,further driving overall deformation and damage to the slopes.Deep and large fissures control the deformation and failure modes of the slopes,with closer and longer deep and large fissures near the slope surface exerting greater influence on the slope mass.The impact of mining in the same coal seam direction on the slopes is mainly reflected in the process of slope deformation and failure.Downslope mining directly leads to overall subsidence of the slope mass,squeezing the front and lower parts of the slope mass.Upslope mining initially causes the foot of the slope to sink and the entire slope mass to move outward,and continuous mining leads to overall settlement and downward compression deformation of the slope.The sequence of mining between multiple coal seams mainly affects the overall and local deformation values of the slope mass.Downward mining leads to increased overall subsidence of the slope mass and exacerbates the backward tilt of the slope top.展开更多
Radiofrequency ablation(RFA)is a form of minimally invasive procedure that precisely ablates abnormal lesions or hyperplastic tissues through thermal energy generated by the radiofrequency current at the tip electrode...Radiofrequency ablation(RFA)is a form of minimally invasive procedure that precisely ablates abnormal lesions or hyperplastic tissues through thermal energy generated by the radiofrequency current at the tip electrode of the flexible catheter,which aims to partially or fully restore the function of the corresponding tissues or organs.Accurate prediction and control of thermal fields are crucial for clinical thermal ablation to ensure precise control of the ablation lesion size and prevent excessive burning of healthy tissues.In this study,an axisymmetric analytical model is developed for the electrothermal analysis of RFA in the cambered tissue surface and verified with the finite element analysis(FEA),which incorporates both the thermal field induced by the radiofrequency current and Pennes'biothermal effect.This model utilizes analytically derived electric and thermal fields to accurately predict the increase in the tissue temperature and the time-varying size of ablation lesion in the tissue.Furthermore,the parameters such as the input current density,curvature,and convective heat transfer coefficient of blood have a significant effect on the thermal field and thus the ablation lesion size.This electrothermal analytical model with a large curvature may provide a theoretical foundation and guidance for the future RFA applications on large-curvature biological surfaces,thereby enhancing accuracy,reducing the need for re-ablation,and lowering the costs associated with the design and production of ablation catheters.展开更多
BACKGROUND Whole-body magnetic resonance imaging(wbMRI)allows general assessment of systemic cancers including lymphomas without radiation burden.AIM To evaluate the diagnostic performance of wbMRI in the staging of d...BACKGROUND Whole-body magnetic resonance imaging(wbMRI)allows general assessment of systemic cancers including lymphomas without radiation burden.AIM To evaluate the diagnostic performance of wbMRI in the staging of diffuse large B-cell lymphoma(DLBCL),determine the value of individual MRI sequences,and assess patients’concerns with wbMRI.METHODS In this single-center prospective study,adult patients newly diagnosed with systemic DLBCL underwent wbMRI on a 3T scanner[diffusion weighted images with background suppression(DWIBS),T2,short tau inversion recovery(STIR),contrast-enhanced T1]and fluorodeoxyglucose(18F-FDG)positron emission tomo-graphy/computed tomography(PET/CT)(reference standard).The involvement of 12 nodal regions and extranodal sites was evaluated on wbMRI and PET/CT.The utility of wbMRI sequences was rated on a five-point scale(0=not useful,4=very useful).Patients received a questionnaire regarding wbMRI.RESULTS Of 60 eligible patients,14(23%)were enrolled and completed the study.The sensitivity of wbMRI in the nodal involvement(182 nodal sites)was 0.84,with 0.99 specificity,positive predictive value of 0.96,negative predictive value of 0.97,and 0.97 accuracy.PET/CT and wbMRI were concordant both in extranodal involvement(13 instances)and staging(κ=1.0).The mean scores of the utility of MRI sequences were 3.71±0.73 for DWIBS,2.64±0.84 for T1,2.14±0.77 for STIR,and 1.29±0.73 for T2(P<0.0001).Patients were mostly concerned about the enclosed environment and duration of the MRI examination(27%of patients).CONCLUSION The wbMRI exhibited excellent sensitivity and specificity in staging DLBCL.DWIBS and contrast-enhanced T1 were rated as the most useful sequences.Patients were less willing to undergo wbMRI as a second examination parallel to PET/CT,especially owing to the long duration and the enclosed environment.展开更多
AIM:To assess the possibility of using different large language models(LLMs)in ocular surface diseases by selecting five different LLMS to test their accuracy in answering specialized questions related to ocular surfa...AIM:To assess the possibility of using different large language models(LLMs)in ocular surface diseases by selecting five different LLMS to test their accuracy in answering specialized questions related to ocular surface diseases:ChatGPT-4,ChatGPT-3.5,Claude 2,PaLM2,and SenseNova.METHODS:A group of experienced ophthalmology professors were asked to develop a 100-question singlechoice question on ocular surface diseases designed to assess the performance of LLMs and human participants in answering ophthalmology specialty exam questions.The exam includes questions on the following topics:keratitis disease(20 questions),keratoconus,keratomalaciac,corneal dystrophy,corneal degeneration,erosive corneal ulcers,and corneal lesions associated with systemic diseases(20 questions),conjunctivitis disease(20 questions),trachoma,pterygoid and conjunctival tumor diseases(20 questions),and dry eye disease(20 questions).Then the total score of each LLMs and compared their mean score,mean correlation,variance,and confidence were calculated.RESULTS:GPT-4 exhibited the highest performance in terms of LLMs.Comparing the average scores of the LLMs group with the four human groups,chief physician,attending physician,regular trainee,and graduate student,it was found that except for ChatGPT-4,the total score of the rest of the LLMs is lower than that of the graduate student group,which had the lowest score in the human group.Both ChatGPT-4 and PaLM2 were more likely to give exact and correct answers,giving very little chance of an incorrect answer.ChatGPT-4 showed higher credibility when answering questions,with a success rate of 59%,but gave the wrong answer to the question 28% of the time.CONCLUSION:GPT-4 model exhibits excellent performance in both answer relevance and confidence.PaLM2 shows a positive correlation(up to 0.8)in terms of answer accuracy during the exam.In terms of answer confidence,PaLM2 is second only to GPT4 and surpasses Claude 2,SenseNova,and GPT-3.5.Despite the fact that ocular surface disease is a highly specialized discipline,GPT-4 still exhibits superior performance,suggesting that its potential and ability to be applied in this field is enormous,perhaps with the potential to be a valuable resource for medical students and clinicians in the future.展开更多
Sea topography information holds significant importance in oceanic research and the climate change detection.Radar imaging altimetry has emerged as the leading approach for global ocean observation,employing synthetic...Sea topography information holds significant importance in oceanic research and the climate change detection.Radar imaging altimetry has emerged as the leading approach for global ocean observation,employing synthetic aperture radar(SAR)interferometry to enhance the spatial resolution of Sea topography.Nevertheless,current payload capacity and satellite hardware limitations prevent the extension of the interferometric baseline by enlarging the physical antenna size.This constraint hinders achieving centimeter-level accuracy in interferometric altimetry.To address this challenge,we conducted a numerical simulation to assess the viability of a large baseline interferometric imaging altimeter(LB-IIA).By controlling the baseline within the range of 600-1000 m through spiral orbit design in two satellites and mitigating baseline de-correlation with the carrier frequency shift(CFS)technique,we aimed to overcome the above limitations.Our findings demonstrate the efficacy of the CFS technique in compensating for baseline decoherence,elevating coherence from less than 0.1 to over 0.85.Concurrently.The height difference accuracy between neighboring sea surfaces reaches 1 cm within a 1 km resolution.This study is anticipated to serve as a foundational reference for future interferometric imaging altimeter development,catering to the demand for high-precision sea topography data in accurate global bathymetry inversion.展开更多
For computer science majors in higher education institutions,programming courses are one of the most important professional foundation courses.Proficiency in independent programming skills is of great help to the stud...For computer science majors in higher education institutions,programming courses are one of the most important professional foundation courses.Proficiency in independent programming skills is of great help to the study of subsequent courses and the personal development of students.In the teaching process of programming courses,online judgement systems are often used to improve students’programming level.Traditional online judgement systems lack guidance for students,and it is often difficult for inexperienced students to find and correct errors in their codes by themselves.We propose an online judgement system that integrates a large model of error correction to help students find errors and improve their programming skills.展开更多
The emergence of artificial intelligence natural language large models has brought new dawn for the in-depth empowerment of the industry.Research on key technologies and applications of railway natural language large ...The emergence of artificial intelligence natural language large models has brought new dawn for the in-depth empowerment of the industry.Research on key technologies and applications of railway natural language large model is of great significance to promoting and coordinating the development of railway artificial intelligence.This paper puts forward the application scenarios of railway natural language large model according to the application requirements of railway artificial intelligence;designs the overall architecture of the railway natural language large model by relying on the railway artificial intelligence platform,studies the key technologies of the natural language large model,builds a railway industry large model oriented to intelligent question-answering,and verifies the model with actual data;finally,this paper prospects for the development and application of railway natural language large model from the aspects of railway traffic organization,railway operation safety and passenger service.展开更多
Since the 1950s,when the Turing Test was introduced,there has been notable progress in machine language intelligence.Language modeling,crucial for AI development,has evolved from statistical to neural models over the ...Since the 1950s,when the Turing Test was introduced,there has been notable progress in machine language intelligence.Language modeling,crucial for AI development,has evolved from statistical to neural models over the last two decades.Recently,transformer-based Pre-trained Language Models(PLM)have excelled in Natural Language Processing(NLP)tasks by leveraging large-scale training corpora.Increasing the scale of these models enhances performance significantly,introducing abilities like context learning that smaller models lack.The advancement in Large Language Models,exemplified by the development of ChatGPT,has made significant impacts both academically and industrially,capturing widespread societal interest.This survey provides an overview of the development and prospects from Large Language Models(LLM)to Large Multimodal Models(LMM).It first discusses the contributions and technological advancements of LLMs in the field of natural language processing,especially in text generation and language understanding.Then,it turns to the discussion of LMMs,which integrates various data modalities such as text,images,and sound,demonstrating advanced capabilities in understanding and generating cross-modal content,paving new pathways for the adaptability and flexibility of AI systems.Finally,the survey highlights the prospects of LMMs in terms of technological development and application potential,while also pointing out challenges in data integration,cross-modal understanding accuracy,providing a comprehensive perspective on the latest developments in this field.展开更多
A large language model(LLM)is constructed to address the sophisticated demands of data retrieval and analysis,detailed well profiling,computation of key technical indicators,and the solutions to complex problems in re...A large language model(LLM)is constructed to address the sophisticated demands of data retrieval and analysis,detailed well profiling,computation of key technical indicators,and the solutions to complex problems in reservoir performance analysis(RPA).The LLM is constructed for RPA scenarios with incremental pre-training,fine-tuning,and functional subsystems coupling.Functional subsystem and efficient coupling methods are proposed based on named entity recognition(NER),tool invocation,and Text-to-SQL construction,all aimed at resolving pivotal challenges in developing the specific application of LLMs for RDA.This study conducted a detailed accuracy test on feature extraction models,tool classification models,data retrieval models and analysis recommendation models.The results indicate that these models have demonstrated good performance in various key aspects of reservoir dynamic analysis.The research takes some injection and production well groups in the PK3 Block of the Daqing Oilfield as an example for testing.Testing results show that our model has significant potential and practical value in assisting reservoir engineers with RDA.The research results provide a powerful support to the application of LLM in reservoir performance analysis.展开更多
Conventional microscopes designed for submicron resolution in biological research are hindered by a limited field of view,typically around 1 mm.This restriction poses a challenge when attempting to simultaneously anal...Conventional microscopes designed for submicron resolution in biological research are hindered by a limited field of view,typically around 1 mm.This restriction poses a challenge when attempting to simultaneously analyze various parts of a sample,such as different brain areas.In addition,conventional objective lenses struggle to perform consistently across the required range of wavelengths for brain imaging in vivo.Here we present a novel mesoscopic objective lens with an impressive field of view of 8 mm,a numerical aperture of 0.5,and a working wavelength range from 400 to 1000 nm.We achieved a resolution of 0.74μm in fluorescent beads imaging.The versatility of this lens was further demonstrated through high-quality images of mouse brain and kidney sections in a wide-field imaging system,a confocal laser scanning system,and a two-photon imaging system.This mesoscopic objective lens holds immense promise for advancing multi-wavelength imaging of large fields of view at high resolution.展开更多
基金supported by the Key Research and Development Program of Science and Technology Department of Guizhou Province(No.20204Y147).
文摘Background:The prognostic significance of the chemokine receptor CCR7 in diffuse large B-cell lymphoma(DLBCL)has been reported previously.However,the detailed mechanisms of CCR7 in DLBCL,particularly regarding its interaction with lenalidomide treatment,are not fully understood.Methods:Our study utilized bioinformatics approaches to identify hub genes in SU-DHL-2 cell lines treated with lenalidomide compared to control groups.Immunohistochemical data and clinical information from 122 patients with DLBCL were analyzed to assess the correlation of CCR7 and p-ERK1/2 expression with the prognosis of DLBCL.Furthermore,in vitro and in vivo experiments were conducted to clarify the role of CCR7 in the response of DLBCL to lenalidomide treatment.Results:Our bioinformatics analysis pinpointed CCR7 as a hub gene in the context of lenalidomide treatment in DLBCL.Notably,31.14%and 36.0%(44/122)of DLBCL cases showed positive expression for CCR7 and ERK1/2 respectively,establishing them as independent prognostic factors for adverse outcomes in DLBCL via multivariate Cox regression analysis.Additionally,our studies demonstrated that the external application of the protein CCL21 promoted proliferation,migration,invasion,and activation of the ERK1/2 pathway in SU-DHL-2 and OCI-LY3 cell lines with high levels of CCR7 expression.This effect was mitigated by CCR7 silencing through siRNA,application of ERK inhibitors,or lenalidomide treatment.In vivo experiments reinforced the efficacy of lenalidomide,significantly reducing tumor growth rate,tumor mass,serum total LDH levels,and expression of CCR7 and p-ERK1/2 in a SUDHL-2 xenograft model in nude mice(p<0.05).Conclusion:Our study clarifies the potential role of the CCL21/CCR7/ERK1/2 axis in the therapeutic effects of lenalidomide in DLBCL treatment.
文摘Significant advancements have been achieved in the field of Single Image Super-Resolution(SISR)through the utilization of Convolutional Neural Networks(CNNs)to attain state-of-the-art performance.Recent efforts have explored the incorporation of Transformers to augment network performance in SISR.However,the high computational cost of Transformers makes them less suitable for deployment on lightweight devices.Moreover,the majority of enhancements for CNNs rely predominantly on small spatial convolutions,thereby neglecting the potential advantages of large kernel convolution.In this paper,the authors propose a Multi-Perception Large Kernel convNet(MPLKN)which delves into the exploration of large kernel convolution.Specifically,the authors have architected a Multi-Perception Large Kernel(MPLK)module aimed at extracting multi-scale features and employ a stepwise feature fusion strategy to seamlessly integrate these features.In addition,to enhance the network's capacity for nonlinear spatial information processing,the authors have designed a Spatial-Channel Gated Feed-forward Network(SCGFN)that is capable of adapting to feature interactions across both spatial and channel dimensions.Experimental results demonstrate that MPLKN outperforms other lightweight image super-resolution models while maintaining a minimal number of parameters and FLOPs.
基金Supported by the National Natural Science Foundation of China(72088101,42372175)PetroChina Science and Technology Innovation Fund Program(2021DQ02-0904)。
文摘This article elucidates the concept of large model technology,summarizes the research status of large model technology both domestically and internationally,provides an overview of the application status of large models in vertical industries,outlines the challenges and issues confronted in applying large models in the oil and gas sector,and offers prospects for the application of large models in the oil and gas industry.The existing large models can be briefly divided into three categories:large language models,visual large models,and multimodal large models.The application of large models in the oil and gas industry is still in its infancy.Based on open-source large language models,some oil and gas enterprises have released large language model products using methods like fine-tuning and retrieval augmented generation.Scholars have attempted to develop scenario-specific models for oil and gas operations by using visual/multimodal foundation models.A few researchers have constructed pre-trained foundation models for seismic data processing and interpretation,as well as core analysis.The application of large models in the oil and gas industry faces challenges such as current data quantity and quality being difficult to support the training of large models,high research and development costs,and poor algorithm autonomy and control.The application of large models should be guided by the needs of oil and gas business,taking the application of large models as an opportunity to improve data lifecycle management,enhance data governance capabilities,promote the construction of computing power,strengthen the construction of“artificial intelligence+energy”composite teams,and boost the autonomy and control of large model technology.
文摘Smart contracts on the Ethereum blockchain continue to revolutionize decentralized applications (dApps) by allowing for self-executing agreements. However, bad actors have continuously found ways to exploit smart contracts for personal financial gain, which undermines the integrity of the Ethereum blockchain. This paper proposes a computer program called SADA (Static and Dynamic Analyzer), a novel approach to smart contract vulnerability detection using multiple Large Language Model (LLM) agents to analyze and flag suspicious Solidity code for Ethereum smart contracts. SADA not only improves upon existing vulnerability detection methods but also paves the way for more secure smart contract development practices in the rapidly evolving blockchain ecosystem.
文摘Cardiac rehabilitation is a crucial multidisciplinary approach to improve patient outcomes.There is a growing body of evidence that suggests that these programs contribute towards reducing cardiovascular mortality and recurrence.Despite this,cardiac rehabilitation is underutilized and adherence to these programs has been a demonstrated barrier in achieving these outcomes.As a result,there is a growing focus on innovating these programs,especially from the standpoint of digital health and personalized medicine.This editorial discusses the possible roles of large language models,such as their role in ChatGPT,in further personalizing cardiac rehabilitation programs through simplifying medical jargon and employing motivational interviewing techniques,thus boosting patient engagement and adherence.However,these possibilities must be further investigated in the clinical literature.Likewise,the integration of large language models in cardiac rehabilitation will be challenging in its nascent stages to ensure accurate and ethical information delivery.
基金Supported by the Pioneer and Leading Goose R&D Program of Zhejiang(No.2023C03130)the National Key R&D Program of China(No.2019YFD0901101)+4 种基金the National Natural Science Foundation of China(No.42076169)the Oceanic Interdisciplinary Program of Shanghai Jiao Tong University(Nos.SL2022ZD203,SL2022MS012)the Zhejiang Provincial Natural Science Founds for Distinguished Young Scientists(No.LR21D060001)the State Key Laboratory of Satellite Ocean Environment Dynamics(No.SOEDZZ1902)the ChinaAPEC Cooperation Fund(No.2029901)。
文摘Microplastics(MPs)have garnered significant international scrutiny as an emerging environmental pollutant,constituting one of the four principal global environmental threats and posing potential health hazards to humans.However,data on the impact of MPs on the early life of the commercially important fish remain limited.In this study,polystyrene microspheres(PS-MPs)(1 and 5μm)were used to investigate the effects of MPs on the growth,development,and metabolism in early life stages of large yellow croaker Pseudosciaena crocea.Results indicate that MPs were enriched in the gastrointestinal tract and gills of the fish.In addition,PS-MPs(1μm)exhibited no obvious effects on embryo hatching and heart rates,while increased the mortality rate(23.00%vs.control 14.99%)and decreased the body length(4098.61±447.03μm vs.control with 2827.04±254.75μm)of the larvae at the highest exposure concentration(5×10^(4)items/L).Metabolomics analysis revealed that PS-MPs(5μm)induced mild perturbations in phospholipid metabolism,specifically alterations in phosphatidylethanolamine(PE)levels.These changes influenced the cell membranes of juvenile fish,and consequently elicited inflammatory responses,disrupted lipid homeostasis,and affected other critical physiological processes.Ultimately,these effects may avoid the growth retardation and potential mortality.Therefore,PS-MPs could affect negatively the fish health in the early life stage,which has implications for aquatic ecosystems.
基金Supported by Key R&D Projects of Hunan Provincial Department of Science and Technology"Study on Key Modern Processing Techniques and Product Development of Huarong Mustard"(2023NK2039).
文摘A survey conducted on the premature bolting of Huarong large leaf mustard from 2018 to 2024 revealed that Huarong large leaf mustard sown in middle August was associated with a higher propensity for premature bolting. Furthermore, it was observed that the earlier being sown, the greater the rate of premature bolting when being sown prior to middle August. The rate of premature bolting observed in seedlings sown on August 8 was recorded at 35.6%. It was noted that as the age of the seedlings increased, the rate of premature bolting correspondingly increased. There were notable differences in the tolerance of various cultivars to elevated temperatures and prolonged sunlight exposure. For instance, cultivars such as Zhangjie 1 and Sichuan Shaguodi, which exhibit greater heat resistance, did not demonstrate premature bolting when sown in early August. The prolonged exposure to elevated temperatures, drought conditions, and extended periods of sunlight during the seedling stage of Huarong large leaf mustard, coupled with delayed irrigation and transplantation, contributed to the occurrence of premature bolting. The Huarong large leaf mustard, when been sown from late August to early September and transplanted at the appropriate time, exhibited normal growth and development, with no instances of premature bolting observed. It is advisable to select heat-resistant varieties, such as Zhangjie 1, prior to middle August. Huarong large leaf mustard should be sown in early to middle September. Additionally, it is essential to ensure centralized production and timely release of seeds, prompt transplantation and harvesting, and enhance the management of pests and diseases.
基金supported by the National Key R&D Program of China under Grant No.2022YFB3103500the National Natural Science Foundation of China under Grants No.62402087 and No.62020106013+3 种基金the Sichuan Science and Technology Program under Grant No.2023ZYD0142the Chengdu Science and Technology Program under Grant No.2023-XT00-00002-GXthe Fundamental Research Funds for Chinese Central Universities under Grants No.ZYGX2020ZB027 and No.Y030232063003002the Postdoctoral Innovation Talents Support Program under Grant No.BX20230060.
文摘The integration of artificial intelligence(AI)technology,particularly large language models(LLMs),has become essential across various sectors due to their advanced language comprehension and generation capabilities.Despite their transformative impact in fields such as machine translation and intelligent dialogue systems,LLMs face significant challenges.These challenges include safety,security,and privacy concerns that undermine their trustworthiness and effectiveness,such as hallucinations,backdoor attacks,and privacy leakage.Previous works often conflated safety issues with security concerns.In contrast,our study provides clearer and more reasonable definitions for safety,security,and privacy within the context of LLMs.Building on these definitions,we provide a comprehensive overview of the vulnerabilities and defense mechanisms related to safety,security,and privacy in LLMs.Additionally,we explore the unique research challenges posed by LLMs and suggest potential avenues for future research,aiming to enhance the robustness and reliability of LLMs in the face of emerging threats.
基金supported in part by the Teaching Reform Project of Chongqing University of Posts and Telecommunications,China under Grant No.XJG23234Chongqing Municipal Higher Education Teaching Reform Research Project under Grant No.203399the Doctoral Direct Train Project of Chongqing Science and Technology Bureau under Grant No.CSTB2022BSXM-JSX0007。
文摘The advent of large language models(LLMs)has made knowledge acquisition and content creation increasingly easier and cheaper,which in turn redefines learning and urges transformation in software engineering education.To do so,there is a need to understand the impact of LLMs on software engineering education.In this paper,we conducted a preliminary case study on three software requirements engineering classes where students are allowed to use LLMs to assist in their projects.Based on the students’experience,performance,and feedback from a survey conducted at the end of the courses,we characterized the challenges and benefits of applying LLMs in software engineering education.This research contributes to the ongoing discourse on the integration of LLMs in education,emphasizing both their prominent potential and the need for balanced,mindful usage.
基金supported by the National Key Research and Development Program(2022YFB3805800)National Natural Science Foundation of China(52473307,22208178,62301290)+9 种基金Taishan Scholar Program of Shandong Province in China(tsqn202211116)Shandong Provincial Universities Youth Innovation Technology Plan Team(2023KJ223)Natural Science Foundation of Shandong Province of China(ZR2023YQ037,ZR2020QE074,ZR2023QE043,ZR2022QE174)Shandong Province Science and Technology Small and Medium sized Enterprise Innovation Ability Enhancement Project(2023TSGC0344,2023TSGC1006)Natural Science Foundation of Qingdao(23-2-1-249-zyyd-jch,24-4-4-zrjj-56-jch)Anhui Province Postdoctoral Researcher Research Activity Funding Project(2023B706)Qingdao Key Technology Research and Industrialization Demonstration Projects(23-1-7-zdfn-2-hz)Qingdao Shinan District Science and Technology Plan Project(2022-3-005-DZ)Suqian Key Research and Development Plan(H202310)Jinan City-School Integration Development Strategy Project for the Year 2023 under Grant(JNSX2023088).
文摘Rehabilitation training is believed to be an effectual strategy that canreduce the risk of dysfunction caused by spasticity.However,achieving visualizationrehabilitation training for patients remains clinically challenging.Herein,wepropose visual rehabilitation training system including iontronic meta-fabrics withskin-friendly and large matrix features,as well as high-resolution image modules fordistribution of human muscle tension.Attributed to the dynamic connection and dissociationof the meta-fabric,the fabric exhibits outstanding tactile sensing properties,such as wide tactile sensing range(0~300 kPa)and high-resolution tactile perception(50 Pa or 0.058%).Meanwhile,thanks to the differential capillary effect,the meta-fabric exhibits a“hitting three birds with one stone”property(dryness wearing experience,long working time and cooling sensing).Based on this,the fabrics can be integrated with garmentsand advanced data analysis systems to manufacture a series of large matrix structure(40×40,1600 sensing units)training devices.Significantly,the tunability of piezo-ionic dynamics of the meta-fabric and the programmability of high-resolution imaging modules allowthis visualization training strategy extendable to various common disease monitoring.Therefore,we believe that our study overcomes theconstraint of standard spasticity rehabilitation training devices in terms of visual display and paves the way for future smart healthcare.
基金supported by the National Key R&D Program of China(Grant No.2018YFC1504802)the National Natural Science Foundation of China(Grant No.52074042)。
文摘Taking the Pusa Collapse in Nayong County,Guizhou Province,China as a case study,this paper investigates the impact of multi-layer coal mining on karst mountains characterized by deep fissures.Based on field investigations and employing discrete element numerical simulations,the deformation and failure mechanisms of karst mountain containing deep and large fissures under multi-seam mining conditions was investigated.The influence of the direction of coal seam extraction and the sequence of extraction between multiple coal seams on the failure modes of karst mountain with deep and large fissures was studied.The results indicate that underground mining primarily manifests in the development of mininginduced fissures in the mountain body,subsidence and deformation of slope masses,and triggering the expansion of existing fissures,further driving overall deformation and damage to the slopes.Deep and large fissures control the deformation and failure modes of the slopes,with closer and longer deep and large fissures near the slope surface exerting greater influence on the slope mass.The impact of mining in the same coal seam direction on the slopes is mainly reflected in the process of slope deformation and failure.Downslope mining directly leads to overall subsidence of the slope mass,squeezing the front and lower parts of the slope mass.Upslope mining initially causes the foot of the slope to sink and the entire slope mass to move outward,and continuous mining leads to overall settlement and downward compression deformation of the slope.The sequence of mining between multiple coal seams mainly affects the overall and local deformation values of the slope mass.Downward mining leads to increased overall subsidence of the slope mass and exacerbates the backward tilt of the slope top.
基金Project supported by the National Natural Science Foundation of China(Nos.U23A20111 and 12372160)。
文摘Radiofrequency ablation(RFA)is a form of minimally invasive procedure that precisely ablates abnormal lesions or hyperplastic tissues through thermal energy generated by the radiofrequency current at the tip electrode of the flexible catheter,which aims to partially or fully restore the function of the corresponding tissues or organs.Accurate prediction and control of thermal fields are crucial for clinical thermal ablation to ensure precise control of the ablation lesion size and prevent excessive burning of healthy tissues.In this study,an axisymmetric analytical model is developed for the electrothermal analysis of RFA in the cambered tissue surface and verified with the finite element analysis(FEA),which incorporates both the thermal field induced by the radiofrequency current and Pennes'biothermal effect.This model utilizes analytically derived electric and thermal fields to accurately predict the increase in the tissue temperature and the time-varying size of ablation lesion in the tissue.Furthermore,the parameters such as the input current density,curvature,and convective heat transfer coefficient of blood have a significant effect on the thermal field and thus the ablation lesion size.This electrothermal analytical model with a large curvature may provide a theoretical foundation and guidance for the future RFA applications on large-curvature biological surfaces,thereby enhancing accuracy,reducing the need for re-ablation,and lowering the costs associated with the design and production of ablation catheters.
基金Supported by the Czech Ministry of Health,General University Hospital in Prague,No.VFN00064165。
文摘BACKGROUND Whole-body magnetic resonance imaging(wbMRI)allows general assessment of systemic cancers including lymphomas without radiation burden.AIM To evaluate the diagnostic performance of wbMRI in the staging of diffuse large B-cell lymphoma(DLBCL),determine the value of individual MRI sequences,and assess patients’concerns with wbMRI.METHODS In this single-center prospective study,adult patients newly diagnosed with systemic DLBCL underwent wbMRI on a 3T scanner[diffusion weighted images with background suppression(DWIBS),T2,short tau inversion recovery(STIR),contrast-enhanced T1]and fluorodeoxyglucose(18F-FDG)positron emission tomo-graphy/computed tomography(PET/CT)(reference standard).The involvement of 12 nodal regions and extranodal sites was evaluated on wbMRI and PET/CT.The utility of wbMRI sequences was rated on a five-point scale(0=not useful,4=very useful).Patients received a questionnaire regarding wbMRI.RESULTS Of 60 eligible patients,14(23%)were enrolled and completed the study.The sensitivity of wbMRI in the nodal involvement(182 nodal sites)was 0.84,with 0.99 specificity,positive predictive value of 0.96,negative predictive value of 0.97,and 0.97 accuracy.PET/CT and wbMRI were concordant both in extranodal involvement(13 instances)and staging(κ=1.0).The mean scores of the utility of MRI sequences were 3.71±0.73 for DWIBS,2.64±0.84 for T1,2.14±0.77 for STIR,and 1.29±0.73 for T2(P<0.0001).Patients were mostly concerned about the enclosed environment and duration of the MRI examination(27%of patients).CONCLUSION The wbMRI exhibited excellent sensitivity and specificity in staging DLBCL.DWIBS and contrast-enhanced T1 were rated as the most useful sequences.Patients were less willing to undergo wbMRI as a second examination parallel to PET/CT,especially owing to the long duration and the enclosed environment.
基金Supported by National Natural Science Foundation of China(No.82160195,No.82460203)Degree and Postgraduate Education Teaching Reform Project of Jiangxi Province(No.JXYJG-2020-026).
文摘AIM:To assess the possibility of using different large language models(LLMs)in ocular surface diseases by selecting five different LLMS to test their accuracy in answering specialized questions related to ocular surface diseases:ChatGPT-4,ChatGPT-3.5,Claude 2,PaLM2,and SenseNova.METHODS:A group of experienced ophthalmology professors were asked to develop a 100-question singlechoice question on ocular surface diseases designed to assess the performance of LLMs and human participants in answering ophthalmology specialty exam questions.The exam includes questions on the following topics:keratitis disease(20 questions),keratoconus,keratomalaciac,corneal dystrophy,corneal degeneration,erosive corneal ulcers,and corneal lesions associated with systemic diseases(20 questions),conjunctivitis disease(20 questions),trachoma,pterygoid and conjunctival tumor diseases(20 questions),and dry eye disease(20 questions).Then the total score of each LLMs and compared their mean score,mean correlation,variance,and confidence were calculated.RESULTS:GPT-4 exhibited the highest performance in terms of LLMs.Comparing the average scores of the LLMs group with the four human groups,chief physician,attending physician,regular trainee,and graduate student,it was found that except for ChatGPT-4,the total score of the rest of the LLMs is lower than that of the graduate student group,which had the lowest score in the human group.Both ChatGPT-4 and PaLM2 were more likely to give exact and correct answers,giving very little chance of an incorrect answer.ChatGPT-4 showed higher credibility when answering questions,with a success rate of 59%,but gave the wrong answer to the question 28% of the time.CONCLUSION:GPT-4 model exhibits excellent performance in both answer relevance and confidence.PaLM2 shows a positive correlation(up to 0.8)in terms of answer accuracy during the exam.In terms of answer confidence,PaLM2 is second only to GPT4 and surpasses Claude 2,SenseNova,and GPT-3.5.Despite the fact that ocular surface disease is a highly specialized discipline,GPT-4 still exhibits superior performance,suggesting that its potential and ability to be applied in this field is enormous,perhaps with the potential to be a valuable resource for medical students and clinicians in the future.
文摘Sea topography information holds significant importance in oceanic research and the climate change detection.Radar imaging altimetry has emerged as the leading approach for global ocean observation,employing synthetic aperture radar(SAR)interferometry to enhance the spatial resolution of Sea topography.Nevertheless,current payload capacity and satellite hardware limitations prevent the extension of the interferometric baseline by enlarging the physical antenna size.This constraint hinders achieving centimeter-level accuracy in interferometric altimetry.To address this challenge,we conducted a numerical simulation to assess the viability of a large baseline interferometric imaging altimeter(LB-IIA).By controlling the baseline within the range of 600-1000 m through spiral orbit design in two satellites and mitigating baseline de-correlation with the carrier frequency shift(CFS)technique,we aimed to overcome the above limitations.Our findings demonstrate the efficacy of the CFS technique in compensating for baseline decoherence,elevating coherence from less than 0.1 to over 0.85.Concurrently.The height difference accuracy between neighboring sea surfaces reaches 1 cm within a 1 km resolution.This study is anticipated to serve as a foundational reference for future interferometric imaging altimeter development,catering to the demand for high-precision sea topography data in accurate global bathymetry inversion.
基金supported by Research and Construction of Experimental Teaching Aid Platform for Programming under the Teaching Reform Research Project of Shandong University。
文摘For computer science majors in higher education institutions,programming courses are one of the most important professional foundation courses.Proficiency in independent programming skills is of great help to the study of subsequent courses and the personal development of students.In the teaching process of programming courses,online judgement systems are often used to improve students’programming level.Traditional online judgement systems lack guidance for students,and it is often difficult for inexperienced students to find and correct errors in their codes by themselves.We propose an online judgement system that integrates a large model of error correction to help students find errors and improve their programming skills.
文摘The emergence of artificial intelligence natural language large models has brought new dawn for the in-depth empowerment of the industry.Research on key technologies and applications of railway natural language large model is of great significance to promoting and coordinating the development of railway artificial intelligence.This paper puts forward the application scenarios of railway natural language large model according to the application requirements of railway artificial intelligence;designs the overall architecture of the railway natural language large model by relying on the railway artificial intelligence platform,studies the key technologies of the natural language large model,builds a railway industry large model oriented to intelligent question-answering,and verifies the model with actual data;finally,this paper prospects for the development and application of railway natural language large model from the aspects of railway traffic organization,railway operation safety and passenger service.
基金We acknowledge funding from NSFC Grant 62306283.
文摘Since the 1950s,when the Turing Test was introduced,there has been notable progress in machine language intelligence.Language modeling,crucial for AI development,has evolved from statistical to neural models over the last two decades.Recently,transformer-based Pre-trained Language Models(PLM)have excelled in Natural Language Processing(NLP)tasks by leveraging large-scale training corpora.Increasing the scale of these models enhances performance significantly,introducing abilities like context learning that smaller models lack.The advancement in Large Language Models,exemplified by the development of ChatGPT,has made significant impacts both academically and industrially,capturing widespread societal interest.This survey provides an overview of the development and prospects from Large Language Models(LLM)to Large Multimodal Models(LMM).It first discusses the contributions and technological advancements of LLMs in the field of natural language processing,especially in text generation and language understanding.Then,it turns to the discussion of LMMs,which integrates various data modalities such as text,images,and sound,demonstrating advanced capabilities in understanding and generating cross-modal content,paving new pathways for the adaptability and flexibility of AI systems.Finally,the survey highlights the prospects of LMMs in terms of technological development and application potential,while also pointing out challenges in data integration,cross-modal understanding accuracy,providing a comprehensive perspective on the latest developments in this field.
基金Supported by the National Talent Fund of the Ministry of Science and Technology of China(20230240011)China University of Geosciences(Wuhan)Research Fund(162301192687)。
文摘A large language model(LLM)is constructed to address the sophisticated demands of data retrieval and analysis,detailed well profiling,computation of key technical indicators,and the solutions to complex problems in reservoir performance analysis(RPA).The LLM is constructed for RPA scenarios with incremental pre-training,fine-tuning,and functional subsystems coupling.Functional subsystem and efficient coupling methods are proposed based on named entity recognition(NER),tool invocation,and Text-to-SQL construction,all aimed at resolving pivotal challenges in developing the specific application of LLMs for RDA.This study conducted a detailed accuracy test on feature extraction models,tool classification models,data retrieval models and analysis recommendation models.The results indicate that these models have demonstrated good performance in various key aspects of reservoir dynamic analysis.The research takes some injection and production well groups in the PK3 Block of the Daqing Oilfield as an example for testing.Testing results show that our model has significant potential and practical value in assisting reservoir engineers with RDA.The research results provide a powerful support to the application of LLM in reservoir performance analysis.
基金supported by National Key R&D Program of China(grant no.2022YFC2404201)the Chinese Academy of Sciences Project for Young Scientists in Basic Research(grant no.YSBR067).
文摘Conventional microscopes designed for submicron resolution in biological research are hindered by a limited field of view,typically around 1 mm.This restriction poses a challenge when attempting to simultaneously analyze various parts of a sample,such as different brain areas.In addition,conventional objective lenses struggle to perform consistently across the required range of wavelengths for brain imaging in vivo.Here we present a novel mesoscopic objective lens with an impressive field of view of 8 mm,a numerical aperture of 0.5,and a working wavelength range from 400 to 1000 nm.We achieved a resolution of 0.74μm in fluorescent beads imaging.The versatility of this lens was further demonstrated through high-quality images of mouse brain and kidney sections in a wide-field imaging system,a confocal laser scanning system,and a two-photon imaging system.This mesoscopic objective lens holds immense promise for advancing multi-wavelength imaging of large fields of view at high resolution.
