Objective To develop a clinical decision and prescription generation system(CDPGS)specifically for diarrhea in traditional Chinese medicine(TCM),utilizing a specialized large language model(LLM),Qwen-TCM-Dia,to standa...Objective To develop a clinical decision and prescription generation system(CDPGS)specifically for diarrhea in traditional Chinese medicine(TCM),utilizing a specialized large language model(LLM),Qwen-TCM-Dia,to standardize diagnostic processes and prescription generation.Methods Two primary datasets were constructed:an evaluation benchmark and a fine-tuning dataset consisting of fundamental diarrhea knowledge,medical records,and chain-ofthought(CoT)reasoning datasets.After an initial evaluation of 16 open-source LLMs across inference time,accuracy,and output quality,Qwen2.5 was selected as the base model due to its superior overall performance.We then employed a two-stage low-rank adaptation(LoRA)fine-tuning strategy,integrating continued pre-training on domain-specific knowledge with instruction fine-tuning using CoT-enriched medical records.This approach was designed to embed the clinical logic(symptoms→pathogenesis→therapeutic principles→prescriptions)into the model’s reasoning capabilities.The resulting fine-tuned model,specialized for TCM diarrhea,was designated as Qwen-TCM-Dia.Model performance was evaluated for disease diagnosis and syndrome type differentiation using accuracy,precision,recall,and F1-score.Furthermore,the quality of the generated prescriptions was compared with that of established open-source TCM LLMs.Results Qwen-TCM-Dia achieved peak performance compared to both the base Qwen2.5 model and five other open-source TCM LLMs.It achieved 97.05%accuracy and 91.48%F1-score in disease diagnosis,and 74.54%accuracy and 74.21%F1-score in syndrome type differentiation.Compared with existing open-source TCM LLMs(BianCang,HuangDi,LingDan,TCMLLM-PR,and ZhongJing),Qwen-TCM-Dia exhibited higher fidelity in reconstructing the“symptoms→pathogenesis→therapeutic principles→prescriptions”logic chain.It provided complete prescriptions,whereas other models often omitted dosages or generated mismatched prescriptions.Conclusion By integrating continued pre-training,CoT reasoning,and a two-stage fine-tuning strategy,this study establishes a CDPGS for diarrhea in TCM.The results demonstrate the synergistic effect of strengthening domain representation through pre-training and activating logical reasoning via CoT.This research not only provides critical technical support for the standardized diagnosis and treatment of diarrhea but also offers a scalable paradigm for the digital inheritance of expert TCM experience and the intelligent transformation of TCM.展开更多
Hydraulic stimulation technology is widely employed to enhance the permeability of geothermal reservoirs.Nevertheless,accurately predicting hydraulic fracture propagation in complex geological conditions remains chall...Hydraulic stimulation technology is widely employed to enhance the permeability of geothermal reservoirs.Nevertheless,accurately predicting hydraulic fracture propagation in complex geological conditions remains challenging,thereby hindering the effective utilization of existing natural fractures.In this study,a phase field model was developed utilizing the finite element method to examine the influence of fluid presence,stress conditions,and natural fractures on the initiation and propagation of hydraulic fractures.The model employs Biot's poroelasticity theory to establish the coupling between the displacement field and the fluid field,while the phase field theory is applied to simulate fracture behavior.The results show that whenσ_(x0)/σ_(y0)<3 or qf<20 kg/(m^(3)·s),the presence of natural fractures can alter the original propagation direction of hydraulic fractures.Conversely,in the absence of these conditions,the propagation path of natural fractures is predominantly influenced by the initial stress field.Furthermore,based on the analysis of breakdown pressure and damage area,the optimal intersection angle between natural fractures and hydraulic fractures is determined to range from 45°to 60°.Finally,once a dominant channel forms,initiating and propagating hydraulic fractures in other directions becomes increasingly difficult,even in highly fractured areas.This method tackles the challenges of initiating and propagating hydraulic fractures in complex geological conditions,providing a theoretical basis for optimizing Enhanced Geothermal System(EGS)projects.展开更多
Objectives This study aimed to explore the lagged and cumulative effects of risk factors on disability in older adults using distributed lag non-linear models(DLNMs).Methods We utilized data from the China Health and ...Objectives This study aimed to explore the lagged and cumulative effects of risk factors on disability in older adults using distributed lag non-linear models(DLNMs).Methods We utilized data from the China Health and Retirement Longitudinal Study(CHARLS).After feature selection via Elastic Net Regularization,we applied DLNMs to evaluate the lagged effects of risk factors.Disability was defined as the presence of any difficulties in basic activities of daily living(BADL).The cumulative relative risk(CRR)was calculated by summing the lag-specific risk estimates,representing the cumulative disability risk over the specified lag period.Effect modifications and sensitivity analyses were also performed.Results This study included a total of 2,318 participants.Early-phase lag factors,such as the difficulty in stooping(CRR=3.58;95%CI:2.31-5.55;P<0.001)and walking(CRR=2.77;95%CI:1.39-5.55;P<0.001),exerted the strongest effects immediately upon occurrence.Mid-phase lag factors,such as arthritis(CRR=1.51;95%CI:1.10-2.06;P=0.001),showed a resurgence in disability risk within 2-3 years.Late-phase lag factors,including depressive symptoms(CRR=2.38;95%CI:1.30-4.35;P<0.001)and elevated systolic blood pressure(CRR=1.64;95%CI:1.06-2.79;P=0.02),exhibited significant long-term cumulative risks.Conversely,grip strength(CRR=0.80;95%CI:0.54-0.95;P=0.02)and social participation(CRR=0.89;95%CI:0.73-0.99;P=0.04)were significant protective factors.Conclusions The findings underscore the importance of tailored interventions that account for various lag characteristics of different factors to effectively mitigate disability risk.Future studies should explore the underlying biological and sociological mechanisms of these lagged effects,identify intervention strategies that target risk factors with different lagged patterns,and evaluate their effectiveness.展开更多
Over the past decade,large-scale pre-trained autoregressive and diffusion models rejuvenated the field of text-guided image generation.However,these models require enormous datasets and parameters,and their multi-step...Over the past decade,large-scale pre-trained autoregressive and diffusion models rejuvenated the field of text-guided image generation.However,these models require enormous datasets and parameters,and their multi-step generation processes are often inefficient and difficult to control.To address these challenges,we propose CAFE-GAN,a CLIP-Projected GAN with Attention-Aware Generation and Multi-Scale Discrimination,which incorporates a pretrained CLIP model along with several key architectural innovations.First,we embed a coordinate attention mechanism into the generator to capture long-range dependencies and enhance feature representation.Second,we introduce a trainable linear projection layer after the CLIP text encoder,which aligns textual embeddings with the generator’s semantic space.Third,we design a multi-scale discriminator that leverages pre-trained visual features and integrates a feature regularization strategy,thereby improving training stability and discrimination performance.Experiments on the CUB and COCO datasets demonstrate that CAFE-GAN outperforms existing text-to-image generation methods,achieving lower Fréchet Inception Distance(FID)scores and generating images with superior visual quality and semantic fidelity,with FID scores of 9.84 and 5.62 on the CUB and COCO datasets,respectively,surpassing current state-of-the-art text-to-image models by varying degrees.These findings offer valuable insights for future research on efficient,controllable text-to-image synthesis.展开更多
With the rise of AI-assisted education,many instructors and engineers seek to deliver high-quality programming courses online.However,crafting effective programming lectures remains a challenge,particularly for instru...With the rise of AI-assisted education,many instructors and engineers seek to deliver high-quality programming courses online.However,crafting effective programming lectures remains a challenge,particularly for instructors lacking pedagogical training or multilingual fluency.We present CourseAgent,a prompt-driven framework that leverages large language models(LLMs)to automatically generate Python tutorials,structured lecture scripts,and accompanying audio narrations.CourseAgent accepts raw code as input and transforms it into segmented,well-commented code blocks,adapting content to different difficulty levels and languages via prompt customization.Our system supports multilingual instruction(e.g.,Chinese,English),fine-grained control of pedagogical depth,and auto-generation of lecture videos.We evaluate the output generated by CourseAgent using real student feedback and feedback from in-service teachers,alongside automated assessments from LLMs.These evaluations demonstrate that the materials produced by CourseAgent are coherent,pedagogically sound,and comparable in quality to those created by experienced instructors.CourseAgent lowers the barrier to quality programming education and shows promise for scalable,personalized,and language-adaptive content generation.展开更多
It remains difficult to automate the creation and validation of Unified Modeling Language(UML)dia-grams due to unstructured requirements,limited automated pipelines,and the lack of reliable evaluation methods.This stu...It remains difficult to automate the creation and validation of Unified Modeling Language(UML)dia-grams due to unstructured requirements,limited automated pipelines,and the lack of reliable evaluation methods.This study introduces a cohesive architecture that amalgamates requirement development,UML synthesis,and multimodal validation.First,LLaMA-3.2-1B-Instruct was utilized to generate user-focused requirements.Then,DeepSeek-R1-Distill-Qwen-32B applies its reasoning skills to transform these requirements into PlantUML code.Using this dual-LLM pipeline,we constructed a synthetic dataset of 11,997 UML diagrams spanning six major diagram families.Rendering analysis showed that 89.5%of the generated diagrams compile correctly,while invalid cases were detected automatically.To assess quality,we employed a multimodal scoring method that combines Qwen2.5-VL-3B,LLaMA-3.2-11B-Vision-Instruct and Aya-Vision-8B,with weights based on MMMU performance.A study with 94 experts revealed strong alignment between automatic and manual evaluations,yielding a Pearson correlation of r=0.82 and a Fleiss’Kappa of 0.78.This indicates a high degree of concordance between automated metrics and human judgment.Overall,the results demonstrated that our scoring system is effective and that the proposed generation pipeline produces UML diagrams that are both syntactically correct and semantically coherent.More broadly,the system provides a scalable and reproducible foundation for future work in AI-driven software modeling and multimodal verification.展开更多
Due to the digital transformation tendency among cultural institutions and the substantial influence of the social media platform,the demands of visual communication keep increasing for promoting traditional cultural ...Due to the digital transformation tendency among cultural institutions and the substantial influence of the social media platform,the demands of visual communication keep increasing for promoting traditional cultural artifacts online.As an effective medium,posters serve to attract public attention and facilitate broader engagement with cultural artifacts.However,existing poster generation methods mainly rely on fixed templates and manual design,which limits their scalability and adaptability to the diverse visual and semantic features of the artifacts.Therefore,we propose CAPGen,an automated aesthetic Cultural Artifacts Poster Generation framework built on a Multimodal Large Language Model(MLLM)with integrated iterative optimization.During our research,we collaborated with designers to define principles of graphic design for cultural artifact posters,to guide the MLLM in generating layout parameters.Later,we generated these parameters into posters.Finally,we refined the posters using an MLLM integrated with a multi-round iterative optimization mechanism.Qualitative results show that CAPGen consistently outperforms baseline methods in both visual quality and aesthetic performance.Furthermore,ablation studies indicate that the prompt,iterative optimization mechanism,and design principles significantly enhance the effectiveness of poster generation.展开更多
Dear Editor,This letter presents techniques to simplify dataset generation for instance segmentation of raw meat products,a critical step toward automating food production lines.Accurate segmentation is essential for ...Dear Editor,This letter presents techniques to simplify dataset generation for instance segmentation of raw meat products,a critical step toward automating food production lines.Accurate segmentation is essential for addressing challenges such as occlusions,indistinct edges,and stacked configurations,which demand large,diverse datasets.To meet these demands,we propose two complementary approaches:a semi-automatic annotation interface using tools like the segment anything model(SAM)and GrabCut and a synthetic data generation pipeline leveraging 3D-scanned models.These methods reduce reliance on real meat,mitigate food waste,and improve scalability.Experimental results demonstrate that incorporating synthetic data enhances segmentation model performance and,when combined with real data,further boosts accuracy,paving the way for more efficient automation in the food industry.展开更多
The application of generative artificial intelligence(AI)is bringing about notable changes in anime creation.This paper surveys recent advancements and applications of diffusion and language models in anime generation...The application of generative artificial intelligence(AI)is bringing about notable changes in anime creation.This paper surveys recent advancements and applications of diffusion and language models in anime generation,focusing on their demonstrated potential to enhance production efficiency through automation and personalization.Despite these benefits,it is crucial to acknowledge the substantial initial computational investments required for training and deploying these models.We conduct an in-depth survey of cutting-edge generative AI technologies,encompassing models such as Stable Diffusion and GPT,and appraise pivotal large-scale datasets alongside quantifiable evaluation metrics.Review of the surveyed literature indicates the achievement of considerable maturity in the capacity of AI models to synthesize high-quality,aesthetically compelling anime visual images from textual prompts,alongside discernible progress in the generation of coherent narratives.However,achieving perfect long-form consistency,mitigating artifacts like flickering in video sequences,and enabling fine-grained artistic control remain critical ongoing challenges.Building upon these advancements,research efforts have increasingly pivoted towards the synthesis of higher-dimensional content,such as video and three-dimensional assets,with recent studies demonstrating significant progress in this burgeoning field.Nevertheless,formidable challenges endure amidst these advancements.Foremost among these are the substantial computational exigencies requisite for training and deploying these sophisticated models,particularly pronounced in the realm of high-dimensional generation such as video synthesis.Additional persistent hurdles include maintaining spatial-temporal consistency across complex scenes and mitigating ethical considerations surrounding bias and the preservation of human creative autonomy.This research underscores the transformative potential and inherent complexities of AI-driven synergy within the creative industries.We posit that future research should be dedicated to the synergistic fusion of diffusion and autoregressive models,the integration of multimodal inputs,and the balanced consideration of ethical implications,particularly regarding bias and the preservation of human creative autonomy,thereby establishing a robust foundation for the advancement of anime creation and the broader landscape of AI-driven content generation.展开更多
Hydrocarbon generation kinetics are influenced by complex factors,including temperature,reaction time,pressure,and molecular structure,which render simple modeling approaches inadequate for accurately simulating metha...Hydrocarbon generation kinetics are influenced by complex factors,including temperature,reaction time,pressure,and molecular structure,which render simple modeling approaches inadequate for accurately simulating methane generation.The closed-system pyrolysis experiment,a common method to study hydrocarbon generation,poses challenges for kinetic parameter regression due to limited data points.This limitation necessitates the application of sophisticated data analysis techniques to extract meaningful insights from sparse experimental data.This paper establishes a quantitative relationship between methane production and the thermal process through closed system pyrolysis experiments.A nonlinear regression model using multiple algorithms is established based on this quantitative relationship.Accordingly,a method that can quantitatively invert the methane generation kinetic parameters corresponding to the samples based on the experimental data is provided.Based on this theoretical model,a computer program capable of processing experimental data is designed and implemented.Practical analyses are performed using the method above for three samples:a coal sample from the Yulong,Guizhou;a solid bitumen sample from Guangyuan,Sichuan;and a marlstone sample containing type Ⅰ kerogen from Luquan,Yunnan.The results obtained agree with the qualitative estimates based on hydrocarbon generation kinetic theory using the previous method.Thus,the validity of the new data processing method,the new mathematical model,and the data processing procedures are verified.展开更多
The task of molecule generation guided by specific text descriptions has been proposed to generate molecules that match given text inputs.Mainstream methods typically use simplified molecular input line entry system(S...The task of molecule generation guided by specific text descriptions has been proposed to generate molecules that match given text inputs.Mainstream methods typically use simplified molecular input line entry system(SMILES)to represent molecules and rely on diffusion models or autoregressive structures for modeling.However,the one-to-many mapping diversity when using SMILES to represent molecules causes existing methods to require complex model architectures and larger training datasets to improve performance,which affects the efficiency of model training and generation.In this paper,we propose a text-guided diverse-expression diffusion(TGDD)model for molecule generation.TGDD combines both SMILES and self-referencing embedded strings(SELFIES)into a novel diverse-expression molecular representation,enabling precise molecule mapping based on natural language.By leveraging this diverse-expression representation,TGDD simplifies the segmented diffusion generation process,achieving faster training and reduced memory consumption,while also exhibiting stronger alignment with natural language.TGDD outperforms both TGM-LDM and the autoregressive model MolT5-Base on most evaluation metrics.展开更多
Objective:This study aimed to develop a Nursing Retrieval-Augmented Generation(NurRAG)system based on large language models(LLMs)and to evaluate its accuracy and clinical applicability in nursing question answering.Me...Objective:This study aimed to develop a Nursing Retrieval-Augmented Generation(NurRAG)system based on large language models(LLMs)and to evaluate its accuracy and clinical applicability in nursing question answering.Methods:A multidisciplinary team consisting of nursing experts,artificial intelligence researchers,and information engineers collaboratively designed the NurRAG framework following the principles of retrieval-augmented generation.The system included four functional modules:1)construction of a nursing knowledge base through document normalization,embedding,and vector indexing;2)nursing question filtering using a supervised classifier;3)semantic retrieval and re-ranking for evidence selection;and 4)evidence-conditioned language model generation to produce citation-based nursing answers.The system was securely deployed on hospital intranet servers using Docker containers.Performance evaluation was conducted with 1,000 expert-verified nursing question–answer pairs.Semantic fidelity was assessed using Recall Oriented Understudy for Gisting Evaluation–Longest Common Subsequence(ROUGE-L),and clinical correctness was measured using Accuracy.Results:The NurRAG system achieved significant improvements in both semantic fidelity and answer accuracy compared with conventional large language models.For ChatGLM2-6B,ROUGE-L increased from(30.73±1.48)%to(64.27±0.27)%,and accuracy increased from(49.08±0.92)%to(75.83±0.35)%.For LLaMA2-7B,ROUGE-L increased from(28.76±0.89)%to(60.33±0.21)%,and accuracy increased from(43.27±0.83)%to(73.29±0.33)%.All differences were statistically significant(P<0.001).A quantitative case analysis further demonstrated that NurRAG effectively reduced hallucinated outputs and generated evidence-based,guideline-concordant nursing responses.Conclusion:The NurRAG system integrates domain-specific retrieval with LLMs generation to provide accurate,reliable,and traceable evidence-based nursing answers.The findings demonstrate the system’s feasibility and potential to improve the accuracy of clinical knowledge access,support evidence-based nursing decision-making,and promote the safe application of artificial intelligence in nursing practice.展开更多
In the era of big data,data-driven technologies are increasingly leveraged by industry to facilitate autonomous learning and intelligent decision-making.However,the challenge of“small samples in big data”emerges whe...In the era of big data,data-driven technologies are increasingly leveraged by industry to facilitate autonomous learning and intelligent decision-making.However,the challenge of“small samples in big data”emerges when datasets lack the comprehensive information necessary for addressing complex scenarios,which hampers adaptability.Thus,enhancing data completeness is essential.Knowledge-guided virtual sample generation transforms domain knowledge into extensive virtual datasets,thereby reducing dependence on limited real samples and enabling zero-sample fault diagnosis.This study used building air conditioning systems as a case study.We innovatively used the large language model(LLM)to acquire domain knowledge for sample generation,significantly lowering knowledge acquisition costs and establishing a generalized framework for knowledge acquisition in engineering applications.This acquired knowledge guided the design of diffusion boundaries in mega-trend diffusion(MTD),while the Monte Carlo method was used to sample within the diffusion function to create information-rich virtual samples.Additionally,a noise-adding technique was introduced to enhance the information entropy of these samples,thereby improving the robustness of neural networks trained with them.Experimental results showed that training the diagnostic model exclusively with virtual samples achieved an accuracy of 72.80%,significantly surpassing traditional small-sample supervised learning in terms of generalization.This underscores the quality and completeness of the generated virtual samples.展开更多
Panoramic images, offering a 360-degree view, are essential in virtual reality(VR) and augmented reality(AR), enhancing realism with high-quality textures. However, acquiring complete and high-quality panoramic textur...Panoramic images, offering a 360-degree view, are essential in virtual reality(VR) and augmented reality(AR), enhancing realism with high-quality textures. However, acquiring complete and high-quality panoramic textures is challenging. This paper introduces a method using generative adversarial networks(GANs) and the contrastive language-image pretraining(CLIP) model to restore and control texture in panoramic images. The GAN model captures complex structures and maintains consistency, while CLIP enables fine-grained texture control via semantic text-image associations. GAN inversion optimizes latent codes for precise texture details. The resulting low dynamic range(LDR) images are converted to high dynamic range(HDR) using the Blender engine for seamless texture blending. Experimental results demonstrate the effectiveness and flexibility of this method in panoramic texture restoration and generation.展开更多
In order to investigate the hydrocarbon generation process and gas potentials of source rocks in deepwater area of the Qiongdongnan Basin, kinetic parameters of gas generation （activation energy distribution and freq...In order to investigate the hydrocarbon generation process and gas potentials of source rocks in deepwater area of the Qiongdongnan Basin, kinetic parameters of gas generation （activation energy distribution and frequency factor） of the Yacheng Formation source rocks （coal and neritic mudstones） was determined by thermal simulation experiments in the closed system and the specific KINETICS Software. The results show that the activation energy （Ea） distribution of C1–C5 generation ranges from 50 to 74 kcal/mol with a frequency factor of 2.4×1015 s–1 for the neritic mudstone and the Ea distribution of C1–C5 generation ranges from 49 to 73 kcal/mol with a frequency factor of 8.92×1013 s–1 for the coal. On the basis of these kinetic parameters and combined with the data of sedimentary burial and paleothermal histories, the gas generation model of the Yacheng Formation source rocks closer to geological condition was worked out, indicating its main gas generation stage at Ro （vitrinite reflectance） of 1.25%–2.8%. Meanwhile, the gas generation process of the source rocks of different structural locations （central part, southern slope and south low uplift） in the Lingshui Sag was simulated. Among them, the gas generation of the Yacheng Formation source rocks in the central part and the southern slope of the sag entered the main gas window at 10 and 5 Ma respectively and the peak gas generation in the southern slope occurred at 3 Ma. The very late peak gas generation and the relatively large gas potential indices （GPI：20×10^8–60×10^8 m^3/km^2） would provide favorable conditions for the accumulation of large natural gas reserves in the deepwater area.展开更多
In the context of power generation companies, vast amounts of specialized data and expert knowledge have been accumulated. However, challenges such as data silos and fragmented knowledge hinder the effective utilizati...In the context of power generation companies, vast amounts of specialized data and expert knowledge have been accumulated. However, challenges such as data silos and fragmented knowledge hinder the effective utilization of this information. This study proposes a novel framework for intelligent Question-and-Answer (Q&A) systems based on Retrieval-Augmented Generation (RAG) to address these issues. The system efficiently acquires domain-specific knowledge by leveraging external databases, including Relational Databases (RDBs) and graph databases, without additional fine-tuning for Large Language Models (LLMs). Crucially, the framework integrates a Dynamic Knowledge Base Updating Mechanism (DKBUM) and a Weighted Context-Aware Similarity (WCAS) method to enhance retrieval accuracy and mitigate inherent limitations of LLMs, such as hallucinations and lack of specialization. Additionally, the proposed DKBUM dynamically adjusts knowledge weights within the database, ensuring that the most recent and relevant information is utilized, while WCAS refines the alignment between queries and knowledge items by enhanced context understanding. Experimental validation demonstrates that the system can generate timely, accurate, and context-sensitive responses, making it a robust solution for managing complex business logic in specialized industries.展开更多
The current researches on the tooth surface mathematical equations and the theory of gearing mainly pay attention to the ordinary type worm gear set（e.g., ZN, ZA, or ZK）. The research of forming mechanism and three-...The current researches on the tooth surface mathematical equations and the theory of gearing mainly pay attention to the ordinary type worm gear set（e.g., ZN, ZA, or ZK）. The research of forming mechanism and three-dimensional modeling method for the double pitch worm gear set is not enough. So there are some difficulties in mathematical model deducing and geometry modeling of double pitch ZN-type worm gear set based on generation mechanism. In order to establish the mathematical model and the precise geometric model of double pitch ZN-type worm gear set, the structural characteristics and generation mechanism of the double pitch ZN-type worm gear set are investigated. Mathematical model of the ZN-type worm gear set is derived based on its generation mechanism and the theory of gearing. According to the mathematical model of the worm gear set which has been developed, a geometry modeling method of the double pitch ZN-type worm and worm gear is presented. Furthermore, a geometrical precision calculate method is proposed to evaluate the geometrical quality of the double pitch worm gear set. As a result, the maximum error is less than 6′10–4 mm in magnitude, thus the model of the double pitch ZN-type worm gear set is available to meet the requirements of finite element analysis and engineering application. The derived mathematical model and the proposed geometrical modeling method are helpful to guiding the design, manufacture and contact analysis of the worm gear set.展开更多
Predicting wind power gen eration over the medium and long term is helpful for dispatchi ng departme nts,as it aids in constructing generation plans and electricity market transactions.This study presents a monthly wi...Predicting wind power gen eration over the medium and long term is helpful for dispatchi ng departme nts,as it aids in constructing generation plans and electricity market transactions.This study presents a monthly wind power gen eration forecast!ng method based on a climate model and long short-term memory(LSTM)n eural n etwork.A non linear mappi ng model is established between the meteorological elements and wind power monthly utilization hours.After considering the meteorological data(as predicted for the future)and new installed capacity planning,the monthly wind power gen eration forecast results are output.A case study shows the effectiveness of the prediction method.展开更多
The innovative Next Generation Subsea Production System(NextGen SPS)concept is a newly proposed petroleum development solution in ultra-deep water areas.The definition of NextGen SPS involves several disciplines,which...The innovative Next Generation Subsea Production System(NextGen SPS)concept is a newly proposed petroleum development solution in ultra-deep water areas.The definition of NextGen SPS involves several disciplines,which makes the design process difficult.In this paper,the definition of NextGen SPS is modeled as an uncertain multidisciplinary design optimization(MDO)problem.The deterministic optimization model is formulated,and three concerning disciplines—cost calculation,hydrodynamic analysis and global performance analysis are presented.Surrogate model technique is applied in the latter two disciplines.Collaborative optimization(CO)architecture is utilized to organize the concerning disciplines.A deterministic CO framework with two disciplinelevel optimizations is proposed firstly.Then the uncertainties of design parameters and surrogate models are incorporated by using interval method,and uncertain CO frameworks with triple loop and double loop optimization structure are established respectively.The optimization results illustrate that,although the deterministic MDO result achieves higher reduction in objective function than the uncertain MDO result,the latter is more reliable than the former.展开更多
A simplified model for SO_(2) generation during spontaneous combustion of coal gangue was put forward and validated using the measured data.Using the proposed model,the effects of initial temperature inside the gangue...A simplified model for SO_(2) generation during spontaneous combustion of coal gangue was put forward and validated using the measured data.Using the proposed model,the effects of initial temperature inside the gangue and fresh air supply on SO_(2) generation were discussed.The results showed that,higher initial temperature inside the gangue could accelerate the oxidation rate of FeS_(2) and increase the maximum concentration of SO_(2).If initial temperature inside the gangue increased by about 37%,the total SO_(2) generation increased by 166%.Fresh air supply had less significant effect on the oxidation rate of FeS_(2).However,the higher the fresh air supply was,the more FeS_(2) could be oxidized,which ultimately produced more SO_(2).Although the computed results and the measured data concerning the inner locations inside the gangue had a certain degree of error,the proposed model can provide a relatively precise total release of SO_(2) within acceptable accuracy.Besides,this method provides a useful prototype to predict the generation of hazardous materials,such as CO,NO_(x),and chlorine during the spontaneous combustion of coal gangue.展开更多
基金National Key Research and Development Program of China(2024YFC3505400)Capital Clinical Project of Beijing Municipal Science&Technology Commission(Z221100007422092)Capital’s Funds for Health Improvement and Research(2024-1-2231).
文摘Objective To develop a clinical decision and prescription generation system(CDPGS)specifically for diarrhea in traditional Chinese medicine(TCM),utilizing a specialized large language model(LLM),Qwen-TCM-Dia,to standardize diagnostic processes and prescription generation.Methods Two primary datasets were constructed:an evaluation benchmark and a fine-tuning dataset consisting of fundamental diarrhea knowledge,medical records,and chain-ofthought(CoT)reasoning datasets.After an initial evaluation of 16 open-source LLMs across inference time,accuracy,and output quality,Qwen2.5 was selected as the base model due to its superior overall performance.We then employed a two-stage low-rank adaptation(LoRA)fine-tuning strategy,integrating continued pre-training on domain-specific knowledge with instruction fine-tuning using CoT-enriched medical records.This approach was designed to embed the clinical logic(symptoms→pathogenesis→therapeutic principles→prescriptions)into the model’s reasoning capabilities.The resulting fine-tuned model,specialized for TCM diarrhea,was designated as Qwen-TCM-Dia.Model performance was evaluated for disease diagnosis and syndrome type differentiation using accuracy,precision,recall,and F1-score.Furthermore,the quality of the generated prescriptions was compared with that of established open-source TCM LLMs.Results Qwen-TCM-Dia achieved peak performance compared to both the base Qwen2.5 model and five other open-source TCM LLMs.It achieved 97.05%accuracy and 91.48%F1-score in disease diagnosis,and 74.54%accuracy and 74.21%F1-score in syndrome type differentiation.Compared with existing open-source TCM LLMs(BianCang,HuangDi,LingDan,TCMLLM-PR,and ZhongJing),Qwen-TCM-Dia exhibited higher fidelity in reconstructing the“symptoms→pathogenesis→therapeutic principles→prescriptions”logic chain.It provided complete prescriptions,whereas other models often omitted dosages or generated mismatched prescriptions.Conclusion By integrating continued pre-training,CoT reasoning,and a two-stage fine-tuning strategy,this study establishes a CDPGS for diarrhea in TCM.The results demonstrate the synergistic effect of strengthening domain representation through pre-training and activating logical reasoning via CoT.This research not only provides critical technical support for the standardized diagnosis and treatment of diarrhea but also offers a scalable paradigm for the digital inheritance of expert TCM experience and the intelligent transformation of TCM.
基金supported by the National Key Research and Development Program(2021YFB150740401)National Natural Science Foundation of China(42202336)the CAS Pioneer Hundred Talents Program in China(Y826031C01)。
文摘Hydraulic stimulation technology is widely employed to enhance the permeability of geothermal reservoirs.Nevertheless,accurately predicting hydraulic fracture propagation in complex geological conditions remains challenging,thereby hindering the effective utilization of existing natural fractures.In this study,a phase field model was developed utilizing the finite element method to examine the influence of fluid presence,stress conditions,and natural fractures on the initiation and propagation of hydraulic fractures.The model employs Biot's poroelasticity theory to establish the coupling between the displacement field and the fluid field,while the phase field theory is applied to simulate fracture behavior.The results show that whenσ_(x0)/σ_(y0)<3 or qf<20 kg/(m^(3)·s),the presence of natural fractures can alter the original propagation direction of hydraulic fractures.Conversely,in the absence of these conditions,the propagation path of natural fractures is predominantly influenced by the initial stress field.Furthermore,based on the analysis of breakdown pressure and damage area,the optimal intersection angle between natural fractures and hydraulic fractures is determined to range from 45°to 60°.Finally,once a dominant channel forms,initiating and propagating hydraulic fractures in other directions becomes increasingly difficult,even in highly fractured areas.This method tackles the challenges of initiating and propagating hydraulic fractures in complex geological conditions,providing a theoretical basis for optimizing Enhanced Geothermal System(EGS)projects.
基金supported by ScientificResearch Fund of National Health Commission of the People’s Republic of China-Major Science and Technology Program for Medicine and Health in Zhejiang Province(WKJ-ZJ-2406).
文摘Objectives This study aimed to explore the lagged and cumulative effects of risk factors on disability in older adults using distributed lag non-linear models(DLNMs).Methods We utilized data from the China Health and Retirement Longitudinal Study(CHARLS).After feature selection via Elastic Net Regularization,we applied DLNMs to evaluate the lagged effects of risk factors.Disability was defined as the presence of any difficulties in basic activities of daily living(BADL).The cumulative relative risk(CRR)was calculated by summing the lag-specific risk estimates,representing the cumulative disability risk over the specified lag period.Effect modifications and sensitivity analyses were also performed.Results This study included a total of 2,318 participants.Early-phase lag factors,such as the difficulty in stooping(CRR=3.58;95%CI:2.31-5.55;P<0.001)and walking(CRR=2.77;95%CI:1.39-5.55;P<0.001),exerted the strongest effects immediately upon occurrence.Mid-phase lag factors,such as arthritis(CRR=1.51;95%CI:1.10-2.06;P=0.001),showed a resurgence in disability risk within 2-3 years.Late-phase lag factors,including depressive symptoms(CRR=2.38;95%CI:1.30-4.35;P<0.001)and elevated systolic blood pressure(CRR=1.64;95%CI:1.06-2.79;P=0.02),exhibited significant long-term cumulative risks.Conversely,grip strength(CRR=0.80;95%CI:0.54-0.95;P=0.02)and social participation(CRR=0.89;95%CI:0.73-0.99;P=0.04)were significant protective factors.Conclusions The findings underscore the importance of tailored interventions that account for various lag characteristics of different factors to effectively mitigate disability risk.Future studies should explore the underlying biological and sociological mechanisms of these lagged effects,identify intervention strategies that target risk factors with different lagged patterns,and evaluate their effectiveness.
文摘Over the past decade,large-scale pre-trained autoregressive and diffusion models rejuvenated the field of text-guided image generation.However,these models require enormous datasets and parameters,and their multi-step generation processes are often inefficient and difficult to control.To address these challenges,we propose CAFE-GAN,a CLIP-Projected GAN with Attention-Aware Generation and Multi-Scale Discrimination,which incorporates a pretrained CLIP model along with several key architectural innovations.First,we embed a coordinate attention mechanism into the generator to capture long-range dependencies and enhance feature representation.Second,we introduce a trainable linear projection layer after the CLIP text encoder,which aligns textual embeddings with the generator’s semantic space.Third,we design a multi-scale discriminator that leverages pre-trained visual features and integrates a feature regularization strategy,thereby improving training stability and discrimination performance.Experiments on the CUB and COCO datasets demonstrate that CAFE-GAN outperforms existing text-to-image generation methods,achieving lower Fréchet Inception Distance(FID)scores and generating images with superior visual quality and semantic fidelity,with FID scores of 9.84 and 5.62 on the CUB and COCO datasets,respectively,surpassing current state-of-the-art text-to-image models by varying degrees.These findings offer valuable insights for future research on efficient,controllable text-to-image synthesis.
基金supported by the Zhejiang Province Leading Geese Plan(Grant No.2025C02025)the Guangdong Province Primary and Secondary School Teachers’Digital Literacy Enhancement Project 2025(Grant No.GDSZSYKT2025244).
文摘With the rise of AI-assisted education,many instructors and engineers seek to deliver high-quality programming courses online.However,crafting effective programming lectures remains a challenge,particularly for instructors lacking pedagogical training or multilingual fluency.We present CourseAgent,a prompt-driven framework that leverages large language models(LLMs)to automatically generate Python tutorials,structured lecture scripts,and accompanying audio narrations.CourseAgent accepts raw code as input and transforms it into segmented,well-commented code blocks,adapting content to different difficulty levels and languages via prompt customization.Our system supports multilingual instruction(e.g.,Chinese,English),fine-grained control of pedagogical depth,and auto-generation of lecture videos.We evaluate the output generated by CourseAgent using real student feedback and feedback from in-service teachers,alongside automated assessments from LLMs.These evaluations demonstrate that the materials produced by CourseAgent are coherent,pedagogically sound,and comparable in quality to those created by experienced instructors.CourseAgent lowers the barrier to quality programming education and shows promise for scalable,personalized,and language-adaptive content generation.
基金supported by the DH2025-TN07-07 project conducted at the Thai Nguyen University of Information and Communication Technology,Thai Nguyen,Vietnam,with additional support from the AI in Software Engineering Lab.
文摘It remains difficult to automate the creation and validation of Unified Modeling Language(UML)dia-grams due to unstructured requirements,limited automated pipelines,and the lack of reliable evaluation methods.This study introduces a cohesive architecture that amalgamates requirement development,UML synthesis,and multimodal validation.First,LLaMA-3.2-1B-Instruct was utilized to generate user-focused requirements.Then,DeepSeek-R1-Distill-Qwen-32B applies its reasoning skills to transform these requirements into PlantUML code.Using this dual-LLM pipeline,we constructed a synthetic dataset of 11,997 UML diagrams spanning six major diagram families.Rendering analysis showed that 89.5%of the generated diagrams compile correctly,while invalid cases were detected automatically.To assess quality,we employed a multimodal scoring method that combines Qwen2.5-VL-3B,LLaMA-3.2-11B-Vision-Instruct and Aya-Vision-8B,with weights based on MMMU performance.A study with 94 experts revealed strong alignment between automatic and manual evaluations,yielding a Pearson correlation of r=0.82 and a Fleiss’Kappa of 0.78.This indicates a high degree of concordance between automated metrics and human judgment.Overall,the results demonstrated that our scoring system is effective and that the proposed generation pipeline produces UML diagrams that are both syntactically correct and semantically coherent.More broadly,the system provides a scalable and reproducible foundation for future work in AI-driven software modeling and multimodal verification.
基金supported by the National Key Research and Development Program of China(2023YFF0906502)the Postgraduate Research and Innovation Project of Hunan Province under Grant(CX20240473).
文摘Due to the digital transformation tendency among cultural institutions and the substantial influence of the social media platform,the demands of visual communication keep increasing for promoting traditional cultural artifacts online.As an effective medium,posters serve to attract public attention and facilitate broader engagement with cultural artifacts.However,existing poster generation methods mainly rely on fixed templates and manual design,which limits their scalability and adaptability to the diverse visual and semantic features of the artifacts.Therefore,we propose CAPGen,an automated aesthetic Cultural Artifacts Poster Generation framework built on a Multimodal Large Language Model(MLLM)with integrated iterative optimization.During our research,we collaborated with designers to define principles of graphic design for cultural artifact posters,to guide the MLLM in generating layout parameters.Later,we generated these parameters into posters.Finally,we refined the posters using an MLLM integrated with a multi-round iterative optimization mechanism.Qualitative results show that CAPGen consistently outperforms baseline methods in both visual quality and aesthetic performance.Furthermore,ablation studies indicate that the prompt,iterative optimization mechanism,and design principles significantly enhance the effectiveness of poster generation.
基金supported by European Union’s Horizon Europe research and innovation programme,project AGILEHAND(Smart Grading,Handling and Packaging Solutions for Soft and Deformable Products in Agile and Reconfigurable Lines)(101092043).
文摘Dear Editor,This letter presents techniques to simplify dataset generation for instance segmentation of raw meat products,a critical step toward automating food production lines.Accurate segmentation is essential for addressing challenges such as occlusions,indistinct edges,and stacked configurations,which demand large,diverse datasets.To meet these demands,we propose two complementary approaches:a semi-automatic annotation interface using tools like the segment anything model(SAM)and GrabCut and a synthetic data generation pipeline leveraging 3D-scanned models.These methods reduce reliance on real meat,mitigate food waste,and improve scalability.Experimental results demonstrate that incorporating synthetic data enhances segmentation model performance and,when combined with real data,further boosts accuracy,paving the way for more efficient automation in the food industry.
基金supported by the National Natural Science Foundation of China(Grant No.62202210).
文摘The application of generative artificial intelligence(AI)is bringing about notable changes in anime creation.This paper surveys recent advancements and applications of diffusion and language models in anime generation,focusing on their demonstrated potential to enhance production efficiency through automation and personalization.Despite these benefits,it is crucial to acknowledge the substantial initial computational investments required for training and deploying these models.We conduct an in-depth survey of cutting-edge generative AI technologies,encompassing models such as Stable Diffusion and GPT,and appraise pivotal large-scale datasets alongside quantifiable evaluation metrics.Review of the surveyed literature indicates the achievement of considerable maturity in the capacity of AI models to synthesize high-quality,aesthetically compelling anime visual images from textual prompts,alongside discernible progress in the generation of coherent narratives.However,achieving perfect long-form consistency,mitigating artifacts like flickering in video sequences,and enabling fine-grained artistic control remain critical ongoing challenges.Building upon these advancements,research efforts have increasingly pivoted towards the synthesis of higher-dimensional content,such as video and three-dimensional assets,with recent studies demonstrating significant progress in this burgeoning field.Nevertheless,formidable challenges endure amidst these advancements.Foremost among these are the substantial computational exigencies requisite for training and deploying these sophisticated models,particularly pronounced in the realm of high-dimensional generation such as video synthesis.Additional persistent hurdles include maintaining spatial-temporal consistency across complex scenes and mitigating ethical considerations surrounding bias and the preservation of human creative autonomy.This research underscores the transformative potential and inherent complexities of AI-driven synergy within the creative industries.We posit that future research should be dedicated to the synergistic fusion of diffusion and autoregressive models,the integration of multimodal inputs,and the balanced consideration of ethical implications,particularly regarding bias and the preservation of human creative autonomy,thereby establishing a robust foundation for the advancement of anime creation and the broader landscape of AI-driven content generation.
文摘Hydrocarbon generation kinetics are influenced by complex factors,including temperature,reaction time,pressure,and molecular structure,which render simple modeling approaches inadequate for accurately simulating methane generation.The closed-system pyrolysis experiment,a common method to study hydrocarbon generation,poses challenges for kinetic parameter regression due to limited data points.This limitation necessitates the application of sophisticated data analysis techniques to extract meaningful insights from sparse experimental data.This paper establishes a quantitative relationship between methane production and the thermal process through closed system pyrolysis experiments.A nonlinear regression model using multiple algorithms is established based on this quantitative relationship.Accordingly,a method that can quantitatively invert the methane generation kinetic parameters corresponding to the samples based on the experimental data is provided.Based on this theoretical model,a computer program capable of processing experimental data is designed and implemented.Practical analyses are performed using the method above for three samples:a coal sample from the Yulong,Guizhou;a solid bitumen sample from Guangyuan,Sichuan;and a marlstone sample containing type Ⅰ kerogen from Luquan,Yunnan.The results obtained agree with the qualitative estimates based on hydrocarbon generation kinetic theory using the previous method.Thus,the validity of the new data processing method,the new mathematical model,and the data processing procedures are verified.
基金supported in part by the National Natural Science Foundation of China(Grant Nos.62476247 and 62072409)the“Pioneer”and“Leading Goose”R&D Program of Zhejiang(Grant No.2024C01214)the Zhejiang Provincial Natural Science Foundation(Grant No.LR21F020003).
文摘The task of molecule generation guided by specific text descriptions has been proposed to generate molecules that match given text inputs.Mainstream methods typically use simplified molecular input line entry system(SMILES)to represent molecules and rely on diffusion models or autoregressive structures for modeling.However,the one-to-many mapping diversity when using SMILES to represent molecules causes existing methods to require complex model architectures and larger training datasets to improve performance,which affects the efficiency of model training and generation.In this paper,we propose a text-guided diverse-expression diffusion(TGDD)model for molecule generation.TGDD combines both SMILES and self-referencing embedded strings(SELFIES)into a novel diverse-expression molecular representation,enabling precise molecule mapping based on natural language.By leveraging this diverse-expression representation,TGDD simplifies the segmented diffusion generation process,achieving faster training and reduced memory consumption,while also exhibiting stronger alignment with natural language.TGDD outperforms both TGM-LDM and the autoregressive model MolT5-Base on most evaluation metrics.
基金supported by the Young and Middle-aged Research Fund Project of Shenzhen People's Hospital(Grant No.SYHL2024-N0010)the Shenzhen Basic Research Program(General Program,Grant No.JCYJ20240813104409013)。
文摘Objective:This study aimed to develop a Nursing Retrieval-Augmented Generation(NurRAG)system based on large language models(LLMs)and to evaluate its accuracy and clinical applicability in nursing question answering.Methods:A multidisciplinary team consisting of nursing experts,artificial intelligence researchers,and information engineers collaboratively designed the NurRAG framework following the principles of retrieval-augmented generation.The system included four functional modules:1)construction of a nursing knowledge base through document normalization,embedding,and vector indexing;2)nursing question filtering using a supervised classifier;3)semantic retrieval and re-ranking for evidence selection;and 4)evidence-conditioned language model generation to produce citation-based nursing answers.The system was securely deployed on hospital intranet servers using Docker containers.Performance evaluation was conducted with 1,000 expert-verified nursing question–answer pairs.Semantic fidelity was assessed using Recall Oriented Understudy for Gisting Evaluation–Longest Common Subsequence(ROUGE-L),and clinical correctness was measured using Accuracy.Results:The NurRAG system achieved significant improvements in both semantic fidelity and answer accuracy compared with conventional large language models.For ChatGLM2-6B,ROUGE-L increased from(30.73±1.48)%to(64.27±0.27)%,and accuracy increased from(49.08±0.92)%to(75.83±0.35)%.For LLaMA2-7B,ROUGE-L increased from(28.76±0.89)%to(60.33±0.21)%,and accuracy increased from(43.27±0.83)%to(73.29±0.33)%.All differences were statistically significant(P<0.001).A quantitative case analysis further demonstrated that NurRAG effectively reduced hallucinated outputs and generated evidence-based,guideline-concordant nursing responses.Conclusion:The NurRAG system integrates domain-specific retrieval with LLMs generation to provide accurate,reliable,and traceable evidence-based nursing answers.The findings demonstrate the system’s feasibility and potential to improve the accuracy of clinical knowledge access,support evidence-based nursing decision-making,and promote the safe application of artificial intelligence in nursing practice.
基金supported by the National Natural Science Foundation of China(No.62306281)the Natural Science Foundation of Zhejiang Province(Nos.LQ23E060006 and LTGG24E050005)the Key Research Plan of Jiaxing City(No.2024BZ20016).
文摘In the era of big data,data-driven technologies are increasingly leveraged by industry to facilitate autonomous learning and intelligent decision-making.However,the challenge of“small samples in big data”emerges when datasets lack the comprehensive information necessary for addressing complex scenarios,which hampers adaptability.Thus,enhancing data completeness is essential.Knowledge-guided virtual sample generation transforms domain knowledge into extensive virtual datasets,thereby reducing dependence on limited real samples and enabling zero-sample fault diagnosis.This study used building air conditioning systems as a case study.We innovatively used the large language model(LLM)to acquire domain knowledge for sample generation,significantly lowering knowledge acquisition costs and establishing a generalized framework for knowledge acquisition in engineering applications.This acquired knowledge guided the design of diffusion boundaries in mega-trend diffusion(MTD),while the Monte Carlo method was used to sample within the diffusion function to create information-rich virtual samples.Additionally,a noise-adding technique was introduced to enhance the information entropy of these samples,thereby improving the robustness of neural networks trained with them.Experimental results showed that training the diagnostic model exclusively with virtual samples achieved an accuracy of 72.80%,significantly surpassing traditional small-sample supervised learning in terms of generalization.This underscores the quality and completeness of the generated virtual samples.
文摘Panoramic images, offering a 360-degree view, are essential in virtual reality(VR) and augmented reality(AR), enhancing realism with high-quality textures. However, acquiring complete and high-quality panoramic textures is challenging. This paper introduces a method using generative adversarial networks(GANs) and the contrastive language-image pretraining(CLIP) model to restore and control texture in panoramic images. The GAN model captures complex structures and maintains consistency, while CLIP enables fine-grained texture control via semantic text-image associations. GAN inversion optimizes latent codes for precise texture details. The resulting low dynamic range(LDR) images are converted to high dynamic range(HDR) using the Blender engine for seamless texture blending. Experimental results demonstrate the effectiveness and flexibility of this method in panoramic texture restoration and generation.
基金The National Science and Technology Major Project of China under contract No.2011ZX05025-002
文摘In order to investigate the hydrocarbon generation process and gas potentials of source rocks in deepwater area of the Qiongdongnan Basin, kinetic parameters of gas generation （activation energy distribution and frequency factor） of the Yacheng Formation source rocks （coal and neritic mudstones） was determined by thermal simulation experiments in the closed system and the specific KINETICS Software. The results show that the activation energy （Ea） distribution of C1–C5 generation ranges from 50 to 74 kcal/mol with a frequency factor of 2.4×1015 s–1 for the neritic mudstone and the Ea distribution of C1–C5 generation ranges from 49 to 73 kcal/mol with a frequency factor of 8.92×1013 s–1 for the coal. On the basis of these kinetic parameters and combined with the data of sedimentary burial and paleothermal histories, the gas generation model of the Yacheng Formation source rocks closer to geological condition was worked out, indicating its main gas generation stage at Ro （vitrinite reflectance） of 1.25%–2.8%. Meanwhile, the gas generation process of the source rocks of different structural locations （central part, southern slope and south low uplift） in the Lingshui Sag was simulated. Among them, the gas generation of the Yacheng Formation source rocks in the central part and the southern slope of the sag entered the main gas window at 10 and 5 Ma respectively and the peak gas generation in the southern slope occurred at 3 Ma. The very late peak gas generation and the relatively large gas potential indices （GPI：20×10^8–60×10^8 m^3/km^2） would provide favorable conditions for the accumulation of large natural gas reserves in the deepwater area.
文摘In the context of power generation companies, vast amounts of specialized data and expert knowledge have been accumulated. However, challenges such as data silos and fragmented knowledge hinder the effective utilization of this information. This study proposes a novel framework for intelligent Question-and-Answer (Q&A) systems based on Retrieval-Augmented Generation (RAG) to address these issues. The system efficiently acquires domain-specific knowledge by leveraging external databases, including Relational Databases (RDBs) and graph databases, without additional fine-tuning for Large Language Models (LLMs). Crucially, the framework integrates a Dynamic Knowledge Base Updating Mechanism (DKBUM) and a Weighted Context-Aware Similarity (WCAS) method to enhance retrieval accuracy and mitigate inherent limitations of LLMs, such as hallucinations and lack of specialization. Additionally, the proposed DKBUM dynamically adjusts knowledge weights within the database, ensuring that the most recent and relevant information is utilized, while WCAS refines the alignment between queries and knowledge items by enhanced context understanding. Experimental validation demonstrates that the system can generate timely, accurate, and context-sensitive responses, making it a robust solution for managing complex business logic in specialized industries.
基金Supported by Major National Basic Research Program of China(973Program,Grant No.2011CB013400-05)PhD Programs Foundation of Ministry of Education of China(Grant No.20110191110005)
文摘The current researches on the tooth surface mathematical equations and the theory of gearing mainly pay attention to the ordinary type worm gear set（e.g., ZN, ZA, or ZK）. The research of forming mechanism and three-dimensional modeling method for the double pitch worm gear set is not enough. So there are some difficulties in mathematical model deducing and geometry modeling of double pitch ZN-type worm gear set based on generation mechanism. In order to establish the mathematical model and the precise geometric model of double pitch ZN-type worm gear set, the structural characteristics and generation mechanism of the double pitch ZN-type worm gear set are investigated. Mathematical model of the ZN-type worm gear set is derived based on its generation mechanism and the theory of gearing. According to the mathematical model of the worm gear set which has been developed, a geometry modeling method of the double pitch ZN-type worm and worm gear is presented. Furthermore, a geometrical precision calculate method is proposed to evaluate the geometrical quality of the double pitch worm gear set. As a result, the maximum error is less than 6′10–4 mm in magnitude, thus the model of the double pitch ZN-type worm gear set is available to meet the requirements of finite element analysis and engineering application. The derived mathematical model and the proposed geometrical modeling method are helpful to guiding the design, manufacture and contact analysis of the worm gear set.
基金National Key R&D Program of China"Study on impact assessment of ecological climate and environment on the wind fann and photovoltaic plants"(2018YFB1502800)Science and Technology Project of State Grid Hebei Electric Power Company"Research and application of medium and long-term forecasting technology for regional wind and photovoltaic resources and generation capacity",(5204BB170007)Special Fund Project of Hebei Provincial Government(19214310D).
文摘Predicting wind power gen eration over the medium and long term is helpful for dispatchi ng departme nts,as it aids in constructing generation plans and electricity market transactions.This study presents a monthly wind power gen eration forecast!ng method based on a climate model and long short-term memory(LSTM)n eural n etwork.A non linear mappi ng model is established between the meteorological elements and wind power monthly utilization hours.After considering the meteorological data(as predicted for the future)and new installed capacity planning,the monthly wind power gen eration forecast results are output.A case study shows the effectiveness of the prediction method.
基金the National Natural Science Foundation of China(Grant No.51709041).
文摘The innovative Next Generation Subsea Production System(NextGen SPS)concept is a newly proposed petroleum development solution in ultra-deep water areas.The definition of NextGen SPS involves several disciplines,which makes the design process difficult.In this paper,the definition of NextGen SPS is modeled as an uncertain multidisciplinary design optimization(MDO)problem.The deterministic optimization model is formulated,and three concerning disciplines—cost calculation,hydrodynamic analysis and global performance analysis are presented.Surrogate model technique is applied in the latter two disciplines.Collaborative optimization(CO)architecture is utilized to organize the concerning disciplines.A deterministic CO framework with two disciplinelevel optimizations is proposed firstly.Then the uncertainties of design parameters and surrogate models are incorporated by using interval method,and uncertain CO frameworks with triple loop and double loop optimization structure are established respectively.The optimization results illustrate that,although the deterministic MDO result achieves higher reduction in objective function than the uncertain MDO result,the latter is more reliable than the former.
基金the financial support provided by the Major Science and Technology Projects of Inner Mongolia Autonomous Region under Grant No.RZ190001148Fund of Education Department of Inner Mongolia Autonomous Region under Grant No.NJZY21480.
文摘A simplified model for SO_(2) generation during spontaneous combustion of coal gangue was put forward and validated using the measured data.Using the proposed model,the effects of initial temperature inside the gangue and fresh air supply on SO_(2) generation were discussed.The results showed that,higher initial temperature inside the gangue could accelerate the oxidation rate of FeS_(2) and increase the maximum concentration of SO_(2).If initial temperature inside the gangue increased by about 37%,the total SO_(2) generation increased by 166%.Fresh air supply had less significant effect on the oxidation rate of FeS_(2).However,the higher the fresh air supply was,the more FeS_(2) could be oxidized,which ultimately produced more SO_(2).Although the computed results and the measured data concerning the inner locations inside the gangue had a certain degree of error,the proposed model can provide a relatively precise total release of SO_(2) within acceptable accuracy.Besides,this method provides a useful prototype to predict the generation of hazardous materials,such as CO,NO_(x),and chlorine during the spontaneous combustion of coal gangue.
