Let G be a group.The family of all sets which are closed in every Hausdorf group topology of G form the family of closed sets of a T_(1) topology M_(G) on G called the Markov topology.Similarly,the family of all algeb...Let G be a group.The family of all sets which are closed in every Hausdorf group topology of G form the family of closed sets of a T_(1) topology M_(G) on G called the Markov topology.Similarly,the family of all algebraic subsets of G forms a family of closed sets for another T_(1)topology Z_(G) on G called the Zarski topology.A subgroup H of G is said to be Markov(resp.Zarski)embedded if the equality M_(G|H)=M_(H)(resp.Z_(G|H)=Z_(H))holds.I's proved that an abirary subgroup of a free group is both Zariski and Markov embedded in it.展开更多
Digital watermarking must balance imperceptibility,robustness,complexity,and security.To address the challenge of computational efficiency in trellis-based informed embedding,we propose a modified watermarking framewo...Digital watermarking must balance imperceptibility,robustness,complexity,and security.To address the challenge of computational efficiency in trellis-based informed embedding,we propose a modified watermarking framework that integrates fuzzy c-means(FCM)clustering into the generation off block codewords for labeling trellis arcs.The system incorporates a parallel trellis structure,controllable embedding parameters,and a novel informed embedding algorithm with reduced complexity.Two types of embedding schemes—memoryless and memory-based—are designed to flexibly trade-off between imperceptibility and robustness.Experimental results demonstrate that the proposed method outperforms existing approaches in bit error rate(BER)and computational complexity under various attacks,including additive noise,filtering,JPEG compression,cropping,and rotation.The integration of FCM enhances robustness by increasing the codeword distance,while preserving perceptual quality.Overall,the proposed framework is suitable for real-time and secure watermarking applications.展开更多
In shale gas reservoir stimulation,proppants are essential for sustaining fracture conductivity.However,increasing closing stress causes proppants to embed into the rock matrix,leading to a progressive decline in frac...In shale gas reservoir stimulation,proppants are essential for sustaining fracture conductivity.However,increasing closing stress causes proppants to embed into the rock matrix,leading to a progressive decline in fracture permeability and conductivity.Furthermore,rock creep contributes to long-term reductions in fracture performance.To elucidate the combined effects of proppant embedding and rock creep on sustained conductivity,this study conducted controlled experiments examining conductivity decay in propped fractures under varying closing stresses,explicitly accounting for both mechanisms.An embedded discrete fracture model was developed to simulate reservoir production under different conductivity decay scenarios,while evaluating the influence of proppant parameters on fracture performance.The results demonstrate that fracture conductivity diminishes rapidly with increasing stress,yet at 50 MPa,the decline becomes less pronounced.Simulated production profiles show strong agreement with actual gas well data,confirming the model’s accuracy and predictive capability.These findings suggest that employing a high proppant concentration with smaller particle size(5 kg/m^(2),70/140 mesh)is effective for maintaining long-term fracture conductivity and enhancing shale gas recovery.This study provides a rigorous framework for optimizing proppant selection and designing stimulation strategies that maximize reservoir performance over time.展开更多
Tibetan medical named entity recognition(Tibetan MNER)involves extracting specific types of medical entities from unstructured Tibetan medical texts.Tibetan MNER provide important data support for the work related to ...Tibetan medical named entity recognition(Tibetan MNER)involves extracting specific types of medical entities from unstructured Tibetan medical texts.Tibetan MNER provide important data support for the work related to Tibetan medicine.However,existing Tibetan MNER methods often struggle to comprehensively capture multi-level semantic information,failing to sufficiently extract multi-granularity features and effectively filter out irrelevant information,which ultimately impacts the accuracy of entity recognition.This paper proposes an improved embedding representation method called syllable-word-sentence embedding.By leveraging features at different granularities and using un-scaled dot-product attention to focus on key features for feature fusion,the syllable-word-sentence embedding is integrated into the transformer,enhancing the specificity and diversity of feature representations.The model leverages multi-level and multi-granularity semantic information,thereby improving the performance of Tibetan MNER.We evaluate our proposed model on datasets from various domains.The results indicate that the model effectively identified three types of entities in the Tibetan news dataset we constructed,achieving an F1 score of 93.59%,which represents an improvement of 1.24%compared to the vanilla FLAT.Additionally,results from the Tibetan medical dataset we developed show that it is effective in identifying five kinds of medical entities,with an F1 score of 71.39%,which is a 1.34%improvement over the vanilla FLAT.展开更多
A complete examination of Large Language Models’strengths,problems,and applications is needed due to their rising use across disciplines.Current studies frequently focus on single-use situations and lack a comprehens...A complete examination of Large Language Models’strengths,problems,and applications is needed due to their rising use across disciplines.Current studies frequently focus on single-use situations and lack a comprehensive understanding of LLM architectural performance,strengths,and weaknesses.This gap precludes finding the appropriate models for task-specific applications and limits awareness of emerging LLM optimization and deployment strategies.In this research,50 studies on 25+LLMs,including GPT-3,GPT-4,Claude 3.5,DeepKet,and hybrid multimodal frameworks like ContextDET and GeoRSCLIP,are thoroughly reviewed.We propose LLM application taxonomy by grouping techniques by task focus—healthcare,chemistry,sentiment analysis,agent-based simulations,and multimodal integration.Advanced methods like parameter-efficient tuning(LoRA),quantumenhanced embeddings(DeepKet),retrieval-augmented generation(RAG),and safety-focused models(GalaxyGPT)are evaluated for dataset requirements,computational efficiency,and performance measures.Frameworks for ethical issues,data limited hallucinations,and KDGI-enhanced fine-tuning like Woodpecker’s post-remedy corrections are highlighted.The investigation’s scope,mad,and methods are described,but the primary results are not.The work reveals that domain-specialized fine-tuned LLMs employing RAG and quantum-enhanced embeddings performbetter for context-heavy applications.In medical text normalization,ChatGPT-4 outperforms previous models,while two multimodal frameworks,GeoRSCLIP,increase remote sensing.Parameter-efficient tuning technologies like LoRA have minimal computing cost and similar performance,demonstrating the necessity for adaptive models in multiple domains.To discover the optimum domain-specific models,explain domain-specific fine-tuning,and present quantum andmultimodal LLMs to address scalability and cross-domain issues.The framework helps academics and practitioners identify,adapt,and innovate LLMs for different purposes.This work advances the field of efficient,interpretable,and ethical LLM application research.展开更多
Multimodal sentiment analysis aims to understand emotions from text,speech,and video data.However,current methods often overlook the dominant role of text and suffer from feature loss during integration.Given the vary...Multimodal sentiment analysis aims to understand emotions from text,speech,and video data.However,current methods often overlook the dominant role of text and suffer from feature loss during integration.Given the varying importance of each modality across different contexts,a central and pressing challenge in multimodal sentiment analysis lies in maximizing the use of rich intra-modal features while minimizing information loss during the fusion process.In response to these critical limitations,we propose a novel framework that integrates spatial position encoding and fusion embedding modules to address these issues.In our model,text is treated as the core modality,while speech and video features are selectively incorporated through a unique position-aware fusion process.The spatial position encoding strategy preserves the internal structural information of speech and visual modalities,enabling the model to capture localized intra-modal dependencies that are often overlooked.This design enhances the richness and discriminative power of the fused representation,enabling more accurate and context-aware sentiment prediction.Finally,we conduct comprehensive evaluations on two widely recognized standard datasets in the field—CMU-MOSI and CMU-MOSEI to validate the performance of the proposed model.The experimental results demonstrate that our model exhibits good performance and effectiveness for sentiment analysis tasks.展开更多
Named Entity Recognition(NER)is vital in natural language processing for the analysis of news texts,as it accurately identifies entities such as locations,persons,and organizations,which is crucial for applications li...Named Entity Recognition(NER)is vital in natural language processing for the analysis of news texts,as it accurately identifies entities such as locations,persons,and organizations,which is crucial for applications like news summarization and event tracking.However,NER in the news domain faces challenges due to insufficient annotated data,complex entity structures,and strong context dependencies.To address these issues,we propose a new Chinesenamed entity recognition method that integrates transfer learning with word embeddings.Our approach leverages the ERNIE pre-trained model for transfer learning and obtaining general language representations and incorporates the Soft-lexicon word embedding technique to handle varied entity structures.This dual-strategy enhances the model’s understanding of context and boosts its ability to process complex texts.Experimental results show that our method achieves an F1 score of 94.72% on a news dataset,surpassing baseline methods by 3%–4%,thereby confirming its effectiveness for Chinese-named entity recognition in the news domain.展开更多
Network virtualization is the development trend and inevitable requirement of hybrid wireless sensor networks(HWSNs).Low mapping efficiency and service interruption caused by mobility seriously affect the reliability ...Network virtualization is the development trend and inevitable requirement of hybrid wireless sensor networks(HWSNs).Low mapping efficiency and service interruption caused by mobility seriously affect the reliability of sensing tasks and ultimately affect the long-term revenue of the infrastructure providers.In response to these problems,this paper proposes an efficient virtual network embedding algorithm with a reliable service guarantee.Based on the topological attributes of nodes,a method for evaluating the physical network resource importance degree is proposed,and the nodes with rich resources are selected to improve embedding efficiency.Then,a method for evaluating the physical network reliability degree is proposed to predict the probability of mobile sensors providing uninterrupted services.The simulation results show that the proposed algorithm improves the acceptance rate of virtual sensor networks(VSN)embedding requests and the long-term revenue of the infrastructure providers.展开更多
Constructing an in vitro vascularized liver tissue model that closely simulates the human liver is crucial for promoting cell proliferation,mimicking physiological heterogeneous structures,and recreating the cellular ...Constructing an in vitro vascularized liver tissue model that closely simulates the human liver is crucial for promoting cell proliferation,mimicking physiological heterogeneous structures,and recreating the cellular microenvironment.However,the layer-by-layer printing method is significantly constrained by the rheological properties of the bioink,making it challenging to form complex three-dimensional vascular structures in low-viscosity soft materials.To overcome this limitation,we developed a cross-linkable biphasic embedding medium by mixing low-viscosity biomaterials with gelatin microgel.This medium possesses yield stress and self-healing properties,facilitating efficient and continuous three-dimensional shaping of sacrificial ink within it.By adjusting the printing speed,we controlled the filament diameter,achieving a range from 250μm to 1000μm,and ensuring precise control over ink deposition locations and filament shapes.Using the in situ endothelialization method,we constructed complex vascular structures and ensured close adhesion between hepatocytes and endothelial cells.In vitro experiments demonstrated that the vascularized liver tissue model exhibited enhanced protein synthesis and metabolic function compared to mixed liver tissue.We also investigated the impact of varying vascular densities on liver tissue function.Transcriptome sequencing revealed that liver tissues with higher vascular density exhibited upregulated gene expression in metabolic and angiogenesis-related pathways.In summary,this method is adaptable to various materials,allowing the rheological properties of the supporting bath and the tissue's porosity to be modified using microgels,thus enabling precise regulation of the liver tissue microenvironment.Additionally,it facilitates the rapid construction of three-dimensional vascular structures within liver tissue.The resulting vascularized liver tissue model exhibits enhanced biological functionality,opening new opportunities for biomedical applications.展开更多
In the domain of knowledge graph embedding,conventional approaches typically transform entities and relations into continuous vector spaces.However,parameter efficiency becomes increasingly crucial when dealing with l...In the domain of knowledge graph embedding,conventional approaches typically transform entities and relations into continuous vector spaces.However,parameter efficiency becomes increasingly crucial when dealing with large-scale knowledge graphs that contain vast numbers of entities and relations.In particular,resource-intensive embeddings often lead to increased computational costs,and may limit scalability and adaptability in practical environ-ments,such as in low-resource settings or real-world applications.This paper explores an approach to knowledge graph representation learning that leverages small,reserved entities and relation sets for parameter-efficient embedding.We introduce a hierarchical attention network designed to refine and maximize the representational quality of embeddings by selectively focusing on these reserved sets,thereby reducing model complexity.Empirical assessments validate that our model achieves high performance on the benchmark dataset with fewer parameters and smaller embedding dimensions.The ablation studies further highlight the impact and contribution of each component in the proposed hierarchical attention structure.展开更多
The increasing fluency of advanced language models,such as GPT-3.5,GPT-4,and the recently introduced DeepSeek,challenges the ability to distinguish between human-authored and AI-generated academic writing.This situati...The increasing fluency of advanced language models,such as GPT-3.5,GPT-4,and the recently introduced DeepSeek,challenges the ability to distinguish between human-authored and AI-generated academic writing.This situation is raising significant concerns regarding the integrity and authenticity of academic work.In light of the above,the current research evaluates the effectiveness of Bidirectional Long Short-TermMemory(BiLSTM)networks enhanced with pre-trained GloVe(Global Vectors for Word Representation)embeddings to detect AIgenerated scientific Abstracts drawn from the AI-GA(Artificial Intelligence Generated Abstracts)dataset.Two core BiLSTM variants were assessed:a single-layer approach and a dual-layer design,each tested under static or adaptive embeddings.The single-layer model achieved nearly 97%accuracy with trainable GloVe,occasionally surpassing the deeper model.Despite these gains,neither configuration fully matched the 98.7%benchmark set by an earlier LSTMWord2Vec pipeline.Some runs were over-fitted when embeddings were fine-tuned,whereas static embeddings offered a slightly lower yet stable accuracy of around 96%.This lingering gap reinforces a key ethical and procedural concern:relying solely on automated tools,such as Turnitin’s AI-detection features,to penalize individuals’risks and unjust outcomes.Misclassifications,whether legitimate work is misread as AI-generated or engineered text,evade detection,demonstrating that these classifiers should not stand as the sole arbiters of authenticity.Amore comprehensive approach is warranted,one which weaves model outputs into a systematic process supported by expert judgment and institutional guidelines designed to protect originality.展开更多
Objective:To explore the effects of acupoint catgut embedding combined with auricular point pressing with beans on symptom management self-efficacy and quality of life in patients with nonalcoholic steatohepatitis(NAS...Objective:To explore the effects of acupoint catgut embedding combined with auricular point pressing with beans on symptom management self-efficacy and quality of life in patients with nonalcoholic steatohepatitis(NASH)of liver depression and spleen deficiency type.Methods:Sixty patients with NASH of liver depression and spleen deficiency type admitted to our hospital from January 2021 to December 2023 were selected and divided into an acupoint catgut embedding group(n=30)and a combined group(n=30)using the envelope lottery method.The acupoint catgut embedding group received acupoint catgut embedding intervention,while the combined group received auricular point pressing with beans on the basis of the acupoint catgut embedding group.The two groups were compared in terms of TCM syndrome scores,symptom management self-efficacy[Chronic Disease Self-Efficacy Scale(CDSES)],and quality of life[Chronic Liver Disease Questionnaire(CLDQ)].Results:After intervention,the combined group had lower TCM syndrome scores for both primary and secondary symptoms compared to the acupoint catgut embedding group(P<0.05).The combined group also had higher scores in all dimensions and total score of the CDSES compared to the acupoint catgut embedding group(P<0.05).Similarly,the combined group had higher scores in all dimensions and total score of the CLDQ compared to the acupoint catgut embedding group(P<0.05).Conclusion:Acupoint catgut embedding combined with auricular point pressing with beans can effectively improve TCM symptoms,enhance symptom management self-efficacy,and improve quality of life in patients with NASH of liver depression and spleen deficiency type.展开更多
Objective To investigate clinical therapeutic effects of acupuncture combined with ear point embedding for treating sleep disorder in depression. Methods Seventy-six patients suffering from sleep disorder in depressio...Objective To investigate clinical therapeutic effects of acupuncture combined with ear point embedding for treating sleep disorder in depression. Methods Seventy-six patients suffering from sleep disorder in depression were randomly divided into an observation group （39 cases） and a control group （37 cases）. In observation group, acupuncture at Baihui （百会 GV 20）, Shangxing （上星 GV 23）, Shenting （神庭 GV 24）, Fengchi （风池 GB 20） and Shenmen （神门 HT 7）, etc., which was combined with ear point embedding, and in control group, Fluoxetine Hydrochloride Dispersible tablet （Prozac） was orally given. Four weeks of treatments were given as a therapeutic course in both groups. Comparison of changes in scores of HAMD and PSQI was made between the two groups. Results After treatment, HAMD scores were obviously lowered in both groups （both P〈0.01）, and the reduction in HAMD score was more obvious in observation group than that in control group （P〈0.05）. After treatment, the reduction in PSQI score was more obvious in observation group （P〈0.01）, it was more significantly decreased than that in control group. Conclusion Acupuncture combined with ear point embedding provids remarkable therapeutic effects in treating sleep disorder in depression, which is superior to those provided by Fluoxetine Hydrochloride Dispersible Fablet orally given.展开更多
Objective To systematically evaluate the effect of acupoint catgut embedding therapy （ACET） for simple obesity. Methods Computer retrieval was used for randomized controlled trials on the treatment effect of simple ...Objective To systematically evaluate the effect of acupoint catgut embedding therapy （ACET） for simple obesity. Methods Computer retrieval was used for randomized controlled trials on the treatment effect of simple obesity with ACET in databases of the China National Knowledge Infrastructure （CNKI）, Wanfang Data system, and the China Biology Medicine disc （CBMDisc）. The Jadad Quality Scale was used in the evaluation of included studies. The outcome indicators were analyzed with the Review Manage 5.1 software. Results A total of 16 randomized controlled trials were included finally. The meta-analysis result showed that compared with the control group, there was statistically significance on the total efficiency of using ACET for simple obesity [OR=2.51, 95% confidence interval （1.74, 3.63）, Z=4.91, P〈0.000 01]. The analysis on the literature quality showed that there was only 2 article marked as 3 points. The other 25 articles marked ≤2 points. The quality of published articles was generally low. There were publication biases and the blinding method was seldom used, the losses of follow-up / drop oup / withdraw were reported with less. There were 27 acupoints used in the treatment, which mainly included Tianshu （天枢ST 25）, Zhongwan （中脘 CV 12）, Fenglong （丰隆 ST 40）, Shufen （水分 CV 9）, Qihai （气海 CV 6）, Sanyinjiao （三阴交 SP 6）, Zusnli （足三里 ST 36）, Ashi point, Daheng （大横 SP 25）. The five kinds of catgut embedding needle were injection needles ＋ acupuncture needle, specialized catgut embedding needle, spinal needle, triangular needle, and skin suture needle. Conclusion There is definite efficiency of using ACET in the treatment of simple obesity. However, the clinical efficiency still lacks of sufficient evidences. Therefore, further clinical research should be conducted in the providing of reliable evidences in the clinical decision-making in the future.展开更多
How to extract optimal composite attributes from a variety of conventional seismic attributes to detect reservoir features is a reservoir predication key,which is usually solved by reducing dimensionality.Principle co...How to extract optimal composite attributes from a variety of conventional seismic attributes to detect reservoir features is a reservoir predication key,which is usually solved by reducing dimensionality.Principle component analysis（PCA） is the most widely-used linear dimensionality reduction method at present.However,the relationships between seismic attributes and reservoir features are non-linear,so seismic attribute dimensionality reduction based on linear transforms can＇t solve non-linear problems well,reducing reservoir prediction precision.As a new non-linear learning method,manifold learning supplies a new method for seismic attribute analysis.It can discover the intrinsic features and rules hidden in the data by computing low-dimensional,neighborhood-preserving embeddings of high-dimensional inputs.In this paper,we try to extract seismic attributes using locally linear embedding（LLE）,realizing inter-horizon attributes dimensionality reduction of 3D seismic data first and discuss the optimization of its key parameters.Combining model analysis and case studies,we compare the dimensionality reduction and clustering effects of LLE and PCA,both of which indicate that LLE can retain the intrinsic structure of the inputs.The composite attributes and clustering results based on LLE better characterize the distribution of sedimentary facies,reservoir,and even reservoir fluids.展开更多
Objective To observe the clinical effect of acupoint catgut embedding combined with abdominal acupuncture for treatment of abdominal obesity under the theoretical guidance of regulating the three energizer.Methods One...Objective To observe the clinical effect of acupoint catgut embedding combined with abdominal acupuncture for treatment of abdominal obesity under the theoretical guidance of regulating the three energizer.Methods One hundred and twenty patients were randomly divided into acupoint catgut embedding group（group A）,abdominal acupuncture group（group B）,acupoint catgut embedding combined with abdominal acupuncture group（group C） and blank control group（group D） based on blocked random method,with 30 cases each;the first three groups based on the principle of regulating three energizer were respectively given acupoint catgut embedding at Zhongwan（中脘 CV12）,Tianshu（天枢 ST 25）,Yinjiao（阴交 CV 7）,Zhigou（支沟 TE 6）,Guanyuan（关元 CV 4）,Zusanli（足三里 ST 36） with catgut embedding once every 7 days,three times for each course of treatment,abdominal acupuncture once every3 days,seven times for each course of treatment.Results The therapeutic effect of each group after treatment was respectively 46.7%（14/30）,43.3%（13/30）,76.7%（23/30） and 26.7%（8/30）,with statistically significant difference.For comparison of waist circumference difference,there was no statistical significance between acupoint catgut embedding combined with abdominal acupuncture and acupoint catgut embedding group（P=0.54）,and for comparison of weight difference,there was statistical significance in terms of therapeutic effect between acupoint catgut embedding combined with abdominal acupuncture and A,B,D groups（4.49±0.59 vs 2.64±0.53,2.87±0.44,1.45±0.26,all P〈0.05）.Conclusion Acupoint catgut embedding combined with abdominal acupuncture had the best effect in terms of total effective rate for treatment of abdominal obesity,equivalent therapeutic effect to simple acupoint catgut embedding in terms of waist circumference reduction,and the best therapeutic effect in terms of weight reduction.展开更多
Objective To compare the therapeutic effect of catgut embedding combined with diet plus exercise with that of diet plus exercise in treating simple obesity complicated with abnormal blood fat. Methods A prescription o...Objective To compare the therapeutic effect of catgut embedding combined with diet plus exercise with that of diet plus exercise in treating simple obesity complicated with abnormal blood fat. Methods A prescription of diet plus exercise was given to the patients in a control group, and catgut embedding was added to the prescription in a treatment group. It was given once every 15 days for the 1st three treatments, and once every month for the 2^nd three treatments. Six treatments constituted a therapeutic course. Body weight and blood fat were measured before and half a year since treatment. Results After treatment, BMI, TO, TG and LDL were very significantly improved in the treatment group （P〈0.01）, and BMI and TG were very significantly （P〈0.01） and TO and LDL were significantly （P〈0.05） improved in the control group. HDL was not obviously improved in both groups （P〉0.05）. The improvement of TO, TG and LDL in the treatment group was significantly more than that in the control group （P〈0.01）, but no difference in improvement of BMI and HDL was found between the two groups （P〉0.05）. Conclusion The therapy of diet plus exercise was effective in body weight reduction and lowering blood fat in patients with simple obesity complicated with abnormal blood fat, and catgut embedding used in combination was even better in lowering blood fat.展开更多
基金Supported by the Grant-in-Aid for Scientific Research(C)by the Japan Society for the Promotion of Science(20K03615)。
文摘Let G be a group.The family of all sets which are closed in every Hausdorf group topology of G form the family of closed sets of a T_(1) topology M_(G) on G called the Markov topology.Similarly,the family of all algebraic subsets of G forms a family of closed sets for another T_(1)topology Z_(G) on G called the Zarski topology.A subgroup H of G is said to be Markov(resp.Zarski)embedded if the equality M_(G|H)=M_(H)(resp.Z_(G|H)=Z_(H))holds.I's proved that an abirary subgroup of a free group is both Zariski and Markov embedded in it.
基金funded by the National Science and Technology Council,Taiwan,under grant number NSTC 114-2221-E-167-005-MY3,and NSTC 113-2221-E-167-006-.
文摘Digital watermarking must balance imperceptibility,robustness,complexity,and security.To address the challenge of computational efficiency in trellis-based informed embedding,we propose a modified watermarking framework that integrates fuzzy c-means(FCM)clustering into the generation off block codewords for labeling trellis arcs.The system incorporates a parallel trellis structure,controllable embedding parameters,and a novel informed embedding algorithm with reduced complexity.Two types of embedding schemes—memoryless and memory-based—are designed to flexibly trade-off between imperceptibility and robustness.Experimental results demonstrate that the proposed method outperforms existing approaches in bit error rate(BER)and computational complexity under various attacks,including additive noise,filtering,JPEG compression,cropping,and rotation.The integration of FCM enhances robustness by increasing the codeword distance,while preserving perceptual quality.Overall,the proposed framework is suitable for real-time and secure watermarking applications.
基金supported by the National Natural Science Foundation of China(Nos.52204051,52304046).
文摘In shale gas reservoir stimulation,proppants are essential for sustaining fracture conductivity.However,increasing closing stress causes proppants to embed into the rock matrix,leading to a progressive decline in fracture permeability and conductivity.Furthermore,rock creep contributes to long-term reductions in fracture performance.To elucidate the combined effects of proppant embedding and rock creep on sustained conductivity,this study conducted controlled experiments examining conductivity decay in propped fractures under varying closing stresses,explicitly accounting for both mechanisms.An embedded discrete fracture model was developed to simulate reservoir production under different conductivity decay scenarios,while evaluating the influence of proppant parameters on fracture performance.The results demonstrate that fracture conductivity diminishes rapidly with increasing stress,yet at 50 MPa,the decline becomes less pronounced.Simulated production profiles show strong agreement with actual gas well data,confirming the model’s accuracy and predictive capability.These findings suggest that employing a high proppant concentration with smaller particle size(5 kg/m^(2),70/140 mesh)is effective for maintaining long-term fracture conductivity and enhancing shale gas recovery.This study provides a rigorous framework for optimizing proppant selection and designing stimulation strategies that maximize reservoir performance over time.
基金supported in part by the National Science and Technology Major Project under(Grant 2022ZD0116100)in part by the National Natural Science Foundation Key Project under(Grant 62436006)+4 种基金in part by the National Natural Science Foundation Youth Fund under(Grant 62406257)in part by the Xizang Autonomous Region Natural Science Foundation General Project under(Grant XZ202401ZR0031)in part by the National Natural Science Foundation of China under(Grant 62276055)in part by the Sichuan Science and Technology Program under(Grant 23ZDYF0755)in part by the Xizang University‘High-Level Talent Training Program’Project under(Grant 2022-GSP-S098).
文摘Tibetan medical named entity recognition(Tibetan MNER)involves extracting specific types of medical entities from unstructured Tibetan medical texts.Tibetan MNER provide important data support for the work related to Tibetan medicine.However,existing Tibetan MNER methods often struggle to comprehensively capture multi-level semantic information,failing to sufficiently extract multi-granularity features and effectively filter out irrelevant information,which ultimately impacts the accuracy of entity recognition.This paper proposes an improved embedding representation method called syllable-word-sentence embedding.By leveraging features at different granularities and using un-scaled dot-product attention to focus on key features for feature fusion,the syllable-word-sentence embedding is integrated into the transformer,enhancing the specificity and diversity of feature representations.The model leverages multi-level and multi-granularity semantic information,thereby improving the performance of Tibetan MNER.We evaluate our proposed model on datasets from various domains.The results indicate that the model effectively identified three types of entities in the Tibetan news dataset we constructed,achieving an F1 score of 93.59%,which represents an improvement of 1.24%compared to the vanilla FLAT.Additionally,results from the Tibetan medical dataset we developed show that it is effective in identifying five kinds of medical entities,with an F1 score of 71.39%,which is a 1.34%improvement over the vanilla FLAT.
文摘A complete examination of Large Language Models’strengths,problems,and applications is needed due to their rising use across disciplines.Current studies frequently focus on single-use situations and lack a comprehensive understanding of LLM architectural performance,strengths,and weaknesses.This gap precludes finding the appropriate models for task-specific applications and limits awareness of emerging LLM optimization and deployment strategies.In this research,50 studies on 25+LLMs,including GPT-3,GPT-4,Claude 3.5,DeepKet,and hybrid multimodal frameworks like ContextDET and GeoRSCLIP,are thoroughly reviewed.We propose LLM application taxonomy by grouping techniques by task focus—healthcare,chemistry,sentiment analysis,agent-based simulations,and multimodal integration.Advanced methods like parameter-efficient tuning(LoRA),quantumenhanced embeddings(DeepKet),retrieval-augmented generation(RAG),and safety-focused models(GalaxyGPT)are evaluated for dataset requirements,computational efficiency,and performance measures.Frameworks for ethical issues,data limited hallucinations,and KDGI-enhanced fine-tuning like Woodpecker’s post-remedy corrections are highlighted.The investigation’s scope,mad,and methods are described,but the primary results are not.The work reveals that domain-specialized fine-tuned LLMs employing RAG and quantum-enhanced embeddings performbetter for context-heavy applications.In medical text normalization,ChatGPT-4 outperforms previous models,while two multimodal frameworks,GeoRSCLIP,increase remote sensing.Parameter-efficient tuning technologies like LoRA have minimal computing cost and similar performance,demonstrating the necessity for adaptive models in multiple domains.To discover the optimum domain-specific models,explain domain-specific fine-tuning,and present quantum andmultimodal LLMs to address scalability and cross-domain issues.The framework helps academics and practitioners identify,adapt,and innovate LLMs for different purposes.This work advances the field of efficient,interpretable,and ethical LLM application research.
基金supported by the Collaborative Tackling Project of the Yangtze River Delta SciTech Innovation Community(Nos.2024CSJGG01503,2024CSJGG01500)Guangxi Key Research and Development Program(No.AB24010317)Jiangxi Provincial Key Laboratory of Electronic Data Control and Forensics(Jiangxi Police College)(No.2025JXJYKFJJ002).
文摘Multimodal sentiment analysis aims to understand emotions from text,speech,and video data.However,current methods often overlook the dominant role of text and suffer from feature loss during integration.Given the varying importance of each modality across different contexts,a central and pressing challenge in multimodal sentiment analysis lies in maximizing the use of rich intra-modal features while minimizing information loss during the fusion process.In response to these critical limitations,we propose a novel framework that integrates spatial position encoding and fusion embedding modules to address these issues.In our model,text is treated as the core modality,while speech and video features are selectively incorporated through a unique position-aware fusion process.The spatial position encoding strategy preserves the internal structural information of speech and visual modalities,enabling the model to capture localized intra-modal dependencies that are often overlooked.This design enhances the richness and discriminative power of the fused representation,enabling more accurate and context-aware sentiment prediction.Finally,we conduct comprehensive evaluations on two widely recognized standard datasets in the field—CMU-MOSI and CMU-MOSEI to validate the performance of the proposed model.The experimental results demonstrate that our model exhibits good performance and effectiveness for sentiment analysis tasks.
基金funded by Advanced Research Project(30209040702).
文摘Named Entity Recognition(NER)is vital in natural language processing for the analysis of news texts,as it accurately identifies entities such as locations,persons,and organizations,which is crucial for applications like news summarization and event tracking.However,NER in the news domain faces challenges due to insufficient annotated data,complex entity structures,and strong context dependencies.To address these issues,we propose a new Chinesenamed entity recognition method that integrates transfer learning with word embeddings.Our approach leverages the ERNIE pre-trained model for transfer learning and obtaining general language representations and incorporates the Soft-lexicon word embedding technique to handle varied entity structures.This dual-strategy enhances the model’s understanding of context and boosts its ability to process complex texts.Experimental results show that our method achieves an F1 score of 94.72% on a news dataset,surpassing baseline methods by 3%–4%,thereby confirming its effectiveness for Chinese-named entity recognition in the news domain.
基金supported by National Natural Science Foundation of China(61901071,61871062,61771082,U20A20157)Science and Natural Science Foundation of Chongqing,China(cstc2020jcyjzdxmX0024)+1 种基金University Innovation Research Group of Chongqing(CXQT20017)Scientific and Technological Research Program of Chongqing Municipal Education Commission(No.KJZD-K201901301).
文摘Network virtualization is the development trend and inevitable requirement of hybrid wireless sensor networks(HWSNs).Low mapping efficiency and service interruption caused by mobility seriously affect the reliability of sensing tasks and ultimately affect the long-term revenue of the infrastructure providers.In response to these problems,this paper proposes an efficient virtual network embedding algorithm with a reliable service guarantee.Based on the topological attributes of nodes,a method for evaluating the physical network resource importance degree is proposed,and the nodes with rich resources are selected to improve embedding efficiency.Then,a method for evaluating the physical network reliability degree is proposed to predict the probability of mobile sensors providing uninterrupted services.The simulation results show that the proposed algorithm improves the acceptance rate of virtual sensor networks(VSN)embedding requests and the long-term revenue of the infrastructure providers.
基金the funding from the National Natural Science Foundation of China No.52275294the National Key Research and Development Program of China(No.2018YFA0703000)。
文摘Constructing an in vitro vascularized liver tissue model that closely simulates the human liver is crucial for promoting cell proliferation,mimicking physiological heterogeneous structures,and recreating the cellular microenvironment.However,the layer-by-layer printing method is significantly constrained by the rheological properties of the bioink,making it challenging to form complex three-dimensional vascular structures in low-viscosity soft materials.To overcome this limitation,we developed a cross-linkable biphasic embedding medium by mixing low-viscosity biomaterials with gelatin microgel.This medium possesses yield stress and self-healing properties,facilitating efficient and continuous three-dimensional shaping of sacrificial ink within it.By adjusting the printing speed,we controlled the filament diameter,achieving a range from 250μm to 1000μm,and ensuring precise control over ink deposition locations and filament shapes.Using the in situ endothelialization method,we constructed complex vascular structures and ensured close adhesion between hepatocytes and endothelial cells.In vitro experiments demonstrated that the vascularized liver tissue model exhibited enhanced protein synthesis and metabolic function compared to mixed liver tissue.We also investigated the impact of varying vascular densities on liver tissue function.Transcriptome sequencing revealed that liver tissues with higher vascular density exhibited upregulated gene expression in metabolic and angiogenesis-related pathways.In summary,this method is adaptable to various materials,allowing the rheological properties of the supporting bath and the tissue's porosity to be modified using microgels,thus enabling precise regulation of the liver tissue microenvironment.Additionally,it facilitates the rapid construction of three-dimensional vascular structures within liver tissue.The resulting vascularized liver tissue model exhibits enhanced biological functionality,opening new opportunities for biomedical applications.
基金supported by the National Science and Technology Council(NSTC),Taiwan,under Grants Numbers 112-2622-E-029-009 and 112-2221-E-029-019.
文摘In the domain of knowledge graph embedding,conventional approaches typically transform entities and relations into continuous vector spaces.However,parameter efficiency becomes increasingly crucial when dealing with large-scale knowledge graphs that contain vast numbers of entities and relations.In particular,resource-intensive embeddings often lead to increased computational costs,and may limit scalability and adaptability in practical environ-ments,such as in low-resource settings or real-world applications.This paper explores an approach to knowledge graph representation learning that leverages small,reserved entities and relation sets for parameter-efficient embedding.We introduce a hierarchical attention network designed to refine and maximize the representational quality of embeddings by selectively focusing on these reserved sets,thereby reducing model complexity.Empirical assessments validate that our model achieves high performance on the benchmark dataset with fewer parameters and smaller embedding dimensions.The ablation studies further highlight the impact and contribution of each component in the proposed hierarchical attention structure.
文摘The increasing fluency of advanced language models,such as GPT-3.5,GPT-4,and the recently introduced DeepSeek,challenges the ability to distinguish between human-authored and AI-generated academic writing.This situation is raising significant concerns regarding the integrity and authenticity of academic work.In light of the above,the current research evaluates the effectiveness of Bidirectional Long Short-TermMemory(BiLSTM)networks enhanced with pre-trained GloVe(Global Vectors for Word Representation)embeddings to detect AIgenerated scientific Abstracts drawn from the AI-GA(Artificial Intelligence Generated Abstracts)dataset.Two core BiLSTM variants were assessed:a single-layer approach and a dual-layer design,each tested under static or adaptive embeddings.The single-layer model achieved nearly 97%accuracy with trainable GloVe,occasionally surpassing the deeper model.Despite these gains,neither configuration fully matched the 98.7%benchmark set by an earlier LSTMWord2Vec pipeline.Some runs were over-fitted when embeddings were fine-tuned,whereas static embeddings offered a slightly lower yet stable accuracy of around 96%.This lingering gap reinforces a key ethical and procedural concern:relying solely on automated tools,such as Turnitin’s AI-detection features,to penalize individuals’risks and unjust outcomes.Misclassifications,whether legitimate work is misread as AI-generated or engineered text,evade detection,demonstrating that these classifiers should not stand as the sole arbiters of authenticity.Amore comprehensive approach is warranted,one which weaves model outputs into a systematic process supported by expert judgment and institutional guidelines designed to protect originality.
文摘Objective:To explore the effects of acupoint catgut embedding combined with auricular point pressing with beans on symptom management self-efficacy and quality of life in patients with nonalcoholic steatohepatitis(NASH)of liver depression and spleen deficiency type.Methods:Sixty patients with NASH of liver depression and spleen deficiency type admitted to our hospital from January 2021 to December 2023 were selected and divided into an acupoint catgut embedding group(n=30)and a combined group(n=30)using the envelope lottery method.The acupoint catgut embedding group received acupoint catgut embedding intervention,while the combined group received auricular point pressing with beans on the basis of the acupoint catgut embedding group.The two groups were compared in terms of TCM syndrome scores,symptom management self-efficacy[Chronic Disease Self-Efficacy Scale(CDSES)],and quality of life[Chronic Liver Disease Questionnaire(CLDQ)].Results:After intervention,the combined group had lower TCM syndrome scores for both primary and secondary symptoms compared to the acupoint catgut embedding group(P<0.05).The combined group also had higher scores in all dimensions and total score of the CDSES compared to the acupoint catgut embedding group(P<0.05).Similarly,the combined group had higher scores in all dimensions and total score of the CLDQ compared to the acupoint catgut embedding group(P<0.05).Conclusion:Acupoint catgut embedding combined with auricular point pressing with beans can effectively improve TCM symptoms,enhance symptom management self-efficacy,and improve quality of life in patients with NASH of liver depression and spleen deficiency type.
文摘Objective To investigate clinical therapeutic effects of acupuncture combined with ear point embedding for treating sleep disorder in depression. Methods Seventy-six patients suffering from sleep disorder in depression were randomly divided into an observation group （39 cases） and a control group （37 cases）. In observation group, acupuncture at Baihui （百会 GV 20）, Shangxing （上星 GV 23）, Shenting （神庭 GV 24）, Fengchi （风池 GB 20） and Shenmen （神门 HT 7）, etc., which was combined with ear point embedding, and in control group, Fluoxetine Hydrochloride Dispersible tablet （Prozac） was orally given. Four weeks of treatments were given as a therapeutic course in both groups. Comparison of changes in scores of HAMD and PSQI was made between the two groups. Results After treatment, HAMD scores were obviously lowered in both groups （both P〈0.01）, and the reduction in HAMD score was more obvious in observation group than that in control group （P〈0.05）. After treatment, the reduction in PSQI score was more obvious in observation group （P〈0.01）, it was more significantly decreased than that in control group. Conclusion Acupuncture combined with ear point embedding provids remarkable therapeutic effects in treating sleep disorder in depression, which is superior to those provided by Fluoxetine Hydrochloride Dispersible Fablet orally given.
基金Supported by the Naional Natural Science Fund Project of China:81072883,81173342
文摘Objective To systematically evaluate the effect of acupoint catgut embedding therapy （ACET） for simple obesity. Methods Computer retrieval was used for randomized controlled trials on the treatment effect of simple obesity with ACET in databases of the China National Knowledge Infrastructure （CNKI）, Wanfang Data system, and the China Biology Medicine disc （CBMDisc）. The Jadad Quality Scale was used in the evaluation of included studies. The outcome indicators were analyzed with the Review Manage 5.1 software. Results A total of 16 randomized controlled trials were included finally. The meta-analysis result showed that compared with the control group, there was statistically significance on the total efficiency of using ACET for simple obesity [OR=2.51, 95% confidence interval （1.74, 3.63）, Z=4.91, P〈0.000 01]. The analysis on the literature quality showed that there was only 2 article marked as 3 points. The other 25 articles marked ≤2 points. The quality of published articles was generally low. There were publication biases and the blinding method was seldom used, the losses of follow-up / drop oup / withdraw were reported with less. There were 27 acupoints used in the treatment, which mainly included Tianshu （天枢ST 25）, Zhongwan （中脘 CV 12）, Fenglong （丰隆 ST 40）, Shufen （水分 CV 9）, Qihai （气海 CV 6）, Sanyinjiao （三阴交 SP 6）, Zusnli （足三里 ST 36）, Ashi point, Daheng （大横 SP 25）. The five kinds of catgut embedding needle were injection needles ＋ acupuncture needle, specialized catgut embedding needle, spinal needle, triangular needle, and skin suture needle. Conclusion There is definite efficiency of using ACET in the treatment of simple obesity. However, the clinical efficiency still lacks of sufficient evidences. Therefore, further clinical research should be conducted in the providing of reliable evidences in the clinical decision-making in the future.
基金National Key Science & Technology Special Projects(Grant No.2008ZX05000-004)CNPC Projects(Grant No.2008E-0610-10).
文摘How to extract optimal composite attributes from a variety of conventional seismic attributes to detect reservoir features is a reservoir predication key,which is usually solved by reducing dimensionality.Principle component analysis（PCA） is the most widely-used linear dimensionality reduction method at present.However,the relationships between seismic attributes and reservoir features are non-linear,so seismic attribute dimensionality reduction based on linear transforms can＇t solve non-linear problems well,reducing reservoir prediction precision.As a new non-linear learning method,manifold learning supplies a new method for seismic attribute analysis.It can discover the intrinsic features and rules hidden in the data by computing low-dimensional,neighborhood-preserving embeddings of high-dimensional inputs.In this paper,we try to extract seismic attributes using locally linear embedding（LLE）,realizing inter-horizon attributes dimensionality reduction of 3D seismic data first and discuss the optimization of its key parameters.Combining model analysis and case studies,we compare the dimensionality reduction and clustering effects of LLE and PCA,both of which indicate that LLE can retain the intrinsic structure of the inputs.The composite attributes and clustering results based on LLE better characterize the distribution of sedimentary facies,reservoir,and even reservoir fluids.
文摘Objective To observe the clinical effect of acupoint catgut embedding combined with abdominal acupuncture for treatment of abdominal obesity under the theoretical guidance of regulating the three energizer.Methods One hundred and twenty patients were randomly divided into acupoint catgut embedding group（group A）,abdominal acupuncture group（group B）,acupoint catgut embedding combined with abdominal acupuncture group（group C） and blank control group（group D） based on blocked random method,with 30 cases each;the first three groups based on the principle of regulating three energizer were respectively given acupoint catgut embedding at Zhongwan（中脘 CV12）,Tianshu（天枢 ST 25）,Yinjiao（阴交 CV 7）,Zhigou（支沟 TE 6）,Guanyuan（关元 CV 4）,Zusanli（足三里 ST 36） with catgut embedding once every 7 days,three times for each course of treatment,abdominal acupuncture once every3 days,seven times for each course of treatment.Results The therapeutic effect of each group after treatment was respectively 46.7%（14/30）,43.3%（13/30）,76.7%（23/30） and 26.7%（8/30）,with statistically significant difference.For comparison of waist circumference difference,there was no statistical significance between acupoint catgut embedding combined with abdominal acupuncture and acupoint catgut embedding group（P=0.54）,and for comparison of weight difference,there was statistical significance in terms of therapeutic effect between acupoint catgut embedding combined with abdominal acupuncture and A,B,D groups（4.49±0.59 vs 2.64±0.53,2.87±0.44,1.45±0.26,all P〈0.05）.Conclusion Acupoint catgut embedding combined with abdominal acupuncture had the best effect in terms of total effective rate for treatment of abdominal obesity,equivalent therapeutic effect to simple acupoint catgut embedding in terms of waist circumference reduction,and the best therapeutic effect in terms of weight reduction.
文摘Objective To compare the therapeutic effect of catgut embedding combined with diet plus exercise with that of diet plus exercise in treating simple obesity complicated with abnormal blood fat. Methods A prescription of diet plus exercise was given to the patients in a control group, and catgut embedding was added to the prescription in a treatment group. It was given once every 15 days for the 1st three treatments, and once every month for the 2^nd three treatments. Six treatments constituted a therapeutic course. Body weight and blood fat were measured before and half a year since treatment. Results After treatment, BMI, TO, TG and LDL were very significantly improved in the treatment group （P〈0.01）, and BMI and TG were very significantly （P〈0.01） and TO and LDL were significantly （P〈0.05） improved in the control group. HDL was not obviously improved in both groups （P〉0.05）. The improvement of TO, TG and LDL in the treatment group was significantly more than that in the control group （P〈0.01）, but no difference in improvement of BMI and HDL was found between the two groups （P〉0.05）. Conclusion The therapy of diet plus exercise was effective in body weight reduction and lowering blood fat in patients with simple obesity complicated with abnormal blood fat, and catgut embedding used in combination was even better in lowering blood fat.
