With the rapid development of intelligent cyber-physical systems(ICPS),diverse services with varying Quality of Service(QoS)requirements have brought great challenges to traditional network resource allocation.Further...With the rapid development of intelligent cyber-physical systems(ICPS),diverse services with varying Quality of Service(QoS)requirements have brought great challenges to traditional network resource allocation.Furthermore,given the open environment and a multitude of devices,enhancing the security of ICPS is an urgent concern.To address these issues,this paper proposes a novel trusted virtual network embedding(T-VNE)approach for ICPS based combining blockchain and edge computing technologies.Additionally,the proposed algorithm leverages a deep reinforcement learning(DRL)model to optimize decision-making processes.It employs the policygradient-based agent to compute candidate embedding nodes and utilizes a breadth-first search(BFS)algorithm to determine the optimal embedding paths.Finally,through simulation experiments,the efficacy of the proposed method was validated,demonstrating outstanding performance in terms of security,revenue generation,and virtual network request(VNR)acceptance rate.展开更多
In recent years,ransomware attacks have become one of the most common and destructive types of cyberattacks.Their impact is significant on the operations,finances and reputation of affected companies.Despite the effor...In recent years,ransomware attacks have become one of the most common and destructive types of cyberattacks.Their impact is significant on the operations,finances and reputation of affected companies.Despite the efforts of researchers and security experts to protect information systems from these attacks,the threat persists and the proposed solutions are not able to significantly stop the spread of ransomware attacks.The latest remarkable achievements of large language models(LLMs)in NLP tasks have caught the attention of cybersecurity researchers to integrate thesemodels into security threat detection.Thesemodels offer high embedding capabilities,able to extract rich semantic representations and paving theway formore accurate and adaptive solutions.In this context,we propose a new approach for ransomware detection based on an ensemblemethod that leverages three distinctLLMembeddingmodels.This ensemble strategy takes advantage of the variety of embedding methods and the strengths of each model.In the proposed solution,each embedding model is associated with an independently trainedMLP classifier.The predictions obtained are then merged using a weighted voting technique,assigning each model an influence proportional to its performance.This approach makes it possible to exploit the complementarity of representations,improve detection accuracy and robustness,and offer a more reliable solution in the face of the growing diversity and complexity of modern ransomware.展开更多
Spam emails remain one of the most persistent threats to digital communication,necessitating effective detection solutions that safeguard both individuals and organisations.We propose a spam email classification frame...Spam emails remain one of the most persistent threats to digital communication,necessitating effective detection solutions that safeguard both individuals and organisations.We propose a spam email classification frame-work that uses Bidirectional Encoder Representations from Transformers(BERT)for contextual feature extraction and a multiple-window Convolutional Neural Network(CNN)for classification.To identify semantic nuances in email content,BERT embeddings are used,and CNN filters extract discriminative n-gram patterns at various levels of detail,enabling accurate spam identification.The proposed model outperformed Word2Vec-based baselines on a sample of 5728 labelled emails,achieving an accuracy of 98.69%,AUC of 0.9981,F1 Score of 0.9724,and MCC of 0.9639.With a medium kernel size of(6,9)and compact multi-window CNN architectures,it improves performance.Cross-validation illustrates stability and generalization across folds.By balancing high recall with minimal false positives,our method provides a reliable and scalable solution for current spam detection in advanced deep learning.By combining contextual embedding and a neural architecture,this study develops a security analysis method.展开更多
Message structure reconstruction is a critical task in protocol reverse engineering,aiming to recover protocol field structures without access to source code.It enables important applications in network security,inclu...Message structure reconstruction is a critical task in protocol reverse engineering,aiming to recover protocol field structures without access to source code.It enables important applications in network security,including malware analysis and protocol fuzzing.However,existing methods suffer from inaccurate field boundary delineation and lack hierarchical relationship recovery,resulting in imprecise and incomplete reconstructions.In this paper,we propose ProRE,a novel method for reconstructing protocol field structures based on program execution slice embedding.ProRE extracts code slices from protocol parsing at runtime,converts them into embedding vectors using a data flow-sensitive assembly language model,and performs hierarchical clustering to recover complete protocol field structures.Evaluation on two datasets containing 12 protocols shows that ProRE achieves an average F1 score of 0.85 and a cophenetic correlation coefficient of 0.189,improving by 19%and 0.126%respectively over state-of-the-art methods(including BinPRE,Tupni,Netlifter,and QwQ-32B-preview),demonstrating significant superiority in both accuracy and completeness of field structure recovery.Case studies further validate the effectiveness of ProRE in practical malware analysis scenarios.展开更多
Objective:In addition to dyspnea and edema,gastrointestinal discomfort is common among patients with heart failure(HF).Reduced cardiac output can lead to inadequate perfusion of the intestinal mucosa and subsequent im...Objective:In addition to dyspnea and edema,gastrointestinal discomfort is common among patients with heart failure(HF).Reduced cardiac output can lead to inadequate perfusion of the intestinal mucosa and subsequent impairment of the intestinal barrier.Levosimendan,a novel inotropic agent,binds to cardiac troponin C to enhance calcium sensitivity,activates ATP-dependent potassium channels in cardiomyocytes and vascular smooth muscle cells,exerts positive inotropic and vasodilatory effects,and reduces free radical generation,thereby improving systemic hemodynamics including intestinal circulation.However,clinical evidence regarding its protective effects on the intestinal barrier in HF patients remains limited,and the underlying mechanisms require further clarification.This study aims to investigate whether levosimendan confers protective effects on the intestinal barrier in HF patients and to explore its potential mechanisms.Methods:Network pharmacology was first used to analyze potential mechanisms of levosimendan in treating intestinal barrier dysfunction among HF patients.A total of 62 hospitalized patients with acute exacerbation of HF with reduced ejection fraction(HFrEF)were enrolled based on echocardiographic left ventricular ejection fraction.According to clinical medication regimens,patients were assigned to a conventional treatment group(n=31)or a levosimendan treatment group(n=31).The conventional treatment group received standard anti-HF therapy,while the levosimendan treatment group received levosimendan in addition to standard therapy.Enzyme-linked immunosorbent assays were used to measure plasma levels and changes in the intestinal-barrier proteins zonulin,intestinal fatty acid binding protein(I-FABP),proinflammatory cytokines[interleukin(IL)-17,IL-6,and tumor necrosis factor(TNF)-α],anti-inflammatory cytokine IL-10,and N-terminal probrain natriuretic peptide(NT-proBNP).Improvements in cardiac function and gastrointestinal symptoms were evaluated using the Kansas City Cardiomyopathy Questionnaire(KCCQ)and the Gastrointestinal Symptom Rating Scale(GSRS).Results:Network pharmacology indicated that the effects of levosimendan on intestinal barrier dysfunction in HF patients may involve inflammation-related pathways such as IL-17 and TNF.Clinically,after treatment,zonulin decreased by 32.94 ng/mL in the levosimendan treatment group versus 15.05 ng/mL in the conventional treatment group(P<0.05).I-FABP decreased by 6.97 pg/mL in the levosimendan treatment group but increased by 35.16 pg/mL in the conventional treatment group(P<0.05).IL-6,IL-17,and TNF-αdecreased by 1.11 pg/mL,1.21 pg/mL,and 2.83 pg/mL,respectively,in the levosimendan treatment group,whereas they increased by 7.68 pg/mL,0.67 pg/mL,and 2.38 pg/mL in the conventional treatment group(all P<0.05).IL-10 decreased by 24.48 pg/mL in the conventional treatment group but increased by 24.98 pg/mL in the levosimendan treatment group(P<0.05).NT-proBNP increased by 7.35 pg/mL in the conventional treatment group but decreased by 4.73 pg/mL in the levosimendan treatment group(P<0.05).KCCQ scores increased by 0.36 in the conventional treatment group and 1.86 in the levosimendan treatment group,GSRS scores decreased by 1.00 in the conventional treatment group and 2.40 in the levosimendan treatment group,respectively,but the differences were not statistically significant(both P>0.05).Conclusion:Levosimendan not only improves HF and gastrointestinal symptoms in hospitalized patients with acute exacerbation of HFrEF but also reduces plasma intestinal barrier factor levels.These effects may be associated with decreased plasma proinflammatory cytokines and increased anti-inflammatory cytokines after treatment,potentially involving IL-17 and TNF signaling pathways.展开更多
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
In this study,it is aimed to develop a generic model which calculates the trajectory of the ejection seat from the jet aircraft,by taking into account the parameters that will affect the seat movement such as the seat...In this study,it is aimed to develop a generic model which calculates the trajectory of the ejection seat from the jet aircraft,by taking into account the parameters that will affect the seat movement such as the seat’s launch speed,ejection direction,ejection angle,altitude of the aircraft,distance/height from the aircraft rudder and canopy,pilot and ejection seat weight.With the model algorithm proposed,the ejection seat trajectory model was developed on MATLAB.The ejection seat trajectory model is based on point mass trajectory mathematical model.In this study,an analytical study of the problem has been made for modeling the flight trajectory of the ejection seat after it has been ejected.Past studies were used as a basis for validation and simulation.By writing a generic MATLAB code,a user interface was developed and presented to the user as a module.This generic code that has been developed could be used for simulations by users in the future by revising it in accordance with their own job descriptions.展开更多
BACKGROUND There is no available data about the trajectory of heart failure(HF)with improved ejection fraction(EF)and patient clinical outcomes in Qatar.AIM To explore the difference in characteristics and outcomes be...BACKGROUND There is no available data about the trajectory of heart failure(HF)with improved ejection fraction(EF)and patient clinical outcomes in Qatar.AIM To explore the difference in characteristics and outcomes between patients with transient and sustained improvement in left ventricular ejection fraction(LVEF)and to determine the independent predictors for sustained improvement in LVEF.METHODS This is a retrospective cohort study that was conducted at the advanced HF clinic of a tertiary care hospital in Qatar between January 2017 and December 2018.This were divided into two groups:HF with transient improvement in EF(HFtimpEF)and HF with sustained improvement in EF(HFsimpEF).RESULTS A total of 175 patients with HF and improved EF were included.Among them 136(77.7%)patients showed sustained improvement in LVEF.The remaining patients with HFtimpEF were predominantly males[37(94.9%)vs 101(74.3%),P=0.005]with a higher incidence of ischemic cardiomyopathy[32(82.1%)vs 68(50.4%),P=0.002],dyslipidemia[24(61.5%)vs 54(39.7%),P=0.03],and hypertension[34(87.2%)vs 93(68.4%),P=0.03]than those with HFsimpEF.The latter experienced significantly lower rates of hospitalization[39(28.7%)vs 20(51.3%),P=0.01]and diagnosis of new cardiovascular conditions during the follow-up(e.g.,acute coronary syndrome,stroke,decompensated HF,and atrial fibrillation)[14(10.3%)vs 10(25.6%),P=0.03]without a difference in emergency department visits or in-hospital death.Sustained improvement in LVEF was positively associated with being female[adjusted odds ratio(aOR)=6.8,95%confidence interval(CI):1.4-32.3,P=0.02],having non-ischemic etiology of HF(aOR=3.1,95%CI:1.03-9.3,P=0.04),and using a mineralocorticoid receptor antagonist(aOR=7.0,95%CI:1.50-31.8,P=0.01).CONCLUSION Patients with HFsimpEF experienced significantly lower rates of hospitalization and diagnosis of new cardiova-scular conditions than patients with HFtimpEF.Sustained improvement in LVEF was positively associated with being a female,having non-ischemic etiology of HF,and using a mineralocorticoid receptor antagonist.展开更多
Heart failure(HF)with preserved ejection fraction(HFpEF)has exceeded HF with reduced ejection fraction(HFrEF),becoming the most common type of HF.Unlike HFrEF,HFpEF is primarily a chronic low-grade inflammatory proces...Heart failure(HF)with preserved ejection fraction(HFpEF)has exceeded HF with reduced ejection fraction(HFrEF),becoming the most common type of HF.Unlike HFrEF,HFpEF is primarily a chronic low-grade inflammatory process closely associated with metabolic disorders.The coexistence of HFpEF and metabolic dysfunction-associated steatotic liver disease(MASLD)presents significant clinical challenges due to shared metabolic pathophysiology and complex inter-play.Management strategies for HFpEF and MASLD remain challenging.Sodium-glucose cotransporter 2 inhibitors have shown benefits in managing both conditions.Additionally,glucagon-like peptide-1 receptor agonists are being actively investigated for their potential benefits,particularly in MASLD.A comprehensive,patient-centered approach that combines metabolic and cardiova-scular care is essential for improving outcomes in patients with HFpEF and MASLD,addressing the global metabolic health challenges.展开更多
The high-pressure mercury intrusion (HPMI) experiment is widely used to assess the pore architecture oftight sandstone reservoirs. However, the conventional analysis of the high- pressure mercury intrusionhas always f...The high-pressure mercury intrusion (HPMI) experiment is widely used to assess the pore architecture oftight sandstone reservoirs. However, the conventional analysis of the high- pressure mercury intrusionhas always focused on the mercury injection curves themselves, neglecting the important geologicalinformation conveyed by the mercury ejection curves. This paper quantitatively describes the fractalcharacteristics of ejection curves by using four fractal models, i.e.,. Menger model, Thermodynamicmodel, Sierpinski model, and multi- fractal model. In comparison with mercury injection curves, weexplore the fractal significance of mercury ejection curves and define the applicability of different fractalmodels in characterizing pore architectures. Investigated tight sandstone samples can be divided intofour types (Types A, B, C and D) based on porosity, permeability, and mercury removal efficiency. Type Dsamples are unique in that they have higher permeability (>0.6 mD) but lower mercury removal effi-ciency (<35%). Fractal studies of the mercury injection curve show that it mainly reflects the pore throatcharacteristics, while the mercury ejection curve serves to reveal the pore features, and porosity andpermeability correlate well with the fractal dimension of the injection curve, while mercury removalefficiency correlates only with the Ds' value of the ejection curve. The studies on the mercury ejectioncurves also reveal that the small pores and micropores of the Type C and Type D samples are moredeveloped, with varying pore architecture. The fractal dimension Ds' value of Type D samples is greaterthan that of Type C samples, and the dissolution of Type D samples is more intense than that of Type Csamples, which further indicates that the Type D samples are smaller in pore size, rougher in surface, andwith greater difficulty for the hydrocarbon to enter, resulting in their reservoir capacity probably lessthan that of Type C samples. In this regard, the important information characterized by the mercuryejection curve should be considered in evaluating the tight sandstone reservoirs. Finally, the Menger andThermodynamic models prove to be more suitable for describing the total pore architecture, while theSierpinski model is better for characterizing the variability of the interconnected pores.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
Knowing the optimal operating parameters of Stirling engines is important for efficient combustion through adaptability to changed pressures and oxygen atmospheres. In this study, the optimum operating conditions for ...Knowing the optimal operating parameters of Stirling engines is important for efficient combustion through adaptability to changed pressures and oxygen atmospheres. In this study, the optimum operating conditions for efficient combustion in a singular Stirling engine combustor at different oxygen atmospheres were investigated and determined. Numerical simulations were performed to investigate the effects of ejection ratio and pressure on combustion performance. In an oxygen/carbon dioxide atmosphere, the results show that increasing the ejection ratio substantially alters the flame distribution in the Stirling engine combustor, increasing heat transfer and external combustion efficiency. In contrast, increasing the ejection ratio reduces the average and maximum temperatures of the Stirling engine combustor. Increased pressure affects the flame distribution in the Stirling engine combustor and impedes the flow and convective heat transfer in the combustor, reducing the overall external combustion efficiency at pressures above 6.5 MPa. In an air/carbon dioxide atmosphere, an increased ejection ratio reduces the average and maximum temperatures in the Stirling engine combustor. However, the overall flame distribution does not change substantially. The external combustion efficiency tends to increase and then decrease because of two opposing factors: the increase in the convective heat transfer coefficient and the decrease in the temperature difference. Increasing pressure inhibits forced convection heat transfer in the Stirling engine combustor, reducing external combustion efficiency, which drops from 78% to 65% when pressure increases from 0.2 MPa to 0.5 MPa.展开更多
Background Elderly patients(age≥60 years)with ST-segment elevation myocardial infarction(STEMI)are at an elevated risk of mortality.This study aimed to investigate the association of the creatinine to ejection fracti...Background Elderly patients(age≥60 years)with ST-segment elevation myocardial infarction(STEMI)are at an elevated risk of mortality.This study aimed to investigate the association of the creatinine to ejection fraction ratio(CER)with in-hospital and 1-year death in elderly patients with STEMI.Methods A total of 753 consecutive elderly patients(age≥60 years)with STEMI undergoing percutaneous coronary intervention(PCI)were enrolled and divided into three groups according to the tertiles of CER at admission:<1.5(n=250),1.5-2.2(n=249)and>2.2(n=254).Multivariate analyses were performed to evaluate the prognostic value of CRE for short-term death in this population.Results The in-hospital and 1-year mortality reached 6.0%and 13.3%,respectively.Patients with higher CER exhibited a higher in-hospital mortality(0.8%vs.2.8%vs.14.2%,P<0.001).An optimal cut-off value of 2.5 for CER was identified for predicting in-hospital death by receiver operating characteristic curve analysis,yielding a sensitivity of 77.8%and a specificity of 76.1%[area under curve(AUC):0.791,95%confidence interval(CI):0.734-0.847,P<0.001].Multivariate regression analyses revealed that CER>2.5 was an independent risk factor for both in-hospital[adjusted odds ratio(OR):9.006,95%CI:2.707-29.967,P<0.001]and 1-year mortality[adjusted hazard ratio(HR):5.082,95%CI:2.462-10.490,P<0.001].Conclusions Elevated CER is associated with adverse short-term mortality in elderly STEMI patients undergone PCI,offering valuable insights for the early identification and management of high-risk individuals in clinical practice.展开更多
BACKGROUND Heart failure with preserved ejection fraction(HFpEF)accounts for approximately half of heart failure cases and is associated with high morbidity and mortality.Beta-blockers(BB)and calcium channel blockers(...BACKGROUND Heart failure with preserved ejection fraction(HFpEF)accounts for approximately half of heart failure cases and is associated with high morbidity and mortality.Beta-blockers(BB)and calcium channel blockers(CCB)are commonly used for symptom control and comorbidity management,but their comparative effectiveness and safety remain unclear.AIM To compare the effectiveness and safety of BB vs CCB in patients with HFpEF using simulated real-world data and propensity score-matched analyses.METHODS Simulated data for 4000 HFpEF patients(2000 BB,2000 CCB)were generated based on distributions extracted from electronic medical records spanning 2014-2023.Inclusion criteria included adults with left ventricular ejection fraction≥50%and initiation of BB or CCB.Effectiveness outcomes encompassed mortality,heart failure hospitalizations,and changes in clinical parameters.Safety outcomes included bradycardia,hypotension,and drug discontinuation.Statistical analyses used t-tests,χ2 tests,Cox proportional hazards models for hazard ratios(HR),and incidence rate ratios(IRR)in R software.Propensity score matching(PSM)was performed to balance baseline characteristics,with outcomes reassessed in the matched cohort.RESULTS Baseline characteristics were largely balanced,with minor differences in sex,chronic kidney disease,systolic blood pressure,and left atrial volume index.BB demonstrated lower all-cause mortality(crude HR 0.78,95%CI:0.70-0.87,P=0.003),heart failure hospitalization(crude HR 0.86,95%CI:0.77-0.96,P=0.031),and composite endpoint(crude HR 0.85,95%CI:0.79-0.91,P<0.001)rates compared to CCB.IRR for heart failure hospitalizations and emergency visits favored BB.Safety profiles showed higher symptomatic bradycardia(9.2%vs 4.9%,P<0.001)and drug discontinuation(11.3%vs 9.3%,P=0.043)with BB,and higher hypotension(7.2%vs 11.5%,P<0.001)with CCB.Matched analyses showed all-cause mortality rates of 0.0622 per person-year for BB vs 0.0649 for CCB(HR 0.96,95%CI:0.85-1.08),heart failure hospitalization rates of 0.0751 vs 0.0888(HR 0.84,95%CI:0.75-0.94),and IRR for number of heart failure hospitalizations of 1.65 for CCB vs BB(95%CI:1.51-1.80,P<0.001).CONCLUSION BB may offer potential advantages in reducing mortality and hospitalizations in HFpEF compared to CCB,with distinct safety considerations.PSM confirmed these trends with reduced confounding.Personalized therapy is recommended,warranting prospective trials for validation.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
Background Coronary microvascular dysfunction(CMD)is a critical pathological mechanism underlying left ventricular ejection fraction(LVEF)decline after primary percutaneous coronary intervention(PPCI)in ST-segment ele...Background Coronary microvascular dysfunction(CMD)is a critical pathological mechanism underlying left ventricular ejection fraction(LVEF)decline after primary percutaneous coronary intervention(PPCI)in ST-segment elevation myocardial infarction(STEMI)patients.Angiography-derived microcirculatory resistance(AMR),a novel wire-free index,offers noninvasive assessment of CMD.This study aimed to validate AMR's predictive value for post-PPCI LVEF impairment.Methods A total of 190 STEMI patients who underwent PPCI at four centers between March 2017 and May 2022 were retrospectively enrolled.Patients were categorized into two groups based on whether their LVEF was below 50%when measured after 72 hours post-PPCI:the normal LVEF group(n=114)and the reduced LVEF group(n=76).The clinical characteristics of the two groups were compared.AMR was computed using quantitative flow ratio(QFR)analysis.Multivariable logistic regression,linear regression,and receiver operating characteristic(ROC)curves were applied to assess predictive performance.Results The reduced LVEF group exhibited significantly higher post-PPCI AMR(268 mmHg·s/m vs.229 mmHg·s/m,P<0.001)compared to the normal LVEF group.Multivariable logistic and linear regression confirmed that elevated AMR(OR:1.01;95%CI:1.00-1.02,P=0.001)was an independent predictor of LVEF decline to<50%after PPCI.ROC analysis revealed a moderate predictive capacity of AMR,with an area under the curve(AUC)of 0.684(95%CI:0.608-0.760).The optimal AMR cutoff of 262.5 mmHg·s/m demonstrated 55.26%sensitivity and 72.81%specificity for identifying LVEF decline in reperfused STEMI patients.Conclusions Post-PPCI AMR was an independent predictor of LVEF decline to<50%in STEMI patients.The data demonstrated that an AMR threshold of≥262.5 mmHg·s/m after PPCI was associated with a statistically significant increase in the likelihood of LVEF reduction compared to in dividuals with AMR values within the normal range.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
基金supported by the National Natural Science Foundation of China under Grant 62471493supported by the Natural Science Foundation of Shandong Province under Grant ZR2023LZH017,ZR2024MF066。
文摘With the rapid development of intelligent cyber-physical systems(ICPS),diverse services with varying Quality of Service(QoS)requirements have brought great challenges to traditional network resource allocation.Furthermore,given the open environment and a multitude of devices,enhancing the security of ICPS is an urgent concern.To address these issues,this paper proposes a novel trusted virtual network embedding(T-VNE)approach for ICPS based combining blockchain and edge computing technologies.Additionally,the proposed algorithm leverages a deep reinforcement learning(DRL)model to optimize decision-making processes.It employs the policygradient-based agent to compute candidate embedding nodes and utilizes a breadth-first search(BFS)algorithm to determine the optimal embedding paths.Finally,through simulation experiments,the efficacy of the proposed method was validated,demonstrating outstanding performance in terms of security,revenue generation,and virtual network request(VNR)acceptance rate.
基金funded by the Deanship of Graduate Studies and Scientific Research at Jouf University under grant No.(DGSSR-2024-02-01176).
文摘In recent years,ransomware attacks have become one of the most common and destructive types of cyberattacks.Their impact is significant on the operations,finances and reputation of affected companies.Despite the efforts of researchers and security experts to protect information systems from these attacks,the threat persists and the proposed solutions are not able to significantly stop the spread of ransomware attacks.The latest remarkable achievements of large language models(LLMs)in NLP tasks have caught the attention of cybersecurity researchers to integrate thesemodels into security threat detection.Thesemodels offer high embedding capabilities,able to extract rich semantic representations and paving theway formore accurate and adaptive solutions.In this context,we propose a new approach for ransomware detection based on an ensemblemethod that leverages three distinctLLMembeddingmodels.This ensemble strategy takes advantage of the variety of embedding methods and the strengths of each model.In the proposed solution,each embedding model is associated with an independently trainedMLP classifier.The predictions obtained are then merged using a weighted voting technique,assigning each model an influence proportional to its performance.This approach makes it possible to exploit the complementarity of representations,improve detection accuracy and robustness,and offer a more reliable solution in the face of the growing diversity and complexity of modern ransomware.
基金funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2026R234)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Spam emails remain one of the most persistent threats to digital communication,necessitating effective detection solutions that safeguard both individuals and organisations.We propose a spam email classification frame-work that uses Bidirectional Encoder Representations from Transformers(BERT)for contextual feature extraction and a multiple-window Convolutional Neural Network(CNN)for classification.To identify semantic nuances in email content,BERT embeddings are used,and CNN filters extract discriminative n-gram patterns at various levels of detail,enabling accurate spam identification.The proposed model outperformed Word2Vec-based baselines on a sample of 5728 labelled emails,achieving an accuracy of 98.69%,AUC of 0.9981,F1 Score of 0.9724,and MCC of 0.9639.With a medium kernel size of(6,9)and compact multi-window CNN architectures,it improves performance.Cross-validation illustrates stability and generalization across folds.By balancing high recall with minimal false positives,our method provides a reliable and scalable solution for current spam detection in advanced deep learning.By combining contextual embedding and a neural architecture,this study develops a security analysis method.
文摘Message structure reconstruction is a critical task in protocol reverse engineering,aiming to recover protocol field structures without access to source code.It enables important applications in network security,including malware analysis and protocol fuzzing.However,existing methods suffer from inaccurate field boundary delineation and lack hierarchical relationship recovery,resulting in imprecise and incomplete reconstructions.In this paper,we propose ProRE,a novel method for reconstructing protocol field structures based on program execution slice embedding.ProRE extracts code slices from protocol parsing at runtime,converts them into embedding vectors using a data flow-sensitive assembly language model,and performs hierarchical clustering to recover complete protocol field structures.Evaluation on two datasets containing 12 protocols shows that ProRE achieves an average F1 score of 0.85 and a cophenetic correlation coefficient of 0.189,improving by 19%and 0.126%respectively over state-of-the-art methods(including BinPRE,Tupni,Netlifter,and QwQ-32B-preview),demonstrating significant superiority in both accuracy and completeness of field structure recovery.Case studies further validate the effectiveness of ProRE in practical malware analysis scenarios.
基金supported by the Hunan Provincial Science and Technology Major Special Fund(2021SK1020)the Natural Science Foundation of Hunan Province(2023JJ30948)+1 种基金the Health Commission of Hunan Province(202203014687)the International Medical Exchange Cardiovascular Multidisciplinary Integrated Thinking Research Foundation(Z-2016-23-2101-20),China.
文摘Objective:In addition to dyspnea and edema,gastrointestinal discomfort is common among patients with heart failure(HF).Reduced cardiac output can lead to inadequate perfusion of the intestinal mucosa and subsequent impairment of the intestinal barrier.Levosimendan,a novel inotropic agent,binds to cardiac troponin C to enhance calcium sensitivity,activates ATP-dependent potassium channels in cardiomyocytes and vascular smooth muscle cells,exerts positive inotropic and vasodilatory effects,and reduces free radical generation,thereby improving systemic hemodynamics including intestinal circulation.However,clinical evidence regarding its protective effects on the intestinal barrier in HF patients remains limited,and the underlying mechanisms require further clarification.This study aims to investigate whether levosimendan confers protective effects on the intestinal barrier in HF patients and to explore its potential mechanisms.Methods:Network pharmacology was first used to analyze potential mechanisms of levosimendan in treating intestinal barrier dysfunction among HF patients.A total of 62 hospitalized patients with acute exacerbation of HF with reduced ejection fraction(HFrEF)were enrolled based on echocardiographic left ventricular ejection fraction.According to clinical medication regimens,patients were assigned to a conventional treatment group(n=31)or a levosimendan treatment group(n=31).The conventional treatment group received standard anti-HF therapy,while the levosimendan treatment group received levosimendan in addition to standard therapy.Enzyme-linked immunosorbent assays were used to measure plasma levels and changes in the intestinal-barrier proteins zonulin,intestinal fatty acid binding protein(I-FABP),proinflammatory cytokines[interleukin(IL)-17,IL-6,and tumor necrosis factor(TNF)-α],anti-inflammatory cytokine IL-10,and N-terminal probrain natriuretic peptide(NT-proBNP).Improvements in cardiac function and gastrointestinal symptoms were evaluated using the Kansas City Cardiomyopathy Questionnaire(KCCQ)and the Gastrointestinal Symptom Rating Scale(GSRS).Results:Network pharmacology indicated that the effects of levosimendan on intestinal barrier dysfunction in HF patients may involve inflammation-related pathways such as IL-17 and TNF.Clinically,after treatment,zonulin decreased by 32.94 ng/mL in the levosimendan treatment group versus 15.05 ng/mL in the conventional treatment group(P<0.05).I-FABP decreased by 6.97 pg/mL in the levosimendan treatment group but increased by 35.16 pg/mL in the conventional treatment group(P<0.05).IL-6,IL-17,and TNF-αdecreased by 1.11 pg/mL,1.21 pg/mL,and 2.83 pg/mL,respectively,in the levosimendan treatment group,whereas they increased by 7.68 pg/mL,0.67 pg/mL,and 2.38 pg/mL in the conventional treatment group(all P<0.05).IL-10 decreased by 24.48 pg/mL in the conventional treatment group but increased by 24.98 pg/mL in the levosimendan treatment group(P<0.05).NT-proBNP increased by 7.35 pg/mL in the conventional treatment group but decreased by 4.73 pg/mL in the levosimendan treatment group(P<0.05).KCCQ scores increased by 0.36 in the conventional treatment group and 1.86 in the levosimendan treatment group,GSRS scores decreased by 1.00 in the conventional treatment group and 2.40 in the levosimendan treatment group,respectively,but the differences were not statistically significant(both P>0.05).Conclusion:Levosimendan not only improves HF and gastrointestinal symptoms in hospitalized patients with acute exacerbation of HFrEF but also reduces plasma intestinal barrier factor levels.These effects may be associated with decreased plasma proinflammatory cytokines and increased anti-inflammatory cytokines after treatment,potentially involving IL-17 and TNF signaling pathways.
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.
文摘In this study,it is aimed to develop a generic model which calculates the trajectory of the ejection seat from the jet aircraft,by taking into account the parameters that will affect the seat movement such as the seat’s launch speed,ejection direction,ejection angle,altitude of the aircraft,distance/height from the aircraft rudder and canopy,pilot and ejection seat weight.With the model algorithm proposed,the ejection seat trajectory model was developed on MATLAB.The ejection seat trajectory model is based on point mass trajectory mathematical model.In this study,an analytical study of the problem has been made for modeling the flight trajectory of the ejection seat after it has been ejected.Past studies were used as a basis for validation and simulation.By writing a generic MATLAB code,a user interface was developed and presented to the user as a module.This generic code that has been developed could be used for simulations by users in the future by revising it in accordance with their own job descriptions.
文摘BACKGROUND There is no available data about the trajectory of heart failure(HF)with improved ejection fraction(EF)and patient clinical outcomes in Qatar.AIM To explore the difference in characteristics and outcomes between patients with transient and sustained improvement in left ventricular ejection fraction(LVEF)and to determine the independent predictors for sustained improvement in LVEF.METHODS This is a retrospective cohort study that was conducted at the advanced HF clinic of a tertiary care hospital in Qatar between January 2017 and December 2018.This were divided into two groups:HF with transient improvement in EF(HFtimpEF)and HF with sustained improvement in EF(HFsimpEF).RESULTS A total of 175 patients with HF and improved EF were included.Among them 136(77.7%)patients showed sustained improvement in LVEF.The remaining patients with HFtimpEF were predominantly males[37(94.9%)vs 101(74.3%),P=0.005]with a higher incidence of ischemic cardiomyopathy[32(82.1%)vs 68(50.4%),P=0.002],dyslipidemia[24(61.5%)vs 54(39.7%),P=0.03],and hypertension[34(87.2%)vs 93(68.4%),P=0.03]than those with HFsimpEF.The latter experienced significantly lower rates of hospitalization[39(28.7%)vs 20(51.3%),P=0.01]and diagnosis of new cardiovascular conditions during the follow-up(e.g.,acute coronary syndrome,stroke,decompensated HF,and atrial fibrillation)[14(10.3%)vs 10(25.6%),P=0.03]without a difference in emergency department visits or in-hospital death.Sustained improvement in LVEF was positively associated with being female[adjusted odds ratio(aOR)=6.8,95%confidence interval(CI):1.4-32.3,P=0.02],having non-ischemic etiology of HF(aOR=3.1,95%CI:1.03-9.3,P=0.04),and using a mineralocorticoid receptor antagonist(aOR=7.0,95%CI:1.50-31.8,P=0.01).CONCLUSION Patients with HFsimpEF experienced significantly lower rates of hospitalization and diagnosis of new cardiova-scular conditions than patients with HFtimpEF.Sustained improvement in LVEF was positively associated with being a female,having non-ischemic etiology of HF,and using a mineralocorticoid receptor antagonist.
基金Supported by Wenzhou Science Technology Bureau Foundation,No.2022Y0726.
文摘Heart failure(HF)with preserved ejection fraction(HFpEF)has exceeded HF with reduced ejection fraction(HFrEF),becoming the most common type of HF.Unlike HFrEF,HFpEF is primarily a chronic low-grade inflammatory process closely associated with metabolic disorders.The coexistence of HFpEF and metabolic dysfunction-associated steatotic liver disease(MASLD)presents significant clinical challenges due to shared metabolic pathophysiology and complex inter-play.Management strategies for HFpEF and MASLD remain challenging.Sodium-glucose cotransporter 2 inhibitors have shown benefits in managing both conditions.Additionally,glucagon-like peptide-1 receptor agonists are being actively investigated for their potential benefits,particularly in MASLD.A comprehensive,patient-centered approach that combines metabolic and cardiova-scular care is essential for improving outcomes in patients with HFpEF and MASLD,addressing the global metabolic health challenges.
基金The research project was co-funded by the National Natural Science Foundation of China(No.42072172,No.41772120)Shandong Province Natural Science Fund for Distinguished Young Scholars(No.JQ201311)the Graduate Scientific and Technological Innovation Project Financially Supported by Shandong University of Science and Technology(No.SDKDYC190313).
文摘The high-pressure mercury intrusion (HPMI) experiment is widely used to assess the pore architecture oftight sandstone reservoirs. However, the conventional analysis of the high- pressure mercury intrusionhas always focused on the mercury injection curves themselves, neglecting the important geologicalinformation conveyed by the mercury ejection curves. This paper quantitatively describes the fractalcharacteristics of ejection curves by using four fractal models, i.e.,. Menger model, Thermodynamicmodel, Sierpinski model, and multi- fractal model. In comparison with mercury injection curves, weexplore the fractal significance of mercury ejection curves and define the applicability of different fractalmodels in characterizing pore architectures. Investigated tight sandstone samples can be divided intofour types (Types A, B, C and D) based on porosity, permeability, and mercury removal efficiency. Type Dsamples are unique in that they have higher permeability (>0.6 mD) but lower mercury removal effi-ciency (<35%). Fractal studies of the mercury injection curve show that it mainly reflects the pore throatcharacteristics, while the mercury ejection curve serves to reveal the pore features, and porosity andpermeability correlate well with the fractal dimension of the injection curve, while mercury removalefficiency correlates only with the Ds' value of the ejection curve. The studies on the mercury ejectioncurves also reveal that the small pores and micropores of the Type C and Type D samples are moredeveloped, with varying pore architecture. The fractal dimension Ds' value of Type D samples is greaterthan that of Type C samples, and the dissolution of Type D samples is more intense than that of Type Csamples, which further indicates that the Type D samples are smaller in pore size, rougher in surface, andwith greater difficulty for the hydrocarbon to enter, resulting in their reservoir capacity probably lessthan that of Type C samples. In this regard, the important information characterized by the mercuryejection curve should be considered in evaluating the tight sandstone reservoirs. Finally, the Menger andThermodynamic models prove to be more suitable for describing the total pore architecture, while theSierpinski model is better for characterizing the variability of the interconnected pores.
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 Shanghai Rising Star Program (Grant No. 21QB1403900)the Shanghai Municipal Commission of Science and Technology (Grant No. 22170712600)。
文摘Knowing the optimal operating parameters of Stirling engines is important for efficient combustion through adaptability to changed pressures and oxygen atmospheres. In this study, the optimum operating conditions for efficient combustion in a singular Stirling engine combustor at different oxygen atmospheres were investigated and determined. Numerical simulations were performed to investigate the effects of ejection ratio and pressure on combustion performance. In an oxygen/carbon dioxide atmosphere, the results show that increasing the ejection ratio substantially alters the flame distribution in the Stirling engine combustor, increasing heat transfer and external combustion efficiency. In contrast, increasing the ejection ratio reduces the average and maximum temperatures of the Stirling engine combustor. Increased pressure affects the flame distribution in the Stirling engine combustor and impedes the flow and convective heat transfer in the combustor, reducing the overall external combustion efficiency at pressures above 6.5 MPa. In an air/carbon dioxide atmosphere, an increased ejection ratio reduces the average and maximum temperatures in the Stirling engine combustor. However, the overall flame distribution does not change substantially. The external combustion efficiency tends to increase and then decrease because of two opposing factors: the increase in the convective heat transfer coefficient and the decrease in the temperature difference. Increasing pressure inhibits forced convection heat transfer in the Stirling engine combustor, reducing external combustion efficiency, which drops from 78% to 65% when pressure increases from 0.2 MPa to 0.5 MPa.
文摘Background Elderly patients(age≥60 years)with ST-segment elevation myocardial infarction(STEMI)are at an elevated risk of mortality.This study aimed to investigate the association of the creatinine to ejection fraction ratio(CER)with in-hospital and 1-year death in elderly patients with STEMI.Methods A total of 753 consecutive elderly patients(age≥60 years)with STEMI undergoing percutaneous coronary intervention(PCI)were enrolled and divided into three groups according to the tertiles of CER at admission:<1.5(n=250),1.5-2.2(n=249)and>2.2(n=254).Multivariate analyses were performed to evaluate the prognostic value of CRE for short-term death in this population.Results The in-hospital and 1-year mortality reached 6.0%and 13.3%,respectively.Patients with higher CER exhibited a higher in-hospital mortality(0.8%vs.2.8%vs.14.2%,P<0.001).An optimal cut-off value of 2.5 for CER was identified for predicting in-hospital death by receiver operating characteristic curve analysis,yielding a sensitivity of 77.8%and a specificity of 76.1%[area under curve(AUC):0.791,95%confidence interval(CI):0.734-0.847,P<0.001].Multivariate regression analyses revealed that CER>2.5 was an independent risk factor for both in-hospital[adjusted odds ratio(OR):9.006,95%CI:2.707-29.967,P<0.001]and 1-year mortality[adjusted hazard ratio(HR):5.082,95%CI:2.462-10.490,P<0.001].Conclusions Elevated CER is associated with adverse short-term mortality in elderly STEMI patients undergone PCI,offering valuable insights for the early identification and management of high-risk individuals in clinical practice.
文摘BACKGROUND Heart failure with preserved ejection fraction(HFpEF)accounts for approximately half of heart failure cases and is associated with high morbidity and mortality.Beta-blockers(BB)and calcium channel blockers(CCB)are commonly used for symptom control and comorbidity management,but their comparative effectiveness and safety remain unclear.AIM To compare the effectiveness and safety of BB vs CCB in patients with HFpEF using simulated real-world data and propensity score-matched analyses.METHODS Simulated data for 4000 HFpEF patients(2000 BB,2000 CCB)were generated based on distributions extracted from electronic medical records spanning 2014-2023.Inclusion criteria included adults with left ventricular ejection fraction≥50%and initiation of BB or CCB.Effectiveness outcomes encompassed mortality,heart failure hospitalizations,and changes in clinical parameters.Safety outcomes included bradycardia,hypotension,and drug discontinuation.Statistical analyses used t-tests,χ2 tests,Cox proportional hazards models for hazard ratios(HR),and incidence rate ratios(IRR)in R software.Propensity score matching(PSM)was performed to balance baseline characteristics,with outcomes reassessed in the matched cohort.RESULTS Baseline characteristics were largely balanced,with minor differences in sex,chronic kidney disease,systolic blood pressure,and left atrial volume index.BB demonstrated lower all-cause mortality(crude HR 0.78,95%CI:0.70-0.87,P=0.003),heart failure hospitalization(crude HR 0.86,95%CI:0.77-0.96,P=0.031),and composite endpoint(crude HR 0.85,95%CI:0.79-0.91,P<0.001)rates compared to CCB.IRR for heart failure hospitalizations and emergency visits favored BB.Safety profiles showed higher symptomatic bradycardia(9.2%vs 4.9%,P<0.001)and drug discontinuation(11.3%vs 9.3%,P=0.043)with BB,and higher hypotension(7.2%vs 11.5%,P<0.001)with CCB.Matched analyses showed all-cause mortality rates of 0.0622 per person-year for BB vs 0.0649 for CCB(HR 0.96,95%CI:0.85-1.08),heart failure hospitalization rates of 0.0751 vs 0.0888(HR 0.84,95%CI:0.75-0.94),and IRR for number of heart failure hospitalizations of 1.65 for CCB vs BB(95%CI:1.51-1.80,P<0.001).CONCLUSION BB may offer potential advantages in reducing mortality and hospitalizations in HFpEF compared to CCB,with distinct safety considerations.PSM confirmed these trends with reduced confounding.Personalized therapy is recommended,warranting prospective trials for validation.
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.
基金supported by the Natural Science Foundation of Guangdong Province(No.2024A1515012943 and No.2023A1515010177)。
文摘Background Coronary microvascular dysfunction(CMD)is a critical pathological mechanism underlying left ventricular ejection fraction(LVEF)decline after primary percutaneous coronary intervention(PPCI)in ST-segment elevation myocardial infarction(STEMI)patients.Angiography-derived microcirculatory resistance(AMR),a novel wire-free index,offers noninvasive assessment of CMD.This study aimed to validate AMR's predictive value for post-PPCI LVEF impairment.Methods A total of 190 STEMI patients who underwent PPCI at four centers between March 2017 and May 2022 were retrospectively enrolled.Patients were categorized into two groups based on whether their LVEF was below 50%when measured after 72 hours post-PPCI:the normal LVEF group(n=114)and the reduced LVEF group(n=76).The clinical characteristics of the two groups were compared.AMR was computed using quantitative flow ratio(QFR)analysis.Multivariable logistic regression,linear regression,and receiver operating characteristic(ROC)curves were applied to assess predictive performance.Results The reduced LVEF group exhibited significantly higher post-PPCI AMR(268 mmHg·s/m vs.229 mmHg·s/m,P<0.001)compared to the normal LVEF group.Multivariable logistic and linear regression confirmed that elevated AMR(OR:1.01;95%CI:1.00-1.02,P=0.001)was an independent predictor of LVEF decline to<50%after PPCI.ROC analysis revealed a moderate predictive capacity of AMR,with an area under the curve(AUC)of 0.684(95%CI:0.608-0.760).The optimal AMR cutoff of 262.5 mmHg·s/m demonstrated 55.26%sensitivity and 72.81%specificity for identifying LVEF decline in reperfused STEMI patients.Conclusions Post-PPCI AMR was an independent predictor of LVEF decline to<50%in STEMI patients.The data demonstrated that an AMR threshold of≥262.5 mmHg·s/m after PPCI was associated with a statistically significant increase in the likelihood of LVEF reduction compared to in dividuals with AMR values within the normal range.
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.
