BACKGROUND Stress ulcers are common complications in critically ill patients,with a higher incidence observed in older patients following gastrointestinal surgery.This study aimed to develop and evaluate the effective...BACKGROUND Stress ulcers are common complications in critically ill patients,with a higher incidence observed in older patients following gastrointestinal surgery.This study aimed to develop and evaluate the effectiveness of a multi-modal intervention protocol to prevent stress ulcers in this high-risk population.AIM To assess the impact of a multi-modal intervention on preventing stress ulcers in older intensive care unit(ICU)patients postoperatively.METHODS A randomized controlled trial involving critically ill patients(aged≥65 years)admitted to the ICU after gastrointestinal surgery was conducted.Patients were randomly assigned to either the intervention group,which received a multimodal stress ulcer prevention protocol,or the control group,which received standard care.The primary outcome measure was the incidence of stress ulcers.The secondary outcomes included ulcer healing time,complication rates,and length of hospital stay.RESULTS A total of 200 patients(100 in each group)were included in this study.The intervention group exhibited a significantly lower incidence of stress ulcers than the control group(15%vs 30%,P<0.01).Additionally,the intervention group demonstrated shorter ulcer healing times(mean 5.2 vs 7.8 days,P<0.05),lower complication rates(10%vs 22%,P<0.05),and reduced length of hospital stay(mean 12.3 vs 15.7 days,P<0.05).CONCLUSION This multi-modal intervention protocol significantly reduced the incidence of stress ulcers and improved clinical outcomes in critically ill older patients after gastrointestinal surgery.This comprehensive approach may provide a valuable strategy for managing high-risk populations in intensive care settings.展开更多
Objective To develop a non-invasive predictive model for coronary artery stenosis severity based on adaptive multi-modal integration of traditional Chinese and western medicine data.Methods Clinical indicators,echocar...Objective To develop a non-invasive predictive model for coronary artery stenosis severity based on adaptive multi-modal integration of traditional Chinese and western medicine data.Methods Clinical indicators,echocardiographic data,traditional Chinese medicine(TCM)tongue manifestations,and facial features were collected from patients who underwent coro-nary computed tomography angiography(CTA)in the Cardiac Care Unit(CCU)of Shanghai Tenth People's Hospital between May 1,2023 and May 1,2024.An adaptive weighted multi-modal data fusion(AWMDF)model based on deep learning was constructed to predict the severity of coronary artery stenosis.The model was evaluated using metrics including accura-cy,precision,recall,F1 score,and the area under the receiver operating characteristic(ROC)curve(AUC).Further performance assessment was conducted through comparisons with six ensemble machine learning methods,data ablation,model component ablation,and various decision-level fusion strategies.Results A total of 158 patients were included in the study.The AWMDF model achieved ex-cellent predictive performance(AUC=0.973,accuracy=0.937,precision=0.937,recall=0.929,and F1 score=0.933).Compared with model ablation,data ablation experiments,and various traditional machine learning models,the AWMDF model demonstrated superior per-formance.Moreover,the adaptive weighting strategy outperformed alternative approaches,including simple weighting,averaging,voting,and fixed-weight schemes.Conclusion The AWMDF model demonstrates potential clinical value in the non-invasive prediction of coronary artery disease and could serve as a tool for clinical decision support.展开更多
BACKGROUND The development of hepatocellular carcinoma(HCC)is influenced by multiple factors.Interventional therapy offers an effective treatment option for patients with unresectable intermediate-to-advanced HCC.Inte...BACKGROUND The development of hepatocellular carcinoma(HCC)is influenced by multiple factors.Interventional therapy offers an effective treatment option for patients with unresectable intermediate-to-advanced HCC.Interventional therapy can induce electrocardiographic(ECG)abnormalities that may be associated with liver dysfunction,electrolyte disorders,and cardiac injury.AIM To explore the ECG alterations and determinants following interventional therapy in patients with HCC.METHODS Sixty patients undergoing interventional treatment for liver cancer were selected as study participants.According to the results of the dynamic ECG examination 1 day after surgery,the patients were divided into an abnormal group(n=21)and a nonabnormal group(n=39).With the help of dynamic ECG examination,the ECG parameters were compared and the baseline data of patients was recorded in the two groups.RESULTS The 24 hours QT interval variability,24 hours normal atrial polarization to ventricular polarization(R-R)interval(standard deviation),24 hours consecutive 5 minutes normal R-R interval,and 24 hours continuous 5 minutes normal R-R interval(standard deviation mean)were lower than patients in the nonabnormal group(P<0.05).The logistic analysis showed that age>60 years,liver function grade B,and postoperative body temperature 38°C were risk factors for abnormal dynamic electrocardiogram in patients with liver cancer intervention(P<0.05).CONCLUSION Interventional therapy for HCC can lead to ECG abnormalities,underscoring the clinical need for enhanced cardiac monitoring to mitigate myocardial complications.展开更多
Objective:To evaluate the value of rehabilitation nursing based on mind mapping model combined with psychological intervention for patients with nephrotic syndrome(NS).Methods:A total of 60 patients with NS who visite...Objective:To evaluate the value of rehabilitation nursing based on mind mapping model combined with psychological intervention for patients with nephrotic syndrome(NS).Methods:A total of 60 patients with NS who visited our hospital from January 2024 to December 2024 were selected as samples and randomly divided into groups.The observation group received rehabilitation nursing based on the mind mapping model combined with psychological intervention,while the control group received routine intervention.The differences in emotional scores,self-care ability scores,compliance,and complications were compared between the two groups.Results:The anxiety(SAS)and depression(SDS)scores of the observation group were lower than those of the control group,while the self-care ability scale(ESCA)score was higher than that of the control group(P<0.05).The compliance rate of the observation group was higher than that of the control group(P<0.05).The complication rate of NS in the observation group was lower than that in the control group(P<0.05).Conclusion:Rehabilitation nursing based on the mind mapping model combined with psychological intervention can enhance self-care ability,reduce negative emotions,and reduce complications in NS nursing,which is efficient and feasible.展开更多
Liver transplantation,as an effective therapy for patients with liver cancer,plays an important role in improving the quality of life of patients.However,the com-plexity and trauma of liver transplantation can easily ...Liver transplantation,as an effective therapy for patients with liver cancer,plays an important role in improving the quality of life of patients.However,the com-plexity and trauma of liver transplantation can easily lead to the occurrence of malnutrition in patients,and then increase the risk of postoperative complica-tions,which has aroused widespread clinical attention.Reasonable nutritional support can not only maintain the stability of the body’s internal environment,reduce the occurrence of complications,but also promote the recovery of liver and other organ functions.In recent years,with the in-depth understanding of nut-ritional metabolism after liver transplantation,the application of enteral nutrition and parenteral nutrition in nutritional support after liver transplantation has been increasingly extensive and achieved remarkable results.This paper discusses the effect of early postoperative nutritional intervention on patients with liver cancer and liver transplantation,and combined with its mechanism of action,can better understand the effectiveness of intervention,and provide reference for the deve-lopment of scientific and reasonable nutritional support programs in clinical pra-ctice.展开更多
This study aims to explore the characteristics of novice teachers’inappropriate behaviors in classroom teaching and their intervention strategies.With the continuous improvement of education quality,novice teachers f...This study aims to explore the characteristics of novice teachers’inappropriate behaviors in classroom teaching and their intervention strategies.With the continuous improvement of education quality,novice teachers face increasing challenges in teaching practice.Their inappropriate behaviors not only affect the classroom atmosphere but may also negatively impact students’learning outcomes.Therefore,researching the characteristics of novice teachers’inappropriate behaviors and their intervention strategies holds significant scientific and social value.This study employs a combination of quantitative and qualitative methods to analyze the behavioral patterns of novice teachers in classroom teaching and proposes corresponding intervention strategies.The results indicate that novice teachers’inappropriate behaviors mainly manifest as poor classroom management,monotonous teaching methods,and insufficient interaction with students.Based on these findings,the study proposes a series of effective intervention strategies,including enhancing teacher training,optimizing teaching design,and promoting positive interactions between teachers and students.The conclusions of the study not only provide practical guidance for educational practice but also point out directions for future research,emphasizing the crucial role of teacher professional development in improving teaching quality.展开更多
BACKGROUND Simultaneous acute ischemic stroke(AIS)and myocardial infarction(cardio-cerebral ischemic attack)have rarely been reported in the literature.Currently,no clear evidence-based guidelines or clinical trials e...BACKGROUND Simultaneous acute ischemic stroke(AIS)and myocardial infarction(cardio-cerebral ischemic attack)have rarely been reported in the literature.Currently,no clear evidence-based guidelines or clinical trials exist to determine the optimal therapeutic strategy for these patients.CASE SUMMARY We present the case of a 27-year-old Chinese man who simultaneously experie-nced acute concomitant cerebrocardiac infarction(CCI)and painless ST-elevation myocardial infarction.The patient was successfully treated with elective percu-taneous coronary intervention(PCI)after receiving urgent systemic thrombolysis at the standard dose for AIS.CONCLUSION Urgent thrombolysis followed by elective PCI was an appropriate strategy for the management of simultaneous CCI.展开更多
Non-communicable diseases (NCDs) are on the rise worldwide and in developing countries like Botswana. Unhealthy eating habits and lack of proper nutrition knowledge cause non-communicable diseases and affect adolescen...Non-communicable diseases (NCDs) are on the rise worldwide and in developing countries like Botswana. Unhealthy eating habits and lack of proper nutrition knowledge cause non-communicable diseases and affect adolescents. It is in adolescence that eating habits are formed that persist till adulthood. Lifestyle interventions are needed to curb NCDs in adolescents. This paper reports the findings of a study that aimed to validate a lifestyle intervention program and its effect on food intake, physical activity, and nutrition knowledge. It was a clustered randomized control trial study conducted in four (4) junior secondary schools. There were 46 participants, 21 in the control and 25 in the intervention arm, who were blindly assigned to each arm by a statistician. Information and skills on nutrition were imparted using the Information, Motivation, and Behavioral Skills model. The program was implemented for eight (8) weeks hourly after school. A questionnaire was used to collect data pre- and post-intervention. Number, proportion, percentage, and independent t-test (mean and SD or median and IQR, p-value) were calculated using numerical and categorical data. The findings showed that the lifestyle intervention was valid, and there was a slight decrease in the intake of sweets among participants in both trial arms (p = 0.066). There was no significant difference in terms of food intake. Only a small number of participants still ate a few fruits, and there was no change in vegetable intake in both trial arms (p = 0.641). There was no change in the intake of fried foods in both trail arms (p = 0.402). Regarding nutrition knowledge, there was a slight significant difference of p = 0.079 between the trial arms. Though the effect of the lifestyle intervention program was not statistically significant, the results are promising, especially if the duration could be increased to a longer period and a larger sample size included.展开更多
Multi-modal Named Entity Recognition(MNER)aims to better identify meaningful textual entities by integrating information from images.Previous work has focused on extracting visual semantics at a fine-grained level,or ...Multi-modal Named Entity Recognition(MNER)aims to better identify meaningful textual entities by integrating information from images.Previous work has focused on extracting visual semantics at a fine-grained level,or obtaining entity related external knowledge from knowledge bases or Large Language Models(LLMs).However,these approaches ignore the poor semantic correlation between visual and textual modalities in MNER datasets and do not explore different multi-modal fusion approaches.In this paper,we present MMAVK,a multi-modal named entity recognition model with auxiliary visual knowledge and word-level fusion,which aims to leverage the Multi-modal Large Language Model(MLLM)as an implicit knowledge base.It also extracts vision-based auxiliary knowledge from the image formore accurate and effective recognition.Specifically,we propose vision-based auxiliary knowledge generation,which guides the MLLM to extract external knowledge exclusively derived from images to aid entity recognition by designing target-specific prompts,thus avoiding redundant recognition and cognitive confusion caused by the simultaneous processing of image-text pairs.Furthermore,we employ a word-level multi-modal fusion mechanism to fuse the extracted external knowledge with each word-embedding embedded from the transformerbased encoder.Extensive experimental results demonstrate that MMAVK outperforms or equals the state-of-the-art methods on the two classical MNER datasets,even when the largemodels employed have significantly fewer parameters than other baselines.展开更多
Multi-modal knowledge graph completion(MMKGC)aims to complete missing entities or relations in multi-modal knowledge graphs,thereby discovering more previously unknown triples.Due to the continuous growth of data and ...Multi-modal knowledge graph completion(MMKGC)aims to complete missing entities or relations in multi-modal knowledge graphs,thereby discovering more previously unknown triples.Due to the continuous growth of data and knowledge and the limitations of data sources,the visual knowledge within the knowledge graphs is generally of low quality,and some entities suffer from the issue of missing visual modality.Nevertheless,previous studies of MMKGC have primarily focused on how to facilitate modality interaction and fusion while neglecting the problems of low modality quality and modality missing.In this case,mainstream MMKGC models only use pre-trained visual encoders to extract features and transfer the semantic information to the joint embeddings through modal fusion,which inevitably suffers from problems such as error propagation and increased uncertainty.To address these problems,we propose a Multi-modal knowledge graph Completion model based on Super-resolution and Detailed Description Generation(MMCSD).Specifically,we leverage a pre-trained residual network to enhance the resolution and improve the quality of the visual modality.Moreover,we design multi-level visual semantic extraction and entity description generation,thereby further extracting entity semantics from structural triples and visual images.Meanwhile,we train a variational multi-modal auto-encoder and utilize a pre-trained multi-modal language model to complement the missing visual features.We conducted experiments on FB15K-237 and DB13K,and the results showed that MMCSD can effectively perform MMKGC and achieve state-of-the-art performance.展开更多
Integrating multiple medical imaging techniques,including Magnetic Resonance Imaging(MRI),Computed Tomography,Positron Emission Tomography(PET),and ultrasound,provides a comprehensive view of the patient health status...Integrating multiple medical imaging techniques,including Magnetic Resonance Imaging(MRI),Computed Tomography,Positron Emission Tomography(PET),and ultrasound,provides a comprehensive view of the patient health status.Each of these methods contributes unique diagnostic insights,enhancing the overall assessment of patient condition.Nevertheless,the amalgamation of data from multiple modalities presents difficulties due to disparities in resolution,data collection methods,and noise levels.While traditional models like Convolutional Neural Networks(CNNs)excel in single-modality tasks,they struggle to handle multi-modal complexities,lacking the capacity to model global relationships.This research presents a novel approach for examining multi-modal medical imagery using a transformer-based system.The framework employs self-attention and cross-attention mechanisms to synchronize and integrate features across various modalities.Additionally,it shows resilience to variations in noise and image quality,making it adaptable for real-time clinical use.To address the computational hurdles linked to transformer models,particularly in real-time clinical applications in resource-constrained environments,several optimization techniques have been integrated to boost scalability and efficiency.Initially,a streamlined transformer architecture was adopted to minimize the computational load while maintaining model effectiveness.Methods such as model pruning,quantization,and knowledge distillation have been applied to reduce the parameter count and enhance the inference speed.Furthermore,efficient attention mechanisms such as linear or sparse attention were employed to alleviate the substantial memory and processing requirements of traditional self-attention operations.For further deployment optimization,researchers have implemented hardware-aware acceleration strategies,including the use of TensorRT and ONNX-based model compression,to ensure efficient execution on edge devices.These optimizations allow the approach to function effectively in real-time clinical settings,ensuring viability even in environments with limited resources.Future research directions include integrating non-imaging data to facilitate personalized treatment and enhancing computational efficiency for implementation in resource-limited environments.This study highlights the transformative potential of transformer models in multi-modal medical imaging,offering improvements in diagnostic accuracy and patient care outcomes.展开更多
Objective:To analyze the effect of interventional nursing on the therapeutic effect,negative emotion and quality of life of patients undergoing cardiovascular and cerebrovascular interventional therapy.Methods:Eighty-...Objective:To analyze the effect of interventional nursing on the therapeutic effect,negative emotion and quality of life of patients undergoing cardiovascular and cerebrovascular interventional therapy.Methods:Eighty-four patients who received cardio-cerebral vascular interventional therapy in a hospital during January 2024–December 2024 were selected,and were divided into the control group and the observation group by the mean score method,each with 42 cases.The control group was given standardized perioperative care,and the observation group was given interventional nursing intervention on this basis.The two groups were compared in terms of clinical efficacy,negative emotion score,incidence of related complications,quality of life score and nursing satisfaction.Results:The total effective rate of treatment of patients in the observation group(95.24%)was significantly higher than that of the control group(78.57%),and the difference was statistically significant(P<0.05);before nursing care,the difference between SDS and SAS scores of patients in the two groups was insignificant(P>0.05);after nursing care,the scores of various indexes of the two groups were significantly reduced and the observation group was lower than that of the control group,and the difference was statistically significant(P<0.05);the patients in the observation group had more negative moods than those in the control group;the complication rate of patients in the observation group(2.38%)was significantly lower than that of the control group(19.04%),and the difference was statistically significant(P<0.05);the quality of life scores of patients in the observation group were higher than that of the control group,and the difference was statistically significant(P<0.05);the satisfaction rate of patients’nursing care in the observation group was 97.62%,which was higher than that of the control group(78.57%),and the difference was statistically significant(P<0.05).The difference is statistically significant(P<0.05).Conclusion:The implementation of interventional nursing intervention for patients with cardiovascular and cerebrovascular interventional therapy can further enhance the clinical therapeutic effect,improve the negative emotions of patients’anxiety and depression,reduce the risk of related complications,improve the quality of life of patients,and obtain higher satisfaction.展开更多
Background Both medication and non-medication therapies are effective approaches to control blood pressure (BP) in hypertension patients.However,the association of joint changes in antihypertensive medication use and ...Background Both medication and non-medication therapies are effective approaches to control blood pressure (BP) in hypertension patients.However,the association of joint changes in antihypertensive medication use and healthy lifestyle index (HLI)with BP control among hypertension patients is seldom reported,which needs to provide more evidence by prospective intervention studies.We examined the association of antihypertensive medication use and HLI with BP control among employees with hypertension in China based on a workplace-based multicomponent intervention program.Methods Between January 2013 and December 2014,a cluster randomized clinical trial of a workplace-based multicomponent intervention program was conducted in 60 workplaces across 20 urban areas in China.Workplaces were randomly divided into intervention (n=40) and control (n=20) groups.Basic information on employees at each workplace was collected by trained professionals,including sociodemographic characteristics,medical history,family history,lifestyle behaviors,medication status and physical measurements.After baseline,the intervention group received a 2-year intervention to achieve BP control,which included:(1) a workplace wellness program for all employees;(2) a guidelines-oriented hypertension management protocol.HLI including nonsmoking,nondrinking,adequate physical activity,weight within reference range and balanced diet,were coded on a 5-point scale (range:0-5,with higher score indicating a healthier lifestyle).Antihypertensive medication use was defined as taking drug within the last 2 weeks.Changes in HLI,antihypertensive medication use and BP control from baseline to 24 months were measured after the intervention.Results Overall,4655 employees were included (age:46.3±7.6 years,men:3547 (82.3%)).After 24 months of the intervention,there was a significant improvement in lifestyle[smoking (OR=0.65,95%CI:0.43-0.99;P=0.045),drinking (OR=0.52,95%CI:0.40-0.68;P<0.001),regular exercise (OR=3.10,95%CI:2.53-3.78;P<0.001),excessive intake of fatty food (OR=0.17,95%CI:0.06-0.52;P=0.002),restrictive use of salt (OR=0.26,95%CI:0.12-0.56;P=0.001)].Compare to employees with a deteriorating lifestyle after the intervention,those with an improved lifestyle had a higher BP control.In the intervention group,compared with employees not using antihypertensive medication,those who consistent used (OR=2.34;95%CI:1.16-4.72;P=0.017) or changed from not using to using antihypertensive medication (OR=2.24;95%CI:1.08-4.62;P=0.030) had higher BP control.Compared with those having lower HLI,participants with a same (OR=1.38;95%CI:0.99-1.93;P=0.056) or high (OR=1.79;95%CI:1.27~2.53;P<0.001) HLI had higher BP control.Those who used antihypertensive medication and had a high HLI had the highest BP control (OR=1.88;95%CI:1.32-2.67,P<0.001).Subgroup analysis also showed the consistent effect as the above.Conclusion These findings suggest that adherence to antihypertensive medication treatment and healthy lifestyle were associated with a significant improvement in BP control among employees with hypertension.展开更多
To address the challenge of missing modal information in entity alignment and to mitigate information loss or bias arising frommodal heterogeneity during fusion,while also capturing shared information acrossmodalities...To address the challenge of missing modal information in entity alignment and to mitigate information loss or bias arising frommodal heterogeneity during fusion,while also capturing shared information acrossmodalities,this paper proposes a Multi-modal Pre-synergistic Entity Alignmentmodel based on Cross-modalMutual Information Strategy Optimization(MPSEA).The model first employs independent encoders to process multi-modal features,including text,images,and numerical values.Next,a multi-modal pre-synergistic fusion mechanism integrates graph structural and visual modal features into the textual modality as preparatory information.This pre-fusion strategy enables unified perception of heterogeneous modalities at the model’s initial stage,reducing discrepancies during the fusion process.Finally,using cross-modal deep perception reinforcement learning,the model achieves adaptive multilevel feature fusion between modalities,supporting learningmore effective alignment strategies.Extensive experiments on multiple public datasets show that the MPSEA method achieves gains of up to 7% in Hits@1 and 8.2% in MRR on the FBDB15K dataset,and up to 9.1% in Hits@1 and 7.7% in MRR on the FBYG15K dataset,compared to existing state-of-the-art methods.These results confirm the effectiveness of the proposed model.展开更多
Traditional Chinese medicine(TCM)demonstrates distinctive advantages in disease prevention and treatment.However,analyzing its biological mechanisms through the modern medical research paradigm of“single drug,single ...Traditional Chinese medicine(TCM)demonstrates distinctive advantages in disease prevention and treatment.However,analyzing its biological mechanisms through the modern medical research paradigm of“single drug,single target”presents significant challenges due to its holistic approach.Network pharmacology and its core theory of network targets connect drugs and diseases from a holistic and systematic perspective based on biological networks,overcoming the limitations of reductionist research models and showing considerable value in TCM research.Recent integration of network target computational and experimental methods with artificial intelligence(AI)and multi-modal multi-omics technologies has substantially enhanced network pharmacology methodology.The advancement in computational and experimental techniques provides complementary support for network target theory in decoding TCM principles.This review,centered on network targets,examines the progress of network target methods combined with AI in predicting disease molecular mechanisms and drug-target relationships,alongside the application of multi-modal multi-omics technologies in analyzing TCM formulae,syndromes,and toxicity.Looking forward,network target theory is expected to incorporate emerging technologies while developing novel approaches aligned with its unique characteristics,potentially leading to significant breakthroughs in TCM research and advancing scientific understanding and innovation in TCM.展开更多
Background:Alzheimer's disease(AD)is a degenerative disease that occurs in the central nervous system and is characterized by progressive cognitive dysfunction and behavioral impairment.The main pathological mecha...Background:Alzheimer's disease(AD)is a degenerative disease that occurs in the central nervous system and is characterized by progressive cognitive dysfunction and behavioral impairment.The main pathological mechanisms are amyloid-βaccumulation(Aβ)and hyperphosphorylation of tau protein(P-TAU).Objective:however,there are no effective drugs for the treatment of AD,so the intervention and treatment of AD still need further research.Methods:We did a search and summary some of the new potential drugs and other interventions for the treatment of AD.Results:We found relevant anti-Aβ,anti-tau,anti-aging,the Apolipoprotein(APOE)control drugs and intervention in AD through exercise and diet.Conclusions:because most of these drugs are in clinical trials,more effort is needed to develop anti-AD drugs.展开更多
As the number and complexity of sensors in autonomous vehicles continue to rise,multimodal fusionbased object detection algorithms are increasingly being used to detect 3D environmental information,significantly advan...As the number and complexity of sensors in autonomous vehicles continue to rise,multimodal fusionbased object detection algorithms are increasingly being used to detect 3D environmental information,significantly advancing the development of perception technology in autonomous driving.To further promote the development of fusion algorithms and improve detection performance,this paper discusses the advantages and recent advancements of multimodal fusion-based object detection algorithms.Starting fromsingle-modal sensor detection,the paper provides a detailed overview of typical sensors used in autonomous driving and introduces object detection methods based on images and point clouds.For image-based detection methods,they are categorized into monocular detection and binocular detection based on different input types.For point cloud-based detection methods,they are classified into projection-based,voxel-based,point cluster-based,pillar-based,and graph structure-based approaches based on the technical pathways for processing point cloud features.Additionally,multimodal fusion algorithms are divided into Camera-LiDAR fusion,Camera-Radar fusion,Camera-LiDAR-Radar fusion,and other sensor fusion methods based on the types of sensors involved.Furthermore,the paper identifies five key future research directions in this field,aiming to provide insights for researchers engaged in multimodal fusion-based object detection algorithms and to encourage broader attention to the research and application of multimodal fusion-based object detection.展开更多
The primary objective of Chinese spelling correction(CSC)is to detect and correct erroneous characters in Chinese text,which can result from various factors,such as inaccuracies in pinyin representation,character rese...The primary objective of Chinese spelling correction(CSC)is to detect and correct erroneous characters in Chinese text,which can result from various factors,such as inaccuracies in pinyin representation,character resemblance,and semantic discrepancies.However,existing methods often struggle to fully address these types of errors,impacting the overall correction accuracy.This paper introduces a multi-modal feature encoder designed to efficiently extract features from three distinct modalities:pinyin,semantics,and character morphology.Unlike previous methods that rely on direct fusion or fixed-weight summation to integrate multi-modal information,our approach employs a multi-head attention mechanism to focuse more on relevant modal information while dis-regarding less pertinent data.To prevent issues such as gradient explosion or vanishing,the model incorporates a residual connection of the original text vector for fine-tuning.This approach ensures robust model performance by maintaining essential linguistic details throughout the correction process.Experimental evaluations on the SIGHAN benchmark dataset demonstrate that the pro-posed model outperforms baseline approaches across various metrics and datasets,confirming its effectiveness and feasibility.展开更多
[Objectives]To explore the effect of a comprehensive intervention strategy based on the ESCAPE program in the prevention of post-ICU syndrome.[Methods]A total of 286 patients treated in the ICU of Taihe Hospital from ...[Objectives]To explore the effect of a comprehensive intervention strategy based on the ESCAPE program in the prevention of post-ICU syndrome.[Methods]A total of 286 patients treated in the ICU of Taihe Hospital from June 2021 to October 2022 were selected as study subjects.138 patients from June 2021 to January 2022 served as the control group,while 130 patients from February 2022 to October 2022 served as the intervention group.The control group received routine nursing care,while the intervention group received a comprehensive intervention strategy based on the ESCAPE protocol.The mental state,sleep quality,fatigue,mechanical ventilation time,ICU hospitalization time,and total hospitalization time were compared between the two groups.[Results]The intervention group showed better sleep quality and lower fatigue at discharge compared to the control group(P<0.05),but no significant difference was observed in mental state(P=0.142).Additionally,the intervention group had shorter mechanical ventilation time,ICU hospitalization time,and total hospitalization time(P<0.05).[Conclusions]The comprehensive intervention strategy based on the ESCAPE protocol effectively improves sleep quality and reduces fatigue,and also shortens the mechanical ventilation time,ICU stay,and total hospitalization duration.展开更多
文摘BACKGROUND Stress ulcers are common complications in critically ill patients,with a higher incidence observed in older patients following gastrointestinal surgery.This study aimed to develop and evaluate the effectiveness of a multi-modal intervention protocol to prevent stress ulcers in this high-risk population.AIM To assess the impact of a multi-modal intervention on preventing stress ulcers in older intensive care unit(ICU)patients postoperatively.METHODS A randomized controlled trial involving critically ill patients(aged≥65 years)admitted to the ICU after gastrointestinal surgery was conducted.Patients were randomly assigned to either the intervention group,which received a multimodal stress ulcer prevention protocol,or the control group,which received standard care.The primary outcome measure was the incidence of stress ulcers.The secondary outcomes included ulcer healing time,complication rates,and length of hospital stay.RESULTS A total of 200 patients(100 in each group)were included in this study.The intervention group exhibited a significantly lower incidence of stress ulcers than the control group(15%vs 30%,P<0.01).Additionally,the intervention group demonstrated shorter ulcer healing times(mean 5.2 vs 7.8 days,P<0.05),lower complication rates(10%vs 22%,P<0.05),and reduced length of hospital stay(mean 12.3 vs 15.7 days,P<0.05).CONCLUSION This multi-modal intervention protocol significantly reduced the incidence of stress ulcers and improved clinical outcomes in critically ill older patients after gastrointestinal surgery.This comprehensive approach may provide a valuable strategy for managing high-risk populations in intensive care settings.
基金Construction Program of the Key Discipline of State Administration of Traditional Chinese Medicine of China(ZYYZDXK-2023069)Research Project of Shanghai Municipal Health Commission (2024QN018)Shanghai University of Traditional Chinese Medicine Science and Technology Development Program (23KFL005)。
文摘Objective To develop a non-invasive predictive model for coronary artery stenosis severity based on adaptive multi-modal integration of traditional Chinese and western medicine data.Methods Clinical indicators,echocardiographic data,traditional Chinese medicine(TCM)tongue manifestations,and facial features were collected from patients who underwent coro-nary computed tomography angiography(CTA)in the Cardiac Care Unit(CCU)of Shanghai Tenth People's Hospital between May 1,2023 and May 1,2024.An adaptive weighted multi-modal data fusion(AWMDF)model based on deep learning was constructed to predict the severity of coronary artery stenosis.The model was evaluated using metrics including accura-cy,precision,recall,F1 score,and the area under the receiver operating characteristic(ROC)curve(AUC).Further performance assessment was conducted through comparisons with six ensemble machine learning methods,data ablation,model component ablation,and various decision-level fusion strategies.Results A total of 158 patients were included in the study.The AWMDF model achieved ex-cellent predictive performance(AUC=0.973,accuracy=0.937,precision=0.937,recall=0.929,and F1 score=0.933).Compared with model ablation,data ablation experiments,and various traditional machine learning models,the AWMDF model demonstrated superior per-formance.Moreover,the adaptive weighting strategy outperformed alternative approaches,including simple weighting,averaging,voting,and fixed-weight schemes.Conclusion The AWMDF model demonstrates potential clinical value in the non-invasive prediction of coronary artery disease and could serve as a tool for clinical decision support.
文摘BACKGROUND The development of hepatocellular carcinoma(HCC)is influenced by multiple factors.Interventional therapy offers an effective treatment option for patients with unresectable intermediate-to-advanced HCC.Interventional therapy can induce electrocardiographic(ECG)abnormalities that may be associated with liver dysfunction,electrolyte disorders,and cardiac injury.AIM To explore the ECG alterations and determinants following interventional therapy in patients with HCC.METHODS Sixty patients undergoing interventional treatment for liver cancer were selected as study participants.According to the results of the dynamic ECG examination 1 day after surgery,the patients were divided into an abnormal group(n=21)and a nonabnormal group(n=39).With the help of dynamic ECG examination,the ECG parameters were compared and the baseline data of patients was recorded in the two groups.RESULTS The 24 hours QT interval variability,24 hours normal atrial polarization to ventricular polarization(R-R)interval(standard deviation),24 hours consecutive 5 minutes normal R-R interval,and 24 hours continuous 5 minutes normal R-R interval(standard deviation mean)were lower than patients in the nonabnormal group(P<0.05).The logistic analysis showed that age>60 years,liver function grade B,and postoperative body temperature 38°C were risk factors for abnormal dynamic electrocardiogram in patients with liver cancer intervention(P<0.05).CONCLUSION Interventional therapy for HCC can lead to ECG abnormalities,underscoring the clinical need for enhanced cardiac monitoring to mitigate myocardial complications.
文摘Objective:To evaluate the value of rehabilitation nursing based on mind mapping model combined with psychological intervention for patients with nephrotic syndrome(NS).Methods:A total of 60 patients with NS who visited our hospital from January 2024 to December 2024 were selected as samples and randomly divided into groups.The observation group received rehabilitation nursing based on the mind mapping model combined with psychological intervention,while the control group received routine intervention.The differences in emotional scores,self-care ability scores,compliance,and complications were compared between the two groups.Results:The anxiety(SAS)and depression(SDS)scores of the observation group were lower than those of the control group,while the self-care ability scale(ESCA)score was higher than that of the control group(P<0.05).The compliance rate of the observation group was higher than that of the control group(P<0.05).The complication rate of NS in the observation group was lower than that in the control group(P<0.05).Conclusion:Rehabilitation nursing based on the mind mapping model combined with psychological intervention can enhance self-care ability,reduce negative emotions,and reduce complications in NS nursing,which is efficient and feasible.
文摘Liver transplantation,as an effective therapy for patients with liver cancer,plays an important role in improving the quality of life of patients.However,the com-plexity and trauma of liver transplantation can easily lead to the occurrence of malnutrition in patients,and then increase the risk of postoperative complica-tions,which has aroused widespread clinical attention.Reasonable nutritional support can not only maintain the stability of the body’s internal environment,reduce the occurrence of complications,but also promote the recovery of liver and other organ functions.In recent years,with the in-depth understanding of nut-ritional metabolism after liver transplantation,the application of enteral nutrition and parenteral nutrition in nutritional support after liver transplantation has been increasingly extensive and achieved remarkable results.This paper discusses the effect of early postoperative nutritional intervention on patients with liver cancer and liver transplantation,and combined with its mechanism of action,can better understand the effectiveness of intervention,and provide reference for the deve-lopment of scientific and reasonable nutritional support programs in clinical pra-ctice.
文摘This study aims to explore the characteristics of novice teachers’inappropriate behaviors in classroom teaching and their intervention strategies.With the continuous improvement of education quality,novice teachers face increasing challenges in teaching practice.Their inappropriate behaviors not only affect the classroom atmosphere but may also negatively impact students’learning outcomes.Therefore,researching the characteristics of novice teachers’inappropriate behaviors and their intervention strategies holds significant scientific and social value.This study employs a combination of quantitative and qualitative methods to analyze the behavioral patterns of novice teachers in classroom teaching and proposes corresponding intervention strategies.The results indicate that novice teachers’inappropriate behaviors mainly manifest as poor classroom management,monotonous teaching methods,and insufficient interaction with students.Based on these findings,the study proposes a series of effective intervention strategies,including enhancing teacher training,optimizing teaching design,and promoting positive interactions between teachers and students.The conclusions of the study not only provide practical guidance for educational practice but also point out directions for future research,emphasizing the crucial role of teacher professional development in improving teaching quality.
文摘BACKGROUND Simultaneous acute ischemic stroke(AIS)and myocardial infarction(cardio-cerebral ischemic attack)have rarely been reported in the literature.Currently,no clear evidence-based guidelines or clinical trials exist to determine the optimal therapeutic strategy for these patients.CASE SUMMARY We present the case of a 27-year-old Chinese man who simultaneously experie-nced acute concomitant cerebrocardiac infarction(CCI)and painless ST-elevation myocardial infarction.The patient was successfully treated with elective percu-taneous coronary intervention(PCI)after receiving urgent systemic thrombolysis at the standard dose for AIS.CONCLUSION Urgent thrombolysis followed by elective PCI was an appropriate strategy for the management of simultaneous CCI.
文摘Non-communicable diseases (NCDs) are on the rise worldwide and in developing countries like Botswana. Unhealthy eating habits and lack of proper nutrition knowledge cause non-communicable diseases and affect adolescents. It is in adolescence that eating habits are formed that persist till adulthood. Lifestyle interventions are needed to curb NCDs in adolescents. This paper reports the findings of a study that aimed to validate a lifestyle intervention program and its effect on food intake, physical activity, and nutrition knowledge. It was a clustered randomized control trial study conducted in four (4) junior secondary schools. There were 46 participants, 21 in the control and 25 in the intervention arm, who were blindly assigned to each arm by a statistician. Information and skills on nutrition were imparted using the Information, Motivation, and Behavioral Skills model. The program was implemented for eight (8) weeks hourly after school. A questionnaire was used to collect data pre- and post-intervention. Number, proportion, percentage, and independent t-test (mean and SD or median and IQR, p-value) were calculated using numerical and categorical data. The findings showed that the lifestyle intervention was valid, and there was a slight decrease in the intake of sweets among participants in both trial arms (p = 0.066). There was no significant difference in terms of food intake. Only a small number of participants still ate a few fruits, and there was no change in vegetable intake in both trial arms (p = 0.641). There was no change in the intake of fried foods in both trail arms (p = 0.402). Regarding nutrition knowledge, there was a slight significant difference of p = 0.079 between the trial arms. Though the effect of the lifestyle intervention program was not statistically significant, the results are promising, especially if the duration could be increased to a longer period and a larger sample size included.
基金funded by Research Project,grant number BHQ090003000X03.
文摘Multi-modal Named Entity Recognition(MNER)aims to better identify meaningful textual entities by integrating information from images.Previous work has focused on extracting visual semantics at a fine-grained level,or obtaining entity related external knowledge from knowledge bases or Large Language Models(LLMs).However,these approaches ignore the poor semantic correlation between visual and textual modalities in MNER datasets and do not explore different multi-modal fusion approaches.In this paper,we present MMAVK,a multi-modal named entity recognition model with auxiliary visual knowledge and word-level fusion,which aims to leverage the Multi-modal Large Language Model(MLLM)as an implicit knowledge base.It also extracts vision-based auxiliary knowledge from the image formore accurate and effective recognition.Specifically,we propose vision-based auxiliary knowledge generation,which guides the MLLM to extract external knowledge exclusively derived from images to aid entity recognition by designing target-specific prompts,thus avoiding redundant recognition and cognitive confusion caused by the simultaneous processing of image-text pairs.Furthermore,we employ a word-level multi-modal fusion mechanism to fuse the extracted external knowledge with each word-embedding embedded from the transformerbased encoder.Extensive experimental results demonstrate that MMAVK outperforms or equals the state-of-the-art methods on the two classical MNER datasets,even when the largemodels employed have significantly fewer parameters than other baselines.
基金funded by Research Project,grant number BHQ090003000X03。
文摘Multi-modal knowledge graph completion(MMKGC)aims to complete missing entities or relations in multi-modal knowledge graphs,thereby discovering more previously unknown triples.Due to the continuous growth of data and knowledge and the limitations of data sources,the visual knowledge within the knowledge graphs is generally of low quality,and some entities suffer from the issue of missing visual modality.Nevertheless,previous studies of MMKGC have primarily focused on how to facilitate modality interaction and fusion while neglecting the problems of low modality quality and modality missing.In this case,mainstream MMKGC models only use pre-trained visual encoders to extract features and transfer the semantic information to the joint embeddings through modal fusion,which inevitably suffers from problems such as error propagation and increased uncertainty.To address these problems,we propose a Multi-modal knowledge graph Completion model based on Super-resolution and Detailed Description Generation(MMCSD).Specifically,we leverage a pre-trained residual network to enhance the resolution and improve the quality of the visual modality.Moreover,we design multi-level visual semantic extraction and entity description generation,thereby further extracting entity semantics from structural triples and visual images.Meanwhile,we train a variational multi-modal auto-encoder and utilize a pre-trained multi-modal language model to complement the missing visual features.We conducted experiments on FB15K-237 and DB13K,and the results showed that MMCSD can effectively perform MMKGC and achieve state-of-the-art performance.
基金supported by the Deanship of Research and Graduate Studies at King Khalid University under Small Research Project grant number RGP1/139/45.
文摘Integrating multiple medical imaging techniques,including Magnetic Resonance Imaging(MRI),Computed Tomography,Positron Emission Tomography(PET),and ultrasound,provides a comprehensive view of the patient health status.Each of these methods contributes unique diagnostic insights,enhancing the overall assessment of patient condition.Nevertheless,the amalgamation of data from multiple modalities presents difficulties due to disparities in resolution,data collection methods,and noise levels.While traditional models like Convolutional Neural Networks(CNNs)excel in single-modality tasks,they struggle to handle multi-modal complexities,lacking the capacity to model global relationships.This research presents a novel approach for examining multi-modal medical imagery using a transformer-based system.The framework employs self-attention and cross-attention mechanisms to synchronize and integrate features across various modalities.Additionally,it shows resilience to variations in noise and image quality,making it adaptable for real-time clinical use.To address the computational hurdles linked to transformer models,particularly in real-time clinical applications in resource-constrained environments,several optimization techniques have been integrated to boost scalability and efficiency.Initially,a streamlined transformer architecture was adopted to minimize the computational load while maintaining model effectiveness.Methods such as model pruning,quantization,and knowledge distillation have been applied to reduce the parameter count and enhance the inference speed.Furthermore,efficient attention mechanisms such as linear or sparse attention were employed to alleviate the substantial memory and processing requirements of traditional self-attention operations.For further deployment optimization,researchers have implemented hardware-aware acceleration strategies,including the use of TensorRT and ONNX-based model compression,to ensure efficient execution on edge devices.These optimizations allow the approach to function effectively in real-time clinical settings,ensuring viability even in environments with limited resources.Future research directions include integrating non-imaging data to facilitate personalized treatment and enhancing computational efficiency for implementation in resource-limited environments.This study highlights the transformative potential of transformer models in multi-modal medical imaging,offering improvements in diagnostic accuracy and patient care outcomes.
文摘Objective:To analyze the effect of interventional nursing on the therapeutic effect,negative emotion and quality of life of patients undergoing cardiovascular and cerebrovascular interventional therapy.Methods:Eighty-four patients who received cardio-cerebral vascular interventional therapy in a hospital during January 2024–December 2024 were selected,and were divided into the control group and the observation group by the mean score method,each with 42 cases.The control group was given standardized perioperative care,and the observation group was given interventional nursing intervention on this basis.The two groups were compared in terms of clinical efficacy,negative emotion score,incidence of related complications,quality of life score and nursing satisfaction.Results:The total effective rate of treatment of patients in the observation group(95.24%)was significantly higher than that of the control group(78.57%),and the difference was statistically significant(P<0.05);before nursing care,the difference between SDS and SAS scores of patients in the two groups was insignificant(P>0.05);after nursing care,the scores of various indexes of the two groups were significantly reduced and the observation group was lower than that of the control group,and the difference was statistically significant(P<0.05);the patients in the observation group had more negative moods than those in the control group;the complication rate of patients in the observation group(2.38%)was significantly lower than that of the control group(19.04%),and the difference was statistically significant(P<0.05);the quality of life scores of patients in the observation group were higher than that of the control group,and the difference was statistically significant(P<0.05);the satisfaction rate of patients’nursing care in the observation group was 97.62%,which was higher than that of the control group(78.57%),and the difference was statistically significant(P<0.05).The difference is statistically significant(P<0.05).Conclusion:The implementation of interventional nursing intervention for patients with cardiovascular and cerebrovascular interventional therapy can further enhance the clinical therapeutic effect,improve the negative emotions of patients’anxiety and depression,reduce the risk of related complications,improve the quality of life of patients,and obtain higher satisfaction.
基金supported by grant 2011BAI11B01 from the Projects in the Chinese National Science and Technology Pillar Program during the 12th Five-year Plan Periodby grant 2017-I2M-1-004 from the Chinese Academy of Medical Science Innovation Fund for Medical Sciencesby the Major science and technology special plan project of Yunnan Province (202302AA310045)。
文摘Background Both medication and non-medication therapies are effective approaches to control blood pressure (BP) in hypertension patients.However,the association of joint changes in antihypertensive medication use and healthy lifestyle index (HLI)with BP control among hypertension patients is seldom reported,which needs to provide more evidence by prospective intervention studies.We examined the association of antihypertensive medication use and HLI with BP control among employees with hypertension in China based on a workplace-based multicomponent intervention program.Methods Between January 2013 and December 2014,a cluster randomized clinical trial of a workplace-based multicomponent intervention program was conducted in 60 workplaces across 20 urban areas in China.Workplaces were randomly divided into intervention (n=40) and control (n=20) groups.Basic information on employees at each workplace was collected by trained professionals,including sociodemographic characteristics,medical history,family history,lifestyle behaviors,medication status and physical measurements.After baseline,the intervention group received a 2-year intervention to achieve BP control,which included:(1) a workplace wellness program for all employees;(2) a guidelines-oriented hypertension management protocol.HLI including nonsmoking,nondrinking,adequate physical activity,weight within reference range and balanced diet,were coded on a 5-point scale (range:0-5,with higher score indicating a healthier lifestyle).Antihypertensive medication use was defined as taking drug within the last 2 weeks.Changes in HLI,antihypertensive medication use and BP control from baseline to 24 months were measured after the intervention.Results Overall,4655 employees were included (age:46.3±7.6 years,men:3547 (82.3%)).After 24 months of the intervention,there was a significant improvement in lifestyle[smoking (OR=0.65,95%CI:0.43-0.99;P=0.045),drinking (OR=0.52,95%CI:0.40-0.68;P<0.001),regular exercise (OR=3.10,95%CI:2.53-3.78;P<0.001),excessive intake of fatty food (OR=0.17,95%CI:0.06-0.52;P=0.002),restrictive use of salt (OR=0.26,95%CI:0.12-0.56;P=0.001)].Compare to employees with a deteriorating lifestyle after the intervention,those with an improved lifestyle had a higher BP control.In the intervention group,compared with employees not using antihypertensive medication,those who consistent used (OR=2.34;95%CI:1.16-4.72;P=0.017) or changed from not using to using antihypertensive medication (OR=2.24;95%CI:1.08-4.62;P=0.030) had higher BP control.Compared with those having lower HLI,participants with a same (OR=1.38;95%CI:0.99-1.93;P=0.056) or high (OR=1.79;95%CI:1.27~2.53;P<0.001) HLI had higher BP control.Those who used antihypertensive medication and had a high HLI had the highest BP control (OR=1.88;95%CI:1.32-2.67,P<0.001).Subgroup analysis also showed the consistent effect as the above.Conclusion These findings suggest that adherence to antihypertensive medication treatment and healthy lifestyle were associated with a significant improvement in BP control among employees with hypertension.
基金partially supported by the National Natural Science Foundation of China under Grants 62471493 and 62402257(for conceptualization and investigation)partially supported by the Natural Science Foundation of Shandong Province,China under Grants ZR2023LZH017,ZR2024MF066,and 2023QF025(for formal analysis and validation)+1 种基金partially supported by the Open Foundation of Key Laboratory of Computing Power Network and Information Security,Ministry of Education,Qilu University of Technology(Shandong Academy of Sciences)under Grant 2023ZD010(for methodology and model design)partially supported by the Russian Science Foundation(RSF)Project under Grant 22-71-10095-P(for validation and results verification).
文摘To address the challenge of missing modal information in entity alignment and to mitigate information loss or bias arising frommodal heterogeneity during fusion,while also capturing shared information acrossmodalities,this paper proposes a Multi-modal Pre-synergistic Entity Alignmentmodel based on Cross-modalMutual Information Strategy Optimization(MPSEA).The model first employs independent encoders to process multi-modal features,including text,images,and numerical values.Next,a multi-modal pre-synergistic fusion mechanism integrates graph structural and visual modal features into the textual modality as preparatory information.This pre-fusion strategy enables unified perception of heterogeneous modalities at the model’s initial stage,reducing discrepancies during the fusion process.Finally,using cross-modal deep perception reinforcement learning,the model achieves adaptive multilevel feature fusion between modalities,supporting learningmore effective alignment strategies.Extensive experiments on multiple public datasets show that the MPSEA method achieves gains of up to 7% in Hits@1 and 8.2% in MRR on the FBDB15K dataset,and up to 9.1% in Hits@1 and 7.7% in MRR on the FBYG15K dataset,compared to existing state-of-the-art methods.These results confirm the effectiveness of the proposed model.
文摘Traditional Chinese medicine(TCM)demonstrates distinctive advantages in disease prevention and treatment.However,analyzing its biological mechanisms through the modern medical research paradigm of“single drug,single target”presents significant challenges due to its holistic approach.Network pharmacology and its core theory of network targets connect drugs and diseases from a holistic and systematic perspective based on biological networks,overcoming the limitations of reductionist research models and showing considerable value in TCM research.Recent integration of network target computational and experimental methods with artificial intelligence(AI)and multi-modal multi-omics technologies has substantially enhanced network pharmacology methodology.The advancement in computational and experimental techniques provides complementary support for network target theory in decoding TCM principles.This review,centered on network targets,examines the progress of network target methods combined with AI in predicting disease molecular mechanisms and drug-target relationships,alongside the application of multi-modal multi-omics technologies in analyzing TCM formulae,syndromes,and toxicity.Looking forward,network target theory is expected to incorporate emerging technologies while developing novel approaches aligned with its unique characteristics,potentially leading to significant breakthroughs in TCM research and advancing scientific understanding and innovation in TCM.
基金National Natural Science Foundation of China Regional Fund Project(Protective effect and mechanism of new antibiotic zunyimycins on Alzheimer's disease model,82060654).
文摘Background:Alzheimer's disease(AD)is a degenerative disease that occurs in the central nervous system and is characterized by progressive cognitive dysfunction and behavioral impairment.The main pathological mechanisms are amyloid-βaccumulation(Aβ)and hyperphosphorylation of tau protein(P-TAU).Objective:however,there are no effective drugs for the treatment of AD,so the intervention and treatment of AD still need further research.Methods:We did a search and summary some of the new potential drugs and other interventions for the treatment of AD.Results:We found relevant anti-Aβ,anti-tau,anti-aging,the Apolipoprotein(APOE)control drugs and intervention in AD through exercise and diet.Conclusions:because most of these drugs are in clinical trials,more effort is needed to develop anti-AD drugs.
基金funded by the Yangtze River Delta Science and Technology Innovation Community Joint Research Project(2023CSJGG1600)the Natural Science Foundation of Anhui Province(2208085MF173)Wuhu“ChiZhu Light”Major Science and Technology Project(2023ZD01,2023ZD03).
文摘As the number and complexity of sensors in autonomous vehicles continue to rise,multimodal fusionbased object detection algorithms are increasingly being used to detect 3D environmental information,significantly advancing the development of perception technology in autonomous driving.To further promote the development of fusion algorithms and improve detection performance,this paper discusses the advantages and recent advancements of multimodal fusion-based object detection algorithms.Starting fromsingle-modal sensor detection,the paper provides a detailed overview of typical sensors used in autonomous driving and introduces object detection methods based on images and point clouds.For image-based detection methods,they are categorized into monocular detection and binocular detection based on different input types.For point cloud-based detection methods,they are classified into projection-based,voxel-based,point cluster-based,pillar-based,and graph structure-based approaches based on the technical pathways for processing point cloud features.Additionally,multimodal fusion algorithms are divided into Camera-LiDAR fusion,Camera-Radar fusion,Camera-LiDAR-Radar fusion,and other sensor fusion methods based on the types of sensors involved.Furthermore,the paper identifies five key future research directions in this field,aiming to provide insights for researchers engaged in multimodal fusion-based object detection algorithms and to encourage broader attention to the research and application of multimodal fusion-based object detection.
基金Supported by the National Natural Science Foundation of China(No.61472256,61170277)the Hujiang Foundation(No.A14006).
文摘The primary objective of Chinese spelling correction(CSC)is to detect and correct erroneous characters in Chinese text,which can result from various factors,such as inaccuracies in pinyin representation,character resemblance,and semantic discrepancies.However,existing methods often struggle to fully address these types of errors,impacting the overall correction accuracy.This paper introduces a multi-modal feature encoder designed to efficiently extract features from three distinct modalities:pinyin,semantics,and character morphology.Unlike previous methods that rely on direct fusion or fixed-weight summation to integrate multi-modal information,our approach employs a multi-head attention mechanism to focuse more on relevant modal information while dis-regarding less pertinent data.To prevent issues such as gradient explosion or vanishing,the model incorporates a residual connection of the original text vector for fine-tuning.This approach ensures robust model performance by maintaining essential linguistic details throughout the correction process.Experimental evaluations on the SIGHAN benchmark dataset demonstrate that the pro-posed model outperforms baseline approaches across various metrics and datasets,confirming its effectiveness and feasibility.
基金Supported by the Philosophy and Social Sciences Research Project of the Hubei Provincial Department of Education(23D108).
文摘[Objectives]To explore the effect of a comprehensive intervention strategy based on the ESCAPE program in the prevention of post-ICU syndrome.[Methods]A total of 286 patients treated in the ICU of Taihe Hospital from June 2021 to October 2022 were selected as study subjects.138 patients from June 2021 to January 2022 served as the control group,while 130 patients from February 2022 to October 2022 served as the intervention group.The control group received routine nursing care,while the intervention group received a comprehensive intervention strategy based on the ESCAPE protocol.The mental state,sleep quality,fatigue,mechanical ventilation time,ICU hospitalization time,and total hospitalization time were compared between the two groups.[Results]The intervention group showed better sleep quality and lower fatigue at discharge compared to the control group(P<0.05),but no significant difference was observed in mental state(P=0.142).Additionally,the intervention group had shorter mechanical ventilation time,ICU hospitalization time,and total hospitalization time(P<0.05).[Conclusions]The comprehensive intervention strategy based on the ESCAPE protocol effectively improves sleep quality and reduces fatigue,and also shortens the mechanical ventilation time,ICU stay,and total hospitalization duration.
