Multimodal Sentiment Analysis Based on a Cross-Modal Multihead Attention Mechanism

Multimodal sentiment analysis aims to understand people’s emotions and opinions from diverse data.Concate-nating or multiplying various modalities is a traditional multi-modal sentiment analysis fusion method.This fusion method does not utilize the correlation information between modalities.To solve this problem,this paper proposes amodel based on amulti-head attention mechanism.First,after preprocessing the original data.Then,the feature representation is converted into a sequence of word vectors and positional encoding is introduced to better understand the semantic and sequential information in the input sequence.Next,the input coding sequence is fed into the transformer model for further processing and learning.At the transformer layer,a cross-modal attention consisting of a pair of multi-head attention modules is employed to reflect the correlation between modalities.Finally,the processed results are input into the feedforward neural network to obtain the emotional output through the classification layer.Through the above processing flow,the model can capture semantic information and contextual relationships and achieve good results in various natural language processing tasks.Our model was tested on the CMU Multimodal Opinion Sentiment and Emotion Intensity(CMU-MOSEI)and Multimodal EmotionLines Dataset(MELD),achieving an accuracy of 82.04% and F1 parameters reached 80.59% on the former dataset.

A Robust Framework for Multimodal Sentiment Analysis with Noisy Labels Generated from Distributed Data Annotation

Multimodal sentiment analysis utilizes multimodal data such as text,facial expressions and voice to detect people’s attitudes.With the advent of distributed data collection and annotation,we can easily obtain and share such multimodal data.However,due to professional discrepancies among annotators and lax quality control,noisy labels might be introduced.Recent research suggests that deep neural networks(DNNs)will overfit noisy labels,leading to the poor performance of the DNNs.To address this challenging problem,we present a Multimodal Robust Meta Learning framework(MRML)for multimodal sentiment analysis to resist noisy labels and correlate distinct modalities simultaneously.Specifically,we propose a two-layer fusion net to deeply fuse different modalities and improve the quality of the multimodal data features for label correction and network training.Besides,a multiple meta-learner(label corrector)strategy is proposed to enhance the label correction approach and prevent models from overfitting to noisy labels.We conducted experiments on three popular multimodal datasets to verify the superiority of ourmethod by comparing it with four baselines.

MAIPFE:An Efficient Multimodal Approach Integrating Pre-Emptive Analysis,Personalized Feature Selection,and Explainable AI

Medical Internet of Things(IoT)devices are becoming more and more common in healthcare.This has created a huge need for advanced predictive health modeling strategies that can make good use of the growing amount of multimodal data to find potential health risks early and help individuals in a personalized way.Existing methods,while useful,have limitations in predictive accuracy,delay,personalization,and user interpretability,requiring a more comprehensive and efficient approach to harness modern medical IoT devices.MAIPFE is a multimodal approach integrating pre-emptive analysis,personalized feature selection,and explainable AI for real-time health monitoring and disease detection.By using AI for early disease detection,personalized health recommendations,and transparency,healthcare will be transformed.The Multimodal Approach Integrating Pre-emptive Analysis,Personalized Feature Selection,and Explainable AI(MAIPFE)framework,which combines Firefly Optimizer,Recurrent Neural Network(RNN),Fuzzy C Means(FCM),and Explainable AI,improves disease detection precision over existing methods.Comprehensive metrics show the model’s superiority in real-time health analysis.The proposed framework outperformed existing models by 8.3%in disease detection classification precision,8.5%in accuracy,5.5%in recall,2.9%in specificity,4.5%in AUC(Area Under the Curve),and 4.9%in delay reduction.Disease prediction precision increased by 4.5%,accuracy by 3.9%,recall by 2.5%,specificity by 3.5%,AUC by 1.9%,and delay levels decreased by 9.4%.MAIPFE can revolutionize healthcare with preemptive analysis,personalized health insights,and actionable recommendations.The research shows that this innovative approach improves patient outcomes and healthcare efficiency in the real world.

Multimodal Discourse Analysis of Public Service Posters on Low-Carbon Life

Low-carbon living represents both a new lifestyle and an environmental responsibility vital for sustainable development,positioning it as a central theme in environmental public service advertisements(PSAs).This research employs a multimodal discourse analysis approach to examining eight public service posters advocating low-carbon living from Xinhua News Agency,primarily utilizing the visual grammar framework established by Kress and Van Leeuwen.The study explores these posters through three key perspectives:representational meaning,interactive meaning,and compositional meaning.By analyzing the interplay between visual and linguistic modalities,this research contributes to the overall understanding of public service posters and supports the promotion of China’s low-carbon development initiatives.

Structural Modal Parameter Recognition and Related Damage Identification Methods under Environmental Excitations: A Review

Modal parameters can accurately characterize the structural dynamic properties and assess the physical state of the structure.Therefore,it is particularly significant to identify the structural modal parameters according to the monitoring data information in the structural health monitoring(SHM)system,so as to provide a scientific basis for structural damage identification and dynamic model modification.In view of this,this paper reviews methods for identifying structural modal parameters under environmental excitation and briefly describes how to identify structural damages based on the derived modal parameters.The paper primarily introduces data-driven modal parameter recognition methods(e.g.,time-domain,frequency-domain,and time-frequency-domain methods,etc.),briefly describes damage identification methods based on the variations of modal parameters(e.g.,natural frequency,modal shapes,and curvature modal shapes,etc.)and modal validation methods(e.g.,Stability Diagram and Modal Assurance Criterion,etc.).The current status of the application of artificial intelligence(AI)methods in the direction of modal parameter recognition and damage identification is further discussed.Based on the pre-vious analysis,the main development trends of structural modal parameter recognition and damage identification methods are given to provide scientific references for the optimized design and functional upgrading of SHM systems.

Dynamic Interaction Analysis of Coupled Axial-Torsional-Lateral Mechanical Vibrations in Rotary Drilling Systems

Maintaining the integrity and longevity of structures is essential in many industries,such as aerospace,nuclear,and petroleum.To achieve the cost-effectiveness of large-scale systems in petroleum drilling,a strong emphasis on structural durability and monitoring is required.This study focuses on the mechanical vibrations that occur in rotary drilling systems,which have a substantial impact on the structural integrity of drilling equipment.The study specifically investigates axial,torsional,and lateral vibrations,which might lead to negative consequences such as bit-bounce,chaotic whirling,and high-frequency stick-slip.These events not only hinder the efficiency of drilling but also lead to exhaustion and harm to the system’s components since they are difficult to be detected and controlled in real time.The study investigates the dynamic interactions of these vibrations,specifically in their high-frequency modes,usingfield data obtained from measurement while drilling.Thefindings have demonstrated the effect of strong coupling between the high-frequency modes of these vibrations on drilling sys-tem performance.The obtained results highlight the importance of considering the interconnected impacts of these vibrations when designing and implementing robust control systems.Therefore,integrating these compo-nents can increase the durability of drill bits and drill strings,as well as improve the ability to monitor and detect damage.Moreover,by exploiting thesefindings,the assessment of structural resilience in rotary drilling systems can be enhanced.Furthermore,the study demonstrates the capacity of structural health monitoring to improve the quality,dependability,and efficiency of rotary drilling systems in the petroleum industry.

Research status and prospects of the fractal analysis of metal material surfaces and interfaces

As a mathematical analysis method,fractal analysis can be used to quantitatively describe irregular shapes with self-similar or self-affine properties.Fractal analysis has been used to characterize the shapes of metal materials at various scales and dimensions.Conventional methods make it difficult to quantitatively describe the relationship between the regular characteristics and properties of metal material surfaces and interfaces.However,fractal analysis can be used to quantitatively describe the shape characteristics of metal materials and to establish the quantitative relationships between the shape characteristics and various properties of metal materials.From the perspective of two-dimensional planes and three-dimensional curved surfaces,this paper reviews the current research status of the fractal analysis of metal precipitate interfaces,metal grain boundary interfaces,metal-deposited film surfaces,metal fracture surfaces,metal machined surfaces,and metal wear surfaces.The relationship between the fractal dimensions and properties of metal material surfaces and interfaces is summarized.Starting from three perspectives of fractal analysis,namely,research scope,image acquisition methods,and calculation methods,this paper identifies the direction of research on fractal analysis of metal material surfaces and interfaces that need to be developed.It is believed that revealing the deep influence mechanism between the fractal dimensions and properties of metal material surfaces and interfaces will be the key research direction of the fractal analysis of metal materials in the future.

Antioxidant and lipoxygenase inhibitory properties of a novel flavonoid from Pistacia chinensis Bunge and its molecular docking analysis

Background:Pistacia chinensis Bunge has been traditionally used to manage various conditions,including asthma,pain,inflammation,hepatoprotection,and diabetes.The study was conducted to investigate the antioxidant and anti-lipoxygenase(LOX)properties of the isolated compound 2-(3,4-dihydroxyphenyl)-5,7-dihydroxy-4H-chromen-4-one from Pistacia chinensis.Methods:LOX assay and antioxidant activity using 2,2-diphenyl-1-picrylhydrazyl(DPPH)assay were performed.Molecular docking studies were conducted using a molecular operating environment.Results:The LOX assay revealed significant inhibitory effects at 0.2µM concentration,with an IC50 value of 37.80µM.The antioxidant effect demonstrated dose-dependency across 5 to 100µg/mL concentrations,reaching 93.09%at 100µg/mL,comparable to ascorbic acid’s 95.43%effect.Molecular docking studies highlighted strong interactions with the lipoxygenase enzyme,presenting an excellent docking score of-10.98 kcal/mol.Conclusion:These findings provide valuable insights into Pistacia chinensis’chemical components and biological effects,reinforcing its traditional medicinal applications.

A Multimodal Critical Discourse Analysis of Lingnan Cultural Promotional Videos on Social Media

In recent years,more and more directors of culture and tourism have taken part in the promotion of local cultural tourism by cross-dressing,talent shows,and pushing their limits on self-media platforms.This study investigates short videos of Lingnan culture promoted by directors general and deputy directors general of the Culture,Radio,Television,Tourism,and Sports Bureau of counties and cities in Guangdong Province on social media by the method of multimodal critical discourse analysis.The analysis of 33 videos shows that Lingnan culture is a domineering and confident culture,historical culture,graceful and elegant culture,and vibrant and active culture.Domineering and confident culture is embedded in the utterances and behaviors of the directors general or deputy directors general in the video.Historical culture is realized through the conversation with historical figures through time travel.Graceful and elegant culture is constructed in the depiction of sceneries and the depiction of characters’manners.Vibrant and active culture is represented in the depiction of the characters’actional process and analytical process.

Prenatal ultrasonography and genetic analysis of fetal cleidocranial dysplasia:A case report

BACKGROUND Cleidocranial dysplasia(CCD)is an infrequent clinical condition with an autosomal dominant inheritance pattern.It is characterized by abnormal clavicles,patent sutures and fontanelles,supernumerary teeth,and short stature.Approximately 60%-70%of patients with CCD have mutations in the RUNX family transcription factor 2 gene.However,prenatal diagnosis of CCD is difficult when the family history is unknown.CASE SUMMARY We report a rare case of fetal CCD with an unknown family history,confirmed by prenatal ultrasonography and genetic testing at a gestational age of 16 weeks.The genetic reports indicated that the fetus carried pathogenic mutations in the RUNX family transcription factor 2 gene(c.674G>A).After careful consideration,the pregnant woman and her family decided to continue the pregnancy.CONCLUSION Definitive prenatal diagnosis of CCD should include family history,ultrasound diagnosis,and genetic analysis,especially if family history is unknown.

Retrospective analysis of pathological types and imaging features in pancreatic cancer: A comprehensive study

BACKGROUND Pancreatic cancer remains one of the most lethal malignancies worldwide,with a poor prognosis often attributed to late diagnosis.Understanding the correlation between pathological type and imaging features is crucial for early detection and appropriate treatment planning.AIM To retrospectively analyze the relationship between different pathological types of pancreatic cancer and their corresponding imaging features.METHODS We retrospectively analyzed the data of 500 patients diagnosed with pancreatic cancer between January 2010 and December 2020 at our institution.Pathological types were determined by histopathological examination of the surgical spe-cimens or biopsy samples.The imaging features were assessed using computed tomography,magnetic resonance imaging,and endoscopic ultrasound.Statistical analyses were performed to identify significant associations between pathological types and specific imaging characteristics.RESULTS There were 320(64%)cases of pancreatic ductal adenocarcinoma,75(15%)of intraductal papillary mucinous neoplasms,50(10%)of neuroendocrine tumors,and 55(11%)of other rare types.Distinct imaging features were identified in each pathological type.Pancreatic ductal adenocarcinoma typically presents as a hypodense mass with poorly defined borders on computed tomography,whereas intraductal papillary mucinous neoplasms present as characteristic cystic lesions with mural nodules.Neuroendocrine tumors often appear as hypervascular lesions in contrast-enhanced imaging.Statistical analysis revealed significant correlations between specific imaging features and pathological types(P<0.001).CONCLUSION This study demonstrated a strong association between the pathological types of pancreatic cancer and imaging features.These findings can enhance the accuracy of noninvasive diagnosis and guide personalized treatment approaches.

A Study of the English Translation of Characteristic Cuisines of the Hexi Corridor from the Perspective of Multimodal Discourse Analysis

Given the diversity of geographical and cultural backgrounds,it is difficult to find precise counterparts of many food-related vocabularies in the unique dietary culture of the Hexi Corridor in the target language,making it challenging to fully convey the essence of its local dietary culture in translation.To achieve the purpose of translation,the theory of multimodal discourse analysis provides an ideal translation approach through the language mode,image mode,sound mode,and the synergistic effect of multimodalities,which can accurately address the deficiencies in the translation of dietary culture.Through this research,it is not only possible to highlight the unique charm of the dietary culture of the Hexi Corridor but also to build a bridge for its international dissemination,promoting cross-border cultural exchanges and mutual understanding.

Comprehensive bibliometric analysis of pharmacotherapy for bipolar disorders:Present trends and future directions

BACKGROUND Bipolar disorder(BD)is a severe mental illness characterized by significant mood swings.Effective drug treatment modalities are crucial for managing BD.AIM To analyze the current status and future trends of global research on BD drug treatment over the last decade.METHODS The Web of Science Core Collection database spanning from 2015 to 2024 was utilized to retrieve literature related to BD drug treatment.A total of 2624 articles were extracted.Data visualization and analysis were conducted using CiteSpace,VOSviewer,Pajek,Scimago Graphica,and R-studio bibliometrix to identify RESULTS The United States,China,and the United Kingdom have made the most significant contributions to research on BD drug treatment and formed notable research collaboration networks.The University of Pittsburgh,Massachusetts General Hospital,and the University of Michigan have been identified as the major research institutions in this field.The Journal of Affective Disorders is the most influential journal.A keyword analysis revealed research hotspots related to clinical symptoms,drug efficacy,and genetic mechanisms.A citation analysis identified the management guidelines published by Yatham et al in 2018 as the most cited paper.CONCLUSION This study provides a detailed overview of the field of BD drug treatment,highlighting key contributors,research hotspots,and future directions.The study findings can be employed as a reference for future research and policymaking,which may enable further development and optimization of BD pharmacotherapy.

Targeted gene sequencing and bioinformatics analysis of patients with gallbladder neuroendocrine carcinoma:A case report

BACKGROUND Gallbladder neuroendocrine carcinoma(NEC)represents a subtype of gallbladder malignancies characterized by a low incidence,aggressive nature,and poor prognosis.Despite its clinical severity,the genetic alterations,mechanisms,and signaling pathways underlying gallbladder NEC remain unclear.CASE SUMMARY This case study presents a rare instance of primary gallbladder NEC in a 73-year-old female patient,who underwent a radical cholecystectomy with hepatic hilar lymphadenectomy and resection of liver segments IV-B and V.Targeted gene sequencing and bioinformatics analysis tools,including STRING,GeneMANIA,Metascape,TRRUST,Sangerbox,cBioPortal and GSCA,were used to analyze the biological functions and features of mutated genes in gallbladder NEC.Twelve mutations(APC,ARID2,IFNA6,KEAP1,RB1,SMAD4,TP53,BTK,GATA1,GNAS,and PRDM3)were identified,and the tumor mutation burden was determined to be 9.52 muts/Mb via targeted gene sequencing.A protein-protein interaction network showed significant interactions among the twelve mutated genes.Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses were used to assess mutation functions and pathways.The results revealed 40 tumor-related pathways.A key regulatory factor for gallbladder NEC-related genes was identified,and its biological functions and features were compared with those of gallbladder carcinoma.CONCLUSION Gallbladder NEC requires standardized treatment.Comparisons with other gallbladder carcinomas revealed clinical phenotypes,molecular alterations,functional characteristics,and enriched pathways.

Analysis of Commuting Modal Shift in Consideration of Social Interaction of Consciousness for Environment

It is the matter for achievement of the low carbon transport system that the excessive use of private vehicles can be controlled appropriately.Not only improvement of service level of modes except private vehicle,but also consciousness for environmental problem of individual trip maker is important for eco-commuting promotion.On the other hand,consciousness for environment would be changed by influence of other person.Accordingly,it is aimed in the study that the structure of decision-making process for modal shift to the eco-commuting mode in the local city is described considering environmental consciousness and social interaction.For the purpose,the consciousness for the environment problem and the travel behavior of the commuter at the suburban area in the local city are investigated by the questionnaire survey.The covariance structure about the eco-consciousness is analyzed with the database of the questionnaire survey by structural equation modeling.As the result,it can be confirmed with the structural equation model that the individual environmental consciousness is strongly related with the intention of self-sacrifice and is influenced with the local interaction of the individual connections.On the other hand,the intention of modal shift for the commuting mode is analyzed with the database of the questionnaire survey.It can be found out that the environmental consciousness is not statistically significant for commuting mode choice with the present poor level of service of public transport.However,the intention of self-sacrifice for the prevention of the global warming is statistically confirmed as the factor of modal shift with the operation of eco-commuting bus service with the RP/SP integrated estimation method.As the result,the multi-agent simulation system with social interaction model for eco consciousness is developed to measure the effect of the eco-commuting promotion.For the purpose,the carbon dioxide emission is estimated based on traffic demand and road network condition in the traffic environment model.On the other hand,the relation between agents is defined based on the small world network.The proposed multi-agent simulation is applied to measure the effect of the eco-commuting promotion such as improvement of level of service on the public transport or education of eco-consciousness.The effect of the promotion plan can be observed with the proposed multi-agent system.Finally,it can be concluded that the proposed multi-agent simulation with social interaction for eco-consciousness is useful for planning of eco-commuting promotion.

Multilevel analysis of the central-peripheral-target organ pathway:contributing to recovery after peripheral nerve injury

Peripheral nerve injury is a common neurological condition that often leads to severe functional limitations and disabilities.Research on the pathogenesis of peripheral nerve injury has focused on pathological changes at individual injury sites,neglecting multilevel pathological analysis of the overall nervous system and target organs.This has led to restrictions on current therapeutic approaches.In this paper,we first summarize the potential mechanisms of peripheral nerve injury from a holistic perspective,covering the central nervous system,peripheral nervous system,and target organs.After peripheral nerve injury,the cortical plasticity of the brain is altered due to damage to and regeneration of peripheral nerves;changes such as neuronal apoptosis and axonal demyelination occur in the spinal cord.The nerve will undergo axonal regeneration,activation of Schwann cells,inflammatory response,and vascular system regeneration at the injury site.Corresponding damage to target organs can occur,including skeletal muscle atrophy and sensory receptor disruption.We then provide a brief review of the research advances in therapeutic approaches to peripheral nerve injury.The main current treatments are conducted passively and include physical factor rehabilitation,pharmacological treatments,cell-based therapies,and physical exercise.However,most treatments only partially address the problem and cannot complete the systematic recovery of the entire central nervous system-peripheral nervous system-target organ pathway.Therefore,we should further explore multilevel treatment options that produce effective,long-lasting results,perhaps requiring a combination of passive(traditional)and active(novel)treatment methods to stimulate rehabilitation at the central-peripheral-target organ levels to achieve better functional recovery.

An Analysis of Modal Verbs in Language Teaching

Through classifying modal verbs from the semantic aspect and the pragmatic aspect, this paper discusses difficulties that students may encounter in modal verb learning. It indicates that modal verbs have complicacy, and a deductive approach and inductive approach should intermingle as an appropriate method to improve students' accuracy in use of modal verbs.

Analysis on the Modality of English News Report

Modality is used to express subjective judgment as well as the speaker’s opinions and attitudes.This paper aims to analyze the reporters’orientation reflected by linguistic modal expressions in English news reports from the perspective of Systemic-Functional Grammar,especially from the perspective of type,value,orientation and polarity.

Effects of Joint Controller on Analytical Modal Analysis of Rotational Flexible Manipulator

Modal analysis is a fundamental and important task for modeling and control of the flexible manipulator. However, almost all of the traditional modal analysis methods view the flexible manipulator as a pure mechanical structure and neglect feedback action of joint controller. In order to study the effects of joint controller on the modal analysis of rotational flexible manipulator, a closed-loop analytical modal analysis method is proposed. Firstly, two exact boundary constraints, namely servo feedback constraint and bending moment constraint, are derived to solve the vibration partial differential equation. It is found that the stiffness and damping gains of joint controller are both included in the boundary conditions, which lead to an unconventional secular term. Secondly, analytical algorithm based on Ritz approach is developed by using Laplace transform and complex modal approach to obtain the natural frequencies and mode shapes. And then, the numerical simulations are performed and the computational results show that joint controller has pronounced influence on the modal parameters： joint controller stiffness reduces the natural frequency, while joint controller damping makes the shape phase non-zero. Furthermore, the validity of the presented conclusion is confirmed through experimental studies. These findings are expected to improve the performance of dynamics simulation systems and model-based controllers.

An operational modal analysis method in frequency and spatial domain

A frequency and spatial domain decomposition method （FSDD） for operational modal analysis （OMA） is presented in this paper, which is an extension of the complex mode indicator function （CMIF） method for experimental modal analysis （EMA）. The theoretical background of the FSDD method is clarified, Singular value decomposition is adopted to separate the signal space from the noise space. Finally, an enhanced power spectrum density （PSD） is proposed to obtain more accurate modal parameters by curve fitting in the frequency domain. Moreover, a simulation case and an application case are used to validate this method.