Text-Image Feature Fine-Grained Learning for Joint Multimodal Aspect-Based Sentiment Analysis

Joint Multimodal Aspect-based Sentiment Analysis(JMASA)is a significant task in the research of multimodal fine-grained sentiment analysis,which combines two subtasks:Multimodal Aspect Term Extraction(MATE)and Multimodal Aspect-oriented Sentiment Classification(MASC).Currently,most existing models for JMASA only perform text and image feature encoding from a basic level,but often neglect the in-depth analysis of unimodal intrinsic features,which may lead to the low accuracy of aspect term extraction and the poor ability of sentiment prediction due to the insufficient learning of intra-modal features.Given this problem,we propose a Text-Image Feature Fine-grained Learning(TIFFL)model for JMASA.First,we construct an enhanced adjacency matrix of word dependencies and adopt graph convolutional network to learn the syntactic structure features for text,which addresses the context interference problem of identifying different aspect terms.Then,the adjective-noun pairs extracted from image are introduced to enable the semantic representation of visual features more intuitive,which addresses the ambiguous semantic extraction problem during image feature learning.Thereby,the model performance of aspect term extraction and sentiment polarity prediction can be further optimized and enhanced.Experiments on two Twitter benchmark datasets demonstrate that TIFFL achieves competitive results for JMASA,MATE and MASC,thus validating the effectiveness of our proposed methods.

Intelligent Recognition Using Ultralight Multifunctional Nano‑Layered Carbon Aerogel Sensors with Human‑Like Tactile Perception

Humans can perceive our complex world through multi-sensory fusion.Under limited visual conditions,people can sense a variety of tactile signals to identify objects accurately and rapidly.However,replicating this unique capability in robots remains a significant challenge.Here,we present a new form of ultralight multifunctional tactile nano-layered carbon aerogel sensor that provides pressure,temperature,material recognition and 3D location capabilities,which is combined with multimodal supervised learning algorithms for object recognition.The sensor exhibits human-like pressure(0.04–100 kPa)and temperature(21.5–66.2℃)detection,millisecond response times(11 ms),a pressure sensitivity of 92.22 kPa^(−1)and triboelectric durability of over 6000 cycles.The devised algorithm has universality and can accommodate a range of application scenarios.The tactile system can identify common foods in a kitchen scene with 94.63%accuracy and explore the topographic and geomorphic features of a Mars scene with 100%accuracy.This sensing approach empowers robots with versatile tactile perception to advance future society toward heightened sensing,recognition and intelligence.

Optoelectronic Synapses Based on MXene/Violet Phosphorus van der Waals Heterojunctions for Visual‑Olfactory Crossmodal Perception

The crossmodal interaction of different senses,which is an important basis for learning and memory in the human brain,is highly desired to be mimicked at the device level for developing neuromorphic crossmodal perception,but related researches are scarce.Here,we demonstrate an optoelectronic synapse for vision-olfactory crossmodal perception based on MXene/violet phosphorus(VP)van der Waals heterojunctions.Benefiting from the efficient separation and transport of photogenerated carriers facilitated by conductive MXene,the photoelectric responsivity of VP is dramatically enhanced by 7 orders of magnitude,reaching up to 7.7 A W^(−1).Excited by ultraviolet light,multiple synaptic functions,including excitatory postsynaptic currents,pairedpulse facilitation,short/long-term plasticity and“learning-experience”behavior,were demonstrated with a low power consumption.Furthermore,the proposed optoelectronic synapse exhibits distinct synaptic behaviors in different gas environments,enabling it to simulate the interaction of visual and olfactory information for crossmodal perception.This work demonstrates the great potential of VP in optoelectronics and provides a promising platform for applications such as virtual reality and neurorobotics.

Edge enhanced depth perception with binocular meta-lens

The increasing popularity of the metaverse has led to a growing interest and market size in spatial computing from both academia and industry.Developing portable and accurate imaging and depth sensing systems is crucial for advancing next-generation virtual reality devices.This work demonstrates an intelligent,lightweight,and compact edge-enhanced depth perception system that utilizes a binocular meta-lens for spatial computing.The miniaturized system comprises a binocular meta-lens,a 532 nm filter,and a CMOS sensor.For disparity computation,we propose a stereo-matching neural network with a novel H-Module.The H-Module incorporates an attention mechanism into the Siamese network.The symmetric architecture,with cross-pixel interaction and cross-view interaction,enables a more comprehensive analysis of contextual information in stereo images.Based on spatial intensity discontinuity,the edge enhancement eliminates illposed regions in the image where ambiguous depth predictions may occur due to a lack of texture.With the assistance of deep learning,our edge-enhanced system provides prompt responses in less than 0.15 seconds.This edge-enhanced depth perception meta-lens imaging system will significantly contribute to accurate 3D scene modeling,machine vision,autonomous driving,and robotics development.

Multimodal Social Media Fake News Detection Based on Similarity Inference and Adversarial Networks

As social networks become increasingly complex, contemporary fake news often includes textual descriptionsof events accompanied by corresponding images or videos. Fake news in multiple modalities is more likely tocreate a misleading perception among users. While early research primarily focused on text-based features forfake news detection mechanisms, there has been relatively limited exploration of learning shared representationsin multimodal (text and visual) contexts. To address these limitations, this paper introduces a multimodal modelfor detecting fake news, which relies on similarity reasoning and adversarial networks. The model employsBidirectional Encoder Representation from Transformers (BERT) and Text Convolutional Neural Network (Text-CNN) for extracting textual features while utilizing the pre-trained Visual Geometry Group 19-layer (VGG-19) toextract visual features. Subsequently, the model establishes similarity representations between the textual featuresextracted by Text-CNN and visual features through similarity learning and reasoning. Finally, these features arefused to enhance the accuracy of fake news detection, and adversarial networks have been employed to investigatethe relationship between fake news and events. This paper validates the proposed model using publicly availablemultimodal datasets from Weibo and Twitter. Experimental results demonstrate that our proposed approachachieves superior performance on Twitter, with an accuracy of 86%, surpassing traditional unimodalmodalmodelsand existing multimodal models. In contrast, the overall better performance of our model on the Weibo datasetsurpasses the benchmark models across multiple metrics. The application of similarity reasoning and adversarialnetworks in multimodal fake news detection significantly enhances detection effectiveness in this paper. However,current research is limited to the fusion of only text and image modalities. Future research directions should aimto further integrate features fromadditionalmodalities to comprehensively represent themultifaceted informationof fake news.

Evolution and Prospects of Foundation Models: From Large Language Models to Large Multimodal Models

Since the 1950s,when the Turing Test was introduced,there has been notable progress in machine language intelligence.Language modeling,crucial for AI development,has evolved from statistical to neural models over the last two decades.Recently,transformer-based Pre-trained Language Models(PLM)have excelled in Natural Language Processing(NLP)tasks by leveraging large-scale training corpora.Increasing the scale of these models enhances performance significantly,introducing abilities like context learning that smaller models lack.The advancement in Large Language Models,exemplified by the development of ChatGPT,has made significant impacts both academically and industrially,capturing widespread societal interest.This survey provides an overview of the development and prospects from Large Language Models(LLM)to Large Multimodal Models(LMM).It first discusses the contributions and technological advancements of LLMs in the field of natural language processing,especially in text generation and language understanding.Then,it turns to the discussion of LMMs,which integrates various data modalities such as text,images,and sound,demonstrating advanced capabilities in understanding and generating cross-modal content,paving new pathways for the adaptability and flexibility of AI systems.Finally,the survey highlights the prospects of LMMs in terms of technological development and application potential,while also pointing out challenges in data integration,cross-modal understanding accuracy,providing a comprehensive perspective on the latest developments in this field.

Correlation linking illness perception,negative emotions,and the post-operative recovery effect in patients with perianal disease

BACKGROUND Improvements in the standard of living have led to increased attention to perianal disease.Although surgical treatments are effective,the outcomes of postoperative recovery(POR)are influenced by various factors,including individual differences among patients,the characteristics of the disease itself,and the psychological state of the patient.Understanding these factors can help healthcare providers develop more personalized and effective post-operative care plans for patients with perianal disease.AIM To investigate the effect of illness perception(IP)and negative emotions on POR outcomes in patients with perianal disease.METHODS A total of 146 patients with perianal disease admitted to the First People's Hospital of Changde City from March to December 2023 were recruited.We employed a general information questionnaire,the Brief Illness Perception Questionnaire(B-IPQ),and the Hospital Anxiety and Depression Scale(HADS).We used the 15-item Quality of Recovery Score(QoR-15)to measure patients’recovery effects.Finally,we conducted Pearson’s correlation analysis to examine the relationship between pre-operative IP and anxiety and depression levels with POR quality.RESULTS Fifty-three(36.3%)had poor knowledge of their disease.Thirty(20.5%)were suspected of having anxiety and 99(67.8%)exhibited symptoms.Forty(27.4%)were suspected of having depression and 102(69.9%)displayed symptoms.The B-IPQ,HADS-A,HADS-D,and QoR-15 scores were 46.82±9.97,12.99±3.60,12.58±3.36,and 96.77±9.85,respectively.There was a negative correlation between pre-operative IP,anxiety,and depression with POR quality.The influence of age and disease course on post-operative rehabilitation effect are both negative.The impact of B-IPQ,HADS-A,and HADS-D on POR was negative.Collectively,these variables accounted for 72.6%of the variance in POR.CONCLUSION The quality of POR in patients with perianal disease is medium and is related to age,disease course,IP,anxiety,and depression.

Perception of dental appearance and aesthetic analysis among patients,laypersons and dentists

BACKGROUND Current concepts of beauty are increasingly subjective,influenced by the viewpoints of others.The aim of the study was to evaluate divergences in the perception of dental appearance and smile esthetics among patients,laypersons and dental practitioners.The study goals were to evaluate the influence of age,sex,education and dental specialty on the participants’judgment and to identify the values of different esthetic criteria.Patients sample included 50 patients who responded to a dental appearance questionnaire(DAQ).Two frontal photographs were taken,one during a smile and one with retracted lips.Laypersons and dentists were asked to evaluate both photographs using a Linear Scale from(0-10),where 0 represent(absolutely unaesthetic)and 10 represent(absolutely aesthetic).One-way analysis of variance(ANOVA)and t-test analysis were measured for each group.Most patients in the sample expressed satisfaction with most aspects of their smiles and dental appearance.Among laypersons(including 488 participants),47 pictures“with lips”out of 50 had higher mean aesthetic scores compared to pictures“without lips”.Among the dentist sample,90 dentists’perception towards the esthetic smile and dental appearance for photos“with lips”and“without lips”were the same for 23 out of 50 patients.Perception of smile aesthetics differed between patients,laypersons and dentists.Several factors can contribute to shape the perception of smile aesthetic.AIM To compare the perception of dental aesthetic among patients,laypersons,and professional dentists,to evaluate the impact of age,sex,educational background,and income on the judgments made by laypersons,to assess the variations in experience,specialty,age,and sex on professional dentists’judgment,and to evaluate the role of lips,skin shade and tooth shade in different participants’judgments.METHODS Patients sample included 50 patients who responded to DAQ.Two frontal photographs were taken:one during a smile and one with retracted lips.Laypersons and dentists were asked to evaluate both photographs using a Linear Scale from(0-10),where 0 represent(absolutely unaesthetic)and 10 represent(absolutely aesthetic).One-way ANOVA and t-test analysis were measured for each group.RESULTS Most patients in the sample expressed satisfaction with most aspects of their smiles and dental appearance.Among laypersons(including 488 participants),47 pictures“with lips”out of 50 had higher mean aesthetic scores compared to pictures“without lips”.Whereas among the dentist sample,90 dentists’perception towards the esthetic smile and dental appearance for photos“with lips”and“without lips”were the same for 23 out of 50 patients.Perception of smile aesthetics differed between patients,laypersons and dentists.CONCLUSION Several factors can contribute to shape the perception of smile aesthetic.

Knowledge, Attitudes and Perceptions of Colorectal Cancer Screening in Brazzaville

Objective: assess the knowledge, attitudes, and perceptions of Brazzaville residents on colorectal cancer and its screening. Population and Methods: a CAP-type cross-sectional study was conducted from June 1 to October 31, 2022, with 803 workers approached at their place of service. Information was collected using a questionnaire administered to participants. The variables studied concerned knowledge, attitudes, and perceptions about colorectal cancer. SPSS software, along with Chi-square and Fisher tests, was used for data entry and analysis. Odds ratios were calculated to determine the strength of the association between variables. Results: The average age of the participants was 33.5 ± 10 years, with a sex ratio of 0.9. There were 231 health workers. The main sources of information were health personnel (78.2%) and the internet (52.6%). The site of the pathology was known to 87% of participants. About 40% identified age, genetic predispositions, and a diet rich in animal fats as risk factors. No signs of the disease were known by 50% of the participants. Colonoscopy was known as a screening method by 40% of participants. Seventy-five percent were willing to participate in a CRC awareness campaign, but only 5% agreed to a screening colonoscopy, although 96% recognized its usefulness. Overall, the level of knowledge was insufficient in 70.4% of cases;attitudes were adapted in 55.7% of cases, and perceptions were adapted in 97.3% of cases. Factors influencing knowledge included young age (p = 0.006), a good level of education, being a healthcare worker, and high socio-economic level. Conclusion: colorectal cancer and its screening are poorly understood by the population. Awareness activities must be organized to improve knowledge and promote screening and early diagnosis of CRC.

An Improved SCMA Detector Based on ResNet Perception Fusion

The received signals used for sparse code multiple access(SCMA)detection are usually contaminated with noise during transmission,which exposes an issue of low decoding efficiency.To address this issue,a novel detector based on a residual network(ResNet)perception fusion framework(RSMPA)is proposed for uplink SCMA system in this paper.Specifically,we first formulate a joint design of perception system and traditional communication module.A perception framework based on ResNet is applied to cancel the noise component and enhance the communication system performance.The ResNet model is designed and trained using the clean and noisy SCMA signal,respectively.Based on the denoised output,information iteration process is executed for multi-user detection.Simulation results indicate that the perception model achieves an excellent denoising performance for SCMA system and the proposed scheme outperforms the conventional detection algorithms in terms of SER performance.

Insights into the enhancement of food flavor perception:strategies,mechanism and emulsion applications

The core drivers of the modern food industry are meeting consumer demand for tasty and healthy foods.The application of food flavor perception enhancement can help to achieve the goals of salt-and sugar-reduction,without compromising the sensory quality of the original food,and this has attracted increasing research attention.The analysis of bibliometric results from 2002 to 2022 reveals that present flavor perception enhancement strategies(changing ingredient formulations,adding salt/sugar substitutes,emulsion delivery systems)are mainly carry out based on sweetness,saltiness and umami.Emulsion systems is becoming a novel research foci and development trends of international food flavor perception-enhancement research.The structured design of food emulsions,by using interface engineering technology,can effectively control,or enhance the release of flavor substances.Thus,this review systematically summarizes strategies,the application of emulsion systems and the mechanisms of action of food flavor perception-enhancement technologies,based on odor-taste cross-modal interaction(OTCMI),to provide insights into the further structural design and application of emulsion systems in this field.

Conditional selection with CNN augmented transformer for multimodal affective analysis

Attention mechanism has been a successful method for multimodal affective analysis in recent years. Despite the advances, several significant challenges remain in fusing language and its nonverbal context information. One is to generate sparse attention coefficients associated with acoustic and visual modalities, which helps locate critical emotional se-mantics. The other is fusing complementary cross‐modal representation to construct optimal salient feature combinations of multiple modalities. A Conditional Transformer Fusion Network is proposed to handle these problems. Firstly, the authors equip the transformer module with CNN layers to enhance the detection of subtle signal patterns in nonverbal sequences. Secondly, sentiment words are utilised as context conditions to guide the computation of cross‐modal attention. As a result, the located nonverbal fea-tures are not only salient but also complementary to sentiment words directly. Experi-mental results show that the authors’ method achieves state‐of‐the‐art performance on several multimodal affective analysis datasets.

Choosing Multimodal Freight Mix: An Integrated Multi-Objective Multicriteria Approach

Multimodal freight transportation emerges as the go-to strategy for cost-effectively and sustainably moving goods over long distances. In a multimodal freight system, where a single contract includes various transportation methods, businesses aiming for economic success must make well-informed decisions about which modes of transport to use. These decisions prioritize secure deliveries, competitive cost advantages, and the minimization of environmental footprints associated with transportation-related pollution. Within the dynamic landscape of logistics innovation, various multicriteria decision-making (MCDM) approaches empower businesses to evaluate freight transport options thoroughly. In this study, we utilize a case study to demonstrate the application of the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) algorithm for MCDM decision-making in freight mode selection. We further enhance the TOPSIS framework by integrating the entropy weight coefficient method. This enhancement aids in assigning precise weights to each criterion involved in mode selection, leading to a more reliable decision-making process. The proposed model provides cost-effective and timely deliveries, minimizing environmental footprint and meeting consumers’ needs. Our findings reveal that freight carbon footprint is the primary concern, followed by freight cost, time sensitivity, and service reliability. The study identifies the combination of Rail/Truck as the ideal mode of transport and containers in flat cars (COFC) as the next best option for the selected case. The proposed algorithm, incorporating the enhanced TOPSIS framework, benefits companies navigating the complexities of multimodal transport. It empowers making more strategic and informed transportation decisions. This demonstration will be increasingly valuable as companies navigate the ever-growing trade within the global supply chains.

Multimodal fusion recognition for digital twin

The digital twin is the concept of transcending reality,which is the reverse feedback from the real physical space to the virtual digital space.People hold great prospects for this emerging technology.In order to realize the upgrading of the digital twin industrial chain,it is urgent to introduce more modalities,such as vision,haptics,hearing and smell,into the virtual digital space,which assists physical entities and virtual objects in creating a closer connection.Therefore,perceptual understanding and object recognition have become an urgent hot topic in the digital twin.Existing surface material classification schemes often achieve recognition through machine learning or deep learning in a single modality,ignoring the complementarity between multiple modalities.In order to overcome this dilemma,we propose a multimodal fusion network in our article that combines two modalities,visual and haptic,for surface material recognition.On the one hand,the network makes full use of the potential correlations between multiple modalities to deeply mine the modal semantics and complete the data mapping.On the other hand,the network is extensible and can be used as a universal architecture to include more modalities.Experiments show that the constructed multimodal fusion network can achieve 99.42%classification accuracy while reducing complexity.

Multiobjective Differential Evolution for Higher-Dimensional Multimodal Multiobjective Optimization

In multimodal multiobjective optimization problems(MMOPs),there are several Pareto optimal solutions corre-sponding to the identical objective vector.This paper proposes a new differential evolution algorithm to solve MMOPs with higher-dimensional decision variables.Due to the increase in the dimensions of decision variables in real-world MMOPs,it is diffi-cult for current multimodal multiobjective optimization evolu-tionary algorithms(MMOEAs)to find multiple Pareto optimal solutions.The proposed algorithm adopts a dual-population framework and an improved environmental selection method.It utilizes a convergence archive to help the first population improve the quality of solutions.The improved environmental selection method enables the other population to search the remaining decision space and reserve more Pareto optimal solutions through the information of the first population.The combination of these two strategies helps to effectively balance and enhance conver-gence and diversity performance.In addition,to study the per-formance of the proposed algorithm,a novel set of multimodal multiobjective optimization test functions with extensible decision variables is designed.The proposed MMOEA is certified to be effective through comparison with six state-of-the-art MMOEAs on the test functions.

Effects of multimodal microstructure on fracture toughness and its anisotropy of LPSO-type extruded Mg-1Zn-2Y alloys

The fracture toughness of extruded Mg-1Zn-2Y(at.%)alloys,featuring a multimodal microstructure containing fine dynamically recrystallized(DRXed)grains with random crystallographic orientation and coarse-worked grains with a strong fiber texture,was investigated.The DRXed grains comprised randomly oriented equiaxedα-Mg grains.In contrast,the worked grains includedα-Mg and long-period stacking ordered(LPSO)phases that extended in the extrusion direction(ED).Both types displayed a strong texture,aligning the(10.10)direction parallel to the ED.The volume fractions of the DRXed and worked grains were controlled by adjusting the extrusion temperature.In the longitudinal-transverse(L-T)orientation,where the loading direction was aligned parallel to the ED,there was a tendency for the conditional fracture toughness,KQ,tended to increase as the volume fraction of the worked grains increased.However,the KQ values in the T-L orientation,where the loading direction was perpendicular to the ED,decreased with an increase in the volume fraction of the worked grains.This suggests strong anisotropy in the fracture toughness of the specimen with a high volume fraction of the worked grains,relative to the test direction.The worked grains,which included the LPSO phase and were elongated perpendicular to the initial crack plane,suppressed the straight crack extension,causing crack deflection,and generating secondary cracks.Thus,these worked grains significantly contributed to the fracture toughness of the extruded Mg-1Zn-2Y alloys in the L-T orientation.

Effect of different anesthetic modalities with multimodal analgesia on postoperative pain level in colorectal tumor patients

BACKGROUND According to clinical data,a significant percentage of patients experience pain after surgery,highlighting the importance of alleviating postoperative pain.The current approach involves intravenous self-control analgesia,often utilizing opioid analgesics such as morphine,sufentanil,and fentanyl.Surgery for colo-rectal cancer typically involves general anesthesia.Therefore,optimizing anes-thetic management and postoperative analgesic programs can effectively reduce perioperative stress and enhance postoperative recovery.The study aims to analyze the impact of different anesthesia modalities with multimodal analgesia on patients'postoperative pain.AIM To explore the effects of different anesthesia methods coupled with multi-mode analgesia on postoperative pain in patients with colorectal cancer.METHODS Following the inclusion criteria and exclusion criteria,a total of 126 patients with colorectal cancer admitted to our hospital from January 2020 to December 2022 were included,of which 63 received general anesthesia coupled with multi-mode labor pain and were set as the control group,and 63 received general anesthesia associated with epidural anesthesia coupled with multi-mode labor pain and were set as the research group.After data collection,the effects of postoperative analgesia,sedation,and recovery were compared.RESULTS Compared to the control group,the research group had shorter recovery times for orientation,extubation,eye-opening,and spontaneous respiration(P<0.05).The research group also showed lower Visual analog scale scores at 24 h and 48 h,higher Ramany scores at 6 h and 12 h,and improved cognitive function at 24 h,48 h,and 72 h(P<0.05).Additionally,interleukin-6 and interleukin-10 levels were significantly reduced at various time points in the research group compared to the control group(P<0.05).Levels of CD3+,CD4+,and CD4+/CD8+were also lower in the research group at multiple time points(P<0.05).CONCLUSION For patients with colorectal cancer,general anesthesia coupled with epidural anesthesia and multi-mode analgesia can achieve better postoperative analgesia and sedation effects,promote postoperative rehabilitation of patients,improve inflammatory stress and immune status,and have higher safety.

Efficient User Identity Linkage Based on Aligned Multimodal Features and Temporal Correlation

User identity linkage(UIL)refers to identifying user accounts belonging to the same identity across different social media platforms.Most of the current research is based on text analysis,which fails to fully explore the rich image resources generated by users,and the existing attempts touch on the multimodal domain,but still face the challenge of semantic differences between text and images.Given this,we investigate the UIL task across different social media platforms based on multimodal user-generated contents(UGCs).We innovatively introduce the efficient user identity linkage via aligned multi-modal features and temporal correlation(EUIL)approach.The method first generates captions for user-posted images with the BLIP model,alleviating the problem of missing textual information.Subsequently,we extract aligned text and image features with the CLIP model,which closely aligns the two modalities and significantly reduces the semantic gap.Accordingly,we construct a set of adapter modules to integrate the multimodal features.Furthermore,we design a temporal weight assignment mechanism to incorporate the temporal dimension of user behavior.We evaluate the proposed scheme on the real-world social dataset TWIN,and the results show that our method reaches 86.39%accuracy,which demonstrates the excellence in handling multimodal data,and provides strong algorithmic support for UIL.

An Immune-Inspired Approach with Interval Allocation in Solving Multimodal Multi-Objective Optimization Problems with Local Pareto Sets

In practical engineering,multi-objective optimization often encounters situations where multiple Pareto sets(PS)in the decision space correspond to the same Pareto front(PF)in the objective space,known as Multi-Modal Multi-Objective Optimization Problems(MMOP).Locating multiple equivalent global PSs poses a significant challenge in real-world applications,especially considering the existence of local PSs.Effectively identifying and locating both global and local PSs is a major challenge.To tackle this issue,we introduce an immune-inspired reproduction strategy designed to produce more offspring in less crowded,promising regions and regulate the number of offspring in areas that have been thoroughly explored.This approach achieves a balanced trade-off between exploration and exploitation.Furthermore,we present an interval allocation strategy that adaptively assigns fitness levels to each antibody.This strategy ensures a broader survival margin for solutions in their initial stages and progressively amplifies the differences in individual fitness values as the population matures,thus fostering better population convergence.Additionally,we incorporate a multi-population mechanism that precisely manages each subpopulation through the interval allocation strategy,ensuring the preservation of both global and local PSs.Experimental results on 21 test problems,encompassing both global and local PSs,are compared with eight state-of-the-art multimodal multi-objective optimization algorithms.The results demonstrate the effectiveness of our proposed algorithm in simultaneously identifying global Pareto sets and locally high-quality PSs.