Multimode Output Research Articles

World Englishes pedagogy: constructing learner identity

Abstract This article examines the impact of a critical pedagogy informed by World Englishes and investment frameworks on one L2 learner in Hong Kong who claims to use ‘Hong Kong English’ (HKE). Taking a critical and proactive stance, we seek to counter insidious (neo)colonial ideologies and practices through our pedagogic investigation in an undergraduate course. Multimodal output during the intervention was jointly analysed to gauge the impact of the pedagogy on one learner’s investment trajectory. The findings revealed that the participant-learner managed to invest in multiple intertwined identities as Hongkonger, legitimate HKE user, and as an L2 English learner. The participant-learner challenged her exonormative orientations to become an invested, self-reflective learner who endorses and embodies endonormative norms in her local English. Overall, the study highlights tasks that create spaces for students to actively resist colonial representations of English.

Enhancing multimodal output in CLIL education: the impact of VR games on fourth-grade students’ English poster designs and presentations in Taiwan

Despite the growing recognition of the importance of multimodal input and digital virtual reality (VR) games in enhancing EFL learners’ productive language skills, a significant gap remains in empirical research examining their impact on multimodal output—particularly writing and speaking—within content and language integrated learning (CLIL) science education. This quasi-experimental study addresses this gap by investigating the potential benefits of using VR games to enhance fourth-grade CLIL students’ productive language skills, specifically writing and speaking, through the analysis of their ability to convey scientific concepts in multimodal output. Grounded in self-regulated learning (SRL) theory, the study compares the effects of multimodal input embedded in VR games with those of traditional PowerPoint (PPT)-led games on students’ English poster designs (writing) and oral presentations (speaking), using the 4Cs (Content, Communication, Cognition, and Culture) framework in multimodal assessments. The study involved 81 fourth-grade students from three Taiwanese public elementary schools, divided into an experimental group (EG = 40) using VR-based games and a control group (CG = 41) using PPT-led games for content review. A mixed-methods approach was employed, combining quantitative evaluations with rubrics based on the 4Cs framework and qualitative rater reflections to provide a comprehensive understanding of how different review methods influenced student performance and creative output. Quantitative findings revealed that students using VR review games significantly outperformed those using traditional PPT games in aspects of Content and Cognition for both poster designs and presentations, demonstrating greater depth, accuracy, and application of scientific concepts and higher-order cognitive skills. In terms of Communication, the EG showed higher target vocabulary usage and sentence complexity in presentations, but no significant differences were found in Culture outcomes between the groups or in Communication in posters. Expert raters’ reflections further highlighted that students using VR games exhibited more innovative and integrated use of scientific content, critical thinking, and multimodal expressions, reflecting deeper engagement with the material. This study empirically demonstrates that game-based virtual reality learning environments (VRLEs) significantly enhance students’ multimodal output in content and cognitive skills. Theoretically, it extends the application of SRL in CLIL contexts by highlighting the potential of VRLEs to foster advanced cognitive skills and emphasizes the importance of multimodal assessments in capturing comprehensive student learning outcomes. Future research should explore integrating cultural content into VR environments to enhance students’ cultural awareness and sensitivity.

Multimodal vehicle trajectory prediction and integrated threat assessment algorithm based on adaptive driving intention

As autonomous driving and connected communication technologies advance swiftly, vehicle trajectory prediction has become increasingly significant. The motion of a vehicle is contingent not only on its historical trajectory but is also subject to the influence of surrounding vehicles, thereby exhibiting intricate social and temporal interdependencies. Furthermore, the inherent randomness and uncertainty in driver behavior render vehicle trajectory prediction inherently multimodal, a factor that is frequently neglected in current research. Against this backdrop, a multimodal vehicle trajectory prediction (MTP) model based on an encoder-decoder architecture is proposed to hierarchically extract historical features of vehicles. The model consists of five key components: temporal feature encoder module, spatial interaction module, spatial-temporal dependence module, driving intention fusion module and multimodal trajectory output module. Experiments on the NGSIM dataset show that the predictive performance of the model has been improved to varying degrees, especially at 3–5 s, where the improvement is more significant. Compared with state-of-the-art models, the Root Mean Square Error (RMSE) error at 5 s time horizon is 3.38 m on NGSIM dataset, which represents a 25 % improvement. To measure the safety of predicted trajectories, we propose a comprehensive threat assessment model that combines collision time (TTC), headway (TH) and time to lateral collision (TLC) metrics based on safe distance theory. This model not only evaluates the longitudinal collision threat in the following state, but also evaluates the lateral collision threat during driving maneuvers in multi lane scenarios, thereby comprehensively improving the safety of vehicle driving. This research also offers new perspectives and insights for the development of autonomous driving.

Reading Between the Lines: An Exploratory Pilot Study of a Co-creation Approach to Life Story Work in Aged Care in Australia.

Life story work (LSW) uses a narrative, reminiscence approach to capturing memories from one's life and has shown positive outcomes for participants. However, LSW in aged care has been criticized for being resource intensive, often involving care staff using pre-determined process and output formats. This pilot study explored participants' lived experiences of a novel co-creation approach to LSW conducted predominantly with university students and older adults in residential aged care and retirement communities, producing multi-modal outputs. Within a 12-month period, 33 LSW projects were completed (21 books, 5 posters, and 7 digital stories). Semi-structured interviews (n = 44) explored participants' lived experience of completing a LSW project. Findings indicate that working with students and adopting a flexible, co-creation approach that empowers participants to make decisions, engage in learning and reflection, and build meaningful relationships can maximize opportunities for transformative impacts, and enable providers to offer a LSW program despite finite resources.

Mechanochromic luminescent material with high quantum yield for multi-mode anti-counterfeiting applications

The multi-mode anti-counterfeiting materials, constructed by mechanochromic luminescent materials with high quantum yield, can effectively prevent illegal counterfeiting, but their design and construction still present significant challenges. Herein, a twisting D-A-D conjugated molecule, FBtF, is obtained with a special hybridized local and charge transfer excited state, and shows approximately 100 % photoluminescence quantum yield in the coating film. The self-assembled FBtF microplates exhibit bathochromic-shifted emission from green to orange, accompanied by the photoluminescence quantum yield increase from 47.8 % to 89.0 % upon grinding, which is the highest in reported MCL materials, to the best of our knowledge. In contrast, a D-π-D type molecule (FBenF) and a D-D’-D molecule (FAnF) maintain the blue-emission after grinding. Through mechanistic investigation, the force-induced molecular conformational planarization and the breakage of the intermolecular charge transfer collaboratively lead to the emission-color change and photoluminescence quantum yield increase. The FBtF microplates are designed as ink for anti-counterfeiting applications based on multi-mode output signals, including the UV light-excited image and the grinding-induced emission color change together with the photoluminescence quantum yield increase. The written tags exhibit high stability in various harsh environments, and demonstrate superior anti-counterfeiting performance in practical applications, such as medicine anti-counterfeiting.

TCHFN: Multimodal sentiment analysis based on Text-Centric Hierarchical Fusion Network

Multimodal sentiment analysis (MSA) has become a popular field of research in recent years. The aim is to combine the three modalities of text, video, and audio to obtain comprehensive emotional information. However, current research often treats these three modalities equally, downplaying the crucial role of text modality in MSA and ignoring the redundant information generated during multimodal fusion. To address these problems, we propose the Text-Centric Hierarchical Fusion Network (TCHFN), employing a hierarchical fusion strategy. In this framework, low-level fusion involves cross-modal interactions between pairs of modalities, while high-level fusion extends these interactions to involve all three modalities. Through the design of the Cross-modal Reinforced Transformer (CRT), we achieve cross-modal enhancement of the target modality, facilitating a nuanced fusion process with text serving as its core. Additionally, we design Text-Centric Contrastive Learning (TCCL) to align non-text modalities with the text modality, emphasising the central role of text in the fusion process. After fusion, a multimodal fusion output gate is employed to mitigate redundant information within the multimodal fusion representation, which is subsequently processed by a linear layer for prediction. Simultaneously, to fully leverage limited labelled datasets, we introduced knowledge distillation. This approach involves preserving the model parameters that yield the best performance during training as a teacher model. The teacher model aids in capturing rich emotional information, enabling the model to transcend local optima and discover more optimal parameters, thereby enhancing overall model performance. Extensive experiments on the CMU-MOSI, CMU-MOSEI, and CH-SIMS datasets demonstrate the superiority of our model over state-of-the-art methods in MSA tasks.

Interactive Output Modalities Design for Enhancement of User Trust Experience in Highly Autonomous Driving

Autonomous driving (AD) technology has gradually matured, but the lack of trust and acceptance from users limits its adoption and diffusion. This paper aims to solve the challenges and alleviate the trust crisis by exploring reasonable interactive output modalities. Firstly, we propose a multimodal interactive output scheme with 9 different feedback level. Subsequently, we conduct simulated driving experiments on the scheme, using three methods: driving trust experience questionnaire (DTEQ), eye-tracking, and takeover desire recording. Finally, we analyze the trends and correlations of results at different interactive output levels. The results indicate that the user trust experience is highly positively correlated with the level of interactive output modalities. Under reasonable design, multimodal and high-level interactive output is beneficial for providing more comprehensive feedback information, reducing users’ visual workload, and improving trust. This work provides a foundation for enhancement of user trust experience, and helps promote the application and popularization of AD.

DAC phase and amplitude noise experimental studies in RZ, NRZ and RF modes

Amplitude and duration of pulses generated by current cells of multimode DAC may differ in real DACs. In this case the problem arises of current pulse parameters defining, their relationship with frequency domain response and noise parameters of DAC, which can be experimentally determined. The article describes the measurements of frequency domain response, phase noise and amplitude noise of multimode DAC output signal in RZ, NRZ and RF (mixed) mode. Phase noise and amplitude noise of multimode DAC in RZ and RF modes calculation uses experimental data obtained in RZ mode. Measurement and consequent calculation of AD9739 DAC phase and amplitude noise confirmed the theoretical analysis.

Design of English Lexical Analysis System for Machine Translation Based on Multimodal Neural Network

Information resources have become a very important source of wealth, and due to differences in language and writing, technological information exchange between countries has become difficult. In this article, we propose a data augmentation method based on statistical models. A framework for a multimodal output model is proposed, taking into account the semantic relevance and importance between different languages. This framework is based on text sequence to sequence framework, decoupled, and a network architecture based on dual stream attention mechanism is designed. A multimodal interactive neural network layer was added between the encoder and decoder, achieving the fusion of multilingual domain information. It compensates for the influence of text on traditional translation generation systems in the process of English Chinese translation, and solves the problems of inaccurate translation results and low similarity with the original text in English Chinese machine translation. The system proposed in this article mainly consists of preprocessing module, lexical analysis and segmentation module, part of speech tagging and phrase analysis module, translation rule construction module, decoding module, translation generation module, etc. The experimental results show that using an improved generation system, compared with traditional generation systems, improves the accuracy of translation generation, has certain advantages, and is more practical.

Ethnocultural empathy development of future language teachers through digital multiliteracy resources for low-literacy adult migrants.

A mixed-method pre-post study was conducted with 48 trainee teachers who participated in stand-alone digital multiliteracy interventions, in which they were encouraged to envisage themselves as future teachers of low-literate migrants. Policy documents such as the reference guide on Literacy and Second Language Learning for the Linguistic Integration of Adult Migrants, journal articles, audiovisual resources as well as examples of existing educational materials aimed at the target audience, were made available to them on an online platform. In two separate studies, trainees were encouraged to collaboratively produce two different multimodal outputs. The Revised Scale of Ethnocultural Empathy was administered before and after the intervention, subjecting the data obtained to quantitative analysis. Qualitative data was also collected to gain a better understanding of the affective and cognitive processes experienced by the participants. Simple statistical analysis coupled with the comparison of means was used to respond to the research questions. Statistical hypothesis testing, including correlations and non-parametric statistics were used to analyze the relationship between each of the factors within the RSEE and the participants, considering the different interventions applied. Non-parametric tests (U-Mann Whitney) were used to compare the differences between the levels of ethnocultural empathy of the participants in the two studies. Significant differences were found in Factor 3 (Empathy) and Factor 5 (Anxiety) between the groups and their post-intervention results, with a p value of 0.053 and 0.038, respectively. The effect size r was calculated, obtaining a size effect of 0.625 for Factor 3 (Empathy) and 0.674 for Factor 5 (Anxiety). These results indicate that the significant differences and the size effect between both groups are large. U-Mann Whitney non-parametric analysis also revealed gender differences in Factor 3 (Empathy), showing females higher levels than males. Effect size r analysis showed a large size effect of 0.708 for Factor 3 (Empathy). The findings pertaining to gender-related differences in empathy levels confirm the conclusions drawn by previous studies. When contrasting study 1 and 2, statistical differences were also shown after the intervention for the 'Anxiety and Lack of Multicultural Self-efficacy' factor. The qualitative data analysis was carried out with Atlas.ti v.8, in order to isolate and categorize the broader themes and the most significant explanatory quotes extracted from the participants' records and interviews. The results reveal the learning strategies that each group of learners applied to successfully complete the task at hand, as well as the participants' deployment of their critical thinking skills and the awakening of a sense of awareness of their own professional competence development process. This study set out to compare how effective two digital multiliteracy interventions were in developing future language teachers' ethnocultural empathy and cognitive abilities when appraising the educational needs of low-literacy migrants. Despite the small sample size, the study certainly adds to our understanding of the impact of multimodal tasks involving critical thinking skills on trainees' cognitive and affective abilities. Besides, it expands the growing body of research that points to the desirability of embedding digitally-based content creation tasks in training curricula for future language teachers.

American Sign Language Detection with Multi-Modal and Multi-Linguistic Output

Abstract: Our cutting-edge project introduces an advanced Sign Language Detection System, emphasizing American Sign Language (ASL) and fundamental prepositions. Utilizing sophis-ticated computer vision techniques for precise gesture recognition, the system delivers real-time video, audio, and text outputs, providing a multifaceted communication experience. A distinctive feature allowing users to select their preferred language for both audio and text outputs enhances inclusivity and customization. The system’s adaptability extends to include additional languages, ensuring its versatility in catering to diverse linguistic prefer-ences. This groundbreaking initiative addresses the pervasive communication challenges faced by the deaf and hard-of-hearing community, offering a comprehensive and highly customizable solution to bridge language gaps and foster seamless, meaningful interactions for individuals using sign language. Through the fusion of advanced technology and usercentric design, our Sign Language Detection System aims to significantly improve accessibility and communication inclusivity.

Transformer-powered surrogates close the ICF simulation-experiment gap with extremely limited data

Recent advances in machine learning, specifically transformer architecture, have led to significant advancements in commercial domains. These powerful models have demonstrated superior capability to learn complex relationships and often generalize better to new data and problems. This paper presents a novel transformer-powered approach for enhancing prediction accuracy in multi-modal output scenarios, where sparse experimental data is supplemented with simulation data. The proposed approach integrates transformer-based architecture with a novel graph-based hyper-parameter optimization technique. The resulting system not only effectively reduces simulation bias, but also achieves superior prediction accuracy compared to the prior method. We demonstrate the efficacy of our approach on inertial confinement fusion experiments, where only 10 shots of real-world data are available, as well as synthetic versions of these experiments.

Multization: Multi-Modal Summarization Enhanced by Multi-Contextually Relevant and Irrelevant Attention Alignment

This article focuses on the task of Multi-Modal Summarization with Multi-Modal Output for China JD.COM e-commerce product description containing both source text and source images. In the context learning of multi-modal (text and image) input, there exists a semantic gap between text and image, especially in the cross-modal semantics of text and image. As a result, capturing shared cross-modal semantics earlier becomes crucial for multi-modal summarization. However, when generating the multi-modal summarization, based on the different contributions of input text and images, the relevance and irrelevance of multi-modal contexts to the target summary should be considered, so as to optimize the process of learning cross-modal context to guide the summary generation process and to emphasize the significant semantics within each modality. To address the aforementioned challenges, Multization has been proposed to enhance multi-modal semantic information by multi-contextually relevant and irrelevant attention alignment. Specifically, a Semantic Alignment Enhancement mechanism is employed to capture shared semantics between different modalities (text and image), so as to enhance the importance of crucial multi-modal information in the encoding stage. Additionally, the IR-Relevant Multi-Context Learning mechanism is utilized to observe the summary generation process from both relevant and irrelevant perspectives, so as to form a multi-modal context that incorporates both text and image semantic information. The experimental results in the China JD.COM e-commerce dataset demonstrate that the proposed Multization method effectively captures the shared semantics between the input source text and source images, and highlights essential semantics. It also successfully generates the multi-modal summary (including image and text) that comprehensively considers the semantics information of both text and image.

Self-Powered and Self-Recoverable Multimodal Force Sensors Based on Trap State and Interfacial Electron Transfer.

Multi-dimensional force sensing that combines intensity, location, area and the like could gather a wealth of information from mechanical stimuli. Developing materials with force-induced optical and electrical dual responses would provide unique opportunities to multi-dimensional force sensing, with electrical signals quantifying the force amplitude and the luminescence output providing spatial distribution of force. However, the reliance on external power supply and high-energy excitation source brings significant challenges to the applicability of multi-dimensional force sensors. Here we reported the mechanical energy-driven and sunlight-activated materials with force-induced dual responses, and investigated the underlying mechanisms of self-sustainable force sensing. Theoretical analysis and experimental data unraveled that trap-controlled luminescence and interfacial electron transfer play a major role in force-induced optical and electrical output. These materials were manufactured into pressure sensor with renewable dual-mode output for quantifying and visualization of pressures by electrical and optical output, respectively, without power supply and high-energy irradiation. The quantification of tactile sensation and stimuli localization of mice highlighted the multi-dimensional sensing ability of the sensor. Overall, this self-powered pressure sensor with multimodal output provides more modalities of force sensing, poised to change the way that intelligent devices sense with the world.

Self‐Powered and Self‐Recoverable Multimodal Force Sensors Based on Trap State and Interfacial Electron Transfer

AbstractMulti‐dimensional force sensing that combines intensity, location, area and the like could gather a wealth of information from mechanical stimuli. Developing materials with force‐induced optical and electrical dual responses would provide unique opportunities to multi‐dimensional force sensing, with electrical signals quantifying the force amplitude and the luminescence output providing spatial distribution of force. However, the reliance on external power supply and high‐energy excitation source brings significant challenges to the applicability of multi‐dimensional force sensors. Here we reported the mechanical energy‐driven and sunlight‐activated materials with force‐induced dual responses, and investigated the underlying mechanisms of self‐sustainable force sensing. Theoretical analysis and experimental data unraveled that trap‐controlled luminescence and interfacial electron transfer play a major role in force‐induced optical and electrical output. These materials were manufactured into pressure sensor with renewable dual‐mode output for quantifying and visualization of pressures by electrical and optical output, respectively, without power supply and high‐energy irradiation. The quantification of tactile sensation and stimuli localization of mice highlighted the multi‐dimensional sensing ability of the sensor. Overall, this self‐powered pressure sensor with multimodal output provides more modalities of force sensing, poised to change the way that intelligent devices sense with the world.

Multiple Hypothesis Dropout: Estimating the Parameters of Multi-Modal Output Distributions

In many real-world applications, from robotics to pedestrian trajectory prediction, there is a need to predict multiple real-valued outputs to represent several potential scenarios. Current deep learning techniques to address multiple-output problems are based on two main methodologies: (1) mixture density networks, which suffer from poor stability at high dimensions, or (2) multiple choice learning (MCL), an approach that uses M single-output functions, each only producing a point estimate hypothesis. This paper presents a Mixture of Multiple-Output functions (MoM) approach using a novel variant of dropout, Multiple Hypothesis Dropout. Unlike traditional MCL-based approaches, each multiple-output function not only estimates the mean but also the variance for its hypothesis. This is achieved through a novel stochastic winner-take-all loss which allows each multiple-output function to estimate variance through the spread of its subnetwork predictions. Experiments on supervised learning problems illustrate that our approach outperforms existing solutions for reconstructing multimodal output distributions. Additional studies on unsupervised learning problems show that estimating the parameters of latent posterior distributions within a discrete autoencoder significantly improves codebook efficiency, sample quality, precision and recall.

Multi-task Hierarchical Heterogeneous Fusion Framework for multimodal summarization

With the rise of multimedia content on the internet, Multimodal Summarization has become a challenging task to help individuals grasp vital information fast. However, previous methods mainly learn the different modalities indistinguishably, which is ineffective in capturing the fine-grained content and hierarchical correlation in multimodal articles. To resolve the present problem, this paper proposes a Multi-task Hierarchical Heterogeneous Fusion Framework (MHHF) to learn the hierarchical structure and heterogeneous correlation existing in the multimodal data. Specifically, we propose a Hierarchical Cross-modality Feature Fusion module to progressively explore the different levels of interaction from object-word features to sentence-scene features. Besides, a Multi-task Cross-modality Decoder is constructed to coalesce different levels of features with three sub-tasks, i.e., Abstractive Summary Generation, Relevant Image Selection, and Extractive Summary Generation. We conduct extensive experiments on three datasets, i.e., MHHF-dataset, CNN, and Daily Mail, which consist of 62880, 1970, and 203 multimodal articles, respectively. Our method achieves state-of-the-art performance on most metrics. Moreover, MHHF consistently outperforms the baseline model on MHHF-dataset by 5.88%, 4.41%, and 0.4% of Rouge-1, Rouge-2, and Rouge-L for the abstractive summarization task. Ablation studies show that both Hierarchical Cross-modality Feature Fusion and Multi-task Cross-modality Decoder can improve the quality of multimodal summarization output. Further diversity analysis and human evaluation also demonstrate that MHHF can generate more informative and fluent summaries.

SCT: Summary Caption Technique for Retrieving Relevant Images in Alignment with Multimodal Abstractive Summary

This work proposes an efficient Summary Caption Technique that considers the multimodal summary and image captions as input to retrieve the correspondence images from the captions that are highly influential to the multimodal summary. Matching a multimodal summary with an appropriate image is a challenging task in computer vision and natural language processing. Merging in these fields is tedious, though the research community has steadily focused on cross-modal retrieval. These issues include the visual question-answering, matching queries with the images, and semantic relationship matching between two modalities for retrieving the corresponding image. Relevant works consider questions to match the relationship of visual information and object detection and to match the text with visual information and employing structural-level representation to align the images with the text. However, these techniques are primarily focused on retrieving the images to text or for image captioning. But less effort has been spent on retrieving relevant images for the multimodal summary. Hence, our proposed technique extracts and merge features in the Hybrid Image Text layer and captions in the semantic embeddings with word2vec where the contextual features and semantic relationships are compared and matched with each vector between the modalities, with cosine semantic similarity. In cross-modal retrieval, we achieve top five related images and align the relevant images to the multimodal summary that achieves the highest cosine score among the retrieved images. The model has been trained with seq-to-seq modal with 100 epochs, besides reducing the information loss by the sparse categorical cross entropy. Further, experimenting with the multimodal summarization with multimodal output dataset, in cross-modal retrieval, helps to evaluate the quality of image alignment with an image-precision metric that demonstrate the best results.

Multi‐agent trajectory prediction with adaptive perception‐guided transformers

AbstractThe ability to predict the trajectory of an autonomous vehicle accurately is crucial for safe and efficient navigation. However, predicting diverse and multimodal futures can be challenging. Recent approaches such as attention and graph neural networks have achieved state‐of‐the‐art performance by considering agent interactions and map contexts. This study focused on multi‐agent prediction using an agent‐centric approach with transformers. This enables parallel computation and a comprehensive understanding of the environment. Two main features are introduced: an adaptive receptive field (ARF) that captures the relevant surroundings for each agent, and perception encoding, which serves as spatial context embeddings. The ARF adapts to the agent's velocity and rotation, focusing attention ahead at high speeds or to the sides when it is slower. Perception encoding divides agents or lanes into levels and encodes the information of each level. This approach enables the efficient encoding of complex spatial relationships. The proposed method combines these advances with transformer modelling for multi‐agent trajectory prediction while ensuring real‐time prediction capabilities. The approach is evaluated on the Argoverse benchmark and better performance than the state‐of‐the‐art baseline is achieved. By addressing challenges such as multimodal outputs and robustness, the study enhances the safety and efficiency of autonomous driving systems by more accurately predicting trajectories.

"Four-In-One" Multifunctional Dandelion-Like Gold@platinum Nanoparticles-Driven Multimodal Lateral Flow Immunoassay.

The traditional lateral flow immunoassay (LFIA) with a single signal output mode may encounter challenges such as low sensitivity, poor detection range, and susceptibility to external interferences. These limitations hinder its ability to meet the growing demand for advanced LFIA. To address these issues, the rational development of multifunctional labels for multimodal LFIA emerges as a promising strategy. Herein, this study reports a multimodal LFIA using "four-in-one" multifunctional dandelion-like gold@platinum nanoparticles (MDGP). The inherent properties of MDGP, such as the broad absorption spectrum, porous dandelion-like nanostructure, and bimetallic composition with gold and platinum, endow them with capacities in dual spectral-overlapped fluorescence quenching, optical readout, catalytic activity, and photothermal effect. Benefiting from their multifunctional properties, the MDGP-LFIA enables multimodal outputs including fluorescent, colorimetric, and photothermal signals. This multimodal MDGP-LFIA allows for the detection of acetamiprid at a range of 0.01-50ngmL-1, with the lowest qualitative and quantitative detection results of 0.5 and 0.008ngmL-1, respectively, significantly better than the traditional gold nanoparticles-based LFIA. The diversity, complementarity, and synergistic effect of integrated output signals in this multimodal MDGP-LFIA improve the flexibility, practicability, and accuracy of detection, holding great promise as a point-of-care testing platform in versatile application scenarios.