Multidimensional Structure Research Articles

Time-frequency analysis of nonlinear structures under nonstationary excitation by directly solving structural dynamic equation through absolute displacement

The time-frequency evolution of seismic ground motion energy under strong earthquakes, which can be described by the evolutionary power spectral density (EPSD) matrix, will amplify the most unfavorable responses of members in nonlinear structural systems. However, the existing general finite element software fails to reduce the dimension of the EPSD matrix and then decouple time from frequency in frequency domain analysis. This failure greatly limits the simulation and calculation of multi-dimensional nonlinear structures with multiple degrees of freedom by the stochastic vibration theory. In this study, the pseudo excitation method (PEM), which can direct solve structural dynamic equations using absolute displacement, is used to derive the self-spectral and cross-spectral densities of seismic ground motions in one-dimensional and multi-dimensional cases. In MATLAB, the EPSD matrix is transformed into a four-dimensional uniform modulation excitation matrix independent of the time variable. Nonlinear iteration is conducted and the precise integration method (PIM) is employed through a transient analysis module. Furthermore, a simple one-dimensional example is presented to verify the correctness of the proposed algorithm herein. Finally, as an example, we select important parameters of the coherence model at different supports of a half-through three-span concrete-filled steel tubular (CFST) tied-arch bridge in view of real engineering conditions, and calculate the time-varying power spectrum and variance of the responses of arch ribs, arch feet and the bridge deck system. The results show that the theoretical derivation and the algorithm constructed in this paper can simulate the time-delay effect of the nonlinear structural system. This proves the feasibility of general finite element software in solving and analyzing random responses to multi-dimensional and multi-support non-stationary excitation of nonlinear structures.

CP factor model for dynamic tensors

Abstract Observations in various applications are frequently represented as a time series of multidimensional arrays, called tensor time series, preserving the inherent multidimensional structure. In this paper, we present a factor model approach, in a form similar to tensor CANDECOMP/PARAFAC (CP) decomposition, to the analysis of high-dimensional dynamic tensor time series. As the loading vectors are uniquely defined but not necessarily orthogonal, it is significantly different from the existing tensor factor models based on Tucker-type tensor decomposition. The model structure allows for a set of uncorrelated one-dimensional latent dynamic factor processes, making it much more convenient to study the underlying dynamics of the time series. A new high-order projection estimator is proposed for such a factor model, utilizing the special structure and the idea of the higher order orthogonal iteration procedures commonly used in Tucker-type tensor factor model and general tensor CP decomposition procedures. Theoretical investigation provides statistical error bounds for the proposed methods, which shows the significant advantage of utilizing the special model structure. Simulation study is conducted to further demonstrate the finite sample properties of the estimators. Real data application is used to illustrate the model and its interpretations.

Cost-effective enhancement of high viscosity modified bitumen anti-aging properties using organic layered double hydroxide/fume silica nanoparticles composite nanomaterials

High-viscosity modified bitumen (HVMB) is prone to thermal-oxidative and photooxidative aging, resulting in pavement diseases. The use of conventional Nano-modifiers to enhance the aging resistance of bitumen is often hindered by high costs, limiting their adoption in widespread applications. This study aims to evaluate the impact of a cost-effective Multi-Dimensional Nano-Composite (MDNC) modifier on the durability of HVMB, which includes organically modified Layered Double Hydroxides (OLDHs) and Fumed Silica Nanoparticles (FSNPs). Additionally, Zero-Dimensional Nanomaterials (ZDNs) such as FSNPs, Nano-SiO2, or Nano-TiO2, in combination with various MDNC modifiers consisting of OLDHs and ZDNs, were also utilized as comparative anti-aging modifiers for HVMB binders. FTIR, XRD, and contact angle tests indicate that LDHs modified with KH560 (OLDHs) exhibit improved hydrophobicity and increased interlayer distance. The temperature sweep test of the unaged binders and the FESEM test of the Nano-modifier indicate that the branched network structure of the FSNPs significantly enhances the high-temperature elasticity of the HVMB binders. Rheological test results have shown that both ZDNs and MDNC modifiers can delay the changes in rheological parameters after aging. Combining FTIR and SARA (Saturates, Aromatics, Resins, Asphaltenes) results, it was found that the combination of OLDHs/FSNPs synergistically inhibited the increase in the content of heavy components in bitumen during thermal oxygen and ultraviolet aging. The enhancement of the HVMB's thermal oxidation aging resistance by OLDHs/FSNPs composite modifiers is primarily due to the multidimensional structure of the composite nanomaterial, which effectively prevents the penetration of oxygen molecules. Meanwhile, the improvement in resistance to ultraviolet aging is mainly due to the absorption and scattering characteristics of FSNPs. Therefore, OLDHs/FSNPs are demonstrated to be a cost-effective multidimensional nano-modifier.

Methods and Applications of Space Understanding in Indoor Environment—A Decade Survey

The demand for digitizing manufacturing and controlling processes has been steadily increasing in recent years. Digitization relies on different techniques and equipment, which produces various data types and further influences the process of space understanding and area recognition. This paper provides an updated view of these data structures and high-level categories of techniques and methods leading to indoor environment segmentation and the discovery of its semantic meaning. To achieve this, we followed the Systematic Literature Review (SLR) methodology and covered a wide range of solutions, from floor plan understanding through 3D model reconstruction and scene recognition to indoor navigation. Based on the obtained SLR results, we identified three different taxonomies (the taxonomy of underlying data type, of performed analysis process, and of accomplished task), which constitute different perspectives we can adopt to study the existing works in the field of space understanding. Our investigations clearly show that the progress of works in this field is accelerating, leading to more sophisticated techniques that rely on multidimensional structures and complex representations, while the processing itself has become focused on artificial intelligence-based methods.

Heterogeneous CuS QDs/BiVO4@Y2O2S Nanoreactor for Monitorable Photocatalysis.

Exploration of multifunctional integrated catalysts is of great significance for photocatalysis toward practical application. Herein, a 1D confined nanoreactor with a heterogeneous core-shell structure is designed for synergies of efficient catalysis and temperature monitoring by custom encapsulation of Z-scheme heterojunction CuS quantum dots/BiVO4 (CuS QDs/BiVO4) and Y2O2S-Er, Yb. The dispersed active sites created by the QDs with high surface energy improve the mass transfer efficiency, and the efficient electron transport channels at the heterogeneous interface extend the carrier lifetime, which endows the nanoreactor with excellent catalytic performance. Meanwhile, real-time temperature monitoring is realized based on the thermally coupled levels 2H11/2/4S3/2→4I15/2 of Er3+ using fluorescence intensity ratio, which enables the monitorable photocatalysis. Furthermore, the nanoreactor with a multidimensional structure increases effective intermolecular collisions to facilitate the catalytic process by restricting the reaction within distinct enclosed spaces and circumvents potential unknown interaction effects. The design of multi-space nanoconfined reactors opens up a new avenue to modulate catalyst function, providing a unique perspective for photocatalytic applications in the mineralization of organic pollutants, hydrogen production, and nitrogen fixation.

Continuous control of structural vibrations using hybrid deep reinforcement learning policy

An efficient active structural control scheme by utilizing the collective concepts of data-driven and physics-inspired reinforcement learning (RL) approaches is being introduced here. The controller is designed to deliver optimal feedback forces based on the full-state information, wherein the training process involves the use of deep neural networks (NNs) and a specially designed gradient descent-based sequence within the RL framework. This integration of algorithms results in a unique active control policy that accelerates the learning process, thereby demands considerably less computational resources to develop an optimal and stable controller as compared to existing data-driven approaches. Most importantly, the mentioned data-driven and physics-inspired approaches encompass deep deterministic policy gradient (DDPG) and an iterative gradient-based state feedback control (SFSC) algorithms, respectively, to establish the architecture of the hybrid control policy, referred to as hybrid RL-controller. Additional advantage of the hybrid RL-controller is its ability to operate in continuous state–action spaces, which allows the designed controller to address diverse structural control problems. The staggered performance of the hybrid RL-controller, both in continuous and discrete time, is evaluated in three case studies that involve structures in linear and nonlinear regimes. The outcomes include a detailed comparison of the hybrid RL-controller with the individually designed DDPG and SFSC RL control strategies, as well as the uncontrolled scenario. Furthermore, this study thoroughly investigates the real-life implementation concerns of control strategies, such as perturbations in model parameters and input forces, and time delays in the feedback loop. Finally, the results from this study corroborate that the designed RL-controller showcases superior performance and faster execution time in the feedback loop, making it suitable for the vibration control of multidimensional complex structures.

Multidimensional response of China’s grassland stability to drought

Drought usually affects the normal functioning of grassland ecosystems. Due to sustained global warming, the frequency and intensity of drought have significantly increased, leading to greater impacts on many aspects of grassland ecosystem functions, such as soil moisture, vegetation physiological structure and ecosystem community structure. Ecosystem stability dimension can help us better understand the changes induced by drought occurred within ecosystems. Using the Normalized Vegetation Index (NDVI), climate and biodiversity data, we quantified resistance, resilience and temporal stability of grassland ecosystems under long term drought in northern China and calculated the ecosystem stability dimension based on the correlation between the three stability indicators. The results indicated that Qinghai Tibet Plateau and Inner Mongolian Plateau showed strong resistance to drought, while Loess Plateau region presented rapid recovery after drought. The resistance, compared to resilience and temporal stability, exhibited stronger sensitivity to drought in space. Drought promoted the transformation of grassland ecosystems into multidimensional structures. The reduction of aboveground biomass caused by frequent droughts accelerated the transformation in grassland ecosystems. Our findings provide the new insights for guiding the response mechanism of drought on grassland ecosystems.

Measuring and validating autistic burnout.

Researchers have begun to explore the characteristics and risk factors for autistic burnout, but assessment tools are lacking. Our study comprehensively examined and compared the psychometric properties of the unpublished 27-item AASPIRE Autistic Burnout Measure (ABM), and personal and work scales of the Copenhagen Burnout Inventory (CBI) to evaluate their efficacy as screening measures for autistic burnout, with a group of 238 autistic adults. Exploratory factor analyses (EFA) revealed a 4-factor structure for the ABM and a 2-factor structure for the CBI personal scale (CBI-P). Factorial validity and dimensionality were examined with four exploratory models which indicated a unidimensional structure for the ABM with an overarching 'Autistic Burnout' construct, and multidimensional CBI-P structure comprising two subscales and overarching 'Personal Burnout' construct. Other reliability and validity indicators included Spearman correlations, analysis of variance, receiver operating characteristics, sensitivity, specificity, and intra-class correlations (ICC). The ABM and CBI-P were strongly correlated with depression, anxiety, stress, and fatigue. Unexpectedly, correlations between the burnout measures and camouflaging, and wellbeing measures were moderate. Potential overlap between burnout and depression and fatigue was examined through EFA, which supported convergent validity of the ABM and depression measure, while correlations and ICC analyses revealed mixed results. We concluded that the ABM and the CBI-P Emotional Exhaustion subscale were valid preliminary screening tools for autistic burnout. Testing with larger and more diverse autistic samples is required to further examine the psychometric properties of the ABM, and to understand the relationships between autistic burnout and depression, and masking.

Analyzing the Direct Effect of Multidimensional Country Image on Purchase Intention: A Preliminary Study on Foreign Student in Turkiye

The study examined direct effect of country image on purchase intention of Turkish product or service in the sample of foreign students. The questionnaire was developed by measures in literature and research was conducted by convenience sampling with foreign students living in Izmir City. In findings, multidimensional country image was confirmed and its seven dimensions were obtained. It was found out that country image explained 19.9% of variance in purchase intention. This study filled the gap by confirming multidimensional structure of Turkiye’s country image and showed direct effects of country image on purchase intention in the foreign student sample.

Which One is the Best for Evaluating the Multidimensional Structure of Meaning in Life Among Chinese: A Comparison of Three Multidimensional Scales

Which One is the Best for Evaluating the Multidimensional Structure of Meaning in Life Among Chinese: A Comparison of Three Multidimensional Scales

Investigating the internal structure of multiple mini interviews-A perspective from Pakistan.

Healthcare professionals require many personal attributes in addition to cognitive abilities and psychomotor skills for competent practice. Multiple Mini- Interviews are being employed globally to assess personality attributes of candidates for selection in health professions education at all level of entry; these attributes are namely, communication skills, critical thinking, honesty, responsibility, health advocacy, empathy and sanctity of life. Considering the high stakes involved for students, faculty, institutions and the society, rigorous quality assurance mechanisms similar to those used for student assessment must be employed for student selection, throughout the continuum of medical education. It is a difficult undertaking as these psychological constructs are difficult to define and measure. Though considered to yield reliable and valid scores, studies providing multiple evidences of internal structure especially dimensionality of Multiple Mini-Interviews are sparse giving rise to questions if they are measuring a single or multiple constructs and even if they are measuring what they are purported to be measuring. The main objective is to provide statistical support of the multi-dimensional nature of our Multiple Mini Interviews, hypothesized a-priori, through CFA. Another objective is to provide multiple evidences for the internal structure. Our study highlights the link between content and internal structure evidences of the constructs, thus establishing that our Multiple Mini Interviews measure what they were intended to measure. After securing permission from the Institutional review board, an a-priori seven factor-model was hypothesized based on the attributes considered most essential for the graduating student of the institution. After operationally defining the attributes through extensive literature search, scenarios were constructed to assess them. A 5-point rating scale was used to rate each item on the station. A total 259 students participated in the multiple mini interviews over a period of three days. A training workshop had been arranged for the participating faculty. The reliability coefficient using Cronbach's alpha were calculated (range from 0.73 to 0.94), Standard Error of Measurement (ranged from 0.80 to1.64), and item to station-total correlation ranged from 0.43-0.50 to 0.75-0.83. Inter-station correlation was also determined. Confirmatory factor analysis endorsed the results of Exploratory factor analysis in the study revealing a seven model fit with multiple indices of Goodness-of-fit statistics such as Root mean square error of approximation (RMSEA) value 0.05, Standardized root mean square residual (SRMR) value with less than 0.08. All these indices showed that model fit is good. The Confirmatory factor analysis confirmed the multi-dimensional nature of our MMIs and also confirmed that our stations measured the attributes that they were supposed to measure. This study adds to the validity evidence of Multiple Mini-Interviews, in selection of candidates, with required personality traits for healthcare profession. It provides the evidence for the multi-dimensional structure of Multiple Mini interviews administered with multiple evidences for its internal structure and demonstrates the independence of different constructs being measured.

A measure of attributions for peer victimization: psychometric properties and differences among bullying roles

ABSTRACT Attributional processes about why outcomes occur constitute an important mediating mechanism that can explain different reactions of both the targets of harassment and their peers. The present study aimed to evaluate the psychometric properties of a measure of attributions for victimization among adolescents, using Confirmatory Factor Analyses, and to examine differences among roles (i.e., not involved, bullies, victims, bully-victims) in self-blame attributions, by means of testing invariance. The sample consisted of 1,207 adolescents (54% females; Mage = 14.89; SD = 1.39). The results showed a multidimensional structure of the scale with three factors (Characterological self-blame, Behavioral self-blame, and External Attribution). Partial invariance was found across different roles, highlighting a difference in the latent means of Characterological Self-Blame, higher in the victim group than in the other roles. The findings suggested how victims showed biases in their interpretations of social situations attributing the negative experience to internal, stable, and uncontrollable causes.

Isomorphic Multidimensional Structures of the Cyclic Random Process in Problems of Modeling Cyclic Signals with Regular and Irregular Rhythms

This paper is devoted to the research of the isomorphic multidimensional cyclic structure and multidimensional phase structure of the cyclic random process (CRP) and to its formation method, which enables a rigorous formalization of intuitive ideas concerning cyclic stochastic motion. The fundamental properties of the cyclic random process and analytical dependencies between the multidimensional cyclic structure, multidimensional phase structure and rhythm structure of the CRP have been established. This work shows that the CRP is able to take into account the cyclicity of multidimensional distribution functions of cyclic signals as well as the variability in the rhythm of the investigated signals. A subclass of the CRP is the periodic random process, which allows for the use of classical processing methods of cyclic signals with a regular rhythm. Based on a series of experiments, significant advantages of the CRP as a mathematical model of electrocardiographic signals (ECG) compared to the periodic random process are shown.

Topological and geometric analysis of cell states in single-cell transcriptomic data.

Single-cell RNA sequencing (scRNA-seq) enables dissecting cellular heterogeneity in tissues, resulting in numerous biological discoveries. Various computational methods have been devised to delineate cell types by clustering scRNA-seq data, where clusters are often annotated using prior knowledge of marker genes. In addition to identifying pure cell types, several methods have been developed to identify cells undergoing state transitions, which often rely on prior clustering results. The present computational approaches predominantly investigate the local and first-order structures of scRNA-seq data using graph representations, while scRNA-seq data frequently display complex high-dimensional structures. Here, we introduce scGeom, a tool that exploits the multiscale and multidimensional structures in scRNA-seq data by analyzing the geometry and topology through curvature and persistent homology of both cell and gene networks. We demonstrate the utility of these structural features to reflect biological properties and functions in several applications, where we show that curvatures and topological signatures of cell and gene networks can help indicate transition cells and the differentiation potential of cells. We also illustrate that structural characteristics can improve the classification of cell types.

Health information craving: Conceptualization, scale development and validation

Cyberchondria, involving the excessive and compulsive seeking of health information, has garnered escalating attention in recent years. One model proposes cyberchondria as an addictive behavior, and craving is considered a key driver of addictive behaviors. However, craving has yet to be systematically studied in the context of cyberchondria. Therefore, this study aimed to conceptualize a contruct of health information craving, develop, and validate a health information craving scale, intended for subsequent empirical examinations in the context of cyberchondria. We employed a rigorous multi-step procedure for scale development and validation, conducting three separate studies with 1633 participants. The resulting scale of health information craving exhibited a multi-dimensional structure with positive and negative reinforcing sub-scales, as confirmed through diverse reliability and validity assessments. As expected, the scale demonstrated a positive relationship with individuals' cyberchondria, thus providing support for the nomological validity of the developed scale. Furthermore, the findings suggest that health information craving should be considered a related but distinct concept from another similar concept, health information need, thereby offering further support for its operationalization. Theoretical and practical implications are discussed.

Scaled aggregation operations over three-dimensional extended intuitionistic fuzzy index matrices

Modern image processing techniques are improving beyond old methods, which include advanced approaches, for example deep learning. Convolutional Neural Networks (CNNs) are excellent at automatic feature extraction, whereas Generative Adversarial Networks (GANs) produce realistic images. Transfer learning uses pre-trained models, whereas semantic segmentation identifies pixels in images. Super-resolution, style transfer, and attention mechanisms can increase the quality of images and understanding. Adversarial defenses address purposeful manipulations, while 3D image processing handles three-dimensional data. These advancements make use of improved computational power and massive datasets to revolutionize image processing capabilities. Traditional image processing algorithms frequently fail to handle the complex and multidimensional structure of color images, particularly when dealing with uncertainty and imprecision. In this study, the 3D-EIFIM frame work is extented and scaled aggregation operations 3D-EIFIM tailored for image data are proposed. By representing each pixel as an entry of 3D-EIFIM and applying aggregation techniques to enable more effective image analysis, manipulation, and enhancement. The practical implications of this research are significant, as it can lead to advancements in fields such as computer vision, medical imaging, and remote sensing.

Construction of multidimensional structure model of the Mesoproterozoic Xiamaling shale kerogen, northern North China

In the southeastern Xuanlong sag of Northern China, the organic-rich black shale of the Mesoproterozoic Xiamaling Formation holds significant potential as a source rock. Through advanced analytical techniques including elemental analysis, 13C NMR, XPS, FTIR, and XRD, the elemental composition and chemical structure were thoroughly characterized, and the molecular structure of the Xiamaling shale kerogen was established. The 13C NMR analysis revealed the carbon composition of kerogen, indicating 75.19 % aliphatic carbon, 22.06 % aromatic carbon, and 2.75 % carbonyl carbon, with quantitative support from FTIR and XRD spectra. The dominant methylene carbons exhibited a low average methylene chain length (Cn) of 3.87, suggesting that they are primarily short-branched or alicyclic compounds rather than long straight chains. This conclusion was verified by the absence of a sharp peak at a wavenumber of 719.95 cm−1 in the FTIR spectrum. Oxygen-containing functional groups observed in 13C NMR spectra were also identified in FTIR spectra at a wavenumber range of 3500–1000 cm−1. XPS analysis indicated that nitrogen-containing groups were amino, quaternary, pyridine, and primary pyrrole groups, and sulfur existed as sulfoxide, aromatic sulfur, aliphatic sulfur, and sulfone. Finally, a relatively rational 2D molecular structure with the chemical formula of C278H346N6O26S2 was established by comparing the predicted and experimental 13C NMR spectra. Using MD simulation, five optimized 2D molecules were combined to construct a robust 3D kerogen model with a physical density of 1.067 g/cm3. Comparatively, the aliphatic structure of the Mesoproterozoic Xiamaling kerogen model consists of some monocyclic alkanes and polycyclic saturated structures, resembling Paleozoic kerogen models but differing from Mesozoic counterparts. The constructed kerogen model addresses the gap in the Precambrian kerogen modeling and sets the stage for in-depth exploration of pyrolysis mechanisms in the future.

Epileptic seizure prediction based on EEG using pseudo-three-dimensional CNN.

Epileptic seizures are characterized by their sudden and unpredictable nature, posing significant risks to a patient's daily life. Accurate and reliable seizure prediction systems can provide alerts before a seizure occurs, as well as give the patient and caregivers provider enough time to take appropriate measure. This study presents an effective seizure prediction method based on deep learning that combine with handcrafted features. The handcrafted features were selected by Max-Relevance and Min-Redundancy (mRMR) to obtain the optimal set of features. To extract the epileptic features from the fused multidimensional structure, we designed a P3D-BiConvLstm3D model, which is a combination of pseudo-3D convolutional neural network (P3DCNN) and bidirectional convolutional long short-term memory 3D (BiConvLstm3D). We also converted EEG signals into a multidimensional structure that fused spatial, manual features, and temporal information. The multidimensional structure is then fed into a P3DCNN to extract spatial and manual features and feature-to-feature dependencies, followed by a BiConvLstm3D input to explore temporal dependencies while preserving the spatial features, and finally, a channel attention mechanism is implemented to emphasize the more representative information in the multichannel output. The proposed has an average accuracy of 98.13%, an average sensitivity of 98.03%, an average precision of 98.30% and an average specificity of 98.23% for the CHB-MIT scalp EEG database. A comparison of the proposed model with other baseline methods was done to confirm the better performance of features through time-space nonlinear feature fusion. The results show that the proposed P3DCNN-BiConvLstm3D-Attention3D method for epilepsy prediction by time-space nonlinear feature fusion is effective.

Enhancing electromagnetic wave absorption performance through construction of three-dimensional multilayered SiCw/Y3Si2C2/Ni0.5Zn0.5Fe2O4 composites

In this study, three-dimensional (3D) multilayer electromagnetic wave absorbing (EMWA) materials SiCw/Y3Si2C2/Ni0.5Zn0.5Fe2O4 were fabricated for the first time. A two-dimensional (2D) Y3Si2C2 coating was synthesized onto the one-dimensional (1D) SiCw surface by the molten salt method. Subsequently, one layer of zero-dimensional (0D) Ni0.5Zn0.5Fe2O4 nanoparticles was deposited onto the Y3Si2C2 coating surface via hydrothermal reaction. The microstructure and electromagnetic wave absorption mechanism of SiCw/Y3Si2C2/Ni0.5Zn0.5Fe2O4 were systematically investigated. The introduction of 2D Y3Si2C2 and 0D Ni0.5Zn0.5Fe2O4 enhanced impedance matching and provided abundant heterogeneous interfaces, thus leading to interface polarization loss. Owing to the promoted impedance matching and superb attenuation capacity, 3D multilayered SiCw/Y3Si2C2/Ni0.5Zn0.5Fe2O4 composites exhibit superior EMWA performance in comparison with origin SiCw. For the SiCw/Y3Si2C2/Ni0.5Zn0.5Fe2O4 composites, their minimum reflection loss (RLmin) can be achieved −43.95 dB and the effective absorption bandwidth (EAB) is 4.08 GHz when the thickness is 2.12 mm. This study could pave a reference for the investigation of multi-dimensional hybrid structure EMWA material.

Flexible vanillin-polyacrylate/chitosan/mesoporous nanosilica-MXene composite film with self-healing ability towards dual-mode sensors

Manufacturing flexible sensors with prominent mechanical properties, multifunctional sensing abilities, and remarkable self-healing capabilities remains a difficult task. In this study, a novel vanillin-modified polyacrylate (VPA), which is capable of forming green dynamic covalent crosslinking with chitosan (CS), was synthesized. The synthesized VPA was combined with mesoporous silica-modified MXene (AMS-MXene) and covalently cross-linked simultaneously with CS, resulting in the formation of a flexible composite conductive film designed for dual-mode sensors. Due to the multidimensional structure formed by the mesoporous silica and MXene layers, the resulting composite film is not only suitable for strain sensing but also excels in gas response sensing. Most importantly, the composite films demonstrate a remarkable self-healing capability through reversible dynamic covalent bonds, specifically Schiff base bonds, coupled with multiple hydrogen bonding interactions with AMS-MXene. This robust self-repair functionality remains effective even at a low temperature of 30 °C. Additionally, the synergistic antibacterial effect exerted by vanillin and CS in the film can endow the composite sensor with excellent antimicrobial properties. This multifunctional composite film holds tremendous potential for applications in green flexible wearable sensors. Furthermore, it can show diverse applications in a wide variety of fields, driving advances in wearable technology and human health monitoring.