Search Results (755)

The current work presents a case study for the state-of-the-art multimodal risk assessment approach, which is especially appropriate for environmental wind-wave dynamic systems that are either directly physically observed or numerically modeled. High dimensionality of the wind-wave environmental system and cross-correlations between its primary dimensions or components make it quite challenging for existing reliability methods. The primary goal of this investigation has been the application of a novel multivariate hazard assessment methodology to a combined windspeed and correlated wave-height unfiltered/raw dataset, which was recorded in 2024 by in situ NOAA buoy located southeast offshore of Greenland. Existing hazard/risk assessment methods are mostly limited to univariate or at most bivariate dynamic systems. It is well known that the interaction of windspeeds and corresponding wave heights results in a multimodal, nonstationary, and nonlinear dynamic environmental system with cross-correlated components. Alleged global warming may represent additional factor/covariate, affecting ocean windspeeds and related wave heights dynamics. Accurate hazard/risk assessment of in situ environmental systems is necessary for naval, marine, and offshore structures that operate within particular offshore/ocean zones of interest, susceptible to nonstationary ocean weather conditions. Benchmarking of the novel spatiotemporal multivariate reliability approach, which may efficiently extract relevant information from the underlying in situ field dataset, has been the primary objective of the current work. The proposed multimodal hazard/risk evaluation methodology presented in this study may assist designers and engineers to effectively assess in situ environmental and structural risks for multimodal, nonstationary, nonlinear ocean-driven wind-wave-related environmental/structural systems. The key result of the presented case study lies within the demonstration of the methodological superiority, compared to a popular bivariate copula reliability approach. Full article

Background: Immersive virtual reality (IVR) serves as a promising tool to provide adolescents with enriched social experience due to its high-fidelity simulations and multimodal interaction. This study aims to design and develop a multi-user IVR collaborative game utilizing simultaneous localization and mapping (SLAM)-based inside-out tracking technique to foster social experience among students. Also, this study explored the mechanism by which technology acceptance affected social experience in the IVR collaboration game, focusing on the mediating effects of presence, collective efficacy, and group effectiveness, as well as the moderating effect of social–emotional competence (SEC). Methods: A total of 104 seventh graders from a middle school in Central China participated in this study and completed the questionnaire. Finally, 87 valid questionnaire responses were retrieved. Results: The results revealed that technology acceptance both directly and indirectly influenced social experience. The mediation analysis revealed a key pathway influencing social experience: technology acceptance → presence → collective efficacy → group effectiveness → social experience. However, no moderating effect of SEC was found in the relationship between technology acceptance and social experience, group effectiveness, and social experience. Conclusions: Based on these results, more appropriate IVR interventions could be developed for social–emotional learning among children and adolescents. Full article

This scoping review examines the broad applications, risks, and ethical challenges associated with Extended Reality (XR) technologies, including Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR), within the context of Metaverse. XR is revolutionizing fields such as immersive learning in education, medical and professional training, neuropsychological assessment, therapeutic interventions, arts, entertainment, retail, e-commerce, remote work, sports, architecture, urban planning, and cultural heritage preservation. The integration of multimodal technologies—haptics, eye-, face-, and body tracking, and brain–computer interfaces—enhances user engagement and interactivity, playing a key role in shaping the immersive experiences in the Metaverse. However, XR’s expansion raises serious concerns, including data privacy risks, cybersecurity vulnerabilities, cybersickness, addiction, dissociation, harassment, bullying, and misinformation. These psychological, social, and security challenges are further complicated by intense advertising, manipulation of public opinion, and social inequality, which could disproportionately affect vulnerable individuals and social groups. This review emphasizes the urgent need for robust ethical frameworks and regulatory guidelines to address these risks while promoting equitable access, privacy, autonomy, and mental well-being. As XR technologies increasingly integrate with artificial intelligence, responsible governance is essential to ensure the safe and beneficial development of the Metaverse and the broader application of XR in enhancing human development. Full article

This study presents an advanced integration of Multi-modal Raman Light Sheet Microscopy with zero-shot learning-based computational methods to significantly enhance the resolution and analysis of complex three-dimensional biological structures, such as 3D cell cultures and spheroids. The Multi-modal Raman Light Sheet Microscopy system incorporates Rayleigh scattering, Raman scattering, and fluorescence detection, enabling comprehensive, marker-free imaging of cellular architecture. These diverse modalities offer detailed spatial and molecular insights into cellular organization and interactions, critical for applications in biomedical research, drug discovery, and histological studies. To improve image quality without altering or introducing new biological information, we apply Zero-Shot Deconvolution Networks (ZS-DeconvNet), a deep-learning-based method that enhances resolution in an unsupervised manner. ZS-DeconvNet significantly refines image clarity and sharpness across multiple microscopy modalities without requiring large, labeled datasets, or introducing artifacts. By combining the strengths of multi-modal light sheet microscopy and ZS-DeconvNet, we achieve improved visualization of subcellular structures, offering clearer and more detailed representations of existing data. This approach holds significant potential for advancing high-resolution imaging in biomedical research and other related fields. Full article

In this work, a novel optical–chemical sensor for the detection of per- and polyfluorinated substances (PFASs) in a real scenario is presented. The proposed sensing approach exploits the multimode characteristics of plastic optical fibers (POFs) to achieve unconventional sensors via surface plasmon resonance (SPR) phenomena. The sensor is realized by the coupling of an SPR-POF platform with a novel chemical chip based on different polymeric nanolayers over the core of a D-shaped POF, one made up of an optical adhesive and one of a molecularly imprinted polymer (MIP) for PFAS. The chemical chip is used to launch the light into the SPR D-shaped POF platform, so the interaction between the analyte and the MIP’s sites can be used to modulate the propagated light in the POFs and the SPR phenomena. Selectivity tests and dose–response curves by standard PFOA water solutions were carried out to characterize the detection range sensor response, obtaining a wide PFAS response range, from 1 ppt to 1000 ppt. Then, tests performed on river water samples collected from the Bormida river paved the way for the applicability of the proposed approach to a real scenario. Full article

With the increasing need for real-time crowd evaluation in military surveillance, public safety, and event crowd management, crowd counting using unmanned aerial vehicle (UAV) captured images has emerged as an essential research topic. While conventional RGB-based methods have achieved significant success, their performance is severely hampered in low-light environments due to poor visibility. Integrating thermal infrared (TIR) images can address this issue, but existing RGB-T crowd counting networks, which employ multi-stream architectures, tend to introduce computational redundancy and excessive parameters, rendering them impractical for UAV applications constrained by limited onboard resources. To overcome these challenges, this research introduces an innovative, compact RGB-T framework designed to minimize redundant feature processing and improve multi-modal representation. The proposed approach introduces a Partial Information Interaction Convolution (PIIConv) module to selectively minimize redundant feature computations and a Global Collaborative Fusion (GCFusion) module to improve multi-modal feature representation through spatial attention mechanisms. Empirical findings indicate that the introduced network attains competitive results on the DroneRGBT dataset while significantly reducing floating-point operations (FLOPs) and improving inference speed across various computing platforms. This study’s significance is in providing a computationally efficient framework for RGB-T crowd counting that balances accuracy and resource efficiency, making it ideal for real-time UAV deployment. Full article

Tropical cyclones (TC) are dynamic atmospheric phenomena featuring extreme low-pressure systems and powerful winds, known for their devastating impacts on weather and the environment. The main purpose of this paper is to consider the subtle involvement of TCs in the air quality index (AQI), focusing on aspects related to the air quality before, during and after cyclones. This research employs multimodal methods, which include meteorological data and different satellite observations. Deep learning approaches, i.e., ConvLSTM, CNN and Real-ESRGAN models, are combined with a regression model to analyze the temporal variability in the air quality associated with tropical cyclones. Deep learning models are deployed to uncover complex patterns and non-linear interdependencies between cyclones’ features and the AQI to give predictive insights into the air quality fluctuations throughout the different stages of tropical cyclones. Furthermore, this study explores the aftermaths of TCs in terms of the air quality with respect to post-cyclone recovery. The findings offer an enhanced view of the role of TCs in the regional or global air quality, which will be useful for policymakers, meteorologists and environmental researchers. Utilizing a CNN for tropical cyclone (TC) classification and the extra trees regressor (ETR) for AQI prediction results in accuracy of 92.02% for the CNN and an R2 of 83.33% for the ETR. Hence, this work adds to our knowledge and enlightens us on the complex interactions between TCs and the air quality, highlighting wider public health concerns regarding climate adaptation and urban renewal. Full article

Accurate postmortem interval (PMI) estimation is critical for forensic investigations, aiding case classification and providing vital trial evidence. Early postmortem signs, such as body temperature and rigor mortis, are reliable for estimating PMI shortly after death. However, these indicators become less useful as decomposition progresses, making late-stage PMI estimation a significant challenge. Decomposition involves predictable microbial activity, which may serve as an objective criterion for PMI estimation. During decomposition, anaerobic microbes metabolize body tissues, producing gases and organic acids, leading to significant changes in skin and soil microbial communities. These shifts, especially the transition from anaerobic to aerobic microbiomes, can objectively segment decomposition into pre- and post-rupture stages according to rupture point. Microbial communities change markedly after death, with anaerobic bacteria dominating early stages and aerobic bacteria prevalent post-rupture. Different organs exhibit distinct microbial successions, providing valuable PMI insights. Alongside microbial changes, metabolic and volatile organic compound (VOC) profiles also shift, reflecting the body’s biochemical environment. Due to insufficient information, unimodal models could not comprehensively reflect the PMI, so a muti-modal model should be used to estimate the PMI. Machine learning (ML) offers promising methods for integrating these multimodal data sources, enabling more accurate PMI predictions. Despite challenges such as data quality and ethical considerations, developing human-specific multimodal databases and exploring microbial–insect interactions can significantly enhance PMI estimation accuracy, advancing forensic science. Full article

We propose boson sampling from a system of coupled photons and Bose–Einstein condensed atoms placed inside a multi-mode cavity as a simulation process testing the quantum advantage of quantum systems over classical computers. Consider a two-level atomic transition far-detuned from photon frequency. An atom–photon scattering and interatomic collisions provide interactions that create quasiparticles and excite atoms and photons into squeezed entangled states, orthogonal to the atomic condensate and classical field driving the two-level transition, respectively. We find a joint probability distribution of atom and photon numbers within a quasi-equilibrium model via a hafnian of an extended covariance matrix. It shows a sampling statistics that is ♯P-hard for computing, even if only photon numbers are sampled. Merging cavity-QED and quantum-gas technologies into a hybrid boson sampling setup has the potential to overcome the limitations of separate, photon or atom, sampling schemes and reveal quantum advantage. Full article

Surface plasmon resonance (SPR) biosensors are extensively utilized for analyzing molecular interactions due to their high sensitivity and label-free detection capabilities. Recent innovations in surface-sensitive biosensors with dielectric surfaces address the inherent limitations associated with traditional gold surfaces, such as thermal effects and biocompatibility issues, which can impede broader applications. This review examines state-of-the-art biosensor configurations, including total internal reflection, optical waveguide, photonic crystal resonators, Bloch surface wave biosensors, and surface electrochemical biosensors, which can enhance analyte signals and augment the molecular detection efficiency at the sensor interface. These technological advancements not only improve the resolution of binding kinetics analysis and single-molecule detection but also extend the analytical capabilities of these systems. Additionally, this review explores prospective advancements in augmenting field enhancement and incorporating multimodal sensing functionalities, emphasizing the significant potential of these sophisticated biosensing technologies to profoundly enhance our understanding of molecular interactions. Full article

Magnetic resonance (MR) imaging is widely used in the clinical field due to its non-invasiveness, but the long scanning time is still a bottleneck for its popularization. Using the complementary information between multi-modal imaging to accelerate imaging provides a novel and effective MR fast imaging solution. However, previous technologies mostly use simple fusion methods and fail to fully utilize their potential sharable knowledge. In this study, we introduced a novel multi-hierarchical complementary feature interaction network (MHCFIN) to realize joint reconstruction of multi-modal MR images with undersampled data and thus accelerate multi-modal imaging. Firstly, multiple attention mechanisms are integrated with a dual-branch encoder–decoder network to represent shared features and complementary features of different modalities. In the decoding stage, the multi-modal feature interaction module (MMFIM) acts as a bridge between the two branches, realizing complementary knowledge transfer between different modalities through cross-level fusion. The single-modal feature fusion module (SMFFM) carries out multi-scale feature representation and optimization of the single modality, preserving better anatomical details. Extensive experiments are conducted under different sampling patterns and acceleration factors. The results show that this proposed method achieves obvious improvement compared with existing state-of-the-art reconstruction methods in both visual quality and quantity. Full article

This review explores electrochemical sensing strategies for synthetic orange dyes, addressing the growing need for sensitive and selective detection methods in various industries. We examine the fundamental principles underlying the electrochemical detection of these compounds, focusing on their redox behavior and interaction with electrode surfaces. The review covers a range of sensor designs, from unmodified electrodes to advanced nanomaterial-based platforms. Chemically modified electrodes incorporating polymers and molecularly imprinted polymers are discussed for their enhanced selectivity. Particular attention is given to nanomaterial-based sensors, including those utilizing carbon nanotubes, graphene derivatives, and metal nanoparticles, which have demonstrated exceptional sensitivity and wide linear ranges. The potential of biological-based approaches, such as DNA interaction sensors and immunosensors, is also evaluated. Current challenges in the field are addressed, including matrix effects in complex samples and long-term stability issues. Emerging trends are highlighted, including the development of multi-modal sensing platforms and the integration of artificial intelligence for data analysis. The review concludes by discussing the commercial potential of these sensors in food safety, environmental monitoring, and smart packaging applications, emphasizing their importance in ensuring the safe use of synthetic orange dyes across industries. Full article

Multimodal sentiment analysis enables machines to interact with people more naturally. The integration of multimodalities can enhance the machines’ ability to accurately predict emotions. The main obstacle to multimodal sentiment analysis is integrating information from different modalities. Previous research has used a variety of techniques, including long short-term memory networks (LSTM) and transformers. However, traditional fusion methods cannot better utilize the information from each modality, and some intra- and inter-modal features may be overlooked due to possible differences in feature representations. Therefore, to address this problem, we use a combined transformer that can connect different modal inputs and introduce SS-Trans (Single-Stream Transformer for Multimodal Sentiment Analysis and Emotion Recognition), a single-stream transformer that fuses textual, visual, and speech modalities. The model was pre-trained on the CMU-MOSI and CMU-MOSEI datasets: multi-modal masked image language modeling (MLM) and text–image matching (TIA). Compared to other existing models, SS-Trans improves ACC-2 on these two datasets by 1.06% and 1.33%, and improves F1 values by 1.50% and 1.62%, respectively. The experimental results show that our method achieves the state-of-the-art level. In addition, ablation experiments validate the model and the pre-training task, proving the effectiveness of the proposed model. Full article

With the rapid development of mobile applications (apps), various types of mobile apps have become the main channels for smartphone interaction. The user’s app switching behavior in mobile search tasks has also received attention from academia. This article uses the push–pull–mooring (PPM) theoretical model to determine the three influencing factors of push, pull, and mooring that affect user’s app switching behavior in mobile search. Data were collected from 374 respondents using a structural equation model. This study can deepen the understanding of app switching in user mobile search from the perspectives of information source selection, user information search path, etc. This study found that in terms of pushing factors, the complexity of search tasks positively affects users’ willingness to switch apps. In terms of pulling factors, the attractiveness of alternative products and users’ follow-up activities will positively affect their switching willingness. Meanwhile, inertia serves as a mooring variable to regulate the relationship between push-pull factors and user switching intentions. This research highlights key insights on user behavior, follow-up activities, and the role of switching costs and inertia, contributing to the broader literature on information-seeking behavior. It also provides actionable recommendations for app developers to enhance search experiences and retain users by integrating personalized, multi-modal features. Full article

A comprehensive design framework is proposed for optimizing sparse MIMO (multiple-input, multiple-output) arrays to enhance multi-target detection. The framework emphasizes efficient utilization of antenna resources, including strategies for minimizing inter-element mutual coupling and exploring alternative grid-based sparse array (GBSA) configurations by efficiently separating interacting elements. Alternative strategies are explored to enhance angular beamforming metrics, including beamwidth (BW), peak-to-sidelobe ratio (PSLR), and grating lobe limited field of view. Additionally, a set of performance metrics is introduced to evaluate virtual aperture effectiveness and beamwidth loss factors. The framework explores optimization strategies for the partial sharing of antenna elements, specifically tailored for multi-mode radar applications, utilizing the desirability function to enhance performance across various operational modes. A novel machine learning initialization approach is introduced for rapid convergence. Key observations include the potential for peak-to-sidelobe ratio (PSLR) reduction in dense arrays and insights into GBSA feasibility and performance compared to uniform arrays. The study validates the efficacy of the proposed framework through simulated and measured results. The study emphasizes the importance of effective sparse array processing in multi-target scenarios and highlights the advantages of the proposed design framework. The proposed design framework for grid-spaced sparse arrays stands out for its superior efficiency and applicability in processing hardware compared to both uniform and non-uniform arrays. Full article