Multimodal Response Research Articles

Effects of long-term volleyball training on multimodal responses in adolescent female athletes: a follow-up study.

The aim of this study was to examine the effects of long-term (10 months) volleyball training on biochemical responses in adolescent female athletes since the cumulative effects of chronic training on this population are not yet clear. Twenty-one adolescent female volleyball players competing at the national level served as the participants. All athletes carried out volleyball training, which consisted of ball handling, specialized drills, and practical game-style exercises, including physical training in the school gymnasium. The average training cycle consisted of 6 days per week, with a total of about 2 to 2.5 hours of volleyball training per day. In order to determine the cumulative effects on autonomic, immune, renal function and bone resorption, salivary and urinary samples were collected before volleyball training on 3 consecutive days (days 1, 3 and 5) at months 0 and 10, respectively. No significant changes in body mass, salivary secretion rate, urinary albumin, L-type fatty acid binding protein and type I collagen cross-linked N-telopeptide concentration were found. In contrast, significant decreases were noted in salivary α-amylase activity (% change: -22.9), total protein (%change: -21.4), and immunoglobulin A concentration (% change: -20.1). The results of this study imply that the autonomic function after chronic volleyball training in adolescent female athletes may be enhanced due to training adaptation, although the immune function may be attenuated as a result of cumulative overtraining. Moreover, long-term volleyball training in adolescent female athletes appears to suppress bone resorption and not to induce cumulative damage to renal function.

AI-Powered Multimodal Disaster Response Enhancement using Social Media Streams

Social media serves as a valuable source of real-time disaster data, providing timely updates that are critical for effective disaster monitoring and response. It plays an essential role in raising awareness, enhancing preparedness, and facilitating prompt action during emergencies. While much of the current research focuses on disaster prediction, real-time updates, which are key to saving lives and coordinating response efforts, are often overlooked. Our project addresses this gap by focusing on the collection and analysis of real-time data from various social media platforms, including disaster-related images and textual data like tweets.We use multiple APIs to gather real-time data from platforms such as Instagram, Facebook, and Twitter. To analyze visual data, we employ state-of-the-art multimodal models like ResNet50 and EfficientNet for image classification. For the textual data, advanced natural language processing models like BERT and XLNet are used for sentiment analysis, event detection, and severity assessment. By integrating and fusing the image and text data, our system is able to identify the type of disaster, estimate its severity, and provide actionable insights.

Digital Storytelling as Multimodal Response to Young Adult Literature Promoting EFL Students’ Multiliteracies, Global Citizenship, and Mediation Skills

This study examines how Digital Storytelling enhanced upper secondary students’ multiliteracy and mediation skills in EFL and their reflections on global issues. Thirteen digital stories, created in response to Young Adult Literature, were analysed using Visual Communication Grammar and Soundscape. This analysis was complemented by students’ diaries and classroom field notes. Results show that students used different remix strategies to reinterpret novel themes, enjoying collaboration and fostering critical and creative skills but struggling with brainstorming, research, and editing.

Oxide Dendrite Transistors Gated with Polyvinyl Alcohol/Chitosan Hybrid Electrolyte for Spatiotemporal Integration

Inspired by the brain computing mode, significant efforts have been dedicated to developing neuromorphic devices to realize energy-efficient neuromorphic systems. Specifically, a neuron can deal with spatiotemporal input patterns with dendrites, meaningful for multi-modal responses of our perceptual system. Here, a multi-gate blended polyvinyl alcohol/chitosan based electrolyte gated oxide dendrite transistor was fabricated. With extremely strong interfacial protonic/electronic coupling, the proposed dendrite transistors exhibit rich neuromorphic functions under heterosynaptic mechanism. Spatiotemporal dendritic integration and linear/nonlinear dendritic algorithms were demonstrated, showing its versatility in complex bionic neuronal processes. Logic operations were successfully implemented, including “OR”, “AND”, “NOR”, “NAND”. Capacitively coupled multi-terminal oxide dendrite transistors are also proposed for data classification and information decoding. Finally, neural addition operation was realized on a single dendrite transistor. These results indicate that the present neuromorphic devices could be building blocks for neuromorphic systems, offering an interesting strategy toward efficient and compact neuromorphic architectures.

Australasian hidradenitis suppurativa management guidelines.

Hidradenitis Suppurativa is a burdensome inflammatory skin disease with significant quality of life impact. These management guidelines were developed to direct appropriate clinical management in the Australasian context. A systematic review was used for the basis of the consensus guidelines. Thirteen clinical experts were involved in a modified Delphi consensus process to develop the guidelines and treatment algorithms. Overall management strategies include appropriate severity assessment of disease and comorbidities, multimodal therapy with systemic and local treatments, and evidence-based progression along the therapeutic ladder in the event of inadequate response. Sequential monotherapy with antibiotics and/or single agent therapy is discouraged and aggressive treatment of moderate to severe disease to capture the window of opportunity is highly emphasised. Specific considerations in the setting of disease comorbidities, pregnancy and breastfeeding are also addressed. Overall, the complex nature of HS requires a complex and multimodal therapeutic response with medical, physical and surgical therapies to achieve best patient outcomes.

Dissociable Effects of Urgency and Evidence Accumulation during Reaching Revealed by Dynamic Multisensory Integration.

When making perceptual decisions, humans combine information across sensory modalities dependent on their respective uncertainties. However, it remains unknown how the brain integrates multisensory feedback during movement and which factors besides sensory uncertainty influence sensory contributions. We performed two reaching experiments on healthy adults to investigate whether movement corrections to combined visual and mechanical perturbations scale with visual uncertainty. To describe the dynamics of multimodal feedback responses, we further varied movement time and visual feedback duration during the movement. The results of our first experiment show that the contribution of visual feedback decreased with uncertainty. Additionally, we observed a transient phase during which visual feedback responses were stronger during faster movements. In a follow-up experiment, we found that the contribution of vision increased more quickly during slow movements when we presented the visual feedback for a longer time. Muscle activity corresponding to these visual responses exhibited modulations with sensory uncertainty and movement speed ca. 100 ms following the onset of the visual feedback. Using an optimal feedback control model, we show that the increased response to visual feedback during fast movements can be explained by an urgency-dependent increase in control gains. Further, the fact that a longer viewing duration increased the visual contributions suggests that the brain accumulates sensory information over time to estimate the state of the arm during reaching. Our results provide additional evidence concerning the link between reaching control and decision-making, both of which appear to be influenced by sensory evidence accumulation and response urgency.

An explainable longitudinal multi-modal fusion model for predicting neoadjuvant therapy response in women with breast cancer

Multi-modal image analysis using deep learning (DL) lays the foundation for neoadjuvant treatment (NAT) response monitoring. However, existing methods prioritize extracting multi-modal features to enhance predictive performance, with limited consideration on real-world clinical applicability, particularly in longitudinal NAT scenarios with multi-modal data. Here, we propose the Multi-modal Response Prediction (MRP) system, designed to mimic real-world physician assessments of NAT responses in breast cancer. To enhance feasibility, MRP integrates cross-modal knowledge mining and temporal information embedding strategy to handle missing modalities and remain less affected by different NAT settings. We validated MRP through multi-center studies and multinational reader studies. MRP exhibited comparable robustness to breast radiologists, outperforming humans in predicting pathological complete response in the Pre-NAT phase (ΔAUROC 14% and 10% on in-house and external datasets, respectively). Furthermore, we assessed MRP’s clinical utility impact on treatment decision-making. MRP may have profound implications for enrolment into NAT trials and determining surgery extensiveness.

Bioinspired Three-Mode Photosensitive Synaptic LED for Optical Information Processing.

Inspired by human sense organs, AI is advancing toward multimodal perception, with display technology evolving into intelligent human-computer interaction tools. However, hardware networks with multimodal responses connected by different devices bring problems such as delayed information transfer and inefficiency. Thus, an innovative three-mode photosensitive synaptic LED (PSSL) is first proposed by adding a photosensitive layer indacenodithiophene-benzothiadiazole (IDTBT) to the quantum-dot light-emitting diode (QLED), switched by changing the bias voltage. The self-powered PSSL has a photoresponse range from 310 nm to 808 nm (ultraviolet-near-infrared, UV-NIR). The device exhibits a bipolar response under red and UV light at 1 V. When the voltage reaches the turn-on voltage, the PSSL device turns into a neuromorphic LED, exhibiting conductivity enhancement under red-light irradiation and suppression under UV-light irradiation. As a result, the PSSLs are expected to be applied in the field of optical encryption communication and in neuromorphic display.

Multi-modal response control with multiple suspension-type tuned vibration absorbers

Due to complicated excitations, engineering structures are often subjected to multi-modal responses. Considering the feasibility in practical installation on slender structures, multiple Suspension-type Tuned Vibration Absorbers (S-TVAs) are investigated for multi-modal response control. Firstly, parametric optimization of a single S-TVA for single-modal response control is investigated analytically. The issues regarding to the optimal tuning, static and dynamic performances, and installation location are addressed. Subsequently, an optimal design method for multi-modal response control with multiple S-TVAs is presented. Two aspects on the optimization strategy are discussed. Consequently, the optimization should be performed with an inverse modal order sequence. And, the modal information should be updated considering the S-TVA optimized in the previous step. Finally, the effectiveness of the presented optimal design method is validated through practical wind-induced response control on a slender chimney. The most unfavorable response can be suppressed up to 59.7 %, which is 47.8 % better than traditional single-modal control approach. Moreover, it is interesting to find that the practical overall control performance may not be achieved with more controlled modes intuitively. It is recommended to select from several practical cases determined by the presented optimal design method. Practical installation and feasibility are highly required to be considered in practice.

Chain Friction and Lubrication Balanced Ultra-Tough Polyacrylates With Wide-Span Switchable Stiffness for Strain-Programmable Deformation.

Natural mollusks perform complex mechanical actions through reversible large-strain deformation and stiffness switching, which are challenging to achieve simultaneously in synthetic materials. Herein, it is shown that a set of polyacrylates designed according to a chain friction and lubrication balanced strategy showsultra-stretchability (λ up to 324), high resilience (near 100% recovery at strain ≥ 100), and wide-span stiffness switching (up to 2073 times). The typical emulsion polymerization method and casting technique are adopted to fabricate the polyacrylate films. Quaternary ammonium surfactants are used as the emulsifier and reserved in the polymer matrix to enhance the chain segment lubrication with their long alkyl group but improve the whole chain friction through the formation of nano-eutectics. These polyacrylates undergo multimodal mechanical responses, including temperature- or time-programmed deformation and load-bearing like artificial muscles. This molecular design principle and synthetic method provide a robust platform for the fabrication of ultra-tough polymers for soft robots with multiple customized functions.

Poetry unveiled: Multimodality and aesthetic responses as a fresh approach to teaching and reading verse

AbstractIn secondary English classrooms, poetry is often a text that is least liked because it is viewed as being “inaccessible,” reserved for the elite, and/or too abstract. Part of the reason for this also lies in the traditional, colonial structures of introducing poetry such as relying on canonical texts and close reading analysis. Yet, outside of the classroom poetry is used in more accessible and engaging manners. With the advancements of technology, there also includes multimodal ways in which to read and write poetry that could be much more interesting for both educators and youth. This paper opens a discussion to consider multimodal and aesthetic responses to including poetry such as using digital apps like PhoneMe, a free accessible platform that allows users to post their written poems, record themselves reciting poems, and pin their poems directly on to an interactive digital map. The uniqueness of PhoneMe—a layered multimodal approach—can provide a more engaging way to teach and learn poetry.

Sequential-dependent synthesis of gold-chromium nanocomposites for multimode colorimetric sensing, advanced logic computing, and tri-layer information protection

Binary materials combine the properties and advantages of their components, but their fabrication paradigm needs updating to fully exploit their characteristics and broaden their application. Here, multifunctional bimetallic gold-chromium nanocomposites (Au–Cr NCs) were synthesized facilely in a sequential-dependent manner, and applied from multi-channel sensing to molecular informatization (including advanced logic computing and tri-layer information protection). By sequentially mixing Cr6+, Au3+, and NaBH4 (reducing agent), Au–Cr NCs with Au nanoparticles attached to Cr nanobelts can be prepared easily and quickly. The resulting Au–Cr NCs exhibited multi-signal sensing capability for hypochlorite, with significantly improved selectivity (about 5 times) and sensitivity (about 1650 times) when analyzing multiple signals. The synthesis process can be used to implement parallel molecular logic gates and customizable keypad lock operations. Moreover, by digitizing logical relationships and multimodal selective responses based on Au–Cr NCs, specific information (wise sayings and literary works sentence) can be encoded, encrypted and hidden to realize nano-cryptography and steganography. When combined with molecular keypad lock, a tri-layer information protection system can be constructed to enhance anti-cracking ability. This study promotes controllable preparation and multi-purpose applications of advanced multifunctional nanocomposites, but also opens up a new direction for nano-based sensing and digitization in multi-dimension.

Flexible porous non-woven silk fabric based conductive composite for efficient multimodal sensing

Silk-based conductive composites have attracted much attention for applications in flexible smart electronics. However, silk-based conductive composites that combine high air permeability, Joule heat management, electromagnetic interference (EMI) shielding and multimodal responsiveness are still challenging. In this paper, flexible porous non-woven silk fabric (nSF)-based conductive composites with the aforementioned properties are designed and developed by a freeze-induced assembly and surface micro-dissolution adhesion (FASMA) process to introduce carbonyl carbon nanotubes (C-CNTs), MXene and thermoplastic polyurethanes (TPU) into the inter-fiber network structure of nSF, with the aim of constructing a high-performance multimodal sensing platform. Functioning as a flexible strain sensor, the nSF-based composite can detect strains as low as 0.05 % with rapid response and recovery time of 125 ms and 146 ms, respectively, and can be used to detect full-size human motion signals. The nSF-based composite has a good humidity response at 0–85 % relative humidity (RH) and a sensitive and reliable thermal response. In additionally, the nSF-based composites also have Joule heat management and EMI shielding functions. All these multi-functional applications demonstrate the advantages of the prepared flexible porous nSF-based composites in the field of flexible smart electronics.

An Improved Vibration Multi Mode-Based Technique for the Characterization of Metallic Adhesion Impulses

Adhesion impulses generated at the separation of metallic surfaces significantly impact the functionality and performance of launch-lock and release space mechanisms. The testing concept adopted here consists of a suspended plate set into contact with an end-effector, which is retracted to simulate an in-flight release. At the retraction, the bonds are stretched up to failure, transferring an impulse to the plate. The proposed technique focuses on plate-free vibration: if at least two amplitudes of the excited vibration modes are measured, it is possible to estimate the impulse intensity and duration. This technique is developed by exploiting the plate multimode response with redundant outputs to the same dynamic input to characterize adhesion dynamics with improved measurement performance.

Designing self-cleaning & flexible multifunctional wearable heater

Wearable fabric heater has aroused wide concern in personalized temperature regulation. However, it is still challenging to develop multifunctional wearable heater with multimodal & fast thermal response, good flexibility, strong abrasion, excellent washability, good antibacterial performance, and recyclability. To accomplish these properties, a self-cleaning & flexible multifunctional wearable heater is designed by spraying mixture of Ti3C2Tx MXene/ZnO composite and PDMS on cotton fabric (CF-MZP). The designed CF-MZP fabric heater shows high photothermal/electrothermal/synergistic conversion properties and fast thermal response (just 30 s). Furthermore, the CF-MZP fabric possesses excellent superhydrophobic and self-cleaning properties that endow the heater with strong abrasion, excellent washability, and good antibacterial performance. Interestingly, the CF-MZP heater also exhibits good breathability, durability and flexible performance, which is demonstrated as a flexible wearable heater attached to knuckle position of a human body, showing steady temperature of around 50.9 °C at a voltage of 3 V or about 80.5 °C upon NIR lamp (0.2 W cm-2) irradiation, either relaxed and bending (90° or 180°) states. Furthermore, the CF-MZP heater can be recycled by separating ordinary oil-water mixture and high viscous heavy oil. Consequently, the designed CF-MZP fabrics provide a good perspective application in self-cleaning, recycled, and flexible wearable heaters.

COVID-19 vaccinated children, adolescents, and young adults with acute lymphoblastic leukemia show spike reactive antibodies and multifunctional T-cells.

Little is known about the efficacy of COVID-19 vaccines during acute lymphoblastic leukemia therapy (ALL); data for COVID-19 vaccine immune responses in pediatric leukemia remain sparse. We conducted a single center study of patients aged 5-25 years undergoing ALL chemotherapy who received COVID-19 vaccination. Twenty-one patients were enrolled; efficacy was evaluable in 20. Twenty were vaccinated while receiving chemotherapy. Twenty received the BNT162b2 mRNA vaccine. Spike reactive antibodies (S-IgG) and/or T-cells (SRT) were detected in 16 of 20 (80%) vaccinated patients; 13 (65%) and 9 (45%) were positive for S-IgG and SRT, respectively. Six (30%) showed both spike reactive B and T-cell responses. Eleven of the 13 with S-IgG positivity were negative for anti-Nucleocapsid IgG, an antibody profile consistent with a vaccine induced immune response. All 13S-IgG+ patients showed neutralizing antibodies. SRT included CD4+ (7) and CD8+ (6) T-cells; both CD4+ and CD8+ SRT were seen in 4. SRT were multifunctional (producing multiple cytokines) in most patients (8 of 9); 4 showed SRT with triple cytokine and B-cell co-stimulatory responses, indicating a multimodal adaptive immune response. Immune responses were seen among patients vaccinated in the settings of lymphopenia (6 of 12) intensive chemotherapy (3 of 4), and Peg allergy (6 of 8). Sequencing revealed public CD4+ and CD8+ TCR sequences reactive to epitopes across the spike protein. In conclusion, COVID-19 vaccination induced B and/or T-cell responses in a majority of children and young adults undergoing ALL chemotherapy.

Dynamic performance evaluation of a magnetorheological damper-based sting support in wind tunnel tests

The sting support faces an issue of being prone to resonance due to its low-damping characteristics, which will compromise the accuracy of test data in wind tunnel experiments. Magnetorheological damper (MRD)-based tail support (MRSS) features varying stiffness and damping abilities, enabling it to suppress the vibration with random, time-variation characteristics in a self-adaptive way, and it also exhibits excellent fail–safe properties. This work aims to validate its controllability and fail–safe property in a wind tunnel environment. First, the natural frequency, root mean square (RMS), and power spectral density (PSD) of the designed MRSS’ response for random excitation were theoretically analyzed. Then, wind tunnel experiments were conducted to test the response under different attack angles, wind speeds, and test currents. Subsequently, the controllability and fail–safe property were demonstrated by analyzing the aircraft model response. Also, the variable stiffness and damping properties of MRD behind the observed phenomena were revealed based on the theoretical analysis. Results demonstrate that as the input current increases and the MRD’s stiffness continues to increase, the damping initially increases and then decreases. Increasing wind speed leads to a decrease in the MRD’s stiffness. Additionally, approximately 50% reduction in RMS and a multi-modal response attenuation was achieved.

Brain effect mechanism of lever positioning manipulation on LDH analgesia based on multimodal MRI: a study protocol

IntroductionThe clinical symptoms of Lumbar Disc Herniation (LDH) can be effectively ameliorated through Lever Positioning Manipulation (LPM), which is closely linked to the brain's pain-regulating mechanisms. Magnetic Resonance Imaging (MRI) offers an objective and visual means to study how the brain orchestrates the characteristics of analgesic effects. From the perspective of multimodal MRI, we applied functional MRI (fMRI) and Magnetic Resonance Spectrum (MRS) techniques to comprehensively evaluate the characteristics of the effects of LPM on the brain region of LDH from the aspects of brain structure, brain function and brain metabolism. This multimodal MRI technique provides a biological basis for the clinical application of LPM in LDH.Methods and analysisA total of 60 LDH patients and 30 healthy controls, matched by gender, age, and years of education, will be enrolled in this study. The LDH patients will be divided into two groups (Group 1, n = 30; Group 2, n = 30) using a random number table method. Group 1 will receive LPM treatment once every two days, for a total of 12 times over 4 weeks. Group 2 will receive sham LPM treatment during the same period as Group 1. All 30 healthy controls will be divided into Group 3. Multimodal MRI will be performed on Group 1 and Group 2 at three time points (TPs): before LPM (TP1), after one LPM session (TP2), and after a full course of LPM treatment. The healthy controls (Group 3) will not undergo LPM and will be subject to only a single multimodal MRI scan. Participants in both Group 1 and Group 2 will be required to complete clinical questionnaires. These assessments will focus on pain intensity and functional disorders, using the Visual Analog Scale (VAS) and the Japanese Orthopaedic Association (JOA) scoring systems, respectively.DiscussionThe purpose of this study is to investigate the multimodal brain response characteristics of LDH patients after treatment with LPM, with the goal of providing a biological basis for clinical applications.Trial registration numberhttps://clinicaltrials.gov/ct2/show/NCT05613179, identifier: NCT05613179.

Long short-term memory (LSTM) neural networks for predicting dynamic responses and application in piezoelectric energy harvesting

Long Short-Time Memory (LSTM) deep neural networks are capable of learning order dependence in sequence problems and capturing long-term, non-linear temporal dependencies between the input and out of a system. With the long-term vision to model dynamical systems to which analytical or numerical methods are impossible or difficult to apply, this paper presents a study of modeling system dynamics and predicting responses using the LSTM networks, which have demonstrated excellent capability in predicting single-mode responses in a prior study. However, the LSTM network exhibits difficulties in modeling and predicting multi-mode responses accurately. To resolve the multi-mode issue, this paper presents an approach that obtains an equivalent network consisting of a set of sub-networks learned on isolated modes, and demonstrates its effectiveness on a simulated 2-degree-of-freedom mass-spring-damper system of nonlinear Duffing springs. The second part of the paper is focused on the application of the proposed approach in piezoelectric energy harvesting. Experiments are conducted on a harvester subjected to random base-motion excitation and exhibiting nonlinearity in its multi-mode response. Both the direct and mode-separation LSTM modeling approaches are applied to predict the output voltage given a random base-motion excitation. The mode-separation approach outperforms the direct approach significantly, and yields an excellent match between the actual and predicted responses. Specifically, for a test electrical voltage response of RMS value 0.2241 V, the difference between the actual test and predicted responses by using the mode-separation approach has an RMS value of 0.0504 V, compared to 0.1645 V obtained by using the direct LSTM approach. It is also much lower than the RMS value of 0.1835 V obtained by using the attention-based LSTM network, another comparison method. Leveraging a deep learning strategy, the validated approach opens up opportunities for accurately modeling energy harvesting systems of high complexities and/or strong nonlinearities.

An amplitude probability density function model under broadband multimodal stochastic vibration fatigue response

In this paper, a fatigue life prediction model based on the amplitude probability density function under broadband multimodal stochastic vibration response is presented. An analysis method is proposed to address the dispersion of the third- and fourth-order normalized moments of rain-flow amplitude distributions. The unified relationships between the first four-order normalized moments of rain-flow amplitude distributions and the spectral parameters are established to determine the model parameters. Through comparison with other frequency-domain methods and based on the tail probability density distribution of rain-flow amplitude, the proposed model offers more precise and stable fitting results under broadband multimodal response power spectra.