Backdoor attacks play a crucial role in assessing model robustness and privacy risks. However, existing multimodal backdoor methods often fail in high-level downstream tasks such as visual emotion analysis (VEA), as they overlook the issue of semantic asynchrony between different modalities, resulting in suboptimal emotional perception. To address this limitation, we propose EmoAttack, a unified emotion-oriented backdoor attack framework that dynamically optimizes tunable prompts and fine-grained triggers through dual latent space modeling. By exploiting the correlation between emotional semantics and the attack mechanism, our approach optimizes these prompts across different training phases to inject fine-grained target-aware knowledge. As a result, EmoAttack effectively decouples prompt injection from task-specific transfer, facilitating precise emotional cue mining while preserving semantic consistency across diverse visual contents. Extensive experiments on seven public VEA datasets (≈166K images) show that EmoAttack achieves both high stealthiness and stability, with an average clean accuracy improvement of 11.6% and attack success rate (ASR) up to 89.3% even under the CleanClip defense, outperforming state-of-the-art methods.
The rotating triboelectric nanogenerator (R-TENG) has emerged as a core technology in micro-energy harvesting and micro-signal detection. However, the high impedance, high-voltage and low-current output characteristics of R-TENG structures, lead to bottlenecks such as impedance mismatch, difficult topological adaptation and weak anti-interference capabilities. Under controlled bench-test conditions with 1-3 Hz mechanical excitation, customized rectification and energy storage circuits improve R-TENG energy harvesting efficiency from 60% to 70%-95%, a 10%-35% gain primarily by mitigating impedance mismatch. Sensing accuracy for rotational and wind speed is ±0.5%-±2% of full scale, varying with methods and environmental interference. This paper systematically reviews the research progresses of the micro-energy conversion and micro-signal processing technologies for R-TENG. Regarding micro-energy harvesting, the key technologies such as rectification, energy storage optimization, and DC-DC buck conversion are analyzed systematically. For micro-signal processing, in-depth analysis is conducted on signal processing methods such as filtering, noise reduction, and feature extraction in typical application scenarios such as motor speed estimation and wind speed measurement. The core contributions include: established a comprehensive analysis framework that bridges theoretical foundations and engineering applications; highlighted the performance metrics and practicality through structured comparisons; identified the research gaps in circuit stability, model generalizability and adaptation to extreme environments. Finally, this article analyzes the development directions of R-TENG such as intelligent adaptability, anti-interference ability, and universal modularization, which will help promote the innovative application of R-TENG in micro-energy harvesting and micro-signal processing.
We introduce and analyze the integral state constrained optimal control problem governed by a space-time fractional diffusion equation in this paper. The optimality conditions are first derived to consider the problem, then spectral Galerkin discretization of the problem is constructed. On the basis of some properties of projection operators and auxiliary equations, a priori error analysis of spectral Galerkin approximation is established rigorously. Moreover, reliable a posteriori error analysis is also investigated for state variable, adjoint state variable, control variable, and Lagrange multiplier in detail. The error estimates theoretical finding denotes that it can obtain spectral accuracy on time direction and space direction under suitable regularity assumptions.
Infectious disease transmission is shaped by tightly coupled biological processes within hosts and social contact patterns between hosts, making cross-scale modeling essential for understanding epidemic dynamics. We develop a data-driven multiscale framework that links within-host viral kinetics to between-host transmission on a dynamic multilayer contact network. Using influenza virus, SARS-CoV-2, and SARS-CoV as representative pathogens, we infer infection-age-dependent infectiousness from longitudinal viral-load data and integrate the resulting profiles into a stochastic individual-based model with contact layers constructed from empirical demographic and contact data. By comparing infectiousness profiles within the same multilayer transmission framework, we show that epidemic trajectories depend not only on overall transmission potential but also on the timing and duration of infectiousness. Early, concentrated infectiousness produces earlier epidemic peaks and shorter outbreaks, whereas later or prolonged infectiousness delays and extends transmission. Layer-resolved analyses show that setting-specific contributions arise from interactions among contact volume, contact intensity, and infectiousness profiles. Intervention simulations highlight the importance of temporal alignment: case isolation and vaccination are most effective when implemented before or during the pathogen-specific period of high infectiousness, whereas physical distancing reduces epidemic burden by lowering contact opportunities. This framework provides a flexible approach for linking viral-load-derived infectiousness with network-mediated transmission and evaluating targeted epidemic control strategies.
Introduction Building on an assessment of the evolution in spatial accessibility to community hospitals for older adults (2012–2024) within the 15-min city framework, this study proposes and evaluates an integrated online-to-offline (O2O) model to enhance healthcare accessibility and equity. Methods Using Nanjing, China, as a case study, an Enhanced Two-Step Floating Catchment Area (E2SFCA) method was applied to assess the spatial accessibility of community hospitals for older adults (stratified into age groups of 60–69, 70–79, and ≥80) from 2012 to 2024. A counterfactual analysis decomposed the effects of infrastructure expansion and demographic change. Furthermore, an “online diagnosis + 15-min medication delivery” service was integrated into the model to evaluate the proposed integrated O2O approach, with a sensitivity analysis conducted on the online service allocation parameter (θ). Results Despite a 24% net increase in community hospitals between 2012 and 2024, spatial accessibility for older adults declined, a trend disproportionately affecting the oldest-old (aged ≥80). Counterfactual analysis attributed this paradox to a significant spatial mismatch between infrastructure development and demographic shifts. The proposed integrated O2O model substantially improved both accessibility and equity. Sensitivity analysis identified an optimal online service allocation (θ = 0.1), which more than doubled accessibility for the most vulnerable group (≥80) and reduced the Gini coefficient by up to 25.8%. Conclusions The findings demonstrate that equitable and resilient urban health systems for aging populations require not only physical infrastructure expansion but also strategic digital integration, reframing the 15-min city as a conditional benchmark that demands age-sensitive calibration rather than a uniform spatial standard.
