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Abstract The presence of bacterial biofilms and the occurrence of excessive inflammatory response greatly imped the healing process of chronic wounds in diabetic patients. However, effective strategies to simultaneously address these issues are still lacking. Here, a microenvironment‐adaptive nanodecoy (GC@Pd) is constructed via the coordination and in situ reduction of palladium ions on gallic acid‐modified chitosan (GC) to promote wound healing by synergistic biofilm eradication, inflammation alleviation, and immunoregulation. During the weakly acidic conditions of the biofilm infection stage, GC@Pd serves as a nanodecoy to induce bacterial aggregation. Subsequently, through its oxidase‐like activity generating reactive oxygen species and the hyperthermia from photothermal effects, it effectively eliminates the biofilm. As the local microenvironment of diabetic wounds transitions to an alkaline inflammatory state, the enzyme‐like activity of GC@Pd adapts to catalase‐like activity, effectively eliminating reactive oxygen species at the site of inflammation. Additionally, GC@Pd could selectively capture pro‐inflammatory cytokines through Michael addition reactions. In vivo experiments and transcriptomic analysis confirmed that GC@Pd could accelerate the wound transition from inflammatory to proliferative phase by eliminating biofilm infection and reducing the inflammatory response, thus promoting diabetic chronic wound healing. The nanodecoy provides a potential therapeutic strategy for treating biofilm‐infected diabetic chronic wounds.

Dispersion in optical coherence tomography (OCT) poses a challenge that is exacerbated by the increased spectral bandwidth, which leads to image blur and feature loss. In this paper, we present a straightforward and cost-effective approach for dispersion compensation in OCT. To achieve this, we employed a pixel-to-pixel (Pix2Pix) generative adversarial network (GAN) architecture customized for image-to-image translation. Two data groups with varying amounts of training image data and epochs were used. The Pix2Pix GAN was trained to generate clear OCT images from the corresponding dispersion-affected OCT images in paired datasets. According to the experimental results, the Pix2Pix GAN technique demonstrated a substantial improvement over the basic GAN. Specifically, it increases the peak signal-to-noise ratio (PSNR) by 159%, structural similarity index (SSIM) by 370%, and Fréchet inception distance (FID) by 274%. These outcomes indicate that the proposed model can generate images with resilience and effectiveness, particularly when dealing with dispersion-affected OCT data.

To resolve the 'Problem of Time'—a foundational challenge in quantum gravity and open quantum systems—this paper proposes Atemporal Topological Information Dynamics (ATID), a novel information-theoretic framework that operates without a background spacetime manifold. We formulate an effective non-Hermitian Hamiltonian H_{\text{eff}} describing open-system dissipation alongside a topological action S_{\text{topo}} defined over an atemporal information manifold \mathcal{M}_{\text{info}}. Applying the variational principle \delta S_{\text{topo}} = 0, we geometrically derive a non-commutative friction tensor \mathcal{F}_{ab}^{\text{fric}} that diverges near Exceptional Points (EPs) due to the non-analytic algebraic structure of the Puiseux series. Furthermore, by introducing a Forgetting Functor \mathcal{F}_{\text{orget}} mapping from a high-dimensional microscopic topological category \mathbf{TopoSys} to a 1D causal order category \mathbf{Time}, we prove that thermodynamic entropy and the arrow of time emerge naturally as the 'kernel residue' of lossy topological information compression. Finally, we establish a non-linear algebraic phase delay scaling law (\Delta \tau \propto \Vert\mathcal{F}_{ab}^{\text{fric}}\Vert \ln(I_c / (I_c - I))) near the critical information injection threshold I_c, numerically verifying the model via a Parity-Time (PT) symmetric coupled RLC electronic circuit system and providing an accessible experimental protocol.

With the increase in the availability of multitemporal hyperspectral images (HSIs), HSIs change detection (CD) methods, including pixel-level and subpixel-level based methods, have attracted great attention in recent years. However, the widespread presence of mixed pixels in HSIs may make it difficult for pixel-level methods to detect subtle changes; meanwhile, the less utilization of spatial information may also lead to limitations in some subpixel-level methods. Therefore, a joint framework, which aims to combine the advantages of pixel-level in spatial utilization and subpixel-level in temporal and spectral exploration, is proposed to enhance the performance of HSIs CD. Two models, convolutional sparse analysis and temporal spectral unmixing, are introduced and presented to characterize different spatial structures and overcome the effects of spectral variability under this framework, respectively. In addition, a multiple CD-based on subpixel analysis is discussed as well. Experiments conducted on three bitemporal HSIs datasets indicate that the proposed framework is robust in capturing effective features and has achieved great detection accuracy.

Abstract Adverse weather conditions, such as snow, heavy rain, fog, or limited illumination, pose significant challenges for autonomous vehicles (AVs) by degrading sensor reliability. This paper introduces E2ETrADS, an end-to-end transformer-based autonomous driving framework designed to operate robustly under such conditions. A comprehensive dataset was generated using the CARLA simulator, encompassing both nominal and adverse weather scenarios. The model is trained via imitation learning from an expert driver equipped with weather-adaptive MPC planner and PID controllers, enabling robust control under perception uncertainty. Experimental results demonstrate that E2ETrADS outperforms the TransFuser baseline in adverse conditions, exhibiting fewer infractions and improved lane adherence. The system dynamically adjusts vehicle speed to maintain control stability and adapts its control policies in response to sensor degradation, resulting in fewer missed turns and reduced lane invasions. Furthermore, E2ETrADS generalizes effectively to safety-critical long-tail scenarios, demonstrating human-like reasoning capabilities.

Offshore wind turbines are prone to structural damage over time due to environmental factors, which increases operational costs and the risk of accidents. Early detection of structural damage through monitoring systems can help reduce maintenance costs. However, under complex external conditions and varying structural parameters, existing methods struggle to accurately and quickly detect damage. Understanding the factors that influence structural health is critical for effective long-term monitoring, as these factors directly affect the accuracy and timeliness of damage identification. This study comprehensively analyzed 5 MW offshore wind turbine measurement data, including constructing a digital twin model, establishing a surrogate model, and performing a sensitivity analysis. For monopile-based turbines, sensors in x and y directions were installed at four heights on the pile foundation and tower. Via Bayesian optimization, the finite element model’s structural parameters were updated to align its modal parameters with sensor data analysis results. The update efficiencies of different objective functions and the impacts of neural network hyperparameters on the surrogate model were examined. The sensitivity of the turbine’s structural parameters to modal parameters was studied. The results showed that the modal flexibility matrix is more effective in iteration. A 128-neuron, double-hidden-layer neural network balanced computational efficiency and accuracy well in the surrogate model for modal analysis. Flange damage and soil degradation near the pile mainly impacted the turbine’s health.

BackgroundProblematic smartphone use (PSU) and attention deficit hyperactivity disorder (ADHD) in children, adolescents, and young adults are of major concern to parents. However, the prevalence and associated factors related to these issues in Bangladeshi adolescents and young adults remain unclear to the best of our knowledge. The aim of this study is to assess PSU and ADHD in the context of adolescent and young adult age groups in Bangladesh.MethodsThe present study collected data from diverse geographical locations in Bangladesh via face-to-face surveys using stratified random sampling methods. We considered age, sex, and geographic location stratification criteria. A total of 578 respondents participated in the survey initially. From this, we discarded 36 responses after screening because the information provided was insufficient or incomplete response. In the end, 542 replies were incorporated into the final analysis. PSU and ADHD depend on several factors, including the individual's demographic background.ResultsThe prevalence of PSU and ADHD symptoms in adolescents and young adults in Bangladesh is 61.44% and 37.45%, respectively based on our findings. The symptoms of PSU are correlated with age, education level, family type (nuclear/joint), sleeping pattern, physical exercise, and residence area. ADHD symptoms are correlated with age, education level, living with family, smoking habit, physical disability, sleeping pattern, physical exercise, residence area, and PSU. Also, we observed that ADHD and PSU symptoms are positively correlated with each other.ConclusionA large proportion of young adults and adolescents reported PSU and ADHD symptoms. The present findings have practical implications in clinical psychology, psychotherapy, and related policy considerations. We propose to develop an inclusive interventional strategy and community-based programs to address PSU and ADHD-related issues.

This study explores the electromagnetic properties of flat textile products enhanced with carbon nanotube (CNT) threads used as the weft. CNT threads, fabricated via dry-spinning, were integrated into fabrics by wrapping them around steel threads to form a solenoid-like structure. To further improve electromagnetic attenuation, the CNT yarn was coated with graphene oxide and silver nanoparticles. The research assessed the impact of these modifications on the fabric’s ability to attenuate alternating electromagnetic fields across a range of frequencies. Results showed enhanced attenuation at 30 MHz and 500 MHz. CNT yarn wrapped around steel threads achieved attenuation efficiencies of 18 dB at 30 MHz and 22 dB at 500 MHz, with a notable 10 dB improvement at 30 MHz over the reference. Fabrics with CNT yarn coated with graphene oxide demonstrated similar performance to the reference fabric at 500 MHz and an 8 dB increase at 30 MHz. Similarly, CNT yarn with silver nanoparticles showed comparable performance at higher frequencies but matched the reference at 30 MHz. These results indicate significant enhancement at lower frequencies, with benefits diminishing at higher. This study underscores the potential of integrating CNTs and metal nanoparticles into textiles to improve electromagnetic shielding, especially across specific frequencies.