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Device-associated thrombosis remains a critical problem in cardiovascular medicine, often requiring lifelong anticoagulation therapy that paradoxically introduces significant bleeding risks. Here, we present a bioinspired passive flow routing approach that mitigates thrombosis by altering local hemodynamics to reduce stagnation, a primary hemodynamic driver of clot formation. Inspired by flow-reattachment mechanisms in avian wings and aeronautical slats, we integrate circumferential routing channels into mechanical heart valve housings to redirect a small fraction of forward flow into peri-ring regions prone to stasis. Computational optimization identifies a configuration that eliminates near-zero-shear pockets and reduces exposure to low shear by several orders of magnitude, while maintaining comparable high-shear exposure relative to control under physiologic conditions. In a fibrin clot deposition assay, the routed design exhibits reduced peri-ring clot accumulation. In an ovine bypass model without anticoagulation, the routed valve demonstrated improved sinus washout across angiographic assessments and, unlike the control, explant examination after three months showed no macroscopic thrombus at the sewing-ring interface. Preliminary computational extensions indicate that passive flow routing can alleviate stagnation in additional cardiovascular geometries. These findings establish bioinspired passive flow routing as a hemodynamic design strategy to mitigate thrombosis in cardiovascular devices by targeting the hemodynamic root cause of stasis.

The aim of this study was to investigate the effects of hypergravity on the biomechanics of the human lumbar spinal musculoskeletal system. We quantitatively analyzed the biomechanics of the intervertebral disc (IVD), lumbar spinal muscles, and ligament biomechanics changes in jet pilots under various hypergravity conditions, namely, 1G (represents the normal gravity on the Earth), 3G, 6G and 9G (represents various hypergravity), strengthened 10%-3G/6G/9G (various hypergravity after muscle strengthened by 10%), strengthened 20%-3G/6G/9G (various hypergravity after muscle strengthened by 20%) using a full body musculoskeletal modeling approach, and whether these biomechanical changes lead to lumbar IVD damages with a finite element method. The compressive and shear forces on the lumbar IVD, the height and water content of the lumbar IVD, muscle activation, and ligament force, as well as the mechanical damage in the IVD under the above various hypergravity were quantitatively compared and analyzed. Compared with 1G, hypergravity produced dose-dependent changes in lumbar spine biomechanics and IVD integrity. Under 3G, 6G, and 9G, average lumbar IVD compressive forces increased by 68%, 190%, and 311%, while shear forces increased by 53%, 140%, and 212%, respectively. IVD height decreased by 3.0%, 8.0%, and 12.4%, and water content decreased by 2.4%, 7.2%, and 12.4%. Most lumbar muscle activations increased by 3.0%, 7.0%, and 13.0%, whereas most ligament forces decreased by 25.0%, 34.0%, and 40.0%. AF damaged volume fraction increased by 0.5%, 3.7%, and 7.3%. IVD stress also increased markedly, with AF stress rising by 73%, 215%, and 358%, and NP stress by 58%, 163%, and 267% under 3G, 6G, and 9G, respectively. The muscle strengthening after training showed no significant change on the loading distribution among different musculoskeletal segments under hypergravity. Our results indicated that loading on the IVD significantly increased, biochemical environment and morphology within the IVD changed, and the risk of annulus fibrosus damage increased; muscle strengthening did not have a significant impact on the load on various tissues under hypergravity. Our findings were important for understanding the hypergravity-induced effect on the health of human lumbar spine.

Ceramic-matrix composites face a persistent challenge: the trade-off between strength and toughness. Inspired by the mineral bridge architecture of nacre, we propose a reverse interphase design that contrasts with conventional dense-laminar pyrolytic carbon given the active incorporation of nanopores. Multiscale characterization and simulations reveal a dual reinforcement mechanism: nanopores reduce the interfacial debonding strength and induce crack deflection that protects fibers from brittle fracture. Meanwhile, the resulting rough fracture paths enhance interfacial frictional stress and load transfer, thereby improving the matrix bearing capacity and energy dissipation. This asymmetric modulation of interfacial properties simultaneously preserves fiber integrity and maximizes energy dissipation. The resulting single-tow Cf/SiC composites exhibit 903 MPa tensile strength, which is 38% higher than that of conventional designs, and a 1.8-fold increase in fracture energy. The interphase-enabled mechanisms identified here are intrinsically scalable, with their effectiveness further demonstrated in architectured ceramic-matrix composites. This work demonstrates a shift from empirical optimization toward theory-driven interface design and establishes a viable route to overcome the classical strength-toughness dilemma in structural composites.

Extreme heat events pose significant challenges for risk management and communication, particularly as many climate governance leaders are viewed as untrustworthy sources. While research shows that trusted sources are more persuasive, strategies for building trust remain unclear. This study employed a randomized, controlled factorial experiment with 12 message conditions (three message trust conditions × three agency levels plus controls) and a politically representative US sample (N = 551) recruited via Prolific Academic. Participants viewed 90 s video messages designed to manipulate trust in emergency management agencies while persuading Americans to take protective actions in response to extreme heat risks. Participants completed validated scales measuring perceived source trustworthiness, attitudes toward extreme heat risk, attribution of heat events to climate change, and behavioral intentions. Results indicate that experimental trust-building messaging and specified government agency levels significantly influenced perceived trustworthiness, with local emergency management agencies rated more trustworthy than state or national agencies. Messages designed to convey trustworthiness significantly improved attitudes toward heat risk perception. These findings suggest that trust-building strategies and local framing can enhance persuasive efficacy in extreme heat preparedness communication. Implications for theory and best practices in emergency risk messaging are discussed.

Flexible capacitive pressure sensors have garnered significant attention in wearable electronics and robotic tactile sensing due to their high flexibility and simple structure. However, non-uniform distribution of conductive fillers in composite dielectric layers often compromises dielectric stability and sensing performance. In this work, a Cu/PDMS composite dielectric layer was fabricated using ultrasonic-assisted homogenization to enhance Cu particle dispersion and suppress sedimentation. A theoretical model and finite element simulations were employed to investigate the effects of particle distribution on permittivity, capacitance, electric field, and current density. The results indicate that uniform Cu dispersion improves dielectric stability and mitigates local electric-field concentration. Compared with conventionally prepared sensors, the ultrasonically treated sensor demonstrated higher sensitivity, enhanced dielectric stability, and a broader working range. Specifically, the sensor achieved a sensitivity of 0.157 kPa-1 within 0-1 kPa and maintained stable performance over 1000 loading cycles. These findings confirm that ultrasonic-assisted homogenization is an effective approach for improving the dielectric and sensing performance of flexible capacitive pressure sensors.

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To address the increasing demand for platinum (Pt) and the accumulation of hazardous waste, this study proposes a novel and sustainable pyrometallurgical co-smelting strategy for recovering Pt from spent automotive catalysts (SACs) using electric arc furnace dust (EAFD) as a co-smelting agent. Thermodynamic analysis confirmed the feasibility of reducing Pt oxides and sulfides to their metallic form under high-temperature, reductive conditions, enabling efficient alloying with iron (Fe). A five-component slag system (SiO2-Al2O3-CaO-MgO-FeO) was designed to lower the melting temperature and viscosity, thereby improving the separation efficiency of slag and alloy. Through systematic single-factor experiments, the effects of alkalinity (CaO/SiO2), EAFD addition amount, reducing agents amount, and smelting temperature on Pt recovery were investigated. Optimal recovery (96.4%) was achieved under the conditions of CaO/SiO2 = 0.7, 20 wt% EAFD addition amount, 6 wt% reducing agents amount, and a smelting temperature of 1550°C. Microstructural characterizations using X-ray diffraction and scanning electron microscopy and energy dispersive spectroscopy revealed that Pt was predominantly incorporated into the Fe matrix through substitutional solid solution mechanisms. Furthermore, the resulting slag exhibited a dense, amorphous glassy microstructure, indicating excellent environmental stability and inertness, thereby minimizing the risk of secondary pollution. Overall, this integrated co-smelting approach not only offers a technically viable and environmentally benign method for the high-efficiency recovery of Pt from SACs but also establishes a novel paradigm for the cross-sectoral recycling of hazardous industrial residues such as EAFD. The proposed strategy thus holds significant potential for advancing circular economy practices within the waste management industries.

The catalytic conversion of CO2 into value-added chemicals via the reverse water-gas shift (RWGS) reaction represents a significant pathway for mitigating climate change and enabling sustainable carbon utilization. However, Pt-based catalysts, despite their superior H2 activation ability, often suffer from inadequate CO selectivity and durability under high-temperature conditions, primarily due to excessive CO adsorption at low-coordinated Pt edge sites. Herein, we present a sulfur (S)-mediated targeted passivation strategy to engineer Pt-CeO2 catalysts with atomically tailored active sites, effectively addressing the critical activity-stability-selectivity trade-off. The incorporation of S into CeO2 support induced optimized electronic modulation, as evidenced by in situ/ex situ characterizations and density functional theory (DFT) calculations, which weakened *CO adsorption strength and suppressed the methanation pathway. The optimized Pt-S-CeO2 catalyst exhibits remarkable performance at 600&#x2009;&#xb0;C: CO selectivity >95%, CO production rate of 8.8&#xd7;10-5&#x2009;mol gcat-1 s-1, and <10% activity loss over 250&#x2009;h. On the other hand, this work establishes a framework for targeted dopant-mediated site engineering in heterogeneous catalysis, offering a generalizable route to reconcile conflicting performance in CO2 hydrogenation systems and beyond.

Dependent censoring, common in medical studies with informative dropout, invalidates standard Cox regression by violating the independent censoring assumption. While copula-based methods offer flexible dependence modeling, their parametric extensions face identifiability barriers. We address this problem through a novel fully identifiable parametric model that synergizes double-Cox marginal structures with copula dependence, which is called the copula-double-Cox model. Using Weibull or generalized exponential (GenExp) distributions, the double-Cox model links both scale and shape parameters to covariates via Cox-type regressions. This structure accommodates non-proportional hazards while containing the standard Cox model as a special case. We establish identifiability under dependent censoring and derive consistent estimators for baseline parameters, regression coefficients, and copula association. Simulations confirm robustness to association structure misspecification and over-parameterization. Estimation accuracy is supported by asymptotic theory and standard error evaluation via the observed information matrix. Finally, we illustrate the proposed approach through a real-world application to a dataset on monoclonal gammopathy of undetermined significance (MGUS), highlighting its practical relevance. The results show that our method provides an interpretable characterization of covariate effects on both failure time and censoring time through its double-Cox structure. An open-source R implementation of the copula-double-Cox model is provided on GitHub.

This review provides a comprehensive analysis of the effects of different exercise modalities on working memory function in middle-aged and older adults. It systematically reviews evidence from interventions including aerobic exercise, resistance training, and traditional mind-body exercises, examining their acute effects and long-term mechanisms of action. The findings indicate that aerobic exercise enhances working memory by improving cerebral blood flow, promoting neuroplasticity, and upregulating brain-derived neurotrophic factor (BDNF) levels. Resistance training activates neuroadaptive remodeling mechanisms and optimizes cerebral hemodynamics, thereby strengthening executive control. Traditional mind-body exercises improve the efficiency of cognitive control networks by restructuring functional neural networks and modulating the stress-inflammation axis. Combined exercise regimens, which integrate the synergistic effects of aerobic and resistance training, can further improve working memory performance. Mechanistic studies indicate that exercise interventions optimize neural network function through multiple pathways, including BDNF-mediated synaptic plasticity enhancement, coordinated regulation of the dopaminergic/noradrenergic systems, and improvements in cerebral blood flow and vascular reactivity. A dynamic individualized prescription design based on physiological and cognitive baseline characteristics may maximize cognitive health benefits. Future research should integrate multimodal neuroimaging technologies to clarify the neural mechanisms underlying exercise interventions and develop artificial intelligence-driven personalized prescription systems. At the policy level, integrating sports and medical services and promoting the large-scale application of exercise interventions in populations with multimorbidity are critical to addressing the public health challenges associated with population aging.

This paper presents the RaDICAL monostatic passive radar framework for target detection and localization using a sparse uniform circular array (SUCA), multifrequency dither, and dictionary-based waveform processing. Rather than forming conventional spatial images or relying on explicit Doppler/TDOA/FDOA estimation, the proposed method encodes target geometry directly into a composite receiver waveform and performs localization through hypothesis testing using a library of predicted waveform responses. A SUCA-based signal model is developed for both point and extended targets, and detection/localization formulated as a waveform-domain dictionary matching problem using normalized complex correlation and QR-domain processing. A reproducible MATLAB-based Monte Carlo study evaluates waveform separability, probability of detection versus input SNR, receiver operating characteristic (ROC) behavior, localization performance, and receiver power balance. The results demonstrate that multifrequency dither produces distinctive composite waveforms with strong hypothesis separability and stable waveform domain recognition performance. ROC analysis and detection simulations showed reliable target detection at input SNR levels on the order of -10 to 0 dB, consistent with the coherent processing gain achieved through waveform-domain correlation processing. The corresponding power-balance analysis indicates that reliable detection and localization are feasible using modest illuminator EIRP and compact receiver dimensions. These results support the feasibility of compact reference-free waveform domain passive sensing for joint target detection and localization.

To address multi-distribution perception and temporal generalization challenges in cross-aircraft aero-engine monitoring, the Lyapunov-Schmidt Multi-distributed Perception Network (LSMPNet) is developed as a continuous-time domain generalization framework grounded in improved Lyapunov-Schmidt reduction (LSR). LSMPNet reformulates state monitoring as a continuous nonlinear system, leveraging a T-LSR Decomposer to perform structured decomposition and employing stacked T-LSR Blocks for hierarchical learning of dynamic features. A time-difference-based operator enhances sensitivity to continuous distribution shifts, while complementary manifold topologies strengthen global distribution perception and noise suppression. The linearized operator design ensures low computational complexity and interpretability. Extensive experiments on public benchmarks and real-world cross-aircraft datasets demonstrate superior performance in degradation modeling and water-wash early warning tasks.

Exposure to ambient air pollution, including ozone and fine particulate matter (PM2.5), is the world's leading environmental health risk factor. Estimating how this burden may change in the future depends on projecting population growth and age structure as well as understanding how future meteorological changes may impact the production and removal of pollutants from the atmosphere. The net impact of these factors on a global scale has not been well-characterized. Here, we leverage recent meteorology, exposure, and mortality output from general circulation, atmospheric chemistry, and health impact models to isolate how changes in meteorology and populations will impact future global air-pollution-related mortality and the associated monetized impacts by the degree of global temperature change. In contrast to previous studies, we estimate that changes in meteorologically driven air pollution, in the absence of pollutant precursor emission changes, will result in 180&#x202f;000 fewer deaths annually by 2100 relative to current levels, an annual monetized benefit of $7.3 trillion. Reductions are driven by decreases in PM2.5-attributable mortality in populated regions but are substantially offset by global increases in ozone-related mortality. We also highlight striking regional differences in the sign of net pollutant impacts by 2100, with net pollution decreases in the Northern Hemisphere driven by reductions in nitrate aerosol, while increases in both ozone and organic aerosol at higher temperatures lead to net increases in pollutant impacts in the Southern Hemisphere. Lastly, we assess sensitivities of these results to meteorological projections, health impact functions, and 10&#x202f;000 future warming scenarios.

Developing catalysts is an important way to modify the hydrogen storage performances of magnesium hydride (MgH2). The hydrogen storage performance of MgH2 was enhanced by employing a mixture of AlV3/Cu9Al4 alloy as a catalytic additive, which was fabricated via arc melting followed by hydrogen plasma treatment. The as-prepared AlV3/Cu9Al4 was mechanically added into MgH2 through ball milling to form MgH2-x wt % AlV3/Cu9Al4 composites (x = 2.5, 5, 7.5). Remarkably, the MgH2-5 wt % AlV3/Cu9Al4 composite demonstrated the best performance: desorbing 6.7 wt % hydrogen within 1 h at 598 K, which is 1.9 times faster than pristine MgH2 under same conditions. Based on kinetic analyses, the activation energy for hydrogen absorption decreased notably to 74.91 kJ/mol, while that for desorption dropped to 150.50 kJ/mol. Furthermore, the composite exhibited exceptional cycling stability, retaining 99% of its initial hydrogen absorption and desorption capacity after 20 cycles. This outstanding reversibility is attributed to the stable dispersion of AlV3/Cu9Al4 particles, which effectively suppresses the coarsening and agglomeration of Mg/MgH2 particles. This work offers a feasible approach for designing transition metal alloy catalysts to address the kinetic constraints of metal hydride-based hydrogen storage systems.

Microwave heating is widely used in daily applications but is fundamentally limited by non-uniform temperature distribution. Despite extensive efforts to manipulate energy distribution around materials, achieving uniform heating remains elusive due to the intrinsic inhomogeneity of electromagnetic fields. Here, we report a self-regulating solution that adaptively modulates the absorbance distribution using a Negative Temperature Coefficient (NTC) Metamaterial Absorber (MA). Distinct from electromagnetic field-shaping strategies, our approach intrinsically suppresses overheating in high-temperature regions and redistributes energy to cooler areas. We demonstrate, for the first time, that uniform heating-quantified by over 90% reduction in the coefficient of variation-can be achieved across diverse configurations, including planar, polyhedral, curved, and multiple objects, as well as under power variations spanning two orders of magnitude. This work not only provides a theoretical resolution to the longstanding challenge of non-uniform microwave heating but also opens new avenues for development and application of temperature-adaptive metamaterials.

Foldable wings significantly improve the portability of flapping-wing micro air vehicles. This study presents a novel multi-stage bio-inspired wing design, inspired by the venation pattern and folding mechanism of the hindwing of Xylotrupes dichotomus, achieving a folding ratio of up to 3.44 while ensuring reliable folding operation. A flapping-wing structure integrating linkages and a membrane is designed with a high-stiffness strategy. A systematic theoretical kinematic analysis is conducted to investigate the motion relationships among the linkages during the folding process, and a kinematic model is established to ensure stable folding operation. A one-way fluid-structure interaction analysis is subsequently performed to verify the high-stiffness assumption and to establish an aerodynamic baseline for the specific corrugation pattern induced by the folding mechanism. Results indicate that the maximum elastic deformation is approximately 5.7% of the wingspan, validating the quasi-rigid treatment. Comparative analysis with a flat-plate model further demonstrates that the corrugation has a negligible effect on aerodynamic performance. The validated high-stiffness configuration thus provides a well-characterized rigid-wing reference for future stiffness-modulated investigations.

Although numerous solutions have been proposed for the automatic detection of malicious components in executable files, malware analysis remains largely a manual task, with the human analyst often representing the main bottleneck. Recent approaches seek to highlight suspicious code regions to reduce analyst effort, but many still rely on signatures or produce high false-positive rates. To address these limitations within the anomaly-based detection paradigm, we present Bifocal Agent, an unsupervised method that analyzes Windows PE executables at two distinct levels of granularity: functions and basic blocks. Our autoencoder-based architecture leverages semantic-aware features and a new strategy for aggregating reconstruction errors to improve the detection of malicious code regions. Results on a dataset comprising three malware families (Rbot, Pegasus, and Carbanak) show that our proposal increases the ROC AUC by 20% (from 0.73 to 0.88) and improves the area under the precision-recall curve by 154% (from 0.13 to 0.32) compared to the baseline. Furthermore, comparative experiments show that the multi-granularity consensus outperforms the state-of-the-art DeepReflect approach. On a curated dataset of labeled malware samples, it reduces false positives by 52% while strictly maintaining the baseline's 80% true positive rate. To confirm generalization and prevent overfitting, we validate the framework on a large-scale, cross-dataset corpus of 1.3 million functions. In this realistic scenario, the Bifocal Agent increases the Matthews correlation coefficient by 3.1 times and achieves a higher coverage rate while dropping the false positive rate from 45 to 16%.

Sluggish kinetics and shuttle effects hinder high-energy rechargeable sodium-chalcogen batteries, requiring nanostructured positive electrodes with excess conductive additives. Here, we show that an acidic binder-induced corrosion strategy enables activity in bulk S/Se/Te positive electrodes. Carboxylic acid-rich binders create an acidic microenvironment (pH&#x2248;3) during processing, triggering spontaneous formation of copper chalcogenide interlayers on Cu current collectors. These interlayers reconstruct into active Cu2X (X&#x2009;=&#x2009;S/Se/Te) catalysts during cycling. Using Na-Se batteries, experimental and DFT analyses confirm Cu2Se reduces the liquid-solid conversion barrier by ~24.1% and accelerates polyselenides conversion. Polar groups in the binders enhance chemisorption, reducing the shuttling effect. Consequently, bulk Se with alginic acid binder achieves 96% theoretical utilization at 0.1&#x2009;A&#x2009;g&#x2012;1, rate capability of 508&#x2009;mAh&#x2009;g&#x2012;1 at 20&#x2009;A&#x2009;g&#x2012;1, stable cyclability at 5&#x2009;A&#x2009;g&#x2012;1 over 16,000 cycles, and low-temperature operation at 0.1&#x2009;A&#x2009;g&#x2012;1 (94% capacity retention at -20&#x2009;&#xb0;C). The strategy of transforming binders into active interface modulators provides a generalizable approach for other chalcogens, enabling practical and host-free rechargeable metal batteries with chalcogen positive electrodes.

Multimodal image fusion is a promising technology designed to fuse information from different medical sensors, which offer structured insights for disease diagnosis and treatment. However, existing 2D-centric fusion methods fail to capture 3D spatial continuity, and conventional wavelet-based approaches lack adaptability to diverse lesion regions and suffer from background artifacts. To address this issue, we propose a 3D multiscale wavelet convolutional neural network for multimodal medical image fusion. Specifically, a 3D Discrete Wavelet Transformation (3D DWT) is introduced to decompose input volumes into multi-frequency bands, isolating anatomical structures and lesion details while reducing 3D spatial redundancy. We embed hierarchical multiple frequency band into a Global and Local Feature Calibration (GLFC) module to adaptively enhance single-modal features by fusing global contextual information and local details. Furthermore, a pyramid group-wise multiscale feature interaction is proposed for capturing complementary features across different spatial scales. Finally, a voxel-wise weighted averaging strategy reconstructs the fused image by adaptively assigning contributions to each modality at every spatial position, effectively eliminating artifacts and improving the visual fidelity of the result. Extensive experiments on the BraTS2020 and Hecktor datasets demonstrate that our proposed method outperforms state-of-the-art (SOTA) fusion methods in both subjective visual quality and objective metrics. Moreover, downstream segmentation validation confirms that fused images from our method significantly improve tumor segmentation accuracy. The source code and pre-trained models will be publicly available.