The rapid development of 6G communications and artificial intelligence (AI) computing clusters urgently demands high-performance organic electro-optic (EO) materials that simultaneously offer giant EO coefficients, outstanding long-term thermal stability, and scalable synthesis. Herein, we address this key challenge by rationally designing and synthesizing a series of binary cross-linked chromophores SY1-SY6 based on a donor-bridge-acceptor (D-pi-A) structure, using salicylaldehyde derivatives as donors, isophorone as bridges, and trifluoromethyl-tricyanofuran (CF3-TCF) as acceptors. Six types of crosslinkable groups-anthracene acrylate, phenyl acrylate, maleimide, furan, azide, and alkyne-are precisely introduced to enable efficient Diels-Alder and Huisgen cycloaddition reactions. After electric-field poling and thermal cross-linking, the binary cross-linked systems SY1/SY2, SY1/SY3, SY3/SY4, and SY5/SY6 exhibit significantly enhanced glass transition temperatures up to 135-181 degrees C and ultrahigh EO coefficients (r33) of 229-265 p.m.& sdot;V-1. Long-term stability tests confirm that over 99.56% of the initial EO response of SY1/SY2 was retained after aging at 85 degrees C for more than 500 h. This binary cross-linking strategy achieves a remarkable balance between strong EO activity and exceptional orientation stability, demonstrating excellent application prospects for next-generation high-speed EO modulators and AI computing photonic interconnects.
To construct the micro collision avoidance intelligent unit, efficient solutions have been pursued by modeling a specific collision-sensitive neuron, the lobula giant movement detector (LGMD), identified in the locust's visual neural system. However, the existing LGMD models cooperating with a threshold-based collision avoidance strategy are less robust in the presence of visual variability, including fluctuations in contrast and noise. Intriguingly, biological research underscores the potential of the single-peak modeling through the a function, suggesting a non-threshold collision avoidance strategy by identifying the peak response moment. Adapting the biological finding to the engineering applications, we establish a novel modeling method based on a-form perceptron, making the response curve behave like the a function. The single peak within the a-function-form response occurs before the collision, offering reliable collision warning cues. Numerical experiments substantiate the effectiveness of the proposed model against visual variability. This study extends the limits of the applications of LGMD models in diverse scenarios, and explores the potential of modeling neural computation in constructing micro-intelligent units.
This study investigated the stable nanocomplexes between Zein (a hydrophobic plant protein), and chitosan (CS) via electrostatic interactions, followed by the co-delivery of different polyphenols, including curcumin, quercetin, and rutin, together with Lactobacillus casei, to construct a dual-encapsulated Pickering emulsion (DEPE) within a layered emulsion carrier. The formation of DEPE was found to depend on CS concentration, pH, and the synergistic effects of polyphenols and probiotics. The results showed that the stability of DEPE was influenced by CS concentration and pH, with optimal performance achieved at pH 3.0 and 0.5 mg/mL CS, yielding high encapsulation efficiencies (EE) for both oil (89.73%) and probiotics (87.76%). The hierarchical interfacial structure effectively protected probiotics in acidic environments and significantly enhanced emulsion clarity, deformation resistance, and thermal stability. Moreover, DEPE improved the survival of Lactobacillus casei during in vitro gastric digestion and storage. In conclusion, this study provides a theoretical basis for the practical application of probiotic-loaded emulsions in the development of innovative functional foods.
Introduction Climate conditions increasingly influence outdoor recreation, including when people travel, which destinations they choose, how far they move, and how long they remain outdoors. This issue is relevant for high-density, heat-prone cities, where urban nature areas support leisure, social interaction, restoration, and climate adaptation. Yet prolonged recreational mobility in green and blue spaces remains underexamined in transport and health research. Methods This study investigated long-duration recreation (LDR) use of urban nature areas in Guangzhou, China, a humid-subtropical metropolis. LDR was defined as stay events lasting more than 1 h within publicly accessible parks, forests, wetlands, waterfronts, and other nature-based open spaces. Large-scale mobile phone signaling data were integrated with urban nature area boundaries, spatially interpolated meteorological data, and the Universal Thermal Climate Index (UTCI). A selection–travel–stay framework was used to describe destination use, travel characteristics, and stay behavior. Associations with background UTCI were examined for daily destination use and stay-duration patterns. Results LDR activity was higher on weekends than on weekdays, with morning and late afternoon peaks in summer and an afternoon peak in winter. Destination use varied by gender and age group and was concentrated in public sites and waterside, woods, and meadow settings. In summer, UTCI associations varied across districts and destination types (r = −0.387 to 0.481), and higher-UTCI days showed lower shares of longer stays. In winter, UTCI was positively correlated with daily stay-record counts across duration categories (r = 0.148–0.433), with r = 0.374 for total LDR records. Conclusions The findings highlight seasonal patterns of prolonged urban nature use associated with background thermal conditions and their relevance to heat-responsive transport, public-space, and green-blue infrastructure planning.
Accurate uncertainty quantification in structural engineering often requires estimating the full response distribution including both the cumulative distribution function (CDF) and the complementary cumulative distribution function (CCDF), rather than a single failure probability. This paper proposes a novel active learning method guided by the probability of misclassification for Gaussian process (GP) modeling to efficiently estimate these distributions for computationally expensive models. The locally defined probability of misclassification, originally meaningful only for a fixed threshold, is generalized to a global version tailored for multi-threshold estimation of CDF and CCDF. This provides a threshold-wise upper bound on the discrepancy between the posterior-predictive estimator and its mean-based approximation under the GP posterior. A tail-weighted stopping criterion placing greater emphasis on low-probability regions is then introduced to control the overall residual error in distribution estimation. Reformulating this criterion leads to a one-step learning function defined entirely in the input space, allowing all thresholds of interest to be considered simultaneously in each learning step and promoting globally efficient sampling. Numerical experiments demonstrate that the proposed method accurately recovers reference CDF and CCDF with fewer true-model evaluations than existing representative distribution-oriented active learning approaches.
