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Inverse vulcanization exploits S 8 to synthesize polysulfides. However, evolution of products and its mechanism during inverse vulcanization remains elusive. Herein, inverse vulcanization curves are obtained to describe the inverse vulcanization process in terms of three stages: induction, curing and over-cure. The typical curves exhibit a moduli increment before declining or plateauing, reflecting the process of polysulfide network formation and loosing depending on monomers. For aromatic alkenes, in the over-cure, the crosslinked polysulfide evolves significantly into a sparse network with accelerated relaxation, due to the degradation of alkenyl moieties into thiocarbonyls. The inverse vulcanization product of olefins degrades slowly with fluctuated relaxation time and modulus because of the generation of thiophene moieties, while the inverse vulcanization curve of dicyclopentadiene has a plateau following curing stage. Confirmed by calculations, the mechanisms reveal the alkenyl groups react spontaneously into thiocarbonyls or thiophenes via similar sulfur-substituted alkenyl intermediates but with different energy barriers. Inverse vulcanization exploits elemental sulfur to synthesize versatile polysulfides but the mechanism of inverse vulcanization remains elusive. Herein, the authors use inverse vulcanization curves to describe the three-stage structural evolution during the inverse vulcanization process.

Separating point clouds into ground and nonground points is an essential step in the processing of airborne laser scanning (ALS) data for various applications. Interpolation-based filtering algorithms have been commonly used for filtering ALS point cloud data. However, most conventional interpolation-based algorithms have exhibited a drawback in terms of retaining abrupt terrain characteristics, resulting in poor algorithmic precision in these regions. To overcome this drawback, this paper proposes an improved adaptive surface interpolation filter with a multilevel hierarchy by using a cloth simulation and relief amplitude. This method uses three hierarchy levels of provisional digital elevation model (DEM) raster surfaces with thin plate spline (TPS) interpolation to separate ground points from unclassified points based on adaptive residual thresholds. A cloth simulation algorithm is adopted to generate sufficient effective initial ground seeds for constructing topographic surfaces with high quality. Residual thresholds are adaptively constructed by the relief amplitude of the examined area to capture complex landscape characteristics during the classification process. Fifteen samples from the International Society for Photogrammetry and Remote Sensing (ISPRS) commission are used to assess the performance of the proposed algorithm. The experimental results indicate that the proposed method can produce satisfying results in both flat areas and steep areas. In a comparison with other approaches, this method demonstrates its superior performance in terms of filtering results with the lowest omission error rate; in particular, the proposed approach retains discontinuous terrain features with steep slopes and terraces.

The multipath effect is a critical factor that prevents the Global Navigation Satellite System (GNSS) from achieving millimeter-level positioning accuracy. A multipath hemispherical map (MHM) is a popular approach to achieving real-time multipath error mitigation. The premise of the constructed MHM model is that the residuals in the grid only contain multipath errors and noise without any outliers. However, when there are numerous obvious outliers in each grid, the traditional quality control method is unable to detect them effectively. Therefore, we propose a multipath hemispherical map with strict quality control (MHM-S) to mitigate multipath errors. This method first uses the maximum phase delay to eliminate obvious outliers. Then, the 3-sigma rule and F-test are applied to remove the remaining few outliers in the grid. After applying the proposed MHM-S method, the experimental results show that when the PRN20 satellite is affected by outliers, the standard deviation (STD) reduction rate of the MHM-S residuals is 12.03% compared with the residual STDs of the MHM model. In addition, we evaluate the capabilities of MHM-S with carrier phase observation (MHM-SC) and carrier phase and pseudo-range observation (MHM-SCP) models in multipath error mitigation. Especially in the east direction, the positioning accuracy of the MHM-SCP model is improved by 48% compared with the MHM-SC model.

•An EEG study using nonauditory visual selective tasks in the elderly•Decreased N2pc amplitude in ANHLs may suggest a visual attentional deficit•N2pc is a potential biomarker for detecting early-stage attentional deficits Evidence from epidemiological studies suggests that hearing loss is associated with an accelerated decline in cognitive function, but the underlying pathophysiological mechanism remains poorly understood. Studies using auditory tasks have suggested that degraded auditory input increases the cognitive load for auditory perceptual processing and thereby reduces the resources available for other cognitive tasks. Attention-related networks are among the systems overrecruited to support degraded auditory perception, but it is unclear how they function when no excessive recruitment of cognitive resources for auditory processing is needed. Here, we implemented an EEG study using a nonauditory visual attentional selection task in 30 individuals with age-related hearing loss (ARHLs, 60–73 years) and compared them with aged (N = 30, 60–70 years) and young (N = 35, 22–29 years) normal-hearing controls. Compared with their normal-hearing peers, ARHLs demonstrated a significant amplitude reduction for the posterior contralateral N2 component, which is a well-validated index of the allocation of selective visual attention, despite the comparable behavioral performance. Furthermore, the amplitudes were observed to correlate significantly with hearing acuities (pure tone audiometry thresholds) and higher-order hearing abilities (speech-in-noise thresholds) in aged individuals. The target-elicited alpha lateralization, another mechanism of visuospatial attention, demonstrated in control groups was not observed in ARHLs. Although behavioral performance is comparable, the significant decrease in N2pc amplitude in ARHLs provides neurophysiologic evidence that may suggest a visual attentional deficit in ARHLs even without extra-recruitment of cognitive resources by auditory processing. It supports the hypothesis that constant degraded auditory input in ARHLs has an adverse impact on the function of cognitive control systems, which is a possible mechanism mediating the relationship between hearing loss and cognitive decline.

The use of mechanical forces to chemically transform polymers dates back decades. In recent years, the use of mechanochemistry to direct constructive transformations in polymers has resulted in a range of engineered molecular responses that span optical, mechanical, electronic and thermal properties. The chemistry that has been developed is now well positioned for use in materials science, polymer physics, mechanics and additive manufacturing. Here, we review the historical backdrop of polymer mechanochemistry, give an overview of the existing toolbox of mechanophores and associated theoretical methods, and speculate as to emerging opportunities in materials science for which current capabilities are seemingly well suited. Non-linear mechanical responses and internal, amplifying stimulus–response feedback loops, including those enabled by, or coupled to, microstructured metamaterial architectures, are seen as particularly promising. Polymer mechanochemistry converts mechanical forces in materials to chemical reactions through the response of functional groups known as mechanophores. This Review discusses the colorimetric, mechanical, chemical and electronic responses of mechanophores that may be useful in materials for strain sensing and strengthening, soft devices and additive manufacturing.

Traditional animal behavior recognition models require extensive labeled datasets and frequent retraining, limiting their adaptability across species and environments. Additionally, existing systems rely on predefined behavior categories, making it difficult for researchers to customize recognition models to specific behavioral patterns relevant to their studies. Different research fields, such as animal welfare monitoring, conservation, and behavioral ecology, often require distinct behavior classifications, yet current systems lack the flexibility to accommodate these varying needs. This study aims to develop an expandable and user-driven animal behavior recognition system utilizing DeepLabCut for pose estimation and a BiLSTM-based classification model. By integrating Dynamic Adaptive Elastic Weight Consolidation (DA-EWC), the system enables incremental learning, allowing new behaviors to be added with minimal annotation while preserving previously learned behaviors. The proposed system is trained on dolphin behavior datasets using DeepLabCut for keypoint extraction and a BiLSTM model for sequence classification. Additionally, a user-friendly interface is implemented to facilitate behavior annotation and efficient model updates. The proposed system achieves 96.5% accuracy in behavior classification, surpassing conventional models such as Faster R-CNN. Compared to standard EWC, DA-EWC maintains an average of 8.3% higher accuracy when incorporating new behaviors. Furthermore, the system reduces annotation efforts by 9.3%, enabling users to expand behavior categories efficiently. This expandable behavior recognition system significantly enhances adaptability and efficiency in animal behavior monitoring. By supporting user-driven incremental learning, it provides a scalable solution for behavior analysis across different research domains, addressing the need for customizable and evolving behavior classification.

Seed vigor is an important agronomic attribute, essentially associated with crop yield. High-temperature and humidity (HTH) stress directly affects seed development of plants, resulting in the decrease of seed vigor. Therefore, it is particularly important to discover HTH-tolerant genes related to seed vigor. Phenylalanine ammonia lyase (PAL, EC 4.3.1.24) is the first rate-limiting enzyme in the phenylpropanoid biosynthesis pathway and a key enzyme involved in plant growth and development and environmental adaptation. However, the biological function of PAL in seed vigor remains unknown. Here, GmPAL1.1 was cloned from soybean, and its protein was located in the cytoplasm and cell membrane. GmPAL1.1 was significantly induced by HTH stress in developing seeds. The overexpression of GmPAL1.1 in Arabidopsis (OE) accumulated lower level of ROS in the developing seeds and in the leaves than the WT at the physiological maturity stage under HTH stress, and the activities of SOD, POD, and CAT and flavonoid contents were significantly increased, while MDA production was markedly reduced in the leaves of the OE lines than in those of the WT. The germination rate and viability of mature seeds of the OE lines harvested after HTH stress were higher than those of the WT. Compared to the control, the overexpression of GmPAL1.1 in Arabidopsis enhanced the tolerance to salt and drought stresses during germination. Our results suggested the overexpression of GmPAL1.1 in Arabidopsis promoted seed vigor at the physiological maturation period under HTH stress and increased the seeds’ tolerance to salt and drought during germination.

Being a bio-sourced and biodegradable polymer, polylactic acid (PLA) has been considered as one of the most promising substitutes for petroleum-based plastics. However, its wide application is greatly limited by its very poor ductility, which has driven PLA-toughening modifications to be a topic of increasing research interest in the past decade. Toughening enhancement is achieved often at the cost of a large sacrifice in strength, with the toughness–strength trade-off having remained as one of the main bottlenecks of PLA modification. In the present study, a bio-elastomeric material of epoxidized soybean oil (ESO) crosslinked with sebacic acid (SA) and enhanced by graphene oxide (GO) nanoparticles (NPs) was employed to toughen PLA with the purpose of simultaneously preserving strength and achieving additional functions. The even dispersion of GO NPs in ESO was aided by ultrasonication and guaranteed during the following ESO-SA crosslinking with GO participating in the carboxyl–epoxy reaction with both ESO and SA, resulting in a nanoparticle-enhanced and dynamically crosslinked elastomer (GESO) via a β-hydroxy ester. GESO was then melt-blended with PLA, with the interfacial reaction between ESO and PLA offering good compatibility. The blend morphology, and thermal and mechanical properties, etc., were evaluated and GESO was found to significantly toughen PLA while preserving its strength, with the GO loading optimized at ~0.67 wt%, which gave an elongation at break of ~274.5% and impact strength of ~10.2 kJ/m2, being 31 times and 2.5 times higher than pure PLA, respectively. Moreover, thanks to the presence of dynamic crosslinks and GO NPs, the PLA-GESO blends exhibited excellent shape memory effect and antistatic properties.

With the development of urbanization and the expansion of floating populations, rental housing has become an increasingly common living choice for many people, and housing rental prices have attracted great attention from individuals, enterprises and the government. The housing rental prices are principally estimated based on structural, locational and neighborhood variables, among which the relationships are complicated and can hardly be captured entirely by simple one-dimensional models; in addition, the influence of the geographic objects on the price may vary with the increase in their quantities. However, existing pricing models usually take those structural, locational and neighborhood variables as one-dimensional inputs into neural networks, and often neglect the aggregated effects of geographical objects, which may lead to fluctuating rental price estimations. Therefore, this paper proposes a rental housing price model based on the convolutional neural network (CNN) and the synthetic spatial density of points of interest (POIs). The CNN can efficiently extract the complex characteristics among the relevant variables of housing, and the two-dimensional locational and neighborhood variables, based on the synthetic spatial density, effectively reflect the aggregated effects of the urban facilities on rental housing prices, thereby improving the accuracy of the model. Taking Wuhan, China, as the study area, the proposed method achieves satisfactory and accurate rental price estimations (coefficient of determination (R2) = 0.9097, root mean square error (RMSE) = 3.5126) in comparison with other commonly used pricing models.

Glioblastoma (GBM) is the most common and aggressive malignant primary brain tumor. E3 ligases play important functions in glioma pathogenesis. CRISPR system offers a powerful platform for genome manipulation, while the screen of E3 ligases in GBM still remains to be explored. Here, we first constructed an E3 ligase small guide RNA (sgRNAs) library for glioma cells growth screening. After four passages, 299 significantly enriched or lost genes (SELGs) were compared with the initial state. Then the clinical significance of SELGs were validated and analyzed with TCGA glioblastoma and CGGA datasets. As RNF185 showed lost signal, decreased expression and favorable prognostic significance, we chose RNF185 for functional analysis. In vitro overexpressed cellular phenotype showed that RNF185 was a tumor suppressor in two glioma cell lines. Finally, the molecular mechanism of decreased RNF185 expression was investigated and increased miR-587 expression and DNA hypermethylation was evaluated. This study would provide a link between the molecular basis and glioblastoma pathogenesis, and a novel perspective for glioblastoma treatment.