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Interactions between carbon (C), nitrogen (N), and phosphorus (P), the vital indicators of ecological restoration, play an important role in signaling the health of ecosystems. Rapidly and accurately mapping foliar C, N, and P is essential for interpreting community structure, nutrient limitation, and primary production during ecosystem recovery. However, research on how to rapidly map C, N, and P in restored areas with mixed plant communities is limited. This study employed laser imaging, detection, and ranging (LiDAR) and hyperspectral data to extract spectral, textural, and height features of vegetation as well as vegetation indices and structural parameters. Causal band, multiple linear regression, and random forest models were developed and tested in a restored area in northern China. Important parameters were identified including (1), for C, red-edge bands, canopy height, and vegetation structure; for N, textural features, height percentile of 40–95%, and vegetation structure; for P, spectral features, height percentile of 80%, and 1 m foliage height diversity. (2) R2 was used to compare the accuracy of the three models as follows: R2 values for C were 0.07, 0.42, and 0.56, for N they were 0.20, 0.48, and 0.53, and for P they were 0.32, 0.39, and 0.44; the random forest model demonstrated the highest accuracy. (3) The accuracy of the concentration estimates could be ranked as C > N > P. (4) The inclusion of LiDAR features significantly improved the accuracy of the C concentration estimation, with increases of 22.20% and 47.30% in the multiple linear regression and random forest models, respectively, although the inclusion of LiDAR features did not notably enhance the accuracy of the N and P concentration estimates. Therefore, LiDAR and hyperspectral data can be used to effectively map C, N, and P concentrations in a mixed plant community in a restored area, revealing their heterogeneity in terms of species and spatial distribution. Future efforts should involve the use of hyperspectral data with additional bands and a more detailed classification of plant communities. The application of this information will be useful for analyzing C, N, and P limitations, and for planning for the maintenance of restored plant communities. [ABSTRACT FROM AUTHOR]

The present paper presents a numerical investigation of heat transfer in an exchanger fitted with a modified conical-shaped turbulator containing water/Fe2O3 nanofluid. The study aims to address the critical need for improved heat exchanger efficiency, a vital component in various industries, including the chemical, power generation, and food industries. The work focuses on achieving enhanced heat transfer performance within a smaller volume, a primary goal of modern technology and industrial processes. The innovation in this study lies in the design and analysis of a novel conical turbulator, which has not been explored extensively in the context of heat exchangers fitted with nanofluids. Unlike traditional methods, which often rely on active or semi-active means to enhance heat transfer, this research introduces a passive approach through the incorporation of turbulators. Specifically, the study investigates the use of perforated cone-shaped turbulators in conjunction with nanofluids to boost heat transfer performance. The research employs state-of-the-art computational fluid dynamics (CFD) models, allowing for a comprehensive evaluation of the turbulator's performance across a wide range of Reynolds numbers (Re = 4000–20,000). It further examines the influence of various turbulator parameters, nanoparticle content, and geometry on heat transfer efficiency. Key findings indicate that the modified turbulator exhibits exceptional performance, increasing Nusselt numbers by 3.4–5.4 times and friction coefficients by 2.3–1.8 times compared to smooth pipes. Particularly noteworthy is the 92 % increase in the Nusselt number achieved with a mere 2 % increase in the Fe2O3 nanoparticle content. The present study introduces a novel passive heat transfer enhancement method using perforated cone-shaped turbulators and nanofluids, filling a significant gap in existing research. The innovative turbulator design and its substantial performance improvements offer promising prospects for achieving higher heat exchanger efficiency, making it a valuable contribution to thermal systems and heat transfer engineering. [ABSTRACT FROM AUTHOR]

The enriched collagens in the extracellular matrix (ECM) of breast cancer substantially impede drug delivery. Halofuginone (HF), a potent antifibrotic agent, was effective to deplete the collagens and remodel the ECM by inhibiting the TGFβ pathway. However, the application of HF was hindered by its strong liver toxicity. Herein, mesoporous platinum (mPt) nanoparticles were constructed to load HF as theranostic nanoplatforms. mPt had a uniform spherical structure with a diameter of 79.83 ± 6.97 nm and an average pore diameter of 20 nm and exhibited good photothermal conversion efficiency of 62.4%. The obtained HF‐loaded nanoplatform (PEG@mPt‐HF) showed enhanced cytotoxicity through the combination of photothermal therapy and the anti‐TGFβ effect induced by HF. The animal imaging and histochemical assays confirmed the PEG@mPt‐HF could efficiently deliver HF to tumors (monitored by CT) and remodel the ECM by TGFβ pathway inhibition, which resulted in increased anti‐cancer efficacy. Importantly, the liver toxicity observed in HF‐treated mice was negligible in those treated by PEG@mPt‐HF. Overall, this study designed a theranostic nanoplatform to remodel the ECM with remarkably reduced systematic toxicity and enhance the therapeutic efficacy through combination treatment. [ABSTRACT FROM AUTHOR]

In recent years, emotion recognition using physiological signals has become a popular research topic. Physiological signal can reflect the real emotional state for individual which is widely applied to emotion recognition. Multimodal signals provide more discriminative information compared with single modal which arose the interest of related researchers. However, current studies on multimodal emotion recognition normally adopt one-stage fusion method which results in the overlook of cross-modal interaction. To solve this problem, we proposed a multi-stage multimodal dynamical fusion network (MSMDFN). Through the MSMDFN, the joint representation based on cross-modal correlation is obtained. Initially, the latent and essential interactions among various features extracted independently from multiple modalities are explored based on specific manner. Subsequently, the multi-stage fusion network is designed to split the fusion procedure into multi-stages using the correlation observed before. This allows us to exploit much more fine-grained unimodal, bimodal and trimodal intercorrelations. For evaluation, the MSMDFN was verified on multimodal benchmark DEAP. The experiments indicate that our method outperforms the related one-stage multi-modal emotion recognition works. [ABSTRACT FROM AUTHOR]