Search Results (45)

This study investigated the effects of planting density and site index on stand attributes and soil nutrients in mature Chinese fir [Cunninghamia lanceolata (Lamb.) Hook.] plantations across Fujian and Sichuan Provinces, elucidating the pathways through which these factors influence standing volume (SV). The results showed that (1) planting density significantly affected stand variables, with average diameter at breast height (ADBH) decreasing and SV initially increasing and then declining with higher density. The number of mortality plants (NMP) and actual stand density (ASD) both increased significantly with higher density. Average tree height (ATH) and dominant height (DH) responses varied by region, with ATH decreasing in Sichuan and DH decreasing in Fujian with higher density. (2) Planting density affected soil nutrients differently in the two provinces, with soil total potassium (TK) increasing in Fujian and phosphorus decreasing in Sichuan. (3) Site index was positively correlated with ATH and ADBH but negatively correlated with ASD and NMP. Its relationship with soil nutrients was province-specific: in Fujian, site index was negatively correlated with total phosphorus (TP) and positively correlated with TK and soil pH, while in Sichuan it was only positively correlated with TK. (4) Structural equation modeling revealed different regulatory pathways: in Fujian, planting density influenced SV through both ASD and soil nutrients, while in Sichuan it affected only through ASD. This study highlights the region-specific interactions between planting density, site index, stand structure, and soil nutrients, providing a foundation for optimized plantation management. Full article

The building sector is responsible for significant carbon emissions and energy consumption, making it a critical field for global energy-saving and emission reduction efforts to combat climate change. This study calculated the building carbon emissions (BCE) of 30 provinces in the Chinese Mainland from 2010 to 2020 using the IPCC carbon emission factor method based on the statistical data of energy consumption and building materials, and then the decoupling relationship between BCE and the construction land area (CLA) was analyzed. The results are as follows: (1) BCE exhibited an overall increase from 2010 to 2020, yet at a descending rate, with a prominent decrease in indirect BCE (IBCE); (2) BCE and direct BCE (DBCE) were higher in the north but lower in the south, while IBCE was higher along the eastern coast; (3) the provinces in North China and Northeast China possess the largest areas of construction land, but the growth of CLA was the slowest or even declined in the later stage of the study; (4) the decoupling relationship between BCE and CLA is dominated by expansive negative decoupling or strong negative decoupling. The growth of BCE is generally much faster than the expansion of construction land. The findings will have important reference for achieving energy-saving and “dual carbon” strategic development goals in China. Full article

The colors of traditional dwellings are an extremely intuitive manifestation of regional culture and an important reference for guiding rural housing. This study takes the Gutian district as the research region and explores the internal influence mechanism of the “color perception–preference–supportive behavior” of the indigenous residents towards traditional dwellings, specifically rammed earth dwellings. After constructing a structural equation model, the results were as follows: (1) The color perception of the indigenous residents towards traditional dwellings has two dimensions: distinctiveness and rootedness. (2) The color perception of the indigenous residents towards traditional dwellings can significantly enhance their color preference, but the two dimensions of color perception have different effects on color preference. (3) Color perception has a direct impact on color supportive behavior, mainly reflected in the dimension of the perception of distinctiveness. On the other hand, the mediating role of color preference has a positive impact on color—supportive behavior, mainly reflected in the dimension of the perception of rootedness. This study constructs a positive—cycle model that goes from the strengthening of color perception to the promotion of color preference and finally to the enhancement of color supportive behavior. The aim is to deeply analyze the multiple values contained in the colors of traditional dwellings, which not only demonstrate regional characteristics but also closely meet the emotional needs of the indigenous residents and have broad application potential in rural housing and cultural inheritance significance. Full article

In integrated energy system modeling, extant research predominantly addresses single-energy system optimization or carbon emission flow models, failing to adequately elucidate the mechanisms of combined energy and carbon flow modeling in complex energy systems. This deficiency hampers a thorough analysis of the coupling relationships between energy and carbon flows, thereby posing significant challenges for resource allocation and carbon mitigation within integrated energy systems. This paper presents an innovative energy–carbon coupling model, constructing a unified framework for energy and carbon flow modeling centered on the energy hub, thereby overcoming the limitations of traditional approaches that are unable to model both energy and carbon flows concurrently. The model comprehensively examines the coupling nodes and carbon density correlations among energy conversion devices within multi-energy systems, precisely quantifying carbon emission paths and distribution across devices. This provides a novel methodology for carbon emission management in integrated energy systems. Case studies on typical integrated energy systems demonstrate the proposed model’s efficacy in low-carbon economic dispatch. The energy–carbon coupling model developed in this study offers a high-adaptability solution for integrated energy systems in multi-energy, low-carbon parks, achieving an optimal balance between economic efficiency and environmental performance under dual objectives of energy demand and carbon emission minimization. Full article

Inorganic/organic composite passivation film can significantly improve the corrosion resistance performance of hot-dip Al-Zn-coated steel. However, yellowing of the passivation film always leads to obvious performance degradation in corrosion resistance. Investigating the yellowing mechanism of the passivation film and its impact on corrosion resistance would provide a foundation for enhancing its yellowing resistance property. This study primarily focuses on the yellowing mechanism of the passivation film based on the copolymer of N-vinylpyrrolidone and N-vinylcaprolactam. It is found that the oxidation and semi-carbonization of butyramide and valeroamide generated by C–N bond cleavage in the copolymer at high temperatures are responsible for the yellowing of the passivation film. The cracking of the passivation film caused by yellowing degree exposes more of the bare Al-Zn coating, further accelerating the degradation in the corrosion resistance. Additionally, it is observed that the impact of yellowing on the corrosion resistance is negligible when the color difference (ΔE*) caused by yellowing is less than 3.0, whereas ΔE* values above 3.0 result in rapid degradation in the corrosion resistance of the passivation film. The formula y = 0.77 − 0.07x + 0.023x² + 0.0039x³ effectively expresses the relationship between corrosion area (y) and ΔE* (x) (R² = 0.995). Full article

Continuous glucose monitoring data have strong time variability as well as complex non-stationarity and nonlinearity. The existing blood glucose concentration prediction models often overlook the impacts of residual components after multi-scale decomposition on prediction accuracy. To enhance the prediction accuracy, a new short-term glucose prediction model that integrates the double decomposition technique, nonlinear marine predator algorithm (NMPA) and deep extreme learning machine (DELM) is proposed. First of all, the initial blood glucose data are decomposed by variational mode decomposition (VMD) to reduce its complexity and non-stationarity. To make full use of the decomposed residual component, the time-varying filter empirical mode decomposition (TVF-EMD) is utilized to decompose the component, and further realize complete decomposition. Then, the NMPA algorithm is utilized to optimize the weight parameters of the DELM network to avoid any fluctuations in prediction performance, and all the decomposed subsequences are predicted separately. Finally, the output results of each model are superimposed to acquire the predicted value of blood sugar concentration. Using actual collected blood glucose concentration data for predictive analysis, the results of three patients show the following: (i) The double decomposition strategy effectively reduces the complexity and volatility of the original sequence and the residual component. Making full use of the important information implied by the residual component has the best decomposition effect; (ii) The NMPA algorithm optimizes DELM network parameters, which can effectively enhance the predictive capabilities of the network and acquire more precise predictive results; (iii) The model proposed in this paper can achieve a high prediction accuracy of 45 min in advance, and the root mean square error values are 5.2095, 4.241 and 6.3246, respectively. Compared with the other eleven models, it has the best prediction accuracy. Full article

To meet the challenges of energy sustainability, the integrated energy system (IES) has become a key component in promoting the development of innovative energy systems. Accurate and reliable multivariate load prediction is a prerequisite for IES optimal scheduling and steady running, but the uncertainty of load fluctuation and many influencing factors increase the difficulty of forecasting. Therefore, this article puts forward a multi-energy load prediction approach of the IES, which combines the fennec fox optimization algorithm (FFA) and hybrid kernel extreme learning machine. Firstly, the comprehensive weight method is used to combine the entropy weight method and Pearson correlation coefficient, fully considering the information content and correlation, selecting the key factors affecting the prediction, and ensuring that the input features can effectively modify the prediction results. Secondly, the coupling relationship between the multi-energy load is learned and predicted using the hybrid kernel extreme learning machine. At the same time, the FFA is used for parameter optimization, which reduces the randomness of parameter setting. Finally, the approach is utilized for the measured data at Arizona State University to verify its effectiveness in multi-energy load forecasting. The results indicate that the mean absolute error (MAE) of the proposed method is 0.0959, 0.3103 and 0.0443, respectively. The root mean square error (RMSE) is 0.1378, 0.3848 and 0.0578, respectively. The weighted mean absolute percentage error (WMAPE) is only 1.915%. Compared to other models, this model has a higher accuracy, with the maximum reductions on MAE, RMSE and WMAPE of 0.3833, 0.491 and 2.8138%, respectively. Full article

Regulation of translation is a crucial step in gene expression. Developmental signals and environmental stimuli dynamically regulate translation via upstream small open reading frames (uORFs) and ribosome pausing. Recent studies have revealed many plant genes that are specifically regulated by uORF translation following changes in growth conditions, but ribosome-pausing events are less well understood. In this study, we performed ribosome profiling (Ribo-seq) of etiolated maize (Zea mays) seedlings exposed to light for different durations, revealing hundreds of genes specifically regulated at the translation level during the early period of light exposure. We identified over 400 ribosome-pausing events in the dark that were rapidly released after illumination. These results suggested that ribosome pausing negatively regulates translation from specific genes, a conclusion that was supported by a non-targeted proteomics analysis. Importantly, we identified a conserved nucleotide motif downstream of the pausing sites. Our results elucidate the role of ribosome pausing in the control of gene expression in plants; the identification of the cis-element at the pausing sites provides insight into the mechanisms behind translation regulation and potential targets for artificial control of plant translation. Full article

Persimmon (Diospyros kaki Thunb.) fruit size variation is abundant. Studying the size of the persimmon fruit is helpful in improving its economic value. At present, the regulatory mechanism of persimmon fruit size formation is still unclear. In this study, the mechanism of fruit size formation was investigated through morphological, cytological and transcriptomic analyses, as well as exogenous ethrel and aminoethoxyinylglycine (AVG: ethylene inhibitor) experiments using the large fruit and small fruit of ‘Yaoxianwuhua’. The results showed that stages 3–4 (June 11–June 25) are the crucial morphological period for differentiation of large fruit and small fruit in persimmon. At this crucial morphological period, the cell number in large fruit was significantly more than that in small fruit, indicating that the difference in cell number is the main reason for the differentiation of persimmon fruit size. The difference in cell number was caused by cell division. CNR1, ANT, LAC17 and EB1C, associated with cell division, may be involved in regulating persimmon fruit size. Exogenous ethrel resulted in a decrease in fruit weight, and AVG treatment had the opposite effect. In addition, LAC17 and ERF114 were upregulated after ethrel treatment. These results indicated that high ethylene levels can reduce persimmon fruit size, possibly by inhibiting cell division. This study provides valuable information for understanding the regulation mechanism of persimmon fruit size and lays a foundation for subsequent breeding and artificial regulation of fruit size. Full article

Breast cancer (BC) poses a significant threat to women’s health, with triple-negative breast cancer (TNBC) representing one of the most challenging and aggressive subtypes due to the lack of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) expression. Traditional TNBC treatments often encounter issues such as low drug efficiency, limited tumor enrichment, and substantial side effects. Therefore, it is crucial to explore novel diagnostic and treatment systems for TNBC. Multifunctional molecular probes (MMPs), which integrate target recognition as well as diagnostic and therapeutic functions, introduce advanced molecular tools for TNBC theranostics. Using an MMP system, molecular drugs can be precisely delivered to the tumor site through a targeted ligand. Real-time dynamic monitoring of drug release achieved using imaging technology allows for the evaluation of drug enrichment at the tumor site. This approach enables accurate drug release, thereby improving the therapeutic effect. Therefore, this review summarizes the recent advancements in MMPs for TNBC theranostics, encompassing the design and synthesis of MMPs as well as their applications in the field of TNBC theranostics. Full article

Oil-tea belongs to the Camellia genus, an important oil crop in China. However, oil-tea is taxonomically challenging due to its morphological variation, polyploidy, and interspecific hybridization. Therefore, the present study aimed to investigate the flower organs’ morphology and pollen micro-morphology of 18 oil-tea genotypes in detail and discussed their significance for oil-tea taxonomy. The quantitative parameters of flowers were measured using Vernier caliper measurements. Pollen morphology was observed and photographed using scanning electron microscopy (SEM). The results indicated that the flower size varied significantly among the tested oil-tea genotypes, with the corolla diameter ranging from 42.25 μm in C. meiocarpa ‘LP’ to 89.51 μm in C. oleifera ‘ASX09’. The pollen grains of oil-tea are monads and medium grade in pollen size. There were two types of polar views, including triangular or subcircular, with a polar axis length (P) ranging from 27.5 μm in C. oleifera ‘CY67’ to 59.04 μm in C. mairei (H. Lév.) Melch. var. lapidea (Y.C. Wu) Sealy. The equatorial views exhibited oblate, spherical, or oblong shapes, with an equatorial axis length (E) of 21.32 to 41.62 μm. The pollen exine sculpture was perforate, verrucate, and reticulate. The perforation lumina diameter (D) ranged from 0.29 μm in C. magniflora Chang to 1.22 μm in C. yuhsienensis Hu, and the perforation width (W) varied from 0.77 μm in C. osmantha to 1.40 μm in C. gauchowensis ‘HM349’, respectively. Qualitative clustering analysis (Q-type cluster) and principal component analysis (PCA) were conducted using eleven indexes of flower and pollen morphology, and the 18 oil-tea genotypes were classified into three categories. In addition, the correlation analysis showed that there was a significant correlation between pollen size and flower morphology or pollen exine sculpture. These results offer valuable information on the classification and identification of the 18 oil-tea germplasm resources. Full article

Gelatin-based hydrogels with excellent mechanical properties and conductivities are desirable, but their fabrication is challenging. In this work, an innovative approach for the preparation of gelatin-based conductive hydrogels is presented that improves the mechanical and conductive properties of hydrogels by integrating Z–Gln–Gly into gelatin polymers via enzymatic crosslinking. In these hydrogels (Gel–TG–ZQG), dynamic π–π stacking interactions are created by the introduction of carbobenzoxy groups, which can increase the elasticity and toughness of the hydrogel and improve the conductivity sensitivity by forming effective electronic pathways. Moreover, the mechanical properties and conductivity of the obtained hydrogel can be controlled by tuning the molar ratio of Z–Gln–Gly to the primary amino groups in gelatin. The hydrogel with the optimal mechanical properties (Gel–TG–ZQG (0.25)) exhibits a high storage modulus, compressive strength, tensile strength, and elongation at break of 7.8 MPa at 10 °C, 0.15 MPa at 80% strain, 0.343 MPa, and 218.30%, respectively. The obtained Gel–TG–ZQG (0.25) strain sensor exhibits a short response/recovery time (260.37 ms/130.02 ms) and high sensitivity (0.138 kPa⁻¹) in small pressure ranges (0–2.3 kPa). The Gel–TG–ZQG (0.25) hydrogel-based sensors can detect full-range human activities, such as swallowing, fist clenching, knee bending and finger pressing, with high sensitivity and stability, yielding highly reproducible and repeatable sensor responses. Additionally, the Gel–TG–ZQG hydrogels are noncytotoxic. All the results demonstrate that the Gel–TG–ZQG hydrogel has potential as a biosensor for wearable devices and health-monitoring systems. Full article

The mechanical LiDAR sensor is crucial in autonomous vehicles. After projecting a 3D point cloud onto a 2D plane and employing a deep learning model for computation, accurate environmental perception information can be supplied to autonomous vehicles. Nevertheless, the vertical angular resolution of inexpensive multi-beam LiDAR is limited, constraining the perceptual and mobility range of mobile entities. To address this problem, we propose a point cloud super-resolution model in this paper. This model enhances the density of sparse point clouds acquired by LiDAR, consequently offering more precise environmental information for autonomous vehicles. Firstly, we collect two datasets for point cloud super-resolution, encompassing CARLA32-128in simulated environments and Ruby32-128 in real-world scenarios. Secondly, we propose a novel temporal and spatial feature-enhanced point cloud super-resolution model. This model leverages temporal feature attention aggregation modules and spatial feature enhancement modules to fully exploit point cloud features from adjacent timestamps, enhancing super-resolution accuracy. Ultimately, we validate the effectiveness of the proposed method through comparison experiments, ablation studies, and qualitative visualization experiments conducted on the CARLA32-128 and Ruby32-128 datasets. Notably, our method achieves a PSNR of 27.52 on CARLA32-128 and a PSNR of 24.82 on Ruby32-128, both of which are better than previous methods. Full article

Organic phosphorus (OP) is an essential component of the soil P cycle, which contributes to barley nutrition after its mineralization into inorganic phosphorus (Pi). However, the dynamics of OP utilization in the barley rhizosphere remain unclear. In this study, phytin was screened out from six OP carriers, which could reflect the difference in OP utilization between a P-inefficient genotype Baudin and a P-efficient genotype CN4027. The phosphorus utilization efficiency (PUE), root morphological traits, and expression of genes associated with P utilization were assessed under P deficiency or phytin treatments. P deficiency resulted in a greater root surface area and thicker roots. In barley fed with phytin as a P carrier, the APase activities of CN4027 were 2–3-fold lower than those of Baudin, while the phytase activities of CN4027 were 2–3-fold higher than those of Baudin. The PUE in CN4027 was mainly enhanced by activating phytase to improve the root absorption and utilization of Pi resulting from OP mineralization, while the PUE in Baudin was mainly enhanced by activating APase to improve the shoot reuse capacity. A phosphate transporter gene HvPHT1;8 regulated P transport from the roots to the shoots, while a purple acid phosphatase (PAP) family gene HvPAPhy_b contributed to the reuse of P in barley. Full article

Amino-functionalized silica@resorcinol–formaldehyde nanocomposites (NH₂-SiO₂@RF) were synthesized for the removal of Cr(VI) from aqueous solutions using the sol–gel technique with two simple preparation steps, including the one-pot synthesis of SiO₂@RF using the Stöber method and (3-aminopropyl)triethoxysilane (APTES) modification. The morphology, particle size, functional group, and thermal stability of the obtained nanocomposites were systematically characterized, with the results indicating a uniform sphericity with a particle size of 200 nm and high thermal stability. The adsorption results demonstrated that the preferred pH value was 2, and the data were well fitted with the Langmuir and Temkin isotherm models and quasi-second-order kinetic equation, indicating a high adsorption capacity. The maximum Cr(VI) adsorption capacity from the nonlinear form of the Langmuir model was 272.6 mg·g⁻¹. The intra-particle diffusion model accurately described the adsorption of Cr(VI) onto NH₂-SiO₂@RF. The changes in Gibb’s free energy, enthalpy, and entropy revealed that Cr(VI) adsorption onto NH₂-SiO₂@RF was a spontaneous and endothermic process. Furthermore, high selectivity was demonstrated in the material for the removal of Cr(VI) from commonly coexisting ions. The obtained nanocomposites had good regeneration properties and maintained a removal rate above 85% in the fifth adsorption–desorption experiments. Moreover, under the optimized adsorption conditions, the obtained nanocomposites were preliminarily applied to tannery wastewater, demonstrating an excellent removal effect, which indicates their potential application value. Full article