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In this study, we examined the application of neurostructural principles to the design of public spaces on university campuses to optimize students’ learning efficiency, social interactions, and psychological well-being. Using Hainan University in China as a case study, a descriptive analysis was used to evaluate the case study design of the data. Data on students’ preferences for and satisfaction with public learning spaces (libraries, student centers, and open learning areas) were also collected through a questionnaire. The questionnaire was based on the four stages of the AIDA (Attention, Interest, Desire, and Action) model and covered basic information about the participants and their first impressions of the learning spaces, design element preferences, emotional and cognitive influences, and willingness to participate in improving the design of campus spaces. Data were analyzed using quantitative methods, including frequency analysis and score aggregation, to assess the students’ satisfaction with the existing design elements of the learning space and their suggestions for potential improvements. A random sample of students enrolled at Hainan University was used to ensure that the data were representative. The results of the study indicate that the rational allocation of natural light, the optimization of the acoustic environment, the adoption of soothing color schemes, and flexible spatial layouts are effective at relieving students’ psychological stress, enhancing their academic performance, and facilitating social interactions. Some of the existing designs are already in line with neurostructural principles, but there is still room for improvement, especially in terms of color schemes and spatial configurations. Students have positive attitudes towards participating in campus space improvement, with especially high interest in light optimization, spatial layout, and the use of natural materials. This study verifies the effectiveness of using neural structure principles in campus public spaces by establishing an empirical model, proves its positive effect on the quality of the campus environment and students’ well-being, and provides empirical evidence and theoretical support for future campus design.

The yellow-fleshed loquat is abundant in carotenoids, which determine the fruit’s color, provide vitamin A, and offer anti-inflammatory and anti-cancer health benefits. In this research, the impact of abscisic acid (ABA), a plant hormone, on carotenoid metabolism and flesh pigmentation in ripening loquat fruits was determined. Results revealed that ABA treatment enhanced the overall content of carotenoids in loquat fruit, including major components like β-cryptoxanthin, lutein, and β-carotene, linked to the upregulation of most genes in the carotenoid biosynthesis pathway. Furthermore, a transcription factor, EjWRKY6, whose expression was induced by ABA, was identified and was thought to play a role in ABA-induced carotenoid acceleration. Transient overexpression of EjWRKY6 in Nicotiana benthamiana and stable genetic transformation in Nicotiana tabacum with EjWRKY6 indicated that both carotenoid production and genes related to carotenoid biosynthesis could be upregulated in transgenic plants. A dual-luciferase assay proposed a probable transcriptional control between EjWRKY6 and promoters of genes associated with carotenoid production. To sum up, pre-harvest ABA application could lead to carotenoid biosynthesis in loquat fruit through the EjWRKY6-induced carotenoid biosynthesis pathway.

Tea ranks among the top three most beloved non-alcoholic beverages worldwide and boasts significant economic and health benefits. In addition to Camellia sinensis (L.) O. Kuntze, and other Camellia plants in China are consumed by residents as tea drinks, which also have important economic value. The present study introduces one of the wild tea species, namely, Camellia tachangensis F. C. Zhang. We analyzed changes in metabolite abundance and gene expression patterns of C. tachangensis and C. sinensis using metabonomics and transcriptomics. We found 1056 metabolites, including 256 differential metabolites (67 upregulated and 189 downregulated). Additionally, transcriptome analysis revealed 8049 differentially expressed genes, with 4418 upregulated and 3631 downregulated genes. C. sinensis boasts a notable abundance of Amino acids, which can be attributed to its specific genetic makeup. In Theanine and Caffeine metabolic pathways, the levels of the majority of amino acids and caffeine tend to decrease. In Flavonoid biosynthesis, the levels of the Flavanone Fustin and Epicatechin are higher in C. tachangensis, while Epigallocatechin and Gallocatechin levels are higher in C. sinensis. This indicates that the metabolic components of C. sinensis and C. tachangensis are not identical, which may result in a unique flavor.

Supercapacitors have gained much attention due to their remarkable merits such as stable cycle performance, high power density and excellent charge and discharge performance at high rate. From this perspective, carbon-based materials are widely explored as the active electrode materials for supercapacitors by taking advantages of hierarchical porous structure, excellent chemical stability, multistage pore structure, high surface area, and excellent conductivity. Various studies have explored many methods to utilize carbon-based electrodes. The large amount of defects inside carbon materials including functional groups, heteroatoms, name as foreign-doping, can provide pseudocapacitance. Meanwhile, self-doping (voids, vacancies, defects, etc.) may also enhance the electric double-layer capacitance. In this context, a brief overview of carbon materials for supercapacitor is expounded, including their characteristics, development process and the future challenges, providing a referential guide to those new to this field.

Metal additive manufacturing (AM) has been extensively studied in recent decades. Despite the significant progress achieved in manufacturing complex shapes and structures, challenges such as severe cracking when using existing alloys for laser powder bed fusion (L-PBF) AM have persisted. These challenges arise because commercial alloys are primarily designed for conventional casting or forging processes, overlooking the fast cooling rates, steep temperature gradients and multiple thermal cycles of L-PBF. To address this, there is an urgent need to develop novel alloys specifically tailored for L-PBF technologies. This review provides a comprehensive summary of the strategies employed in alloy design for L-PBF. It aims to guide future research on designing novel alloys dedicated to L-PBF instead of adapting existing alloys. The review begins by discussing the features of the L-PBF processes, focusing on rapid solidification and intrinsic heat treatment. Next, the printability of the four main existing alloys (Fe-, Ni-, Al- and Ti-based alloys) is critically assessed, with a comparison of their conventional weldability. It was found that the weldability criteria are not always applicable in estimating printability. Furthermore, the review presents recent advances in alloy development and associated strategies, categorizing them into crack mitigation-oriented, microstructure manipulation-oriented and machine learning-assisted approaches. Lastly, an outlook and suggestions are given to highlight the issues that need to be addressed in future work.

Among ornamental plants, the colour of the petals is an important feature. However, the reason for the colour differences of Rhododendron liliiflorum remains unclear. To reveal the differences in the colour of R. liliiflorum, high-efficiency liquid chromatographic collar (UPLC–MS/MS) technology was used to study the yellow and white parts of R. liliiflorum. A total of 1187 metabolites were identified in R. liliiflorum petals, including 339 flavonoid metabolites. Seventy-eight types of flavonoids in these metabolites were found in the yellow and white parts of R. liliiflorum petals, along with 11 other significantly enriched substances. Combining gene expression-related data with differential metabolite data demonstrated effects of enrichment in the flavanonols (fustin), flavonols (epiafzelechin and afzelechin), and flavanones (pinocembrin) of flavonoid biosynthesis; glyccitin, 6″-O-malonylgenistin, and 6-hydroxydaidzein of isoflavonoid biosynthesis; and anthocyanin biosynthesis of malvidin-3-O-galactoside (primulin), delphinidin-3-O-rutinoside, cyanidin-3-O-glucoside (kuromanin), and cyanidin-3-O-rutinoside (keracyanin), which are potentially the contributing factors responsible for the differences in petal colour in R. liliiflorum. This study establishes a connection between the differential metabolites underlying the color differences in the petals of R. liliiflorum and the gene expression in R. liliiflorum. This will provide a foundation for subsequent research on the regulation of flower color in R. liliiflorum and have profound implications for horticultural applications of R. liliiflorum.

Hydrogen-rich water (HRW) treatment has been reported to delay the softening and senescence of postharvest okras, but its regulatory mechanism remains unclear. In this paper, we investigated the effects of HRW treatment on the metabolism of several phytohormones in postharvest okras, which act as regulatory molecules in fruit ripening and senescence processes. The results showed that HRW treatment delayed okra senescence and maintained fruit quality during storage. The treatment upregulated all of the melatonin biosynthetic genes such as AeTDC, AeSNAT, AeCOMT and AeT5H, contributing to the higher melatonin content in the treated okras. Meanwhile, increased transcripts of anabolic genes but lower expression of catabolic genes involved in indoleacetic acid (IAA) and gibberellin (GA) metabolism were observed in okras when treated with HRW, which was related to the enhanced levels of IAA and GA. However, the treated okras experienced lower abscisic acid (ABA) content as compared to the non-treated fruit due to the down-regulation of its biosynthetic genes and up-regulation of the degradative gene AeCYP707A. Additionally, there was no difference in γ-aminobutyric acid between the non-treated and HRW-treated okras. Collectively, our results indicated that HRW treatment increased levels of melatonin, GA and IAA, but decreased ABA content, which ultimately delayed fruit senescence and prolonged shelf life in postharvest okras.

DNA computing and coding have good application prospects in data storage, data computing, data encryption and other fields. Meanwhile, it is very important to design a set of DNA coding set that meets a variety of constraints in today’s research. The purpose of DNA coding is to find as many qualified code sets as possible under given conditions or to keep each DNA code in the DNA code set as far as possible from other codes. The former is used in this paper. The algorithm uses the Bloch system to initialize the DNA coding population, and uses the Ant Colony Algorithm to find the optimal DNA coding. At the same time, crossover and mutation operations are added to make the generated population more random and diverse. Experimental results show that the number of code sets obtained by this algorithm under certain specific conditions is better than the number of code sets obtained by other algorithms.

With the development of recent years, the field of deep learning has made great progress. Compared with the traditional machine learning algorithm, deep learning can better find the rules in the data and achieve better fitting effect. In this paper, we propose a hybrid stock forecasting model based on Feature Selection, Convolutional Neural Network and Bidirectional Gated Recurrent Unit (FS-CNN-BGRU). Feature Selection (FS) can select the data with better performance for the results as the input data after data normalization. Convolutional Neural Network (CNN) is responsible for feature extraction. It can extract the local features of the data, pay attention to more local information, and reduce the amount of calculation. The Bidirectional Gated Recurrent Unit (BGRU) can process the data with time series, so that it can have better performance for the data with time series attributes. In the experiment, we used single CNN, LSTM and GRU models and mixed models CNN-LSTM, CNN-GRU and FS-CNN-BGRU (the model used in this manuscript). The results show that the performance of the hybrid model (FS-CNN-BGRU) is better than other single models, which has a certain reference value.

Urban land subsidence is a slow-density geology disaster caused by the withdrawal of groundwater or the application of water at the land surface. Developing a method of effectively monitoring, predicting and preventing land subsidence has become an urgent urban disaster issue and a great challenge. Traditional mathematical statistics prediction models lack physical meaning. Thus, in this paper, an improved third-order gray prediction GM(1, 3) model that combines terrain factors and neural network error corrections is proposed for urban land subsidence prediction. First, the correlation between land subsidence and terrain factors is verified through a correlation analysis. Second, a geospatial weight matrix based on the terrain factors is presented for auxiliary variable selection. Finally, a neural network is used to model the prediction errors to improve the model accuracy. Precision levelling data for Haiyan County, Zhejiang Province, China from 1999 to 2016 are used as the experimental data. The results show that the proposed method can achieve the same accuracy level as precision levelling. The average absolute error of three-phrase prediction is less than 10 mm, and the relative error can reach 0.4%. The proposed method can be expected to replace precision levelling for the prediction of long-term land subsidence and provide decision support for urban settlement disaster prevention.

Cu-doped boron nitride nanosheets (Cu-BNNS) were first reported as promising adsorbents for the solid-phase extraction and determination of rhodamine B (RhB) dye in a food matrix. Different characterizations, including XRD, FTIR, XPS, SEM, and TEM, were performed to confirm the formation of the adsorbent. Then, the adsorption performance of Cu-BNNS was investigated by adsorption kinetics, isotherms, and thermodynamics. Multiple extraction parameters were optimized by single-factor experiments. Under optimized conditions, the recoveries in the food matrix were in the range of 89.8–95.4%, with the spiked levels of 100 ng/mL and 500 ng/mL, respectively. This novel system was expected to have great potential to detect RhB in a wide variety of real samples.