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The reconstruction of land spatial planning and the increasing severity of carbon emissions pose significant challenges to carbon peak and carbon neutrality strategies. To establish low-carbon and sustainable agricultural spatial planning while achieving dual carbon strategy goals, it is essential to accurately analyze the mechanisms of agricultural spatial transfer and their carbon emission effects, as well as the key factors influencing carbon emissions from agricultural spatial transfer. Therefore, this study, based on land use remote sensing data from 2000 to 2020, proposes a carbon emission accounting system for agricultural space transfer. The carbon emission total from agricultural space transfer in the Harbin-Changchun urban agglomeration over the 20-year period is calculated using the carbon emission coefficient method. Additionally, the spatiotemporal patterns and influencing factors are analyzed using the standard deviation ellipse method and the geographical detector model. The results indicate that: (1) The agricultural space in the Harbin-Changchun urban agglomeration has increased, with a reduction in living space and an expansion of production space. Among land type conversions, the conversion between cultivated land and forest land has been the most intense. (2) The conversion of agricultural space to grassland and built-up land has been the primary source of net carbon emissions. The carbon emission center has shown a migration path characterized by “eastward movement and southward progression,” with a high-north to low-south distribution pattern. Significant carbon emission differences were observed at different spatial scales. (3) Natural environmental factors dominate the carbon emissions from agricultural space transfer, while socioeconomic and policy factors act as driving forces. Elevation is the primary factor influencing carbon emissions from agricultural space transfer. Interactions between factors generally exhibit nonlinear enhancement, with the interaction between elevation, annual precipitation, and industrial structure showing a strong explanatory power. Notably, the interactions between elevation, average annual precipitation, and industrial structure demonstrate significant explanatory power. These findings highlight the necessity for government action to balance agricultural spatial use with ecological protection and economic development, thereby providing scientific references for optimizing future land spatial structures and formulating regional carbon balance policies.

The current efficiency of residential land utilization is witnessing a decline, attributable to accelerated urbanization and inefficient resource allocation, thereby presenting unprecedented threats and challenges to the quality of urban living and the pursuit of high-quality urban development. To enhance residents’ satisfaction and well-being, and to effectively activate existing land resources, it is imperative to accurately identify inefficient residential landuse and its driving factors. While the literature on identifying inefficient urban landuse is expanding, research specifically focusing on residential land, which is closely linked to residents’ lives, remains limited. Furthermore, the factors contributing to inefficient land use are relatively inadequate. Therefore, this study employs a “two-step identification method” to comprehensively identify inefficient residential landuse and utilizes standard deviation ellipses and kernel density assessment methods to analyze the spatial distribution characteristics of such land. Subsequently, the study employs the Random Forest (RF) model to quantitatively analyze factors such as building quality, economic, social, and ecological factors, aiming to provide a scientific basis for subsequent redevelopment initiatives. The findings reveal that inefficient residential landuse is primarily concentrated in city centers, particularly in districts such as Nangang and Xiangfang. In relative inefficient residential areas, aside from Nangang District and Xiangfang District, Songbei District also holds a significant proportion. The intensity of these associations with inefficient residential landuse formation varies depending on urban development history and regional development intensity. In areas other than Songbei District, factors such as aging residential neighborhoods and inadequate green spaces are major contributors to inefficient land use efficiency, whereas in Songbei District, insufficient medical and educational facilities are the primary factors. The RF algorithm, distinguished by its flexibility and accuracy, offers novel perspectives and methods for analyzing issues related to inefficient residential landuse. Moreover, it effectively manages nonlinear relationships between the data, avoiding overfitting and generating precise regression and classification results. Thus, the RF algorithm demonstrates significant promise for widespread application in urban land studies.

Abstract. Objective:. Only a few studies have investigated the development trends in spatial working memory and attention among a large sample of primary school students over a wide range of ages, while the efficiency of learning and memory processes is fundamental to academic performance, particularly for children, who are in a key developmental stage when their life opportunities can be altered. We aimed to explore how the spatial working memory and attention of primary school children are affected by grade, academic performance, and sex. Methods:. In this cross-sectional observational study, students (144 boys, 139 girls, 6–13 years old) were recruited from Experimental Primary School Affiliated to Shanghai Jiao Tong University in Shanghai, China in 2018. The study protocol was reviewed and approved by the Bio-Ethics Board of the Bio-X Institutes, Shanghai Jiao Tong University. Based on a simple Spatial Working Memory and Attention Test on Paired Symbols test, we evaluate 283 participants’ working memory and attention ability. Results:. Attention and working memory performance were enhanced as a positive function of grade in primary school children, and students who showed better academic achievement also performed better on the working memory task. However, attention and working memory performance were not affected by sex. Conclusion:. Attention and working memory performance of primary school students develop with grade and corresponds to Better academic performance. Attention and working memory ability do not differ significantly between boys and girls.

At present, the accuracy of remote sensing estimation models of plant alpha diversity is generally low, and high-precision estimation models in deciduous broadleaved forest (DBF), deciduous coniferous forest (DCF) and evergreen coniferous forest (ECF) are still lacking. The main purpose of this study is to construct high-precision remote sensing models for plant alpha diversity in multiple ecosystems at global scale. Normalized Difference Vegetation Index (NDVI) were derived from Sentinel-2 data. NDVI and NDVI based spectral diversity/heterogeneity indices were selected as predictive variables, and alpha diversity indices were selected as response variables. Simple linear regression (SLR), partial linear regression (PLSR), and random forest (RF) models were used to evaluate the predictive ability of the predictive variables against the response variables under six ecosystems (evergreen broadleaved forest (EBF), DBF, ECF, DCF, shrub, and grassland), and to compare the estimated robustness of various spectral diversity indices. In terms of prediction accuracy, the SLR models were the worst, and the PLSR model were average. RF performed best, outperforming most current models. Especially in DBF, ECF, shrub and grassland, the determination coefficient R2 of RF models can be as high as 0.9. In terms of the prediction of α-diversity, the prediction effect of species richness was better than that of Shannon index, Simpson index and Pielou index. The higher the vegetation complexity, the more accurate the assessment of vegetation α-diversity tends to be, especially in DBF, shrub and grassland. According to the importance of predictive variables and model stability evaluation results, NDVI, standard deviation of NDVI (SD), and NDVI derived Shannon’s diversity index (Sha), Cumulative Residual Entropy (CRE), Pielou’s evenness index (Pie), Hill’s numbers (Hill), Berger-Parker’s diversity index (Ber), Parametric Rao’s index of quadratic entropy (paRao) are all powerful indicators for predicting plant alpha diversity. Among them, the prediction performance of NDVI and SD is better. This study is not only an exploration of the practicability of R package “rasterdiv”, but also an attempt to construct high-precision remote sensing estimation models of plant alpha diversity at global scale.

The effective and safe treatment of red mud has become a pressing global issue in recent years. The purpose of this study is to prepare different systems of low-carbon cementitious materials by combining various solid wastes (slag powder, red brick of construction waste) with different systems of low-carbon cementitious materials and to observe the effects of different cementitious compositions on the construction performance, mechanical properties, freeze–thaw resistance, and heavy metal leaching properties by designing different systems of low-carbon cementitious materials, as well as to analyze the microscopic morphology, mineral composition, and strength-forming mechanisms of the different systems of low-carbon cementitious materials through the use of X-ray fluorescence (XRF), scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), X-ray diffraction (XRD), and Fourier transform infrared spectroscopy (FTIR) techniques. The findings reveal that a ternary cementitious system containing 16% red brick powder exhibits the most favorable overall performance. Compared to a binary system, this mixture improves fluidity by 4.5%, increases compressive strength by 18.27%, reduces drying shrinkage by 39.56%, and lowers the mass loss rate during dry–wet cycling by 11.07%. Furthermore, the leaching levels of heavy metals such as Cr, As, Pb, Ni, and Cu in the red mud-based cementitious materials, combined with multiple solid wastes, are within the safe limits for non-hazardous environmental release, as specified by Chinese regulations, under both freeze–thaw and non-freeze–thaw conditions. This study demonstrates for the first time the potential of combining red mud with construction waste brick dust and provides a scientific basis and theoretical guidance for the synergistic utilization of alkaline solid waste, calcium solid waste, and silica–aluminum solid waste.

Understanding urban sprawl and its drivers is crucial for sustainable urban development. Most studies on Chinese urbanization have focused on coastal areas, paying little attention to urban centers in western China. This study examines urban expansion based on the Google Earth Engine (GEE), remotely sensed image, urban expansion model, and analysis of buffer and quadrant location in the Geographic Information System (GIS). Additionally, driving forces of urban expansion are examined based on the principle component analysis (PCA). Results indicate that urban land area increased more than 5.60 times, reaching 124,723 ha, an increase of over 400 % during 1990–2020. The urban expansion rate and intensity significantly increased and exhibited spatio-temporal heterogeneity. We identified that urban spatial expansion patterns changed from patch filling to patch border expansion, and urban expansion direction was mainly in the southern, northeastern, southwestern, and northwestern regions, extending along the traffic corridor, ring road, and adjacent cities. We suggest that economic development, population, and urbanization have become the driving factors of urban expansion. The GEE provides a new geographic processing algorithm based on massive image datasets, facilitating remote sensing processing. The results revealed that Chengdu is following trends witnessed in coastal cities of China; however, the significance of various drivers of urban expansion in these cities differs from that of the eastern cities. This study will help formulate policies for better urban land management and sustainable land development.

Vegetation cover is a crucial indicator of biodiversity and ecological processes, but there are still uncertainties about the factors driving changes in vegetation. In this study, we conducted a comprehensive analysis of vegetation cover changes in Sichuan Province from 2000 to 2020 using Formation Vegetation Cover (FVC) derived from MODIS13Q1 data. Our results revealed a consistent increase in vegetation FVC, rising from 0.506 to 0.624 over the 21-year period, with an annual growth rate of 0.0028. The turning point in this growth occurred in 2006. Of significance, the expansion of vegetation covered a substantial portion, accounting for 84.76%, while the decrease constituted 13%. Elevation proved to be an effective explanatory factor, with a coefficient of 0.417, indicating its role in explaining vegetation cover changes. It is important to note that FVC trends and averages exhibited distinct patterns concerning elevation, land use, population density, topography, and soil type, while their correlation with meteorological factors was relatively weak. Concurrently, the increase in construction and urban development had a negative impact on vegetation cover.

The research on vegetation changes plays a crucial role in the assessment of ecosystem health, monitoring environmental changes, providing early warnings for natural disasters, and supporting decision-making for sustainable development. However, understanding the nonstationary characteristics of drivers affecting vegetation change remains challenging. This study used Enhanced Vegetation Index (EVI) data obtained through Google Earth Engine (GEE), Theil-Sen, and Mann-Kendall methods to analyze the spatial-temporal patterns and trends of vegetation changes in Sichuan, western China from 2000 to 2020. The Geographical and Temporal Weighted Regression (GTWR) method was applied to deal with spatial and temporal nonstationarity simultaneously. Results showed that vegetation cover in Sichuan was good overall, with medium and high vegetation covering more than 78% of the area. About 72.75% of the area showed an increasing trend in vegetation cover, and areas with extremely significant and significant EVI growth (p

In the breeding process of Exopalaemon carinicauda from the same family, noticeable differences often occur in growth characteristics under the same conditions. To investigate this issue, we performed a comparative transcriptome analysis to identify the critical genes involved in weight gain and compared the intestinal flora to screen for potential intestinal probiotics. In total, we identified 1311 differentially expressed genes (DEGs), of which 735 genes, including troponin C, chitinase, cathepsin B, cathepsin L, and trypsin, were upregulated exclusively in the fast growth group. Several DEGs were validated by quantitative real-time PCR and assigned to the ribosome, phagosome, apoptosis, purine metabolism, and Hippo signaling pathways. The microbiome community richness and diversity of the fast-growing individuals (FGIs) were higher than those of the slow-growing individuals (SGIs). In the current study, the intestinal microflora was mainly distributed in the Proteobacteria, Firmicutes, and Bacteroidota. The species with significant differences (P

Phytoremediation can help remediate potential toxic elements (PTE) in soil. Microorganisms and soil amendments are effective means to improve the efficiency of phytoremediation. This study selected three microorganisms that may promote phytoremediation, including bacteria (Ceratobasidium), fungi (Pseudomonas mendocina), and arbuscular-mycorrhizal fungi (AMF, Funneliformis caledonium). The effects of single or mixed inoculation of three microorganisms on the phytoremediation efficiency of Paspalum vaginatum and Pennisetum alopecuroides were tested under three different degrees of cadmium-contaminated soil (low 10 mg/kg, medium 50 mg/kg, and high 100 mg/kg). The results showed that single inoculation of AMF or Pseudomonas mendocina could significantly increase the biomass of two plants under three different degrees of cadmium-contaminated soil, and the growth-promoting effect of AMF was better than Pseudomonas mendocina. However, simultaneous inoculation of these two microorganisms did not show a better effect than the inoculation of one. Inoculation of Ceratobasidium reduced the biomass of the two plants under high concentrations of cadmium-contaminated soil. Among all treatments, the remediation ability of the two plants was the strongest when inoculated with AMF alone. On this basis, this study explored the effect of AMF combined with corn-straw-biochar on the phytoremediation efficiency of Paspalum vaginatum and Pennisetum alopecuroides. The results showed that biochar could affect plant biomass and Cd concentration in plants by reducing Cd concentration in soil. The combined use of biochar and AMF increased the biomass of Paspalum vaginatum by 8.9–48.6% and the biomass of Pennisetum alopecuroides by 8.04–32.92%. Compared with the single use of AMF or biochar, the combination of the two is better, which greatly improves the efficiency of phytoremediation.

Overgrazing leads to the degradation of grazing lands, which seriously threatens the stability of grassland ecosystems. Root-invading fungi, as one of the main influencing factors, can cause plant diseases in grasslands, reduce the proportion of dominant plant species, increase the proportion of invasive poisonous weeds, and further aggravate degradation. In order to predict and improve the effects of root-invading fungi on grassland degradation, we conducted an in situ soil indoor control experiment using soils collected from non-degraded, moderately degraded, and severely degraded areas of Nanshan pasture in Hunan Province, China. We used monoculture or mixed grasslands of dominant plant species, including Lolium perenne, Trifolium repens, and the invasive weed Persicaria hydropiper, and inoculated them with local strains of pathogenic Fusarium species (Fusarium boothii and Fusarium circinatum) and beneficial fungi Arbuscular Mycorrhizal Fungi (AMF) and Trichoderma hamatum to explore how different strains of fungi affect plant growth and community dynamics. The results showed that Fusarium species (Fusarium boothii and Fusarium circinatum), as a major pathogenic fungus, inhibited the growth of the dominant grass Lolium perenne in moderately and severely degraded soils, which provided growth space and resources for invasive weeds Persicaria hydropiper and further aggravated the degree of grassland degradation. However, the collaborative effect of beneficial fungi (AMF and Trichoderma) and their inhibitory effect on Fusarium species (Fusarium boothii and Fusarium circinatum) could promote the growth of dominant plants and weeds in soils with varying degrees of degradation, which is beneficial to maintaining the stability and diversity of grassland plant communities. The collaborative effect of beneficial fungi could also increase the availability of nutrients in severely degraded soils. Therefore, using beneficial fungi (AMF and Trichoderma) for soil improvement and reducing the harm of pathogenic Fusarium species (Fusarium boothii and Fusarium circinatum) to plant growth is of great significance for promoting the protection and management of grassland ecosystems, as well as for the restoration and recovery of grasslands.

Intense human activities break the grassland–livestock balance and accelerate grassland degradation. We evaluated the use of native dominant species combined with arbuscular mycorrhizal fungi (AMF) in order to recover grassland and restrain grassland degradation. We conducted a full factorial greenhouse experiment to evaluate the interaction effects of native species of distinct traits grass Lolium perenne (L) and legume Trifolium repens (T) with arbuscular mycorrhizal fungi (AMF) inoculation on grass productivity and soil properties across non-degraded, lightly degraded, and severely degraded soils. The grass–legume mixture was manipulated with five ratios (T:L = 1:0, T:L = 1:1, T:L = 3:1, T:L = 1:3, T:L = 0:1). The results showed that L. perenne significantly increased grassland productivity at different grass–legume ratios, regardless of AMF presence or absence. AMF inoculation increased plant N and P content uptake and improved the productivity of degraded grasslands, especially in severely degraded grasslands. The NO3−-N and available P concentrations increased in soil when the legume component increased from T:L = 0:1 (grass monoculture) to T:L = 1:0 (legume monoculture). This may be because the presence of Lolium perenne (L) can promote nitrogen fixation in legumes. Structural equation modeling indicated that grass–legume mixtures directly affected plant biomass, whereas AMF affected plant biomass via providing plant nutrients. A soil quality index based on minimum datasets indicated a significant positive effect of artificial grassland establishment on soil quality. We conclude that planting T:L = 0:1 and T:L = 1:3 combined with AMF inoculation can be used to recover degraded grassland production, and planting T:L = 1:1 and T:L = 1:3 plus AMF inoculation can be applied for grassland nutrient accumulation and stability maintenance.