Inner Mongolia Normal University

Effective separation and recovery of rare earth elements from industrial wastewater using eco-friendly adsorbents has been highly emphasized. A three-dimensional magnetic alginate biogel composite (Ca-SA@Fe3O4) was synthesized in the study, and its morphological structure and physicochemical characteristics were analyzed by multiple techniques. Critical preparing and adsorbing requirements, adsorption capacity, mechanism, and reusability of Ca-SA@Fe3O4 composite for La(III) ions from water were systematically investigated by serial experiments. The results show that the approximately 1.3-mm-diameter Ca-SA@Fe3O4 gel beads have a peculiar fully folded surface structure with many pores, good stability, and a sensitive magnetic response. La(III)-ion removal from water by the composite reachs 90.2% at pH 7.0, contact time of 20 h, and 298 K. The spontaneous adsorption kinetics and equilibrium data are conformed to the pseudo-second-order rate model and Langmuir isothermal model. The maximum adsorption capacity is up to 91.0 mg/g at 298 K. The ionic strength and commonly coexisting ions hardly interfere with the La(III) adsorption, apart from a minor influence of Ca²⁺ ions. Strong adsorption of La(III) ions by Ca-SA@Fe3O4 primarily involves complexation and micropore filling. Ca-SA@Fe3O4 macroparticles can be totally and fast recovered after adsorption by external magnetic field and regenerate with 0.05 mol/L HCl and recycle at least four times. The remarkable adsorption efficiency, convenient separation, and environmental friendliness of Ca-SA@Fe3O4 composite make it a prospective biosorbent in recycling La(III) of La(III)-containing wastewater.

In China, the New Quality Productive Forces (NQPF) and ecological restoration have become a national strategy. Investigating the interrelated mechanisms between NQPF and Territorial Spatial Ecological Restoration (TSERS) is essential for promoting high-quality development. We analyse the non-linear relationship between NQPF and TSERS employing fixed effects, moderating effects, and threshold models. The following results were observed: 1) The differences in NQPF among provinces gradually decreased and exhibited significant spatial agglomeration, while the differences in TSERS gradually increased and showed no significant spatial agglomeration; 2)The NQPF and TSERS of provincial units in China exhibited a centripetal clustering trend in the northeast-southwest direction; 3) China’s NQPF exhibited a significant and positive promotional effect on TSERS, with the regression coefficient of the main effect at 0.435 (including time-lagged effects); 4) There was a double threshold for the influence of NQPF on TSERS, with land green use efficiency being the threshold variable (the threshold values were 0.246 and 1.414, respectively); moreover, the influence of NQPF on TSERS gradually decreased after crossing the threshold value. We provide a novel perspective on examining the relationship between NQPF and ecological restoration while simultaneously evaluating the regulatory effectiveness of land green use efficiency in improving the broader scope of territorial ecological restoration. The findings suggest that the advancement of NQPF can significantly enhance TSERS. Policy recommendations include regulating the expansion of construction land, establishing criteria for ecological management, and implementing taxes on ecological restoration.

3D Single Object Tracking (SOT) plays an important role in real-world visual applications such as autonomous driving and planning. How to realize effective 3D SOT is still a valuable challenge due to its carrier-sparse point clouds and its role-complex influencing factors. Inspired by the remote modeling of popular transformers, we further propose a V ersatile P oint T racking T ransformer (VPTT) method for 3D SOT, with object guidance from the template point cloud to the search area point cloud under the siamese-based tracking paradigm. Specifically, VPTT employs self- and cross- attention mechanisms and extends four matching operations, resulting in leveraging the contextual information of consecutive frames to improve the tracking results. By constructing a deep network VerFormer consisting of four successive transformer layers, which performs matching operations involving fusional transformation, separative discrimination, intersectional interaction, and unidirectional propagation from shallow to deep. Considering that the tracking task involves multiple processes, VPTT further learns how to forecast intermediate outputs including mask probability, trailing distance, and heading angle at each stage. Such a specialized design allows our VPTT to revisit the end-to-end training paradigm used for 3D tracking while developing a versatile transformer that is a perfect fit for the 3D SOT task. Experiments on three benchmarks, KITTI, nuScenes, and Waymo, show that VPTT achieves state-of-the-art tracking performance on siamese-based tracking running at $\sim$ 62 FPS.

Amorphous NiB produces thicker reconstruction layer than crystalline NiB in terms of capacitance during the electrochemical activation process. Charge transfer at the reconstructed NiB/NiOOH interfaces play a crucial role in...

In professional basketball games, athlete action recognition is an important part of sports performance analysis and intelligent assisted training. The complex scene background, frequent target occlusion, and varied motion patterns pose challenges to traditional detection and recognition methods in terms of accuracy and real-time performance. In view of this, the study proposes an improved object detection model for single shot multi-box detectors by combining pyramid feature integration module and dual axis convolutional perception module. At the same time, an action recognition model based on an improved 3D convolutional network is designed through adaptive weight fusion and attention mechanism. In the testing of the object detection model, when the number of iterations reached 500, the average accuracy improved by 6.14% and the frame rate decreased by 8.56%. The missed detection rate under low light conditions was 7.3%, the false detection rate was 8.7%, and the detection time was 30.8 ms. The highest detection accuracy of the action recognition model in complex backgrounds was 89.3%, and the robustness score was 91.9. The results indicate that the proposed model can maintain high accuracy and efficiency in complex backgrounds and fast movements in professional basketball game scenarios. The research model significantly improves the performance and robustness of action recognition, which can provide certain technical support for intelligent sports training systems.

The surface charge plays a crucial role in electrokinetic flow within micro/nanochannels. The charge on silica surfaces originates from the protonation and deprotonation of the silanol functional groups. Instead of being set at a constant value, the surface charge density in this paper depends on the solution pH and ion concentration. Analytical and semi-analytical solutions for the electric potential, velocity, and flow rate of time-periodic pressure-driven flow in a pH-regulated slit nanochannel have been derived. The results show that the solution pH and background salt concentration significantly influence the electric potential. At high pH values and low background salt concentrations, there is a notable reduction in velocity amplitude. The electroviscous effect increases with the distance from the pH value to the isoelectric point and decreases with increasing the background salt concentration. The electroviscous effect is more pronounced in channels with smaller heights. The thinner the electric double layer is, the smaller the electroviscous effect will be. Generally, the pressure frequency does not significantly influence the electroviscous effect because the oscillating Reynolds number is much less than unity in nanochannels.

The frequency of extreme drought events is increasing, significantly impacting ecosystems worldwide. In China, the grasslands of dryland Inner Mongolia provide a vital ecological resource, highlight the urgent need to understand their response to drought in these arid regions. We investigate vegetation dynamics and soil moisture fluctuations across three grassland types—meadows, typical grasslands, and desert grasslands—utilizing solar-induced fluorescence (SIF), normalized difference vegetation index (NDVI), and soil moisture data from 2001 to 2020. By employing event coincidence analysis and the maximum coincidence rate, we quantified the grasslands’ responses to drought events. Our findings indicate a significant upward trend in SIF across all grassland types over the past two decades, while NDVI and soil moisture levels remained relatively stable. Under drought conditions, the probability of grassland degradation ranged from 40% to 60%. Notably, desert grasslands exhibited a considerably slower response rate compared to other grassland types, while meadow grasslands responded the fastest to drought; however, all grassland types displayed a response delay of 1–2 months. When soil moisture levels fell below −1.8 standard deviations (STDs), a reversal in grassland response patterns was observed, particularly in meadow grasslands, which showed a substantial increase in response probability. At soil moisture levels below −2.4 STD, this probability stabilized at approximately 90%, significantly higher than that of other grassland types. These insights provide a vital basis for understanding and mitigating drought impacts on resilient grassland ecosystems under climate change.

Potentilleae Sweet is a large tribe within Rosaceae Juss., with over 1700 species across 13 genera worldwide. Some achievements have been obtained in the study of phylogenetic relationships among Potentilleae genera and its origin and diversification, while its chloroplast (cp.) genome characteristics, mutational hotspots and adaptive evolution are still open questions. In this study, we conducted comparative genomic study on 79 complete cp. genomes of Potentilleae. The Potentilleae cp. genome has a typical quadripartite structure, with a total length of 150,586 − 156,798 bp and Guanine and cytosine (GC) content of 36.7–37.3%. Although some slight differences were present in cp. genome size, GC content, and inverted repeat (IR)/single-copy (SC) boundary regions, gene structure, gene content and gene order of the Potentilleae cp. genome were conserved. Thirteen regions (psaJ-rpl33, rpl16-rps3, petA-psbJ, rpl16 intron, rpl32-trnL-UAG, trnH-GUG-psbA, trnR-UCU-atpA, ndhG-ndhI, accD-psaI, trnL-UAA-trnF-GAA, trnP-UGG-psaJ, ycf4-cemA, ndhC-trnV-UAC) were identified as excellent molecular markers for phylogenetic application. Twenty-three genes (rps16, rpl20, rpl22, rpl23, rpoA, rpoC2, psaA, psbB, psbC, psbH, psbF, psbJ, rbcL, ndhA, ndhB, ndhD, ndhF, ndhI, accD, ccsA, matK, ycf1, ycf2) were positively selected. The adaptive evolution of these genes might play essential roles in the long evolutionary history of Potentilleae. This study will lay a foundation for the future research on identification, phylogeny and adaptive evolution of Potentilleae species.

Based on the surface elasticity model and complex variable function theory, this study investigates the model III fracture problem of a nano-lip shaped hole with four cracks in one-dimensional (1D) hexagonal piezoelectric quasicrystals (PEQCs) by constructing a new conformal mapping. The analytical solution field for intensity factors and energy release rate (ERR) were obtained. By degenerating the relevant parameters, results for some classical defects can be derived. Numerical examples were then used to dynamic analyze the effects of the hole size, crack length, external mechanical, electrical loads, and phonon-phason ASE coupling coefficient on the fracture mechanical behavior. The results show that when defects reach nano sizes, surface effects are generated under the conditions of mutual coupling between the phonon field, phason field, and electric field. The smaller the defect size, the more pronounced the surface effect. As the defect size increases, the impact of surface effects on fracture behavior gradually diminishes, eventually converging to the results of classical fracture theory. The findings of this study can offer theoretical guidance for the structural design of nano-quasicrystal materials and the fracture mechanics research of nanoscale defects, providing a strong theoretical foundation for the development and utilization of engineered materials.

Objectives As the digital wave reshapes the future of cities, smart city construction (SCC) is emerging as a key driver for improving public health and bridging social gaps through its inclusive potential. This research aims to investigate the impact of SCC on the health of middle-aged and older disabilities individuals, focusing on its dynamic trends, underlying mechanisms, unequal selection effects, and overall welfare. Methods Based on data from the China Health and Retirement Longitudinal Study, this paper estimates the impact of SCC on the health of middle-aged and older disabilities using the double machine learning method with the random forest algorithm, and examines the dynamic trend of the health effects using event study method. Results The results indicate that SCC significantly enhances the health of middle-aged and older disabilities, without resulting in a concomitant increase in healthcare expenditure. In the long term, the positive health effect increases year by year. Mechanism analysis reveals that SCC promotes health improvements through two key channels: enhanced accessibility of living infrastructure, including access to running water and internet, and increased household annual income. Heterogeneity analysis reveals that there are unequal selection effects in impact, the health-enhancing effects of SCC on middle-aged and older disabilities are more pronounced among non-retired individuals and those with a more extensive household composition. Finally, welfare analysis shows that SCC reduced their healthcare expenditures and improved labor market performance, with a conservatively estimated welfare benefit of US$3448.65. Conclusion By unraveling the underlying mechanisms and inequality selection effects, this study provides a comprehensive analysis of the potential for digital inclusion to inform scientific understanding and guide policy aimed at promoting health equity and improving the well-being of disadvantaged populations.

Problematic smartphone use is a prevalent issue among adolescents; however, there is limited understanding of how different components of problematic smartphone use are related to cognitive failures. The aim of the current study was to explore the relationship between these components in adolescents. Involving 1439 adolescents (44.54% junior high school students), the study employed a network analysis approach. The results indicated that withdrawal was central for junior high school students, while tolerance and motor failure were central for senior high school students. Additionally, both junior and senior high school students exhibited an association between tolerance and attention failure, as well as between loss control and memory failure. Moreover, the impact of the withdrawal component on memory failure weakened in the senior high school students. Identifying these key components can aid in developing targeted interventions to address the link between problematic smartphone use and cognitive failures.

Rogue waves on the background of periodic standing waves in the (3+1)-dimensional nonlinear evolution equation are presented by the nonlinearization of the Lax pair and multi-fold Darboux transformation. The (3+1)-dimensional nonlinear evolution equation is decomposed into a Schrödinger equation and two (1+1)-dimensional soliton equations. By combining the nonlinearization of the Lax pair and Darboux transformation, two types rogue wave solutions to the (3+1)-dimensional nonlinear evolution equation on the background of the Jacobian elliptic functions dn and cn are derived. In addition, the evolution process of the equation is demonstrated through numerical simulation. This paper enriches the rogue wave solutions of multi-dimensional equations on the periodic background.

Entity relationship extraction tasks conducted in low-resource language domains (such as the medical and military domains) have long faced significant challenges, and data augmentation (DA) is considered an effective solution for addressing this issue. Compared with monolingual DA methods, cross-lingual augmentation methods more effectively enhance the diversity of data in low-resource language settings by leveraging and extending data resources derived from other languages. Unlike traditional DA methods, large language model (LLM)-based DA reduces the reliance on manual evaluations and generates more fluent and diverse data. The superior performance of LLMs is attributed to their large-scale corpora and computational resources, but these advantages also limit their applicability in cases involving low-resource languages. To address these issues, this paper proposes LS-CLDARE, an entity relationship extraction framework based on collaborative cross-lingual DA that employs both large and small models. Specifically, the small language model (SLM) is responsible for extracting entity information, while the LLM generates cross-lingual samples by combining this entity information and its high-resource language description via chain-of-thought (CoT) prompts, which guide the model through step-by-step reasoning to better handle complex tasks. To achieve enhanced cross-lingual transfer performance, the generated cross-lingual samples are combined with cross-lingual soft prompts and input into an SLM pretrained on a high-resource language domain dataset. Through transfer learning and data expansion, the entity recognition and relation extraction capabilities for the low-resource languages of the SLM are continuously improved. Extensive experiments conducted on ultralow-resource languages in the Mongolian medical domain and classical Chinese texts validate the effectiveness of LS-CLDARE. Compared with those of other DA methods, the F1 score was improved by 3.52% to 9.42%, and compared with those of other prompt-based relation extraction methods, the F1 score was improved by 1.8% to 16.93%.

Internal waves(IWs), a significant phenomenon in various marine environments, play a crucial role in sustaining marine ecosystem balance. Accurate extraction of IWs information is essential for studying their properties. Most existing deep-learning-based internal wave extraction models rely heavily on large training datasets. However, large amounts of IWs SAR images are difficult to access in practice. To address this issue, this paper developed a Generative Adversarial Network with Multi-Scale Downsampling and Upsampling(GAN-MSDU) consisting of a series of adversarial networks at multiple scales. The model determines the scale through upsampling and downsampling procedures. Moreover, the adversarial network corresponding to each scale consists of a generator and a discriminator. The generator is designed to generate an IW as realistically as possible. The objective of the discriminator is to distinguish the generated image from the real data as much as possible. Through the design of multiscale architecture, this model successfully captures the characteristics of global and local IWs, and its adversarial training mode enhances the model’s representational ability via the generated data. The main contributions are: 1)To address the scarcity of internal wave image data, this study proposes a framework that requires only four images as the training set, providing a novel approach to the problem of data scarcity. 2)To address the elongated features of internal wave crests, this study proposes a novel downsampling method that preserves the crest features during the downsampling process. As a result, the overall mean accuracy, F1-score, MIoU, and FWIoU of the GAN-MSDU model are 99.48%, 42.64%, 63.77%, and 99.30%, respectively. The comparison and quantitative evaluation with other methods for small data problems show that the GAN-MSDU method is efficient and robust in internal wave extraction.

This study investigates the mass transfer characteristics of an oscillatory electro-osmotic flow (EOF) of generalized Maxwell fluids within hydrophobic nanochannels with mobile surface charges. We focus on the combined effects of surface charge mobility and non-Newtonian behavior on flow dynamics and mass transfer characteristics. To analyze this, we employ the finite difference method to derive the numerical solutions for electric potential, velocity, and concentration profiles within hydrophobic nanochannels. The mass transfer rate is computed through numerical integration of the product of velocity and concentration. Additionally, we derive analytical solutions for this problem under conditions of low zeta potential. By examining how variations in surface charge mobility, oscillating Reynolds number, and normalized relaxation time influence electro-osmotic velocity, concentration, and mass transfer rate, we aim to elucidate the intricate behaviors governing fluid motion and mass transport in nanoscale environments. Unlike the continuous reduction in velocity observed in the oscillatory EOF of Newtonian fluids, which is attributed to surface charge mobility, our findings reveal that, at low oscillating Reynolds numbers, surface charge mobility can positively impact the electro-osmotic velocity of Maxwell fluids. Remarkably, we observe an enhancement in the mass transfer rate ranging from 25% to threefold by considering the effects of surface charge mobility. These results hold significant theoretical importance for the optimization of nanofluidic devices, particularly in the context of nano-mixers and nano-reactors, which play a crucial role in enhancing mass transfer processes.

In the era of global climate change, existing evidence indicates that dominant species play a crucial role in regulating grassland structure and function. However, what remains overlooked are the factors that regulate the growth of dominant species under climate change. Some studies have indicated that the future climate of the Inner Mongolia grasslands will specifically show a trend of warming and humidification. Hence, in 2013, we conducted a controlled warming and precipitation addition experiment in a temperate steppe in Inner Mongolia, China. Open-top chambers (OTCs) were used to simulate warming (by 1.5 °C) and rainfall (twice a month, 10% of the average precipitation between 1960 and 2011 of the same month each time) during the growing season. In 2023, the resource utilization efficiency, morphological characteristics, leaf anatomical structure, and population quantity characteristics of the dominant species (Leymus chinensis), and community species diversity were monitored under control (CK), warming (T), precipitation addition (P), and warming plus precipitation addition (TP) conditions. We found that the plant height of L. chinensis significantly increased under warming; its height was 41.97 cm under TP, 41.84 cm under T, 29.48 cm under P, and 28.88 cm under CK. We observed that L. chinensis primarily obtains more light by increasing leaf area and height under warming conditions. Environmental changes also alter the tissue structure of L. chinensis leaves, leading to a decrease in lignification after increasing the water content. In this study, warming significantly increased the L. chinensis leaf area but decreased the leaf carbon content. Warming and precipitation addition regulated the height of L. chinensis by affecting the leaf area, leaf–stem ratio, and distance of the bottom leaf from the ground. Our results provide reasonable predictions regarding the succession direction of the L. chinensis steppe under global climate change in the future.