Acquisition Ability Research Articles

Prediction of Suitable Habitat of Alien Invasive Plant Ambrosia trifida in Northeast China under Various Climatic Scenarios

Ambrosia trifida is an invasive alien plant species, which has very high reproductive and environmental adaptability. Through strong resource acquisition ability and allelopathy, it could inhibit the growth and reproduction of surrounding plants and destroy the stability of an invasive ecosystem. It is very important to predict the change of suitable distribution area of A. trifida with climate change before implementing scientific control measures. Based on 106 A. trifida distribution data and 14 points of environmental data, the optimal parameter combination (RM = 0.1, FC = LQ) was obtained using the MaxEnt (version 3.4.1) model optimized by Kuenm package, and thus the potential suitable areas of A. trifida in Northeast China under three different climate scenarios (RCP2.6, RCP4.5, RCP8.5) with different emission intensities in the future (2050, 2070) were predicted. The changes of A. trifida suitable area in Northeast China under three climate scenarios were compared, and the relationship between the change of suitable area and emission intensity was analyzed. In general, the suitable area of A. trifida in Northeast China will expand gradually in the future, and the area of its highly suitable area will also increase with the increasing emission intensity, which is unfavorable to the control of A. trifida.

Brain tumor segmentation in MRI with multi-modality spatial information enhancement and boundary shape correction

Brain tumor segmentation is currently of a priori guiding significance in medical research and clinical diagnosis. Brain tumor segmentation techniques can accurately partition different tumor areas on multi-modality images captured by magnetic resonance imaging (MRI). Due to the unpredictable pathological process of brain tumor generation and growth, brain tumor images often show irregular shapes and uneven internal gray levels. Existing neural network-based segmentation methods with an encoding/decoding structure can perform image segmentation to some extent. However, they ignore issues such as differences in multi-modality information, loss of spatial information, and under-utilization of boundary information, thereby limiting the further improvement of segmentation accuracy. This paper proposes a multimodal spatial information enhancement and boundary shape correction method consisting of a modality information extraction (MIE) module, a spatial information enhancement (SIE) module, and a boundary shape correction (BSC) module. The above three modules act on the input, backbone, and loss functions of deep convolutional networks (DCNN), respectively, and compose an end-to-end 3D brain tumor segmentation model. The three proposed modules can solve the low utilization rate of effective modality information, the insufficient spatial information acquisition ability, and the improper segmentation of key boundary positions can be solved. The proposed method was validated on BraTS2017, 2018, and 2019 datasets. Comparative experimental results confirmed the effectiveness and superiority of the proposed method over state-of-the-art segmentation methods.

Intelligent recognition and parameter acquisition of blastholes in rock tunnel based on improved Faster R-CNN

In rock tunnel excavation using the drilling and blasting method, controlling overbreak and underbreak relies heavily on the careful selection and optimization of blasthole parameters, such as number and spacing. To achieve accurate blasting control, it is essential to automatically detect, acquire, and optimize the number and spacing of blastholes on the tunnel face. With the development of image recognition techniques based on convolutional neural networks (CNNs), there are new opportunities for blasthole detection. This study proposes an improved faster region-based CNN (Faster R-CNN) for blasthole detection and automatic acquisition of blasthole number and spacing. The proposed network adopts modular design concepts, including top-down feature fusion, two-stage training, false detection blasthole filtering, and blasthole parameter acquisition. These strategies enable efficient feature extraction, aggregation, filtering, and range finding, thereby significantly enhancing the intelligent acquisition ability of the overall blasthole parameters on the tunnel face. The improved CNNs are validated by an experimental study, and their precision, recall, and F1 score are superior to those of SqueezeNet, VGG-16, and ResNet-50. The distance data from the blasthole image-based detection results can effectively indicate the horizontal and vertical spacings of different types of blastholes on the tunnel face.

Analysis on the Training Path of Data Literacy Ability of Financial Talents in the Era of Digital Economy

With the rapid development of informatization, data has become a new element, and data literacy is a key element in the training of new financial talents. This study demonstrates the necessity of financial data literacy talent construction, analyzes the core ability of financial data literacy talent training in the era of digital economy. It includes capabilities such as data acquisition ability, real-time data analysis ability, system model construction ability, programming ability, etc. In addition, a financial data literacy training framework is built, data literacy content is reconstructed from the two dimensions of value literacy and skill literacy, a three-dimensional integrated teaching environment of "classroom + laboratory + experience site" is created and teaching mode innovation is achieved. A multi-dimensional assessment system is implemented, and a financial data literacy training path based on teaching framework, teaching content, teaching mode and teaching assessment is finally formed.

Laser protection by using vortex wavefront coding imaging system

Laser blindness can reduce or disable the information acquisition ability of photoelectric imaging systems. In this paper, numerical simulation and experimental verification are both performed to systematically study the laser protection performance of the vortex phase mask. First, the imaging model and laser transmission model of the vortex wavefront coding imaging system are introduced in detail. Then, the experimental setup of the imaging system is built, and the imaging result of the imaging system is obtained. Finally, the influence of propagation distance on the maximum single-pixel receiving power and suppression ratio of the imaging system is measured experimentally. The simulation and experimental results both show that the energy suppression ratio of this method can reach more than two orders of magnitude compared with the conventional imaging system, and the probability of laser blindness can be effectively reduced.

Balancing Language and Content: A Study of Negotiation Learning in Applied English Courses

This study examines the impact of subject content versus the English language on students whose first language is non-English. Focusing on Applied English juniors and above, the course in question required negotiation learning through English. Surprisingly, students often reverted to their native language (Mandarin Chinese) during scenario discussions and role-play, despite the assumption that English should be exclusively used. The semester-end survey, based on a 5-point Likert scale, revealed several key findings as follows. First, the course facilitated diverse problem-solving approaches. Secondly, negotiation skills were effectively put into practice. Thirdly, role-play activities played a critical role. Fourthly, teacher-delivered lectures enhanced content understanding. Interestingly, when asked about continuous English usage, approval significantly dropped when foreign students were present. These results suggest that prioritizing knowledge acquisition, skill internalization, and higher-order abilities is essential, even if the “whole English” pattern is not strictly enforced during class.

Review of Invasive Plant Functional Traits and Management Using Remote Sensing in Sub-Saharan Africa

Biodiversity and sustainable development in Sub-Saharan Africa (SSA) are considerably impacted by invasive alien plants (IAPs). Increasing plant invasions in SSA threaten agricultural productivity, biodiversity conservation, and other socioeconomic activities, which in turn put the United Nations Sustainable Development Goals (SDGs) in peril. In order to effectively combat IAPs, understanding their functional traits (morphological, physiological, and phenological traits) and integrating them into remote sensing (RS) is vital. While functional traits influence IAPs’ fitness to invade and establish in a new geographical range, RS aids in studying them remotely, delineating and mapping them, and predicting their potential invasions. The information on this study topic was gathered by reviewing various existing studies published between 2000 and 2024. Based on this review, it was deduced that the majority of IAPs are fast-growing (or acquisitive), with a shorter leaf lifespan, bigger leaves, and higher plant height, ultimately resulting in a higher resource acquisition ability. We established further that in SSA, there are limited studies on IAP functional traits and their integration in RS. Many studies conducted in the region focus mostly on IAP distribution. Evidence from prior studies revealed that functional trait remote sensing (FTRS)-based research not only improves detection and mapping but also predicts whether a certain alien plant can become invasive or expand its distribution range. Thus, using the FTRS approach could help IAP management in SSA, ultimately achieving the SDGs. Our review discusses IAP implications in SSA (e.g., Angola, Tanzania, Benin, Kenya, Uganda, Rwanda, Zambia, Burundi, Zimbabwe, Botswana, Malawi, etc.) and for the achievement of SDGs; functional traits and their impact on alien invasions; and the importance of incorporating functional traits into RS.

How Does Information Acquisition Ability Affect Farmers’ Green Production Behaviors: Evidence from Chinese Apple Growers

Green production is crucial in promoting sustainable agricultural practices, ensuring food safety, and protecting the rural ecological environment. Farmers, as the main decision makers of agricultural production, and their green production behaviors (GPBs), directly determine the process of agricultural green development. Based on the survey data of 656 apple growers in Shaanxi and Gansu provinces in 2022, this paper uses a graded response model to measure the information acquisition ability (IAA) of farmers and constructs an ordered Logit model to empirically explore the influence mechanisms of IAA, green benefit cognition (GBC), and new technology learning attitude (NTLA) on farmers’ GPBs. The results show the following: (1) IAA has a significantly positive impact on the adoption of GPBs by farmers, and farmers with a high IAA are more conscious to adopt green production technologies; (2) in the process of IAA affecting farmers’ adoption of GPBs, GBC plays a positive mediating role; (3) NTLAs have a positive moderating effect on the process of GBC affecting farmers’ GPB adoption; (4) there are generational, educational and regional differences in the impact of IAA on farmers’ GPBs. Policy makers should improve rural information facilities, strengthen agricultural technology promotion and training, improve farmers’ IAA and benefit awareness level, and formulate relevant policies to mobilize farmers’ enthusiasm for learning new technologies.

T1 mapping for Head and Neck Cancer Patients undergoing Chemoradiotherapy: Feasibility of 3D Stack of Star Imaging

BackgroundMeasuring tissue oxygen concentration is crucial in understanding the pathophysiological process of hypoxia in head and neck cancer (HNC) and its significant role in cancer biology. This study aimed to determine the feasibility of T1 mapping using a variable flip angle (VFA) technique with stack of stars (SOS) trajectory sampling in HNC patients undergoing chemoradiotherapy (CRT). MethodsTo evaluate the ability of SOS acquisition to detect T1, a phantom study was conducted and compared to conventional Cartesian acquisition (CART). Additionally, four newly diagnosed patients were recruited and underwent two scans each at baseline and inter-treatment. The repeatability of SOS and CART acquisitions was assessed by comparing the T1 measurements of CSF from the baseline and intra-treatment MRI studies. The changes in ∆T1 of the tumors during air and oxygen inhalation between baseline and inter-treatment scans were also evaluated. ResultsOur study found that the 3D VFA SOS sequence was effective in reducing motion artifacts compared to the conventional VFA sequence with CART sampling and the same scan time, as demonstrated by the results from the phantom and patient studies. In terms of repeatability, no significant correlation was observed between the variability in ΔT1 measurements of CSF obtained from SOS T1 maps. The SOS ΔT1 measurements showed higher consistency, as evidenced by the ICC values ranging from 0.52 to 0.92. The ∆T1 measurements on the primary tumors increased after the first CRT (p<0.05) for all patients who showed a positive treatment response, except for one patient (0.05<p<0.1) who did not respond to the treatment. ConclusionThe 3D VFA SOS sequence is a feasible and reliable method for T1 mapping in HNC patients undergoing CRT. The use of this technique could potentially aid in the assessment of treatment response and contribute to improving patient outcomes.

Does Transportation Infrastructure Construction Enhance Enterprise Innovation Resilience in China?

With increasing uncertainty and ambiguity in the external business environment, the risks and challenges faced by enterprises also increase accordingly; resilience has become a necessary characteristic for the evolution and upgrading of enterprise innovation systems, and improving enterprise innovation resilience becomes the key for enterprises to establish sustainable competitive advantages and achieve sustainable development. Based on the panel data of Chinese listed companies and cities, we employ the common factor method to measure enterprise innovation resilience and explore the impact of transportation infrastructure construction on enterprise innovation resilience. The results reveal that, firstly, enterprise innovation resilience shows an overall upward trend, but there is a certain degree of temporal–spatial and industrial disparity. Secondly, transportation infrastructure construction, represented by HSR opening, can significantly improve enterprise innovation resilience. However, this effect performs the following heterogeneity: (1) Regionally, the promotion effect is more obvious in eastern regions, central cities, and non-central cities within 107 km and 764 km away from the central city. (2) For enterprises, compared to state-owned enterprises and non-high-tech industries, transportation infrastructure construction has a greater effect in non-state-owned enterprises and high-tech industries. (3) The higher the degree of centrality and closeness centrality, the more obvious the promotion effect of transportation infrastructure construction. Finally, mechanism tests show that enterprise resource acquisition and resource allocation abilities are important channels for transportation infrastructure construction, to enhance enterprise innovation resilience.

Does negative feedback affect a firm’s R&D efficiency? An empirical study of Chinese listed firms

ABSTRACT Based on the behavioral theory of the firm, we investigate the impact of negative feedback on R&D efficiency. The results show that when performance falls below aspirations, firms pay more attention to R&D projects with high efficiency to get rid of the operating pressure and reputation pressure caused by the decline in performance. This effect may be influenced by resource differences. In the case of negative feedback, firms with fewer idle resources have more incentive to improve R&D efficiency. Compared with state-owned firms and high-tech firms with higher resource acquisition ability, the impact of negative feedback on R&D efficiency is statistically significant only in the sample of private firms and non-high-tech firms. A firm’s strategy to increase its R&D efficiency may be correct, as the improvement in R&D efficiency effectively reverses investors’ expectations of the firm’s future market value.

The impact of nonlinear pedagogy on communication and skill acquisition

Study Purpose. The current investigation assessed the impact of hybrid nonlinear pedagogical models on skill acquisition and communication in physical education. Methods. The intervention group was instructed in physical education applying hybrid pedagogical models for a duration of 12 weeks. Conversely, the control groups adhered to the physical education curricula of their respective schools. Scales for skill acquisition and communication, which had been previously developed for use with students, were implemented both prior to and subsequent to the program. Comparing the two groups required the application of paired sample t-tests, two-way and one-way analyses of variance, and a Pearson correlation analysis. Results. In comparison to the control group, the intervention group demonstrated a substantial increase in communication and skill acquisition, according to the research findings. Enhancement of communication proficiency is observed in individuals who receive instruction through invasion game learning utilizing nonlinear pedagogy. The aforementioned conclusions are supported by the outcomes of paired sample t-tests, two-way Analysis of Variance (ANOVA), and one-way ANOVA: F &lt; 0.05 (F = 0.000 &lt; 0.05) and ρ &lt; 0.05 (ρ = 0.000 &lt; 0, 05) respectively. Additionally, the Skill Acquisition ability exhibited an improvement subsequent to receiving the intervention based on nonlinear pedagogy and invasion game learning. The significance of this is supported by the outcomes of the paired sample t-tests, two-way ANOVA, and one-way ANOVA: F &lt; 0.05 (F = 0.000 &lt; 0.05) and ρ &lt; 0.05 (ρ = 0.000 &lt; 0.05), respectively. Conclusions. The integration of hybrid nonlinear pedagogical models into physical education instruction through invasion games may facilitate students' communication development and skill acquisition. The results of this study provide motivation for physical education instructors to implement instructional techniques. Additionally, research is conducted into the effects of nonlinear pedagogy

Braiding drive data processing through rote learning

It is difficult for traditional data processing methods to make full use of the potential of braiding driven data, unable to quickly collect and preprocess data, and difficult to ensure the accuracy of data. Rote learning (RL) is part of the research field of artificial intelligence, which aims to enable computers to learn autonomously, just like humans. This allows understanding of relationships and patterns between data and helps computers process information quickly. In order to solve the problems of poor data integrity, slow data processing efficiency and poor information sharing in traditional data processing, and further optimize the braiding driven data processing technology, this paper combined RL with braiding driven data. Through the method of mechanical learning, the potential of weaving driving data is fully exerted, so that it can better cope with nonlinear relations and high-dimensional features. It used the effective method provided by the RL to process the braiding drive data, collect the data, and preprocessed the collected data to ensure the correctness of the data. It extracted the features of the data, which was convenient to classify the data according to its attributes. At the same time, this paper verified it by the steps of feature extraction, model training and data analysis. In order to test whether braiding drive data processing by RL can effectively solve the problems existing in traditional drive data technology, this paper tested the performance of compiled drive data processing, and the analysis results were as follows. The data integrity rate of braiding drive data was as low as 81%, which was much higher than that of traditional drive data processing. The recognition ability of data acquisition and matching was much higher than that of traditional drive data processing. Compared with the traditional drive data processing, the information sharing has been greatly improved. In terms of data processing efficiency, it is also much higher than the traditional drive data processing. It can be seen that the method of braiding drive data processing through RL effectively improves the accuracy of data processing. It strengthens the identification ability of data collection and matching, improves the sharing of information, enables users to obtain data and analyze it faster, and also improves the processing efficiency of data.

Illiteracy and cognitive sciences: A psycholinguistic perspective

This text emphasises the importance of considering literacy and nonverbal IQ (NVIQ) in cognitive psychology research, highlighting their significant impact on language acquisition and cognitive abilities. Inclusion of diverse populations is crucial to avoid overgeneralizations and gain a comprehensive understanding of these influences.

Time–frequency dual-domain electromagnetic detection technology for buried pipelines

ABSTRACT Monitoring and detecting buried pipelines under concrete pavement are often complex and difficult tasks. The time–frequency electromagnetic technology, with the fast information acquisition and strong anti-interference ability, is first applied in the buried pipeline inspection. The purpose is to verify if this method can monitor and detect the pipelines status change effectively, even if covered by concrete pavement. Three representative scenarios (normal, coating-damaged and leaking) and two types of pavement (plain concrete and reinforced concrete) are created and applied in the simulations with the finite element method. The simulated results are processed and analysed with the proposed method. In the frequency domain, the maximum change of the electric and magnetic field signals for the coating-damaged scenario from the normal one are 0.4% and 9.9% respectively, and 97.7% and 157.7% for the leaking scenario. In the time domain, the shifts of the electric and magnetic field signal peak are 6.2 μs and 6.5 μs respectively when the coating damage occurs, and 11.4 μs and 13.1 μs when leakage appears. The results indicate that the above technology is valid for buried pipelines. Moreover, it can be used to locate and identify the abnormalities and types of concrete pavement can be differentiated.

STD-YOLOv8: A lightweight small target detection algorithm for UAV perspectives

&lt;p&gt;When recognizing targets by unmanned aerial vehicles (UAVs), problems such as small size, dense dispersion, and complex background are likely to occur, resulting in low recognition rates. In order to solve the above problems, this work proposed a lightweight small target detection algorithm based on the YOLOv8n: STD-YOLOv8 algorithm. First, the regression problem of small targets in different training periods was optimized, the penalty term in the original loss was improved, and a new LIoU loss function was proposed, so that the size of the penalty term could be dynamically adjusted before and after training, thus improving the performance of the algorithm. Second, in order to better adapt to the small target scale and enhance the ability of small target feature acquisition, the SPD-Conv module was integrated in the backbone network, replacing the original stepwise convolutional layer and pooling layer, so as to solve the problems of loss of fine-grained information and low efficiency of feature representation existing in the current convolutional neural network (CNN) structure. In the neck part, nearest-neighbor upsampling was replaced by the feature reassembly assembly of features operator CARAFE (content-aware reassembly of features), which enabled the model to aggregate contextual information in a larger perceptual field and enhanced the feature representation in the neck. Finally, validation experiments were conducted by comparing different algorithms under the same VisDrone-2021 dataset. The results of the ablation experiments show that the algorithms proposed in this thesis have improved the recall (R), mAP50, and mAP95 by 4.7, 5.8 and 5.7%, respectively, compared with YOLOv8n. The results of the model generalization experiments on the TinyPerson dataset show that the algorithm in this paper has superior small target detection performance with only 1.2 M model parameters (1 M = 10&lt;sup&gt;6&lt;/sup&gt;).&lt;/p&gt;

Impact of different zinc concentrations on growth, yield, fruit quality, and nutrient acquisition traits of tomato (Lycopersicon esculentum L.) grown under salinity stress

Salinity stress affects plant growth, development, nutrient uptake, and yield. Applications of micronutrients, specifically zinc (Zn), can mitigate the harmful consequences of salt stress. During the winter season of 2022, an experiment was conducted in the net house of BINA substation Satkhira, Bangladesh, to examine the impact of different Zn concentrations (5 and 10 kg ha-1) on tomato (Lycopersicon esculentum L.) growth, yield, fruit quality, and nutrient acquisition abilities under different salt stress (SS) conditions (SS0.5%, SS1.0%, and SS1.5% NaCl). The result of the study showed that different stress conditions lowered the plant height, the number of branches per plant, flower clusters, and fruits per plant, plant yield, vitamin C, protein and lycopene contents, and the acquisition of different nutrients, i.e., nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), zinc (Zn) and iron (Fe). The application of 10 kg Zn ha-1 (Zn10) increased all previously mentioned parameters in both saline and usual conditions. On the other hand, a decrease in the amount of Na in fruit was observed when Zn application was increased from 5 to 10 kg ha-1. Plant Na/K ratios were consequently lowest at the highest Zn concentration. Therefore, the findings indicate that Zn application improves tomato growth, yield, fruit quality, and nutrient acquisition traits by mitigating the negative impacts of saline environments.

Research on the Language Acquisition Model of Non-Native English Learners Based on Artificial Intelligence

Abstract The widest application field of intelligent teaching supported by artificial intelligence technology is language learning, and enhancing learners’ language acquisition ability is the top priority. On the basis of analyzing the guidelines for the construction of a language acquisition model for non-native English learners, this paper combs through the main advantages of intelligent technology-assisted English teaching and establishes an intelligent language acquisition model based on artificial intelligence. Fuzzy cognitive diagnosis of the language acquisition level of non-native English learners is carried out using the Fuzzy-CDF model, the language acquisition ability of learners is modeled, and the regression model is constructed by combining with artificial intelligence technology so as to realize the bidirectional assessment of language acquisition level of learners. When the Fuzzy-CDF model is utilized for the learner’s language acquisition level, its MAE value is the lowest at 0.1385, and the influence coefficient of the teacher’s teaching method on the learner’s language acquisition ability is the largest at 23.506. The difference between the learner’s language acquisition response time at different levels on the experimental example sentences and the native learners is small, and close to 90% of the students indicated that they were more satisfied with the intelligent language acquisition model. Relying on artificial intelligence technology to construct intelligent language acquisition models can help non-native English learners improve their language acquisition ability.

Research on the Impact of AI Application on Capital Chain Resilience

Unfavorable external factors such as COVID-19 and economy recession have affected the abilities of enterprises to continue operating. Among them, capital chain resilience has become a key issue for enterprises. In the new era, artificial intelligence (AI) technology can provide new solutions for avoiding the breakage of the capital chain. Using data from listed companies in China, we find that AI technology can improve capital chain resilience. The main impact mechanism is to reduce the level of corporate financial constraints and improve internal control efficiency, and when corporate governance efficiency and resource acquisition capability are lower, such as poor levels of executive supervision and incentive, governance, executive resource acquisition ability, financial statement tone, business and financing environment, the effect of AI technology on improving capital chain resilience is more obvious. We enrich the research on AI and capital chain resilience, provide references for enterprises to use AI technology to help enterprises obtain more funds, warn of risks, and make correct decisions quickly in a crisis to help enterprises survive the crisis smoothly.

Impacts of Economy, Systems and Regional Inclusiveness on Rural-Urban Inequality of English Education in China

Since the 21st century, China has made great efforts in education, and the overall cultural level of the people has been significantly improved. However, with the improvement of people's living standards and the country's degree of openness, the issue of large regional differences in English education has gradually attracted the public's attention. This article aims to analyze the impact of economy, system, and regional inclusion on the inequality of English education between urban and rural areas in China and put forward relevant suggestions to alleviate or change these negative impacts. The author employs a literature analysis approach and concludes that economic, systemic, and regional inclusiveness have profound and interactive effects on rural-urban differences in teacher quality, students' English acquisition ability, and access to quality English-language instructional resources. Finally, this paper suggests using the central government's direct investment in education and teacher allocation, improving the household registration system and related laws, and increasing students' exposure to English in rural areas to alleviate the urban-rural disparity in English education in China.