Education and Information Technologies
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2023
·Vol. 28
·期 8
·10593-10615
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IF 5.5
He Jie,Ma Tingjuan,Zhang Yongliang
This study implemented a blended learning mode with the aid of artificial intelligence teaching platform in the English language course. In this study, 110 students majoring in computer network were randomly selected as subjects, 55 of whom was the experimental group, the rest of 55 was the control group. Before the experiment, this study carried out a pretest on the two groups to determine whether there were differences between them in English learning, and verified the feasibility of the experiment. Meanwhile, a questionnaire was conducted on the learning attitude, style and evaluation of them. Then, traditional classroom teaching was adopted in the control group, and the blended learning mode based on the intelligent cloud teaching platform was used in the experimental group. After a semester of experimental research, through comparative analytics of questionnaire, experimental data and platform data with SPSS 21.0, it was found that this mode was helpful to stimulate the endogenous motivation of learning, develop the ability of autonomous learning and cooperative learning, enhance self-efficacy and educate critical thinking and speculative ability. The study concluded that blended learning can effectively promote learners to devote themselves to the whole process of learning, improve academic performance and achieve the goal of deep learning. Furthermore, this mode could also compensate for the defects of online learning and face-to-face teaching, realize big data recording and representation of teaching, learning and curriculum behavior, give full play to the multiple roles of instructors and reflect the real learning effect of learners.
研究领域:
教育技术学
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计算机辅助教学
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语言教育
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学习科学
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教育心理学
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课程与教学论
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人工智能教育应用
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ECOTOXICOLOGY
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2010
·Vol. 19
·期 8
·1612-1619
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IF 3.507
Han Peng,Niu Chang-Ying,Lei Chao-Liang,Cui Jin-Jie,Desneux Nicolas
Transgenic Cry1Ac+CpTI cotton (CCRI41) is a promising cotton cultivar throughout China but side effects and especially sublethal effects of this transgenic cultivar on beneficial insects remain poorly studied. More specifically potential sublethal effects on behavioural traits of the honey bee Apis mellifera L. have not been formally assessed despite the importance of honey bees for pollination. The goal of our study was to assess potential effects of CCRI41 cotton pollen on visual and olfactory learning by honey bees. After a 7-day oral chronic exposure to honey mixed with either CCRI41 pollen, imidacloprid-treated conventional pollen (used as positive sublethal control) or conventional pollen (control), learning performance was evaluated by the classical proboscis extension reflex (PER) procedure as well as a T-tube maze test. The latter assay was designed as a new device to assess potential side effects of pesticides on visual associative learning of honey bees. These two procedures were complementary because the former focused on olfactory learning while the latter was involved in visual learning based on visual orientation ability. Oral exposure to CCRI41 pollen did not affect learning capacities of honey bees in both the T-tube maze and PER tests. However, exposure to imidacloprid resulted in reduced visual learning capacities in T-tube maze evaluation and decreased olfactory learning performances measured with PER. The implications of these results are discussed in terms of risks of transgenic CCRI41 cotton crops for honey bees.
研究领域:
昆虫学
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生态毒理学
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植物保护
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PubMed
高影响因子
Frontiers in Education
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2024
·
IF 1.9
Yi Yan,Wei Sun,Xiufeng Zhao
The unveiling of ChatGPT 4o by OpenAI, a multimodal large language model powered by Generative Artificial Intelligence (GenAI), has injected interest and incited debate throughout the echelon of education institutions regarding its prospective benefits and drawbacks. Nonetheless, investigations into the learners’ perceptions of GenAI use in learning English as a Foreign Language (EFL) remain markedly insufficient. The study adopts an explorative stance and aims to explore the attitudes and perceptions of Chinese EFL learners toward GenAI use in language learning through the application of metaphor analysis. Data were collected from 281 EFL students of varying majors in four key universities across China by completing a sentence using metaphors to elicit their attitudes and perceptions toward GenAI use in language learning. Through qualitative analysis of metaphorical constructs, including HUMANS, TOOL/MACHINE, BRAIN, RESOURCES, FOOD/DRINK, and MEDICINE metaphors, the study unveils a spectrum of attitudes toward GenAI. While some language learners perceived GenAI as supportive, helpful, and intelligent, others expressed concerns about over-reliance and potential loss of critical thinking skills. The findings underscore the importance of considering learners’ diverse attitudes and beliefs toward GenAI use and application in language learning pedagogy. The implications of these findings for the future integration of GenAI in language education are discussed, complemented by recommendations for further research and pedagogical practice.
研究领域:
应用语言学
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外语教学
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教育技术学
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二语习得
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人工智能教育应用
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学习科学
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高影响因子
ACS Sensors
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2024
·
IF 8.2
Yingying Jian,Nan Zhang,Yunzhe Bi,Xiyang Liu,Jinhai Fan,Weiwei Wu,Taoping Liu
Utilizing electronic noses (e-noses) with pattern recognition algorithms offers a promising noninvasive method for the early detection of urinary bladder cancer (UBC). However, limited clinical samples often hinder existing artificial intelligence (AI)-assisted diagnosis. This paper proposes TC-Sniffer, a novel bibranch framework for few-shot UBC diagnosis, leveraging easily obtainable UBC-related volatile organic components (VOCs) as auxiliary classification categories. These VOCs are biomarkers of UBC, helping the model learn more UBC-specific features, reducing overfitting in small sample scenarios, and reflecting the imbalanced distribution of clinical samples. TC-Sniffer employs intensity-based augmentation to address small sample size issues and focal loss to alleviate model bias due to the class imbalance caused by auxiliary VOCs. The architecture combines transformers and temporal convolutional neural networks to capture long- and short-range dependencies, achieving comprehensive representation learning. Additionally, feature-level constraints further enhance the learning of distinctive features for each class. Experimental results using e-nose data collected from a custom-designed sensor array show that TC-Sniffer significantly surpasses existing approaches, achieving a mean accuracy of 92.95% with only five UBC training samples. Moreover, the fine-grained classification results show that the model can distinguish between nonmuscle-invasive bladder cancer (NMIBC) and muscle-invasive bladder cancer (MIBC), both of which are subtypes of UBC. The superior performance of TC-Sniffer highlights its potential for timely and accurate cancer diagnosis in challenging clinical settings.
研究领域:
医学
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人工智能
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生物标志物研究
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PubMed
高影响因子
Cell Reports Physical Science
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2023
·
IF 8.9
Qinan Wang,Sixuan Duan,Jiahao Qin,Yi Sun,Shihang Wei,Pengfei Song,Wen Liu,Jiangmin Gu,Li Yang,Xin Tu,Hao Gao,Chun Zhao
Dynamic deep learning is considered to simulate the nonlinear memory process of the human brain during long-term potentiation and long-term depression. Here, we propose a photoelectrically modulated synaptic transistor based on MXenes that adjusts the nonlinearity and asymmetry by mixing controllable pulses. According to the advantage of residual deep learning, the rule of dynamic learning is thus elaborately developed to improve the accuracy of a highly homologous database (colorimetric enzyme-linked immunosorbent assay [c-ELISA]) from 80.9% to 87.2% and realize the fast convergence. Besides, mixed stimulation also remarkably shortens the iterative update time to 11.6 s as a result of the photoelectric effect accelerating the relaxation of ion migration. Finally, we extend the dynamic learning strategy to long short-term memory (LSTM) and standard datasets (Cifar10 and Cifar100), which well proves the strong robustness of dynamic learning. This work paves the way toward potential synaptic bionic retina for computer-aided detection in immunology.
研究领域:
神经网络
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生物医学工程
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材料科学
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高影响因子
IEEE Access
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2020
·
IF 3.745
Shaoping Hu,Yuan Gao,Zhangming Niu,Yinghui Jiang,Lao Li,Xianglu Xiao,Minhao Wang,Evandro Fei Fang,Wade Menpes-Smith,Jun Xia,Hui Ye,Guang Yang
An outbreak of a novel coronavirus disease (i.e., COVID-19) has been recorded in Wuhan, China since late December 2019, which subsequently became pandemic around the world. Although COVID-19 is an acutely treated disease, it can also be fatal with a risk of fatality of 4.03% in China and the highest of 13.04% in Algeria and 12.67% Italy (as of 8th April 2020). The onset of serious illness may result in death as a consequence of substantial alveolar damage and progressive respiratory failure. Although laboratory testing, e.g., using reverse transcription polymerase chain reaction (RT-PCR), is the golden standard for clinical diagnosis, the tests may produce false negatives. Moreover, under the pandemic situation, shortage of RT-PCR testing resources may also delay the following clinical decision and treatment. Under such circumstances, chest CT imaging has become a valuable tool for both diagnosis and prognosis of COVID-19 patients. In this study, we propose a weakly supervised deep learning strategy for detecting and classifying COVID-19 infection from CT images. The proposed method can minimise the requirements of manual labelling of CT images but still be able to obtain accurate infection detection and distinguish COVID-19 from non-COVID-19 cases. Based on the promising results obtained qualitatively and quantitatively, we can envisage a wide deployment of our developed technique in large-scale clinical studies.
研究领域:
放射学
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传染病学
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医学影像处理
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人工智能在医学中的应用
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呼吸系统疾病
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高影响因子
CHINESE JOURNAL OF CHEMICAL ENGINEERING
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2023
·
IF 3.8
Kai Ge,Yiping Huang,Yuanhui Ji
The high throughput prediction of the thermodynamic phase behavior of active pharmaceutical ingredients (APIs) with pharmaceutically relevant excipients remains a major scientific challenge in the screening of pharmaceutical formulations. In this work, a developed machine learning model efficiently predicts the solubility of APIs in polymers by learning the phase equilibrium principle and using a few molecular descriptors. Under the few-shot learning framework, thermodynamic theory (Perturbed-Chain Statistical Associating Fluid Theory, PC-SAFT) was employed for data augmentation, and computational chemistry was applied for molecular descriptors screening. The results showed that the developed machine learning model can predict the API-polymer phase diagram accurately, broaden the solubility data of APIs in polymers, and reproduce the relationship between API solubility and the interaction mechanisms between API and polymer successfully, which provided efficient guidance for the development of pharmaceutical formulations.
研究领域:
药学
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化学工程
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材料科学
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计算化学
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机器学习
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高影响因子
Applied Materials Today
·
2025
·
IF 6.9
An Huang,Kai Tan,Xiulan He,Miao Wang,Mingyue Zhou
Porous ZrO 2 /Al 2 O 3 (ZTA) ceramic has shown great potential for fluoride removal from contaminated water owing to its tunable structure, high strength, and excellent chemical stability. The porosity, compressive strength, and fluoride adsorption rate of porous ZTA ceramics prepared by the direct foaming method were systematically studied. A machine learning framework integrating transfer learning, Bayesian optimization, and continuous model refinement was developed to predict and optimize performances of porous ceramics under small-sample conditions. It leverages prior knowledge and limited experimental data to simulate variable-performance relationships. Based on this model, it searches for optimal processing configurations and iteratively improves prediction accuracy. Under the optimal process parameters suggested by the model, the predicted performance (porosity of 91.51 %, compressive strength of 1.58 MPa, and fluoride adsorption rate of 71.71 %) shows strong agreement with experimental results (92.42 %, 1.49 MPa, and 72.49 %), confirming the predictive accuracy achieved through Bayesian optimization. Continuous learning further reduces the mean absolute percentage error of the three performances metrics from 1.02 %∼6.15 % to 0.87 %∼2.74 %, realizing more efficient and reliable material design under data-scarce conditions.
研究领域:
材料科学
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环境工程
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化学工程
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计算材料学
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水处理技术
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高影响因子
CURRENT BIOLOGY
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2022
·
IF 10.9
Jingda Qiao,Shengxi Yang,Hongyan Geng,Wing-Ho Yung,Ya Ke
Summary Aversive olfactory conditioning in Drosophila is a valuable model for elucidating the mechanism of associative learning . Much effort has centered around the role of neuroplasticity at the mushroom body (MB)-mushroom body output neuron (MBON) synapses in mapping odors to specific behaviors . By electrophysiological recordings from MB neurons, we discovered a form of input-timing-dependent plasticity at the incoming synapses from projection neurons that controls the efficacy of aversive olfactory memory formation. Importantly, this plasticity is facilitated by the neural activity of PPL1, the neuronal cluster that also modulates MB-MBON connections at the output stage of MB. Unlike the MB-MBON synapses that probably utilize dopamine D1-like receptors, this neuroplasticity is dependent on D2-like receptors that are expressed mainly by γ Kenyon cells noticeably in their somato-dendritic region. The D2-like receptors recruit voltage-gated calcium channels , leading to calcium influx in the soma and dendrites of γ neurons. Together, our results reveal a previously unrecognized synaptic component of the MB circuit architecture that not only could increase the salience of a conditioning odor but also couples the process of memory encoding and valency mapping to drive-associative learning.
研究领域:
神经科学
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行为生物学
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分子生物学
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PubMed
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FUEL
·
2025
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IF 7.5
Songtao Wu,Modi Guan,Xiaohan Wang,Jing Zhang,Yuhang Zhou,Xiu Huang,Bin Pan
Wettability is a pivotal parameter to determine hydrocarbon reserves and production in shale reservoirs, typically characterized quantitatively by shale-oil-brine contact angles. This parameter is a complex function of shale composition, fluid properties, temperature etc. and is therefore very difficult for conventional theoretical methods to predict accurately and efficiently. Therefore, herein machine learning methods [eXtreme gradient boosting (XGBoost) and the Shapley additive explanations (SHAP)] were integrated to predict this parameter and analyze its sensitivity. To make up the shortage of available data in literature (only 162 data points), another 100 data points about shale/mineral-alkane-water contact angles were measured under in-situ reservoir conditions using the sessile droplet method, thus totally 262 data points were used for machine learning. Experimental results showed that shale, quartz, mica and albite became more hydrophilic with increasing temperature from 25 ℃ to 70 ℃, while K-feldspar and dolomite demonstrated the opposite trend; shale-alkane-water contact angle increased from 60° to 149° (thus wettability shifted from water-wet to oil-wet) with increasing TOC content from 1.9 wt% to 10 wt%. The XGBoost model demonstrated superior predictive accuracy than the gradient boosting regressor and support vector machine models (e.g. R 2 is 0.913, 0.876 and 0.623, respectively). The SHAP sensitivity analysis revealed that brine ionic strength, TOC content, calcite content and quartz content were the four most influential factors affecting wettability. This work presents an efficient artificial intelligence method for shale wettability prediction, which is beneficial for hydrocarbon reserves estimation and production in shale reservoirs.
研究领域:
石油工程
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地质学
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人工智能
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高影响因子
Biology-Basel
·
2022
·Vol. 11
·期 8
·1243
·
IF 5.168
Michael Edbert Suryanto,Ferry Saputra,Kevin Adi Kurnia,Ross D. Vasquez,Marri Jmelou M. Roldan,Kelvin H.-C. Chen,Jong-Chin Huang,Chung-Der Hsiao
Simple SummaryWith the advancement of existing technology, artificial intelligence is widely applied in various fields of research, including cardiovascular studies. In this study, we explored the feasibility of conducting a markerless cardiac physiology assessment in zebrafish embryos by using DeepLabCut (DLC), a deep learning tool for motion analysis. Several cardiac parameters, such as heart rate, diastolic–systolic volumes (EDV/ESV), stroke volume, cardiac output, shortening fraction, and ejection fraction were obtained by the DLC-trained model and then compared to the previous published methods, Time Series Analysis and Kymograph. This new method has several advantages, having full automation, precise detection, and real-time labelling. This network was also trained to analyze zebrafish with cardiovascular defects (pericardial edema) induced by chemical treatments with ethanol and ponatinib. It was revealed that the heart rate, EDV/ESV, stroke volume, and cardiac output from both the ethanol and ponatinib groups displayed significant reductions compared with the control. Hopefully, this trained DLC network can contribute to a better understanding and investigation of the existing cardiovascular system and abnormalities.AbstractDeepLabCut (DLC) is a deep learning-based tool initially invented for markerless pose estimation in mammals. In this study, we explored the possibility of adopting this tool for conducting markerless cardiac physiology assessment in an important aquatic toxicology model of zebrafish (Danio rerio). Initially, high-definition videography was applied to capture heartbeat information at a frame rate of 30 frames per second (fps). Next, 20 videos from different individuals were used to perform convolutional neural network training by labeling the heart chamber (ventricle) with eight landmarks. Using Residual Network (ResNet) 152, a neural network with 152 convolutional neural network layers with 500,000 iterations, we successfully obtained a trained model that can track the heart chamber in a real-time manner. Later, we validated DLC performance with the previously published ImageJ Time Series Analysis (TSA) and Kymograph (KYM) methods. We also evaluated DLC performance by challenging experimental animals with ethanol and ponatinib to induce cardiac abnormality and heartbeat irregularity. The results showed that DLC is more accurate than the TSA method in several parameters tested. The DLC-trained model also detected the ventricle of zebrafish embryos even in the occurrence of heart abnormalities, such as pericardial edema. We believe that this tool is beneficial for research studies, especially for cardiac physiology assessment in zebrafish embryos.Keywords:DeepLabCut;zebrafish;cardiac physiology;deep learning
研究领域:
生物学
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医学
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毒理学
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PubMed
高影响因子
JOURNAL OF ANIMAL ECOLOGY
·
2024
·Vol. 94
·期 1
·112-124
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IF 3.5
Gaoying Gu,Ziqi Wang,Tao Lin,Sainan Wang,Jianjun Li,Shihao Dong,James C. Nieh,Ken Tan
en This link goes to a English section zh This link goes to a Chinese section Predatory threats, even when they do not involve direct consumption (non-consumptive effects, NCEs), can profoundly influence the physiology and behaviour of prey. For example, honeybees that encounter hornet predators show responses similar to fear. However, the physiological mechanisms that are connected with this fear-like response and their effects on bee cognition and olfaction remain largely unknown. We show that bees decreased time spent near the hornet, demonstrated fearful behaviour and moved with greater velocity to escape. After a prolonged 24-h exposure, bees adopted defensive clustering, displayed greater predator avoidance, and experienced a decline in brain dopamine levels. Prolonged predator exposure also diminished bee olfactory sensitivity to odours and their mechanical sensitivity to air currents, contributing to impaired olfactory learning. However, boosting brain dopamine reversed one fear-like behaviour (average bee velocity in the presence of a hornet) and rescued olfactory sensitivity and learning. We therefore provide evidence linking dopamine to sensory and cognitive declines associated with fear in an insect. 摘要 en This link goes to a English section zh This link goes to a Chinese section 即便对猎物不构成直接捕杀(非消耗性捕食效应)，捕食者也可以对猎物的生理和行为造成严重的影响。例如，遭遇胡蜂捕食者时，蜜蜂会产生类似恐惧的反应。 然而，该类似恐惧反应相关的生理机制还没有详细研究，该类似恐惧反应对蜜蜂的认知嗅觉的影响也没有报道。 我们的实验结果表明，蜜蜂在遭遇胡蜂时表现出惧怕行为，表现为减少了在胡蜂附近的停留时间，并以更快的速度逃离。但在24小时的胡蜂胁迫后，蜜蜂表现出防御性集群行为，大脑多巴胺水平下降。长时间的捕食者暴露还降低了蜜蜂对气味的嗅觉敏感度以及气流的机械敏感性，导致嗅觉联想学习能力受损。 然而，人为提高蜜蜂大脑多巴胺水平缓解了它们对胡蜂的恐惧，并在一定程度上恢复了蜜蜂的嗅觉敏感度和学习能力。 因此，我们的研究表明多巴胺参与昆虫恐惧行为的调控，与嗅觉和认知下降密切相关。
研究领域:
昆虫学
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神经生物学
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行为生态学
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高影响因子
危险品化学品经营许可证（带存储）