An attention-based teacher-student model for multivariate short-term landslide displacement prediction incorporating weather forecast data

Predicting the displacement of landslide is of utmost practical importance as the landslide can pose serious threats to both human life and property.However,traditional methods have the limitation of random selection in sliding window selection and seldom incorporate weather forecast data for displacement prediction,while a single structural model cannot handle input sequences of different lengths at the same time.In order to solve these limitations,in this study,a new approach is proposed that utilizes weather forecast data and incorporates the maximum information coefficient(MIC),long short-term memory network(LSTM),and attention mechanism to establish a teacher-student coupling model with parallel structure for short-term landslide displacement prediction.Through MIC,a suitable input sequence length is selected for the LSTM model.To investigate the influence of rainfall on landslides during different seasons,a parallel teacher-student coupling model is developed that is able to learn sequential information from various time series of different lengths.The teacher model learns sequence information from rainfall intensity time series while incorporating reliable short-term weather forecast data from platforms such as China Meteorological Administration(CMA)and Reliable Prognosis(https://rp5.ru)to improve the model’s expression capability,and the student model learns sequence information from other time series.An attention module is then designed to integrate different sequence information to derive a context vector,representing seasonal temporal attention mode.Finally,the predicted displacement is obtained through a linear layer.The proposed method demonstrates superior prediction accuracies,surpassing those of the support vector machine(SVM),LSTM,recurrent neural network(RNN),temporal convolutional network(TCN),and LSTM-Attention models.It achieves a mean absolute error(MAE)of 0.072 mm,root mean square error(RMSE)of 0.096 mm,and pearson correlation coefficients(PCCS)of 0.85.Additionally,it exhibits enhanced prediction stability and interpretability,rendering it an indispensable tool for landslide disaster prevention and mitigation.

基于BiLSTM-Attention的F_(10.7)指数预测模型与中国自主数据集的应用

F_(10.7)指数是太阳活动的重要指标,准确预测F_(10.7)指数有助于预防和缓解太阳活动对无线电通信、导航和卫星通信等领域的影响.基于F_(10.7)射电流量的特性,在双向长短时记忆网络(Bidirectional Long Short-Term Memory Network,BiLSTM)基础上融入注意力机制(Attention),提出了一种基于BiLSTM-Attention的F_(10.7)预报模型.在加拿大DRAO数据集上其平均绝对误差(MAE)为5.38,平均绝对百分比误差(MAPE)控制在5%以内,相关系数(R)高达0.987,与其他RNN模型相比拥有优越的预测性能.针对中国廊坊L&S望远镜观测的F_(10.7)数据集,提出了一种转换平均校准(Conversion Average Calibration,CAC)方法进行数据预处理,处理后的数据与DRAO数据集具有较高的相关性.基于该数据集对比分析了RNN系列模型的预报效果,实验结果表明,BiLSTM-Attention和BiLSTM两种模型在预测F_(10.7)指数方面具有较好的优势,表现出较好的预测性能和稳定性.

An Assisted Diagnosis of Alzheimer’s Disease Incorporating Attention Mechanisms Med-3D Transfer Modeling

Alzheimer’s disease(AD)is a complex,progressive neurodegenerative disorder.The subtle and insidious onset of its pathogenesis makes early detection of a formidable challenge in both contemporary neuroscience and clinical practice.In this study,we introduce an advanced diagnostic methodology rooted in theMed-3D transfermodel and enhanced with an attention mechanism.We aim to improve the precision of AD diagnosis and facilitate its early identification.Initially,we employ a spatial normalization technique to address challenges like clarity degradation and unsaturation,which are commonly observed in imaging datasets.Subsequently,an attention mechanism is incorporated to selectively focus on the salient features within the imaging data.Building upon this foundation,we present the novelMed-3D transfermodel,designed to further elucidate and amplify the intricate features associated withADpathogenesis.Our proposedmodel has demonstrated promising results,achieving a classification accuracy of 92%.To emphasize the robustness and practicality of our approach,we introduce an adaptive‘hot-updating’auxiliary diagnostic system.This system not only enables continuous model training and optimization but also provides a dynamic platform to meet the real-time diagnostic and therapeutic demands of AD.

Review of the evidence for the management of co-morbid Tic disorders in children and adolescents with attention deficit hyperactivity disorder

Attention deficit hyperactivity disorder(ADHD) is the most common neurodevelopmental disorder in children and adolescents, with prevalence ranging between 5% and 12% in the developed countries. Tic disorders(TD) are common co-morbidities in paediatric ADHD patients with or without pharmacotherapy treatment. There has been conflicting evidence of the role of psychostimulants in either precipitating or exacerbating TDs in ADHD patients. We carried out a literature review relating to the management of TDs in children and adolescents with ADHD through a comprehensive search of MEDLINE, EMBASE, CINAHL and Cochrane databases. No quantitative synthesis(meta-analysis) was deemed appropriate. Metaanalysis of controlled trials does not support an association between new onset or worsening of tics and normal doses of psychostimulant use. Supratherapeutic doses of dextroamphetamine have been shown to exacerbate TD. Most tics are mild or moderate and respond to psychoeducation and behavioural management. Level A evidence support the use of alpha adrenergic agonists, including Clonidine and Guanfacine, reuptake noradrenenaline inhibitors(Atomoxetine) and stimulants(Methylphenidate and Dexamphetamines) for the treatment of Tics and comorbid ADHD. Priority should be given to the management of co-morbid Tourette's syndrome(TS) or severely disabling tics in children and adolescents with ADHD. Severe TDs may require antipsychotic treatment. Antipsychotics, especially Aripiprazole, are safe and effective treatment for TS or severe Tics, but they only moderately control the co-occurring ADHD symptomatology. Short vignettes of different common clinical scenarios are presented to help clinicians determine the most appropriate treatment to consider in each patient presenting with ADHD and co-morbid TDs.

Pervasive Attentive Neural Network for Intelligent Image Classification Based on N-CDE’s

The utilization of visual attention enhances the performance of image classification tasks.Previous attentionbased models have demonstrated notable performance,but many of these models exhibit reduced accuracy when confronted with inter-class and intra-class similarities and differences.Neural-Controlled Differential Equations(N-CDE’s)and Neural Ordinary Differential Equations(NODE’s)are extensively utilized within this context.NCDE’s possesses the capacity to effectively illustrate both inter-class and intra-class similarities and differences with enhanced clarity.To this end,an attentive neural network has been proposed to generate attention maps,which uses two different types of N-CDE’s,one for adopting hidden layers and the other to generate attention values.Two distinct attention techniques are implemented including time-wise attention,also referred to as bottom N-CDE’s;and element-wise attention,called topN-CDE’s.Additionally,a trainingmethodology is proposed to guarantee that the training problem is sufficiently presented.Two classification tasks including fine-grained visual classification andmulti-label classification,are utilized to evaluate the proposedmodel.The proposedmethodology is employed on five publicly available datasets,including CUB-200-2011,ImageNet-1K,PASCAL VOC 2007,PASCAL VOC 2012,and MS COCO.The obtained visualizations have demonstrated that N-CDE’s are better appropriate for attention-based activities in comparison to conventional NODE’s.

China's Largest Granite-Type Gas Field was Discovered in Qinghai——The Inorganic Theory has Aroused Attention again

A growing number of oil and gas reservoirs have been discovered in granite and metamorphic crystallized rock areas. Statistics show that, about 157 oil and gas fields were found in crystallized bedrocks, with oil reserves of 5048x 10^8 t, and gas reserves of 2681x10^8m3. Among the discovered industrial oil and gas fields hosted in crystallized rocks, most occurred in granite rocks, occupying 40% in quantity and 75% in reserves, followed by those hosted in mafic and ultra-mafic rocks （about 3%）, and then tbllowed by those in volcanic rocks and metamorphic rocks.

The Role of Teachers’Apperception in Herbart’s Theory for Chinese Teaching in Shenzhen Primary Schools of China

This paper deeply analyzes the practical application of Herbart’s educational concept in contemporary education and teaching,especially its guiding significance to the primary school Chinese classroom in Shenzhen area.Herbart proposed that teachers should play a key role in students’cognitive process,that is,help students better understand and master new knowledge by combining it with existing knowledge.The survey results show that although teachers have some understanding of Herbart’s educational philosophy,it is not widely used in the concrete teaching process.However,the study also shows that there is a positive relationship between Herbart’s theory and students’academic performance,indicating that it plays an important role in improving students’interest and engagement in learning.The research also reveals the differences between educational concepts and practices,as well as the uneven distribution of educational resources,and puts forward measures to alleviate these contradictions,such as strengthening teacher training,improving teaching methods,and emphasizing moral education.At the same time,the study also highlights the critical role of teachers in promoting knowledge integration,arousing learning enthusiasm,shaping students’moral character,and promoting personal and professional growth.

RT-YOLO:A Residual Feature Fusion Triple Attention Network for Aerial Image Target Detection

In recent years,target detection of aerial images of unmannedaerial vehicle(UAV)has become one of the hottest topics.However,targetdetection of UAV aerial images often presents false detection and misseddetection.We proposed a modified you only look once(YOLO)model toimprove the problems arising in object detection in UAV aerial images:(1)A new residual structure is designed to improve the ability to extract featuresby enhancing the fusion of the inner features of the single layer.At the sametime,triplet attention module is added to strengthen the connection betweenspace and channel and better retain important feature information.(2)Thefeature information is enriched by improving the multi-scale feature pyramidstructure and strengthening the feature fusion at different scales.(3)A newloss function is created and the diagonal penalty term of the anchor frame isintroduced to improve the speed of training and the accuracy of reasoning.The proposed model is called residual feature fusion triple attention YOLO(RT-YOLO).Experiments showed that the mean average precision(mAP)ofRT-YOLO is increased from 57.2%to 60.8%on the vehicle detection in aerialimage(VEDAI)dataset,and the mAP is also increased by 1.7%on the remotesensing object detection(RSOD)dataset.The results show that theRT-YOLOoutperforms other mainstream models in UAV aerial image object detection.

Attention-Based Deep Learning Model for Early Detection of Parkinson’s Disease

Parkinson’s disease(PD),classified under the category of a neurological syndrome,affects the brain of a person which leads to the motor and non-motor symptoms.Among motor symptoms,one of the major disabling symptom is Freezing of Gait(FoG)that affects the daily standard of living of PD patients.Available treatments target to improve the symptoms of PD.Detection of PD at the early stages is an arduous task due to being indistinguishable from a healthy individual.This work proposed a novel attention-basedmodel for the detection of FoG events and PD,andmeasuring the intensity of PD on the United Parkinson’s Disease Rating Scale.Two separate datasets,that is,UCF Daphnet dataset for detection of Freezing of Gait Events and PhysioNet Gait in PD Dataset were used for training and validating on their respective problems.The results show a definite rise in the various performance metrics when compared to landmark models on these problems using these datasets.These results strongly suggest that the proposed state of the art attention-based deep learning model provide a consistent as well as an efficient solution to the selected problem.High valueswere obtained for various performance metrics like accuracy of 98.74%for detection FoG,98.72%for detection of PD and 98.05%for measuring the intensity of PD on UPDRS.The model was also analyzed for robustness against noisy samples,where also model exhibited consistent performance.These results strongly suggest that the proposed model provides a better classification method for selected problem.

An Efficient 3D CNN Framework with Attention Mechanisms for Alzheimer’s Disease Classification

Neurodegeneration is the gradual deterioration and eventual death of brain cells,leading to progressive loss of structure and function of neurons in the brain and nervous system.Neurodegenerative disorders,such as Alzheimer’s,Huntington’s,Parkinson’s,amyotrophic lateral sclerosis,multiple system atrophy,and multiple sclerosis,are characterized by progressive deterioration of brain function,resulting in symptoms such as memory impairment,movement difficulties,and cognitive decline.Early diagnosis of these conditions is crucial to slowing down cell degeneration and reducing the severity of the diseases.Magnetic resonance imaging(MRI)is widely used by neurologists for diagnosing brain abnormalities.The majority of the research in this field focuses on processing the 2D images extracted from the 3D MRI volumetric scans for disease diagnosis.This might result in losing the volumetric information obtained from the whole brain MRI.To address this problem,a novel 3D-CNN architecture with an attention mechanism is proposed to classify whole-brain MRI images for Alzheimer’s disease(AD)detection.The 3D-CNN model uses channel and spatial attention mechanisms to extract relevant features and improve accuracy in identifying brain dysfunctions by focusing on specific regions of the brain.The pipeline takes pre-processed MRI volumetric scans as input,and the 3D-CNN model leverages both channel and spatial attention mechanisms to extract precise feature representations of the input MRI volume for accurate classification.The present study utilizes the publicly available Alzheimer’s disease Neuroimaging Initiative(ADNI)dataset,which has three image classes:Mild Cognitive Impairment(MCI),Cognitive Normal(CN),and AD affected.The proposed approach achieves an overall accuracy of 79%when classifying three classes and an average accuracy of 87%when identifying AD and the other two classes.The findings reveal that 3D-CNN models with an attention mechanism exhibit significantly higher classification performance compared to other models,highlighting the potential of deep learning algorithms to aid in the early detection and prediction of AD.

Neural correlates of focused attention in patients with mild Alzheimer’s disease

Alzheimer’s Disease (AD) is characterized by an early and significant memory impairment, and progresses to affect other cognitive domains. Impairments in Focused Attention (FA) have been observed in patients diagnosed with mild AD. A functional magnetic resonance imaging (fMRI) Stroop paradigm with verbal responses was used to investigate the neural correlates of FA in AD patients. Twenty-one patients diagnosed with mild AD performed a verbal Stroop—fMRI paradigm. Colour words were printed in an incongruent ink colour. Series 1 consisted of four blocks “Read the word” followed by four blocks “Say the colour of the ink”;Series 2 alternated between the two conditions. Functional data were analyzed using SPM5 to detect anatomical areas with significant signal intensity differences between the conditions. Within-group analyses of the colour minus word contrast yielded significant activation in the following left hemisphere regions: precentral gyrus, inferior frontal gyrus, fusiform gyrus and supplementary motor area (p < 0.05, uncorrected). Relative to cognitively normal older adults who underwent the same experimental task, Stroop performance was significantly worse in AD patients. The fMRI task yielded similar activated brain regions between the two groups. The use of verbal responses in this novel fMRI Stroop task avoids the confusion and memorizing of button locations seen with the manual response modality, allowing the neural correlates of FA to be investigated in AD patients.

Roles of cholinergic receptors during attentional modulation of cue detection

Basal forebrain corticopetal cholinergic neurons are known to be necessary for normal attentional process-ing. Alterations of cholinergic system functioning have been associated with several neuropsychiatric diseases, such as Alzheimer’s disease and schizophrenia, in which attentional dysfunction is thought to be a key contrib-uting factor. Loss of cortical cholinergic inputs impairs performance in attention-demanding tasks. Moreover, measures of acetylcholine with microdialysis and, more recently, of choline with enzyme-coated microelectrodes have begun to elucidate the precise cognitive demands that activate the cholinergic system on distinct time scales. However, the receptor actions following acetyl-choline release under attentionally-challenging condi-tions are only beginning to be understood. The present review is designed to summarize the evidence regarding the actions of acetylcholine at muscarinic and nicotinic receptors under cognitively challenging conditions in order to evaluate the functions mediated by these two different cholinergic receptor classes. Moreover, evi-dence that supports beneficial effects of muscarinic muscarinic-1 receptor agonists and selective nicotinic receptor subtype agonists for cognitive processing will be discussed. Finally, some challenges and limitations of targeting the cholinergic system for treating cognitive defcits along with future research directions will be mentioned. In conclusion, multiple aspects of cholinergic neurotransmission must be considered when attempting to restore function of this neuromodulatory system.

China's Development of Coalbed Methane Attracting World Attention

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CNTAC Hosted the Most Successful ITMF's Annual Meeting——Shanghai, Focus the Attention of Global Textile Industry

This October, besides the usual China International Trade Fair for Apparel Fabrics and Accessories held in the city of Shanghai, another important event was set to take place in Shanghai—the 2009 Annual Conference of the International Textile Manufacturers Federation (ITMF), making Shanghai the place focusing all the attention of the global textile industries markedly.

A Survey into Teachers＇ Roles in Web-based College English Autonomous Learning

The paper, with the backdrop of web-based autonomous learning put forward by the recent college English teaching reform, aims to explore teachers＇ roles in this learning process in students＇ perception through the means of questionnaires and interviews. It further analyzes the possible reasons why students perceive their teachers＇ roles in such a way, in the hope of providing some implications for web-based college English autonomous learning.

基于改进YOLOv5s的输电线路螺栓缺销检测方法

针对无人机输电线路巡检图像中螺栓缺销检测精度较低、漏检较多的问题,提出了一种基于改进YOLOv5s的输电线路螺栓缺销检测方法。在Backbone部分嵌入Coordinate Attention注意力模块;在Neck部分原有的“FPN+PAN”结构的基础上,新增一条“自顶向下”的特征信息传递路径,跨越临近的同尺度特征层,与较浅层网络以加权融合的方式进行特征融合;将Head部分设置为解耦检测头,将对螺栓检测的分类任务与定位任务分开进行。改进后的YOLOv5s算法增强了对螺栓特征信息的学习能力。使用本方法在螺栓缺销数据集上实验,精确率提升了2.3%,召回率提升了3.4%,平均精度提升了3.1%,检测速度达到了41.1帧/秒,表明改进后的方法能提升输电线路螺栓缺销的检测能力,在智能巡检中具有一定的应用价值。

基于FasterNet和YOLOv5改进的玻璃绝缘子自爆缺陷快速检测方法

为了实现对电力输电线路中绝缘子缺陷实时快速的巡检需求,提出了一种结合FasterNet-tiny和YOLOv5-s-v6.1网络模型改进的缺陷快速检测算法FasterNet-YOLOv5。首先引入参数量小推理速度更快的FasterNet网络替换原先的CSPDarkNet53主干网络,加快网络的检测速度。然后结合由GhostNetv2网络提出的解耦全连接注意力机制(decoupled fully connected,DFC),在主干特征提取网络中设计了DFC-FasterNet模块,模块中的DFC Attention机制可以在特征提取过程中增大感受野,提升网络的检测精度。最后针对玻璃绝缘子自爆缺陷目标较小和背景较复杂的情况,重新设计Neck模块,提出BiFPN-F特征融合模块,使网络更精确地定位绝缘子缺陷区域。实验结果表明:改进后的算法可以快速精准定位,其均值平均精度(mean average precision,mAP)达到93.3%,相较于改进前提升5.67%,检测速度达到45.7 Hz,较改进前提升近1倍。同时与最新的YOLOv8n和YOLOv7-tiny相比,改进后的FasterNet-YOLOv5在自爆缺陷上的检测精度和速度更具优势,该文所提算法能够更快速地对绝缘子及其自爆缺陷实时定位识别。

On Teacher's Feedback from the Perspective of Communication

Classroom interaction involves many aspects,among which teacher's feedback is an important element.This paper aimes at studying teacher's feedback from the perspective of communication.It first discusses the definition of teacher's feedback and analyses the functions of teacher's feedback in the classroom interaction and then explores some strategies for effective feedback based on communication theory.