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.

Coupled DEM-FDM analyses of the effects of falling rock’s shape and impact angle on response of granular cushion and rock shed

Rock shed is an effective protection measure against rockfall.To investigate the influences of falling rock’s shape and impact angle on the impact effect of the cushioned rock shed,a modeling approach for a rock shed with a cushion layer using PFC-FLAC.The granular cushion is modeled as an aggregate of discrete non-cohesion particles,while the concrete plate and the beam are modeled as zones.The falling rock with different sphericities and impact angles is modeled as a rigid assembly.The numerical model is validated by comparing the simulation results with experimental and numerical results from previous literature.This model is applied to analyze the effects of rock shape and impact angle on the dynamic interaction effects between falling rock and cushioned rock shed,including the impact force,transmitted bottom force,penetration depth,and plate deflection.The numerical results show that the variation in the falling rock’s shape has different effects on the falling rock with different impact angles.These findings could support rock shed design by revealing the limitations of the assumptions in the past research,which may result in unsafe rock sheds for some rockfall cases.

A Spectral Convolutional Neural Network Model Based on Adaptive Fick’s Law for Hyperspectral Image Classification

Hyperspectral image classification stands as a pivotal task within the field of remote sensing,yet achieving highprecision classification remains a significant challenge.In response to this challenge,a Spectral Convolutional Neural Network model based on Adaptive Fick’s Law Algorithm(AFLA-SCNN)is proposed.The Adaptive Fick’s Law Algorithm(AFLA)constitutes a novel metaheuristic algorithm introduced herein,encompassing three new strategies:Adaptive weight factor,Gaussian mutation,and probability update policy.With adaptive weight factor,the algorithmcan adjust theweights according to the change in the number of iterations to improve the performance of the algorithm.Gaussianmutation helps the algorithm avoid falling into local optimal solutions and improves the searchability of the algorithm.The probability update strategy helps to improve the exploitability and adaptability of the algorithm.Within the AFLA-SCNN model,AFLA is employed to optimize two hyperparameters in the SCNN model,namely,“numEpochs”and“miniBatchSize”,to attain their optimal values.AFLA’s performance is initially validated across 28 functions in 10D,30D,and 50D for CEC2013 and 29 functions in 10D,30D,and 50D for CEC2017.Experimental results indicate AFLA’s marked performance superiority over nine other prominent optimization algorithms.Subsequently,the AFLA-SCNN model was compared with the Spectral Convolutional Neural Network model based on Fick’s Law Algorithm(FLA-SCNN),Spectral Convolutional Neural Network model based on Harris Hawks Optimization(HHO-SCNN),Spectral Convolutional Neural Network model based onDifferential Evolution(DE-SCNN),SpectralConvolutionalNeuralNetwork(SCNN)model,and SupportVector Machines(SVM)model using the Indian Pines dataset and PaviaUniversity dataset.The experimental results show that the AFLA-SCNN model outperforms other models in terms of Accuracy,Precision,Recall,and F1-score on Indian Pines and Pavia University.Among them,the Accuracy of the AFLA-SCNN model on Indian Pines reached 99.875%,and the Accuracy on PaviaUniversity reached 98.022%.In conclusion,our proposed AFLA-SCNN model is deemed to significantly enhance the precision of hyperspectral image classification.

A Procedure for Trisecting an Acute Angle (Method 2)

This paper presents an alternate graphical procedure (Method 2), to that presented in earlier publications entitled, “A Procedure for Trisecting an Acute Angle” and “A Key to Solving the Angle Trisection Problem”. The procedure, when applied to the 30˚ and 60˚ angles that have been “proven” to be nottrisectable and the 45˚ benchmark angle that is known to be trisectable, in each case produced a construction having an identical angular relationship with Archimedes’ Construction, as in Section 2 on THEORY of this paper, where the required trisection angle was found to be one-third of its respective angle (i.e. DE’MA = 1/3 DE’CG). For example, the trisection angle for the 30˚, 45˚ and 60˚ angles were 10.00000˚, 15.00000˚, and 20.00000˚, respectively, and Section 5 on PROOF in this paper. Therefore, based on this identical angular relationship and the numerical results (i.e. to five decimal places), which represent the highest degree of accuracy and precision attainable by The Geometer’s Sketch Pad software, one can only conclude that not only the geometric requirements for arriving at an exact trisection of the 30˚ and 60˚ angle (which have been “proven” to be not-trisectable) have been met, but also, the construction is valid for any arbitrary acute angle, despite theoretical proofs to the contrary by Wantzel, Dudley, and others.

Tertiary structure-based protein classification by virtual-bond-angles series

Structure-based protein classification can be based on the similarities in primary, second or tertiary structures of proteins. A method using virtual-bond-angles series that transformed the protein space configuration into a sequence was used for the classification of three-dimensional structures of proteins. By transforming the main chains formed by Cα atoms of proteins into sequences, the series of virtual-bond-angles corresponding to the tertiary structure of the proteins were constructed. Then a distance-based hierarchical clustering method similar to Ward method was introduced to classify these virtual-bond-angles series of proteins. 200 files of protein structures were selected from Brookheaven protein data bank, and 11 clusters were classified.

Phylogeny,character evolution,and classification of Selaginellaceae(lycophytes)

Selaginella is the largest and most taxonomically complex genus in lycophytes.The fact that over 750 species are currently treated in a single genus makes Selaginellales/Selaginellaceae unique in pteridophytes.Here we assembled a dataset of six existing and newly sampled plastid and nuclear loci with a total of 684 accessions(74%increase of the earlier largest sampling)representing ca.300 species to infer a new phylogeny.The evolution of 10 morphological characters is studied in the new phylogenetic context.Our major results include:(1)the nuclear and plastid phylogenies are congruent with each other and combined analysis well resolved and strongly supported the relationships of all but two major clades;(2)the Sinensis group is resolved as sister to S.subg.Pulviniella with strong support in two of the three analyses;(3)most morphological characters are highly homoplasious but some characters alone or combinations of characters well define the major clades in the family;and(4)an infrafamilial classification of Selaginellaceae is proposed and the currently defined Selaginella s.l.is split into seven subfamilies(corresponding to the current six subgenera t the Sinensis group)and 19 genera(the major diagnosable clades)with nine new species-poor genera.We support the conservation of Selaginella with a new type,S.flabellata,to minimize nomenclatural instability.We provide a key to subfamilies and genera,images illustrating their morphology,their morphological and geographical synopses,a list of constituent species,and necessary new combinations.This new classification will hopefully facilitate communication,promote further studies,and help conservation.

Study on strength properties and soil behaviour type classification of Huanghe River Delta silts based on variable rate piezocone penetration test

Fine-grained silt is widely distributed in the Huanghe River Delta(HRD)in China,and the sedimentary structure is complex,meaning that the clay content in the silt is variable.The piezocone penetration test(CPTu)is the most widely approved in situ test method.It can be used to invert soil properties and interpret soil behavior.To analyse the strength properties of surface sediments in the HRD,this paper evaluated the friction angle and its inversion formula through the CPTu penetration test and monotonic simple shear test and other soil unit experiments.The evaluation showed that the empirical formula proposed by Kulhawy and Mayne had better prediction and inversion effect.The HRD silts with clay contents of 9.2%,21.4%and 30.3%were selected as samples for the CPTu variable rate penetration test.The results show as follows.(1)The effects of the clay content on the tip resistance and the pore pressure of silt under different penetration rates were summarized.The tip resistance Q_t is strongly dependent on the clay content of the silt,the B_(q)value of the silt tends to 0 and is not significantly affected by the change of the CPTu penetration rate.(2)Five soil behavior type classification charts and three soil behavior type indexes based on CPTu data were evaluated.The results show that the soil behavior type classification chart based on soil behavior type index ISBT,the Robertson 2010 behavior type classification chart are more suitable for the silty soil in the HRD.

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.

Parkinson’s Disease Classification Using Random Forest Kerb Feature Selection

Parkinson’s disease(PD)is a neurodegenerative disease cause by a deficiency of dopamine.Investigators have identified the voice as the underlying symptom of PD.Advanced vocal disorder studies provide adequate treatment and support for accurate PD detection.Machine learning(ML)models have recently helped to solve problems in the classification of chronic diseases.This work aims to analyze the effect of selecting features on ML efficiency on a voice-based PD detection system.It includes PD classification models of Random forest,decision Tree,neural network,logistic regression and support vector machine.The feature selection is made by RF mean-decrease in accuracy and mean-decrease in Gini techniques.Random forest kerb feature selection(RFKFS)selects only 17 features from 754 attributes.The proposed technique uses validation metrics to assess the performance of ML models.The results of the RF model with feature selection performed well among all other models with high accuracy score of 96.56%and a precision of 88.02%,a sensitivity of 98.26%,a specificity of 96.06%.The respective validation score has an Non polynomial vector(NPV)of 99.47%,a Geometric Mean(GM)of 97.15%,a Youden’s index(YI)of 94.32%,and a Matthews’s correlation method(MCC)90.84%.The proposed model is also more robust than other models.It was also realised that using the RFKFS approach in the PD results in an effective and high-performing medical classifier.

A Simplified Graphical Procedure for Constructing a 10˚or 20˚Angle

This paper presents a simplified graphical procedure for constructing, using an unmarked straightedge and a compass only, a 10˚ to 20˚ angle, which is in other words, trisecting a 30˚ or 60˚ angle. The procedure, when applied to the 30˚ and 60˚ angles that have been “proven” to be not trisectable, produced a construction having the identical angular relationship with Archimedes’ Construction, in which the required trisection angles were found to be 10.00000˚ and 20.00000˚ respectively (i.e. exactly one-third of the given angle or ∠E’MA = 1/3∠E’CG). Based on this identical angular relationship as well as the numerical results obtained, one can only conclude that the geometric requirements for arriving at an exact trisection of the 30˚ or 60˚ angle, and therefore the construction of a 10˚ or 20˚ angle, have been met, notwithstanding the theoretical proofs of Wantzel, Dudley, and others. Thus, the solution to the age-old trisection problem, with respect to these two angles, has been accomplished.

Cesarean Sections according to the Robson’s Classification in Two University Hospitals of Yaoundé: Indications and Maternofetal Outcome

Introduction: Cesarean section is a surgical intervention which consists in the extraction of a fetus from the uterus after its incision. The rate of cesarean section varies depending on the country and the health facility. For this reason, in 2015, the World Health Organization (WHO) recommended the use of Robson’s classification to evaluate the practice of cesarean sections in order to identify the groups of women who had abnormally high rates. The objective of our study was to evaluate cesarean sections using the Robson’s classification in CHRACERH and in the Yaoundé Central Hospital (YCH). Methodology: We carried out a retrospective cross sectional and descriptive study in two (02) university hospitals in Yaoundé which took place from December 2017 to May 2018. We included in our study all women who gave birth over a period of two (02) years from January 2016 to December 2017 in these two health facilities. Our sampling was exhaustive over the study period. The parturients’ information was collected using an anonymous and pretested questionnaire. The Robson’s group of every parturient was determined. Descriptive parameters like mean and proportions were calculated. We compared the rates and indications of cesarean sections between the both hospitals using Chi2 test. Results: Out of 330 deliveries realized in CHRACERH, we had 90 cesarean sections;hence, a rate of 27.2%. Out of 1863 deliveries carried out at the YCH, 462 were by cesarean section, hence a rate of 24.8%. The women who belonged to groups 1, 3 and 5 contributed to the highest rates of cesarean sections in both hospitals: in CHRACERH, group 5 (31.1%), group 3 (20%) and group 1 (15.6%), at YCH: group 3 (22.5%), group 1 (21.6%) and group 5 (17.3%). The indications of the cesarean sections varied depending on the Robson’s group and the hospital, the principal indication in group 1 was acute fetal distress (28.6%) in CHRACERH and cephalopelvic disproportion (36.7%) at YCH. Cephalopelvic disproportion was the predominant indication in groups 3 of CHRACERH (44.4%) and YCH (39.2%). In groups 5, CHRACERH and of YCH, a scarred uterus was the principal indication for the cesarean section at 82.4% and 78.4% respectively. At CHRACERH, the maternofetal complications were more frequent in groups 1 and 2 at the YCH, this was the case mostly in groups 1 and 3. Conclusion: The Robson’s classification is an adequate tool for the evaluation and comparison of the rates of cesarean sections. The rates of cesarean section in CHRACERH (27.2%) and at YCH (24.8%) were higher than the rates recommended by WHO. Robson’s groups 1, 3 and 5 were identified as the groups most at risk for cesarean sections in the both hospitals.

A Key to Solving the Angle Trisection Problem

This paper describes the methodology (or approach) that was key to the solution of the angle trisection problem published earlier in article entitled, “A Procedure For Trisecting An Acute Angle.” It was an approach that required first, designing a working model of a trisector mechanism, second, studying the motion of key elements of the mechanism and third, applying the fundamental principles of kinematics to arrive at the desired results. In presenting these results, since there was no requirement to provide a detailed analysis of the final construction, this information was not included. However, now that the publication is out, it is considered appropriate as well as instructive to explain more fully the mechanism analysis of the trisector in graphical detail, as covered in Section 3 of this paper, that formed the basis of the long sought solution to the age-old Angle Trisection Problem.

骨性Angle′s Ⅲ类错患者正畸手术前后下颌角矢量点位置的变化及意义

目的观察骨性Angle′sⅢ类错患者下颌角矢量点(Gov点)位置变化,并探讨其临床意义。方法取天津地区健康人29例为对照组,接受正手术治疗的骨性Angle′sⅢ类错畸形患者15例为观察组。均拍摄头颅侧位X线片,测量Gov点的座标。结果观察组术前、术后及对照组Gov点横坐标分别为(-4.54±0.51)、(-5.10±0.69)、(-5.02±0.35)cm,纵坐标分别为(-7.76±0.40)、(-7.65±0.57)、(-7.50±0.43)cm,观察组术前Gov点横、纵坐标与术后及对照组比较P均<0.05,观察组术后与对照组Gov点横、纵坐标比较P均>0.05。对照组下颌神经孔与Gov点间距为(1.35±0.16)cm,观察组术前为(1.27±0.19)cm,两组相比P>0.05。结论骨性Angle′sⅢ类错患者Gov点位置向前下移位,正手术后得以纠正。Gov点位置可能与下颌功能状态存在内在联系,可以作为正手术术前制定手术计划及术后评估矫治效果的指标。

ADASYN与类别逆比例加权法在阿尔茨海默病不平衡数据中的应用

目的利用自适应合成抽样(adaptive synthetic sampling,ADASYN)与类别逆比例加权法处理类别不平衡数据,结合分类器构建模型对阿尔茨海默病(alzheimer′s disease,AD)患者疾病进程进行分类预测。方法数据源自阿尔茨海默病神经影像学计划(Alzheimer′s disease neuroimaging initiative,ADNI),经随机森林填补缺失值,弹性网络筛选特征子集后,利用ADASYN与类别逆比例加权法处理类别不平衡数据。分别结合随机森林(random forest,RF)、支持向量机(support vector machine,SVM)构建四种模型:ADASYN-RF、ADASYN-SVM、加权随机森林(weighted random forest,WRF)、加权支持向量机(weighted support vector machine,WSVM),与RF、SVM比较分类性能。模型评价指标为宏观平均精确率(macro-average of precision,macro-P)、宏观平均召回率(macro-average of recall,macro-R)、宏观平均F1值(macro-average of F1-score,macro-F1)、准确率(accuracy,ACC)、Kappa值和AUC(area under the ROC curve)。结果ADASYN-RF的分类性能最优(Kappa值为0.938,AUC为0.980),ADASYN-SVM次之。利用ADASYN-RF预测得到的重要分类特征分别为CDRSB、LDELTOTAL、MMSE,在临床上均可得到证实。结论ADASYN与类别逆比例加权法都能辅助提升分类器性能,但ADASYN算法更优。

Diagnostic classification of endosonography for differentiating colorectal ulcerative diseases: A new statistical method

AIM To establish a classification method for differential diagnosis of colorectal ulcerative diseases, especially Crohn's disease(CD), primary intestinal lymphoma(PIL) and intestinal tuberculosis(ITB).METHODS We searched the in-patient medical record database for confirmed cases of CD, PIL and ITB from 2008 to 2015 at our center, collected data on endoscopic ultrasound(EUS) from randomly-chosen patients who formed the training set, conducted univariate logistic regression analysis to summarize EUS features of CD, PIL and ITB, and created a diagnostic classification method. All cases found to have colorectal ulcers using EUS were obtained from the endoscopy database and formed the test set. We then removed the cases which were easily diagnosed, and the remaining cases formed the perplexing test set. We re-diagnosed the cases in the three sets using the classification method, determined EUS diagnostic accuracies, and adjusted the classification accordingly. Finally, the re-diagnosing and accuracy-calculating steps were repeated.RESULTS In total, 272 CD, 60 PIL and 39 ITB cases were diagnosed from 2008 to 2015 based on the in-patient database, and 200 CD, 30 PIL and 20 ITB cases were randomly chosen to form the training set. The EUS features were summarized as follows: CD: Thickened submucosa with a slightly high echo level and visible layer; PIL: Absent layer and diffuse hypoechoic mass; and ITB: Thickened mucosa with a high or slightly high echo level and visible layer. The test set consisted of 77 CD, 30 PIL, 23 ITB and 140 cases of other diseases obtained from the endoscopy database. Seventy-four cases were excluded to form the perplexing test set. After adjustment of the classification, EUS diagnostic accuracies for CD, PIL and ITB were 83.6%(209/250), 97.2%(243/250) and 85.6%(214/250) in the training set, were 89.3%(241/270), 97.8%(264/270) and 84.1%(227/270) in the test set, and were 86.7%(170/196), 98.0%(192/196) and 85.2%(167/196) in the perplexing set, respectively.CONCLUSION The EUS features of CD, PIL and ITB are different. The diagnostic classification method is reliable in the differential diagnosis of colorectal ulcerative diseases.

Modified viscocanalostomy in the Chinese population with open angle glaucoma: a 10-year follow-up results

AIM: To study the long-term efficacy and safety of modified viscocanalostomy in Chinese people with open angle glaucoma(OAG).METHODS: This retrospective study included a total of 100 eyes from 100 Chinese patients with medically uncontrolled OAG. All the patients underwent modified viscocanalostomy with injection of viscoelastic material in the surgically created ostia of Schlemm's canal(SC). The modifications included peeling of the inner wall of SC and the juxtacanalicular meshwork, use of mitomycin C, and loosely suturing the superficial scleral flap. Intraocular pressure(IOP), visual acuity, number of medications, laser goniopuncture data and complications were recorded. The definition of complete(qualified) success was an IOP equal to or lower than 21, 18, 16 mm Hg without(with or without) anti-glaucoma medications. RESULTS: The mean IOP was 33.5±9.9 mm Hg before surgery, 15.2±3.6 mm Hg(mean IOP reduction of 51%) at 5 y after surgery, and 15.6±2.8 mm Hg(mean IOP reduction of 49.9%) at 10 y after surgery(P<0.001). The number of anti-glaucoma medications dropped from 2.39±0.5 preoperatively to 0.47±0.8 at 5 y and 0.67±0.8 at 10 y postoperatively(P<0.001). The follow-up period was 104.5±37.0 mo. The qualified success rate for an IOP of 21, 18 or 16 mm Hg or less was 84% [95% confidence interval(CI): 0.80-0.88], 73%(95%CI: 0.68-0.78), and 59%(95%CI: 0.52-0.66) after 5 y, and 80%(95%CI: 0.76-0.84), 69%(95%CI: 0.64-0.74), 51%(95%CI: 0.44-0.58) after 10 y, respectively. There was a relationship between age, preoperative IOP and success rate(P<0.01, P<0.05). A total of 31 eyes(31.3%)in 31 patients underwent laser goniopuncture, decreasing the IOP from 22.9±4.3 mm Hg to 16.3±2.5 mm Hg(P<0.01). Neither blebitis nor endophthalmitis occurred.CONCLUSION: Modified viscocanalostomy could be performed to lower IOP, decrease multiple anti-glaucoma drops use as well. It's a safe procedure with less complications over 10 y in Chinese individuals with OAG.

Elevated urine formaldehyde in elderly patients with primary open angle glaucoma

AIM：To investigate the risk factor of primary open angle glaucoma（POAG）,which is the leading cause of irreversible blindness worldwide.An abnormally high level of endogenous formaldehyde（FA） has recently been found correlated with cell death and neurodegenerative disease,raising the possibility of a putative correlation of abnormal endogenous FA with POAG.· METHODS：Thirty-four elderly patients with POAG and sixteen healthy controls were enrolled.Glaucomatous visual defects were present at both the functional（visual field） and structural[retinal nerve fiber layer（RNFL）thickness]levels.Morning urine samples were obtained and were analyzed by high-performance liquid chromatography（HPLC） to detect the endogenous FA level in a double blind manner.· RESULTS：Patients with POAG（P 〈0.05） had significantly higher urine FA levels.The urine FA level of patients with severe visual field defects[mean deviation（MD）≥12 dB]was significantly（P〈0.001） greater than that of patients with mild to moderate defects（MD〈12 dB）.By optical coherence tomography（OCT）,the superior and inferior RNFL thickness of POAG group was significantly（P〈0.001） thinner than in controls.Furthermore,the superior and inferior thinning of the RNFL was correlated with the elevation of urine FA concentration.CONCLUSION：Endogenous FA level is positively correlated with the neuronal defects of POAG.

Classification of Short Time Series in Early Parkinson’s Disease With Deep Learning of Fuzzy Recurrence Plots

There are many techniques using sensors and wearable devices for detecting and monitoring patients with Parkinson’s disease(PD).A recent development is the utilization of human interaction with computer keyboards for analyzing and identifying motor signs in the early stages of the disease.Current designs for classification of time series of computer-key hold durations recorded from healthy control and PD subjects require the time series of length to be considerably long.With an attempt to avoid discomfort to participants in performing long physical tasks for data recording,this paper introduces the use of fuzzy recurrence plots of very short time series as input data for the machine training and classification with long short-term memory(LSTM)neural networks.Being an original approach that is able to both significantly increase the feature dimensions and provides the property of deterministic dynamical systems of very short time series for information processing carried out by an LSTM layer architecture,fuzzy recurrence plots provide promising results and outperform the direct input of the time series for the classification of healthy control and early PD subjects.

Classification and 3-D distribution of upper layer water masses in the northern South China Sea

Using the fuzzy cluster analysis and the temperature-salinity(T-S) similarity number analysis of cruise conductivity-temperature-depth(CTD) data in the upper layer(0–300 m) of the northern South China Sea(NSCS), we classify the upper layer water of the NSCS into six water masses: diluted water(D), surface water(SS),the SCS subsurface water mass(U_S), the Pacific Ocean subsurface water mass(U_P), surface-subsurface mixed water(SU) and subsurface-intermediate mixed water(UI). A new stacked stereogram is used to illustrate the water mass distribution, and to examine the source and the distribution of U_P, combining with the sea surface height data and geostrophic current field. The results show that water mass U_P exists in all four seasons with the maximum range in spring and the minimum range in summer. In spring and winter, the U_P intrudes into the Luzon Strait and the southwest of Taiwan Island via the northern Luzon Strait in the form of nonlinear Rossby eddies, and forms a high temperature and high salinity zone east of the Dongsha Islands. In summer, the U_P is sporadically distributed in the study area. In autumn, the U_P is located in the upper 200 m layer east of Hainan Island.