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Journal articles on the topic 'Nearest neighboring methods'
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1
Hou, Z., Y. Chen, K. Tan, and P. Du. "NOVEL HYPERSPECTRAL ANOMALY DETECTION METHODS BASED ON UNSUPERVISED NEAREST REGULARIZED SUBSPACE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (April 30, 2018): 539–46. http://dx.doi.org/10.5194/isprs-archives-xlii-3-539-2018.
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Anomaly detection has been of great interest in hyperspectral imagery analysis. Most conventional anomaly detectors merely take advantage of spectral and spatial information within neighboring pixels. In this paper, two methods of Unsupervised Nearest Regularized Subspace-based with Outlier Removal Anomaly Detector (UNRSORAD) and Local Summation UNRSORAD (LSUNRSORAD) are proposed, which are based on the concept that each pixel in background can be approximately represented by its spatial neighborhoods, while anomalies cannot. Using a dual window, an approximation of each testing pixel is a representation of surrounding data via a linear combination. The existence of outliers in the dual window will affect detection accuracy. Proposed detectors remove outlier pixels that are significantly different from majority of pixels. In order to make full use of various local spatial distributions information with the neighboring pixels of the pixels under test, we take the local summation dual-window sliding strategy. The residual image is constituted by subtracting the predicted background from the original hyperspectral imagery, and anomalies can be detected in the residual image. Experimental results show that the proposed methods have greatly improved the detection accuracy compared with other traditional detection method.
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Sironen, S., A. Kangas, M. Maltamo, and J. Kangas. "Estimating individual tree growth with nonparametric methods." Canadian Journal of Forest Research 33, no. 3 (March 1, 2003): 444–49. http://dx.doi.org/10.1139/x02-162.
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The aim of the study was to demonstrate the use of nonparametric methods in estimating tree-level growth models. In the nonparametric methods the growth of a tree is predicted as a weighted mean of the values of neighboring observations. The selection of the nearest neighbors is based on the similarities between tree- and stand-level characteristics of the target tree and the neighbors. The data for the models were collected from Kuusamo in northeastern Finland. Models for the 5-year diameter growth were constructed for Scots pine (Pinus sylvestris L.) with three different nonparametric methods: the k-nearest neighbor regression, k-most-similar neighbor, and generalized additive model.
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Maltamo, Matti, and Annika Kangas. "Methods based on k-nearest neighbor regression in the prediction of basal area diameter distribution." Canadian Journal of Forest Research 28, no. 8 (August 1, 1998): 1107–15. http://dx.doi.org/10.1139/x98-085.
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In the Finnish compartmentwise inventory systems, growing stock is described with means and sums of tree characteristics, such as mean height and basal area, by tree species. In the calculations, growing stock is described in a treewise manner using a diameter distribution predicted from stand variables. The treewise description is needed for several reasons, e.g., for predicting log volumes or stand growth and for analyzing the forest structure. In this study, methods for predicting the basal area diameter distribution based on the k-nearest neighbor (k-nn) regression are compared with methods based on parametric distributions. In the k-nn method, the predicted values for interesting variables are obtained as weighted averages of the values of neighboring observations. Using k-nn based methods, the basal area diameter distribution of a stand is predicted with a weighted average of the distributions of k-nearest neighbors. The methods tested in this study include weighted averages of (i)Weibull distributions of k-nearest neighbors, (ii)distributions of k-nearest neighbors smoothed with the kernel method, and (iii)empirical distributions of the k-nearest neighbors. These methods are compared for the accuracy of stand volume estimation, stand structure description, and stand growth prediction. Methods based on the k-nn regression proved to give a more accurate description of the stand than the parametric methods.
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Cui, Liang Yu, Wei Liu, Yan Chun Xu, Shu Hui Yang, and Thomas D. Dahmer. "A new method to estimate hair density of small mammal pelage." Journal of Mammalogy 101, no. 4 (May 16, 2020): 1205–12. http://dx.doi.org/10.1093/jmammal/gyaa048.
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Abstract Hair density is the most important structural parameter contributing to insulation performance of mammalian pelage, and often is measured in ecophysiological, thermal biological, and evolutionary studies. To date, hair density has been measured using invasive methods on research objects; however, such methods remain challenging despite efforts to increase their ease of use. In this paper, we develop a new method to estimate hair density without skin sampling. We expressed hair density as the inverse of the number of hairs per unit area, that is, the surface area occupied by a single hair (Ah). This area could be further estimated by measuring distances between nearest neighboring hairs (Ln) and calculating the areas of triangles (A) defined by three randomly selected nearest neighboring hairs and representing half of Ah. Empirical tests using 11 skin samples from specimens of six small mammal species showed this to be a simple, lightly invasive, but accurate and widely applicable method.
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Cariou, Claude, Steven Le Moan, and Kacem Chehdi. "Improving K-Nearest Neighbor Approaches for Density-Based Pixel Clustering in Hyperspectral Remote Sensing Images." Remote Sensing 12, no. 22 (November 14, 2020): 3745. http://dx.doi.org/10.3390/rs12223745.
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We investigated nearest-neighbor density-based clustering for hyperspectral image analysis. Four existing techniques were considered that rely on a K-nearest neighbor (KNN) graph to estimate local density and to propagate labels through algorithm-specific labeling decisions. We first improved two of these techniques, a KNN variant of the density peaks clustering method dpc, and a weighted-mode variant of knnclust, so the four methods use the same input KNN graph and only differ by their labeling rules. We propose two regularization schemes for hyperspectral image analysis: (i) a graph regularization based on mutual nearest neighbors (MNN) prior to clustering to improve cluster discovery in high dimensions; (ii) a spatial regularization to account for correlation between neighboring pixels. We demonstrate the relevance of the proposed methods on synthetic data and hyperspectral images, and show they achieve superior overall performances in most cases, outperforming the state-of-the-art methods by up to 20% in kappa index on real hyperspectral images.
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Mao, Jia Li, Hong Ying Jin, Ming Dong Li, and Jia Li. "Ml-KNN Algorithm Based on Frequent Item Sets." Applied Mechanics and Materials 380-384 (August 2013): 1533–37. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.1533.
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In order to solve the problem of ignoring the correlation between class labels, this paper describes a new method for multi-label classification based on the frequent item sets to classify an unseen instance on the basis of its k nearest neighbors ( MLFI-KNN). For each unseen instance, MLFI-KNN takes its k-nearest neighbors in the training set and counts the number of occurrences of each label in this neighborhood, and then utilizes the FP-growth algorithm to obtain the frequent item sets between the labels that these neighboring instances include, in order to determine the predicted label set. Experiments on benchmark dataset demonstrate the effectiveness of the proposed approach as compared to some existing well-known methods.
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Wang, Hongxinag, Gongqiao Zhang, Gangying Hui, Yuanfa Li, Yanbo Hu, and Zhonghua Zhao. "The influence of sampling unit size and spatial arrangement patterns on neighborhood-based spatial structure analyses of forest stands." Forest Systems 25, no. 1 (April 1, 2016): 056. http://dx.doi.org/10.5424/fs/2016251-07968.
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Aim of the study: Neighborhood-based stand spatial structure parameters can quantify and characterize forest spatial structure effectively. How these neighborhood-based structure parameters are influenced by the selection of different numbers of nearest-neighbor trees is unclear, and there is some disagreement in the literature regarding the appropriate number of nearest-neighbor trees to sample around reference trees. Understanding how to efficiently characterize forest structure is critical for forest management.Area of study: Multi-species uneven-aged forests of Northern ChinaMaterial and methods: We simulated stands with different spatial structural characteristics and systematically compared their structure parameters when two to eight neighboring trees were selected.Main results: Results showed that values of uniform angle index calculated in the same stand were different with different sizes of structure unit. When tree species and sizes were completely randomly interspersed, different numbers of neighbors had little influence on mingling and dominance indices. Changes of mingling or dominance indices caused by different numbers of neighbors occurred when the tree species or size classes were not randomly interspersed and their changing characteristics can be detected according to the spatial arrangement patterns of tree species and sizes.Research highlights: The number of neighboring trees selected for analyzing stand spatial structure parameters should be fixed. We proposed that the four-tree structure unit is the best compromise between sampling accuracy and costs for practical forest management.
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Amin, Faris Muslihul. "Identifikasi Citra Daging Ayam Berformalin Menggunakan Metode Fitur Tekstur dan K-Nearest Neighbor (K-NN)." Jurnal Matematika "MANTIK" 4, no. 1 (May 30, 2018): 68–74. http://dx.doi.org/10.15642/mantik.2018.4.1.68-74.
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The research aimed to create a fresh chicken meat identification system to detect differences between formalin and non-formalin chicken meat based on the image of raw chicken meat. Feature extraction method used is the Feature Texture method which is included in the statistical method where the statistical calculation uses a gray degree distribution (histogram) by measuring the level of contrast, granularity, and roughness of an area from the neighboring relationships between pixels in the image then feature extraction, results feature extraction is then classified by K-NN. With the classification using K-NN results obtained high classification accuracy. The K-NN method is a very good method of dealing with the problem of recognizing complex patterns in the form of data training and processing calibration, based on very fast and high accurate literature methods more than other methods. Observation images will be carried out at various distances between the smartphone camera and chicken meat samples.
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Zheng, Min, Malgorzata Biczysko, Yanting Xu, Nigel W. Moriarty, Holger Kruse, Alexandre Urzhumtsev, Mark P. Waller, and Pavel V. Afonine. "Including crystallographic symmetry in quantum-based refinement: Q|R#2." Acta Crystallographica Section D Structural Biology 76, no. 1 (January 1, 2020): 41–50. http://dx.doi.org/10.1107/s2059798319015122.
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Three-dimensional structure models refined using low-resolution data from crystallographic or electron cryo-microscopy experiments can benefit from high-quality restraints derived from quantum-chemical methods. However, nonperiodic atom-centered quantum-chemistry codes do not inherently account for nearest-neighbor interactions of crystallographic symmetry-related copies in a satisfactory way. Here, these nearest-neighbor effects have been included in the model by expanding to a super-cell and then truncating the super-cell to only include residues from neighboring cells that are interacting with the asymmetric unit. In this way, the fragmentation approach can adequately and efficiently include nearest-neighbor effects. It has previously been shown that a moderately sized X-ray structure can be treated using quantum methods if a fragmentation approach is applied. In this study, a target protein (PDB entry 4gif) was partitioned into a number of large fragments. The use of large fragments (typically hundreds of atoms) is tractable when a GPU-based package such as TeraChem is employed or cheaper (semi-empirical) methods are used. The QM calculations were run at the HF-D3/6-31G level. The models refined using a recently developed semi-empirical method (GFN2-xTB) were compared and contrasted. To validate the refinement procedure for a non-P1 structure, a standard set of crystallographic metrics were used. The robustness of the implementation is shown by refining 13 additional protein models across multiple space groups and a summary of the refinement metrics is presented.
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Karimzadeh, Sadra, Masashi Matsuoka, Jianming Kuang, and Linlin Ge. "Spatial Prediction of Aftershocks Triggered by a Major Earthquake: A Binary Machine Learning Perspective." ISPRS International Journal of Geo-Information 8, no. 10 (October 22, 2019): 462. http://dx.doi.org/10.3390/ijgi8100462.
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Small earthquakes following a large event in the same area are typically aftershocks, which are usually less destructive than mainshocks. These aftershocks are considered mainshocks if they are larger than the previous mainshock. In this study, records of aftershocks (M > 2.5) of the Kermanshah Earthquake (M 7.3) in Iran were collected from the first second following the event to the end of September 2018. Different machine learning (ML) algorithms, including naive Bayes, k-nearest neighbors, a support vector machine, and random forests were used in conjunction with the slip distribution, Coulomb stress change on the source fault (deduced from synthetic aperture radar imagery), and orientations of neighboring active faults to predict the aftershock patterns. Seventy percent of the aftershocks were used for training based on a binary (“yes” or “no”) logic to predict locations of all aftershocks. While untested on independent datasets, receiver operating characteristic results of the same dataset indicate ML methods outperform routine Coulomb maps regarding the spatial prediction of aftershock patterns, especially when details of neighboring active faults are available. Logistic regression results, however, do not show significant differences with ML methods, as hidden information is likely better discovered using logistic regression analysis.
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Tan, Kun, Zengfu Hou, Fuyu Wu, Qian Du, and Yu Chen. "Anomaly Detection for Hyperspectral Imagery Based on the Regularized Subspace Method and Collaborative Representation." Remote Sensing 11, no. 11 (June 1, 2019): 1318. http://dx.doi.org/10.3390/rs11111318.
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Most of the conventional anomaly detectors only take advantage of the spectral information and do not consider the spatial information within neighboring pixels. Recently, the spectral-spatial based local summation anomaly detection (LSAD) algorithm has achieved excellent detection performances. In order to obtain various local spatial distributions with the neighboring pixels of the pixels under test, the LSAD algorithm exploits a multiple-window sliding filter, which can be computationally expensive and time-consuming. In this paper, to address these issues, two modified LSAD-based methods are proposed. The first method, called local summation unsupervised nearest regularized subspace with an outlier removal anomaly detector (LSUNRSORAD), is based on the concept that each pixel in the background can be approximately represented by its spatial neighborhood. The second method, called local summation anomaly detection based on collaborative representation and inverse distance weight (LSAD-CR-IDW), uses the surrounding pixels collected inside the outer window, while outside the inner window, to linearly represent the test pixel and introduces collaborative representation and inverse distance weight to further improve the computational speed and detection precision, respectively. The proposed methods were applied to a synthetic dataset and three real datasets. The experimental results show that the proposed methods have a better detection accuracy and computational speed when compared with the LSAD algorithm and others.
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Qi, Yong Feng, Hong Wei Yang, and Yuan Lian Huo. "Two-Dimensional Weighted and Locality Preserved Discriminant Analysis for Face Recognition." Applied Mechanics and Materials 701-702 (December 2014): 418–23. http://dx.doi.org/10.4028/www.scientific.net/amm.701-702.418.
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In this paper, a novel method for face recognition, called two-dimensional weighted and locality preserved discriminant analysis (2D-WLPDA) is proposed. The new algorithm is developed based on three techniques: (1) locality preserved embedding, by embedding nearest-neighbor graphs which characterize the within-class compactness of the same class samples, 2D-WLPDA discovers the submanifold of images space; (2) image based projection which can avoids the small sample problem and improves the computation efficiency;(3) weighting contributions of individual class pairs which alleviates the overlap of neighboring classes in Fisher criterion for a k-class problem with k>2. We experimentally compare 2D-WLPDA to other feature extraction methods, such as 2D-LDA, 2D-PCA and 2D-DLPP, 2D-WLPDA has better recognition performance.
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Darghan, Aquiles, Sinha Surendra, and Julio Monroy. "A score test for the agronomical overlap effect in a two-way classification model." Agronomía Colombiana 32, no. 3 (September 1, 2014): 417–22. http://dx.doi.org/10.15446/agron.colomb.v32n3.46086.
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In some agricultural research, a treatment applied to an experimental unit may affect the response in the neighboring experimental units. This phenomenon is known as overlap. In this article, a test to evaluate this effect in the Draper and Guttman model was developed by imposing side conditions on the parameters of a two-way classification model to obtain a re-parameterized model which can be used in different neighboring patterns of experimental units, usually plants within a crop, whenever the nearest neighbor is considered a directly affected experimental unit and the two-way model is used. Three methods, namely maximum likelihood, least squares with side conditions and generalized inverse, were used to estimate the parameters of the original model in order to calculate the value of the test statistics for the null hypothesis associated with the absence of the overlapping effect. The three alternatives were invariant with respect to the use of test. The proposed test is simple to adopt and can be implemented in agronomy since its asymptotic nature is in agreement with the large number of experimental units which generally exist in this type of research, where each plant represents the experimental unit being assessed.
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González-Solís, J. L., L. A. Torres-González, and J. R. Villafán-Bernal. "Superparamagnetic Clustering of Diabetes Patients Raman Spectra." Journal of Spectroscopy 2019 (November 5, 2019): 1–8. http://dx.doi.org/10.1155/2019/4296153.
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In this paper, we present a different way to the standard methods to classify Raman spectra whose grouping process is based on a phenomenon of clustering observed in nature at the atomic level and correctly described by the statistical physics model known as the Potts model, which represents the interacting spins on a crystalline lattice. This clustering method is known as the super paramagnetic clustering (SPC), which allows identifying hierarchical structures in data banks. In this novel method, we assigned a Potts spin to each data point (Raman spectrum) and introduced an interaction between neighboring points whose coupling strength is a decreasing function of the distance between the nearest neighboring sites. We found a hierarchical tree structure in our data bank of Raman spectra allowing us to discriminate between the spectra from control and diabetes patients. The sensitivity and specificity of the diabetes detection technique by Raman spectroscopy were calculated directly because the SPC method achieves an accurate determination of the members of each cluster. As a cross-check, SPC results were compared with published results of multivariate analysis, observing excellent agreements; however, the SPC method allows determining the members of all identified clusters explicitly.
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Nelson, Marcy L., James R. Gilbert, and Kevin J. Boyle. "The influence of siting and deterrence methods on seal predation at Atlantic salmon (Salmo salar) farms in Maine, 2001–2003." Canadian Journal of Fisheries and Aquatic Sciences 63, no. 8 (August 1, 2006): 1710–21. http://dx.doi.org/10.1139/f06-067.
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We document the nature and frequency of seal predation at Atlantic salmon (Salmo salar) farms in Maine and determine whether the severity of predation is related to the proximity of farms from one another and nearby harbor seal (Phoca vitulina concolor) haul-outs. We surveyed farm managers annually from 2001–2003 to document management techniques, husbandry practices, and predator deterrence methods employed for comparison with the extent of seal predation. Biweekly aerial surveys were conducted between January and March of each year to document harbor seal presence. An empirical estimate from a negative binomial model showed seal predation at farms declined significantly with distance to the nearest haul-out, suggesting that seal predation may be deterred by maximizing the distance between farms and seal haul-outs. Farms located further than 4 km from harbor seal haul-outs experienced minimal losses. At farms located within 4 km of harbor seal haul-outs, seal predation decreased with increasing distance from neighboring farms, indicating that areas where farms are concentrated may be more vulnerable. The regular replacement of primary and secondary cage netting was negatively correlated with seal predation. Finally, this study documents the apparent ineffectiveness of acoustic harassment devices at deterring seal predation.
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LU, CHANG-TIEN, RAIMUNDO F. DOS SANTOS, XUTONG LIU, and YUFENG KOU. "A GRAPH-BASED APPROACH TO DETECT ABNORMAL SPATIAL POINTS AND REGIONS." International Journal on Artificial Intelligence Tools 20, no. 04 (August 2011): 721–51. http://dx.doi.org/10.1142/s0218213011000309.
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Spatial outliers are the spatial objects whose nonspatial attribute values are quite different from those of their spatial neighbors. Identification of spatial outliers is an important task for data mining researchers and geographers. A number of algorithms have been developed to detect spatial anomalies in meteorological images, transportation systems, and contagious disease data. In this paper, we propose a set of graph-based algorithms to identify spatial outliers. Our method first constructs a graph based on k-nearest neighbor relationship in spatial domain, assigns the differences of nonspatial attribute as edge weights, and continuously cuts high-weight edges to identify isolated points or regions that are much dissimilar to their neighboring objects. The proposed algorithms have three major advantages compared with other existing spatial outlier detection methods: accurate in detecting both point and region outliers, capable of avoiding false outliers, and capable of computing the local outlierness of an object within subgraphs. We present time complexity of the algorithms, and show experiments conducted on US housing and Census data to demonstrate the effectiveness of the proposed approaches.
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Cao, Mingwei, Wei Jia, Zhihan Lv, Liping Zheng, and Xiaoping Liu. "Superpixel-Based Feature Tracking for Structure from Motion." Applied Sciences 9, no. 15 (July 24, 2019): 2961. http://dx.doi.org/10.3390/app9152961.
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Feature tracking in image collections significantly affects the efficiency and accuracy of Structure from Motion (SFM). Insufficient correspondences may result in disconnected structures and incomplete components, while the redundant correspondences containing incorrect ones may yield to folded and superimposed structures. In this paper, we present a Superpixel-based feature tracking method for structure from motion. In the proposed method, we first propose to use a joint approach to detect local keypoints and compute descriptors. Second, the superpixel-based approach is used to generate labels for the input image. Third, we combine the Speed Up Robust Feature and binary test in the generated label regions to produce a set of combined descriptors for the detected keypoints. Fourth, the locality-sensitive hash (LSH)-based k nearest neighboring matching (KNN) is utilized to produce feature correspondences, and then the ratio test approach is used to remove outliers from the previous matching collection. Finally, we conduct comprehensive experiments on several challenging benchmarking datasets including highly ambiguous and duplicated scenes. Experimental results show that the proposed method gets better performances with respect to the state of the art methods.
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Hua, Yan, Yingyun Yang, and Jianhe Du. "Deep Multi-Modal Metric Learning with Multi-Scale Correlation for Image-Text Retrieval." Electronics 9, no. 3 (March 10, 2020): 466. http://dx.doi.org/10.3390/electronics9030466.
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Multi-modal retrieval is a challenge due to heterogeneous gap and a complex semantic relationship between different modal data. Typical research map different modalities into a common subspace with a one-to-one correspondence or similarity/dissimilarity relationship of inter-modal data, in which the distances of heterogeneous data can be compared directly; thus, inter-modal retrieval can be achieved by the nearest neighboring search. However, most of them ignore intra-modal relations and complicated semantics between multi-modal data. In this paper, we propose a deep multi-modal metric learning method with multi-scale semantic correlation to deal with the retrieval tasks between image and text modalities. A deep model with two branches is designed to nonlinearly map raw heterogeneous data into comparable representations. In contrast to binary similarity, we formulate semantic relationship with multi-scale similarity to learn fine-grained multi-modal distances. Inter-modal and intra-modal correlations constructed on multi-scale semantic similarity are incorporated to train the deep model in an end-to-end way. Experiments validate the effectiveness of our proposed method on multi-modal retrieval tasks, and our method outperforms state-of-the-art methods on NUS-WIDE, MIR Flickr, and Wikipedia datasets.
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Tubbs, R. Shane, Joshua Dixon, Marios Loukas, and Aaron A. Cohen-Gadol. "Regional vascular relationships to the foramen ovale: an anatomical study with application to approaches to the external skull base with an emphasis on transcutaneous procedures for the treatment of trigeminal neuralgia." Journal of Neurosurgery 113, no. 3 (September 2010): 493–97. http://dx.doi.org/10.3171/2010.3.jns091454.
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Object The foramen ovale and its neighboring vascular structures may be seen via external approaches to the skull base. More commonly, however, transcutaneous approaches to the foramen ovale are performed. Although complications with this latter technique are uncommon, studies of the distances to the surrounding extracranial vascular structures are lacking in the literature. The present study aimed to elucidate such anatomical relationships. Methods Twenty adult cadavers (40 sides) underwent dissection of the region surrounding the foramen ovale at the external skull base. Measurements between the external surface of the foramen ovale and surrounding vascular structures were made. Results From the nearest aspect of the undersurface of the foramen ovale, the authors found that the mean distances to the middle meningeal artery, maxillary artery, superior bulb of the internal jugular vein, and internal carotid artery at its entrance to and exit from the carotid canal were 3, 19, 20, 9, and 12 mm, respectively. Distances tended to be shorter in females, but this did not reach statistical significance. On the basis of these data, the authors also determined a safe zone while approaching the undersurface of the foramen ovale. Conclusions Additional knowledge of the neurovascular relationships surrounding the foramen ovale may be useful to the neurosurgeon and may help decrease the potential for complications.
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Sun, Fangjin, Tiantian Liu, Daming Zhang, and Zhonghao Xu. "A Conditional Simulation Method for Predicting Wind Pressure Fields of Large-Span Spatial Structures." Shock and Vibration 2021 (July 6, 2021): 1–8. http://dx.doi.org/10.1155/2021/8829509.
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Wind load is among the control loads for large-span spatial structures. Wind tunnel test is one of the commonly used methods for measuring wind pressure fields of different kinds of structures. However, due to the limited wind pressure data obtained from wind tunnel testing, it is quite meaningful to employ the limited measured data to predict the unknown wind pressure at target points. Considering the complexity of wind pressure fields of large-span spatial structures, a simplified nonparametric method based on conditional simulation is proposed to predict the unknown pressures using the existing data. The Karhunen–Loève (KL for short) expansion is employed to represent wind pressure random variants as eigenfunctions of the covariance operator. To reduce the variant dimensionality, the nearest neighboring estimator is given for the transition distribution of the KL expansion. The targeted wind pressure fields are obtained by expanding the Fourier basis of the eigenfunction and estimating its expansion coefficients. The proposed method is applied to estimate wind pressures on a gable roof building. The relevant parameters of the wind pressure field are obtained, and the results compare well with those from wind tunnel testing, with higher efficiency. The proposed method effectively reduces the dimensionality of the predicted wind pressures, with reduced errors, higher accuracy, and increased efficiency.
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Fırat, Orhan, Mete Özay, Itır Önal, Ilke Öztekin, and Fatoş T. Yarman Vural. "Enhancing Local Linear Models Using Functional Connectivity for Brain State Decoding." International Journal of Cognitive Informatics and Natural Intelligence 7, no. 3 (July 2013): 46–57. http://dx.doi.org/10.4018/ijcini.2013070103.
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The authors propose a statistical learning model for classifying cognitive processes based on distributed patterns of neural activation in the brain, acquired via functional magnetic resonance imaging (fMRI). In the proposed learning machine, local meshes are formed around each voxel. The distance between voxels in the mesh is determined by using functional neighborhood concept. In order to define functional neighborhood, the similarities between the time series recorded for voxels are measured and functional connectivity matrices are constructed. Then, the local mesh for each voxel is formed by including the functionally closest neighboring voxels in the mesh. The relationship between the voxels within a mesh is estimated by using a linear regression model. These relationship vectors, called Functional Connectivity aware Mesh Arc Descriptors (FC-MAD) are then used to train a statistical learning machine. The proposed method was tested on a recognition memory experiment, including data pertaining to encoding and retrieval of words belonging to ten different semantic categories. Two popular classifiers, namely k-Nearest Neighbor and Support Vector Machine, are trained in order to predict the semantic category of the item being retrieved, based on activation patterns during encoding. The classification performance of the Functional Mesh Learning model, which range in 62-68% is superior to the classical multi-voxel pattern analysis (MVPA) methods, which range in 40-48%, for ten semantic categories.
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Picht, Thomas, Sven Mularski, Bjoern Kuehn, Peter Vajkoczy, Theodoros Kombos, and Olaf Suess. "Navigated Transcranial Magnetic Stimulation for Preoperative Functional Diagnostics in Brain Tumor Surgery." Operative Neurosurgery 65, suppl_6 (December 1, 2009): ons93—ons99. http://dx.doi.org/10.1227/01.neu.0000348009.22750.59.
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Abstract Objective: Transcranial magnetic stimulation (TMS) is a noninvasive method for analyzing cortical function. To utilize TMS for presurgical functional diagnostics, the magnetic impulse must be precisely targeted by stereotactically positioning the coil. The aim of this study was to evaluate the usefulness of TMS for operation planning when combined with a sensor-based electromagnetic navigation system (nTMS). Methods: Preoperative functional mapping with nTMS was performed in 10 patients with rolandic tumors. Intraoperative mapping was performed with the “gold standard” of direct cortical stimulation. Stimulation was performed in the same predefined 5-mm raster for both modalities, and the results were compared. Results: In regard to the 5-mm mapping raster, the centers of gravity of nTMS and direct cortical stimulation were located at the same spot in 4 cases and at neighboring spots in the remaining 6 cases. The mean distance between the tumor and the nearest motor response (“safety margin”) was 7.9 mm (range, 5–15 mm; standard deviation, 3.2 mm) for nTMS and 6.6 mm (range, 0–12 mm; standard deviation, 3.4 mm) for direct cortical stimulation. Conclusion: nTMS allowed for reliable, precise application of the magnetic impulse, and the peritumoral somatotopy corresponded well between the 2 modalities in all 10 cases. nTMS is a promising method for preoperative functional mapping in motor cortex tumor surgery.
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Pietsch, U., and N. K. Hansen. "A critical review of the experimental valence charge density of GaAs." Acta Crystallographica Section B Structural Science 52, no. 4 (August 1, 1996): 596–604. http://dx.doi.org/10.1107/s0108768196003576.
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The valence charge and difference densities of GaAs have been calculated without previous refinements of a charge density model using six different data sets of X-ray structure amplitudes published until now. Since the data sets have been measured by means of different experimental methods and due to the different data treatment, the individual structure factors differ on the absolute scale. Furthermore, different temperature factors have been published. In order to bring the data to a common level, we used the same two harmonic temperature factors and the same algorithm for correcting the different sets of experimental data for anomalous dispersion. Because of the non-centrosymmetry of the zinc blende structure, these procedures are not strictly model-independent. A simple bond charge model was used to obtain phases of the structure amplitudes and to perform the above-mentioned corrections. In general, the details of `experimental' charge densities depend sensitively on the balanced ratio among the structure factor moduli used. A smooth density map is only obtained if all F have the same high level of accuracy [δ(F)/F < 1%] and if `outliers' are omitted. Only four of the six data sets describe the covalent bond and the partial charge transfer between neighboring atoms, in qualitative agreement with our expectation based on the results of pseudo-potential calculations. However, some quantitative discrepancies remain, particularly in the height of the charge density maximum between nearest neighbours and in some details outside the bonding region.
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Guilloteau, Clément, and Efi Foufoula-Georgiou. "Beyond the Pixel: Using Patterns and Multiscale Spatial Information to Improve the Retrieval of Precipitation from Spaceborne Passive Microwave Imagers." Journal of Atmospheric and Oceanic Technology 37, no. 9 (September 1, 2020): 1571–91. http://dx.doi.org/10.1175/jtech-d-19-0067.1.
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AbstractThe quantitative estimation of precipitation from orbiting passive microwave imagers has been performed for more than 30 years. The development of retrieval methods consists of establishing physical or statistical relationships between the brightness temperatures (TBs) measured at frequencies between 5 and 200 GHz and precipitation. Until now, these relationships have essentially been established at the “pixel” level, associating the average precipitation rate inside a predefined area (the pixel) to the collocated multispectral radiometric measurement. This approach considers each pixel as an independent realization of a process and ignores the fact that precipitation is a dynamic variable with rich multiscale spatial and temporal organization. Here we propose to look beyond the pixel values of the TBs and show that useful information for precipitation retrieval can be derived from the variations of the observed TBs in a spatial neighborhood around the pixel of interest. We also show that considering neighboring information allows us to better handle the complex observation geometry of conical-scanning microwave imagers, involving frequency-dependent beamwidths, overlapping fields of view, and large Earth incidence angles. Using spatial convolution filters, we compute “nonlocal” radiometric parameters sensitive to spatial patterns and scale-dependent structures of the TB fields, which are the “geometric signatures” of specific precipitation structures such as convective cells. We demonstrate that using nonlocal radiometric parameters to enrich the spectral information associated to each pixel allows for reduced retrieval uncertainty (reduction of 6%–11% of the mean absolute retrieval error) in a simple k-nearest neighbors retrieval scheme.
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Tan, Kun, Yusha Zhang, Xue Wang, and Yu Chen. "Object-Based Change Detection Using Multiple Classifiers and Multi-Scale Uncertainty Analysis." Remote Sensing 11, no. 3 (February 11, 2019): 359. http://dx.doi.org/10.3390/rs11030359.
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The drawback of pixel-based change detection is that it neglects the spatial correlation with neighboring pixels and has a high commission ratio. In contrast, object-based change detection (OBCD) depends on the accuracy of the segmentation scale, which is of great significance in image analysis. Accordingly, an object-based approach for automatic change detection using multiple classifiers and multi-scale uncertainty analysis (OB-MMUA) in high-resolution (HR) remote sensing images is proposed in this paper. In this algorithm, the gray-level co-occurrence matrix (GLCM), morphological, and Gabor filter texture features are extracted to construct the input data, along with the spectral features, to utilize the respective advantages of the features and to compensate for the insufficient spectral information. In addition, random forest is used to select the features and determine the optimal feature vectors for the change detection. Change vector analysis (CVA) based on uncertainty analysis is then implemented to select the initial training samples. According to the diversity, support vector machine (SVM), k-nearest neighbor (KNN), and extra-trees (ExT) classifiers are then chosen as the base classifiers for Dempster-Shafer (D-S) evidence theory fusion, and unlabeled samples are selected using an active learning method with spatial information. Finally, multi-scale object-based D-S evidence theory fusion and uncertainty analysis is used to classify the difference image. To validate the proposed approach, we conducted experiments using multispectral images collected by the ZY-3 and GF-2 satellites. The experimental results confirmed the effectiveness and superiority of the proposed approach, which integrates the respective advantages of the pixel-based and object-based methods.
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Bassier, Maarten, and Maarten Vergauwen. "Clustering of Wall Geometry from Unstructured Point Clouds Using Conditional Random Fields." Remote Sensing 11, no. 13 (July 4, 2019): 1586. http://dx.doi.org/10.3390/rs11131586.
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The automated reconstruction of Building Information Modeling (BIM) objects from point cloud data is still subject of ongoing research. A vital step in the process is identifying the observations for each wall object. Given a set of segmented and classified point clouds, the labeled segments should be clustered according to their respective objects. The current processes to perform this task are sensitive to noise, occlusions, and the associativity between faces of neighboring objects. The proper retrieval of the observed geometry is especially important for wall geometry as it forms the basis for further BIM reconstruction. In this work, a method is presented to automatically group wall segments derived from point clouds according to the proper walls of a building. More specifically, a Conditional Random Field is employed that evaluates the context of each wall segment in order to determine which wall it belongs to. First, a set of classified planar primitives is obtained through algorithms developed in prior work. Next, both local and contextual features are extracted based on the nearest neighbors and a number of seeds that are heuristically determined. The final wall clusters are then computed by decoding the graph. The method is tested on our own data as well as the 2D-3D-Semantics (2D-3D-S) benchmark data of Stanford. Compared to a conventional region growing method, the proposed method reduces the rate of false positives, resulting in better wall clusters. Overall, the method computes a more balanced clustering of the observations. A key advantage of the proposed method is its capability to deal with wall geometry in complex configurations in multi-storey buildings opposed to the presented methods in current literature.
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Yao, N., M. M. Hillemeier, and R. T. Anderson. "Breast cancer treatment resources and guideline-concordant treatment in Appalachia." Journal of Clinical Oncology 29, no. 27_suppl (September 20, 2011): 216. http://dx.doi.org/10.1200/jco.2011.29.27_suppl.216.
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216 Background: Appalachia has poorer cancer outcomes, but little research has been done regarding availability of cancer care resources in this region and how resource availability may relate to cancer outcomes. This study 1) examines associations between radiation therapy resources and receipt of radiotherapy after BCS in counties within Kentucky - a SEER state; and 2) describes spatial patterning of breast cancer treatment resources in all 13 Appalachian states. Methods: For the Kentucky analyses, county-level data from the Area Resource File and SEER registry are analyzed. Bivariate analyses and spatial lag regression using a 6-nearest neighboring counties matrix are conducted. The sample includes stage I or II primary breast cancer patients age 18+ years diagnosed in Kentucky during 2000-2007. The dependent variable is the county-level percentage of patients received BCS without radiation; independent variables include density of radiation therapy providers and facilities and other socioeconomic covariates. For the analyses of entire Appalachian region, descriptive analyses and exploratory spatial data analysis are conducted including 420 Appalachian counties and 644 non-Appalachian counties in 13 states. Results: In Kentucky 16.44% of 17,227 early stage breast cancer patients received BCS without radiation therapy (21.08% in Appalachia versus 14.80% in non-Appalachia, p<0.001). Appalachian Kentucky had significantly fewer radiation oncologists and radiation therapy facilities per capita than non-Appalachian Kentucky. The number of radiation therapy facilities per capita is negatively associated with rates of BCS without radiation when controlling for covariates. Analysis of 13 Appalachian states shows that Appalachian counties, especially in the Central and Southern regions, had significant fewer physicians per capita in Surgery, Anesthesia, Clinical Pathology, and Radiation Oncology. Clustering of scarce breast cancer care resources was observed in Central Appalachia. Conclusions: Appalachian counties, especially in central Appalachia, have fewer breast cancer treatment resources than non-Appalachian counties, and resource availability is associated with cancer health disparities.
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Iyer, Hari S., Nicholas G. Wolf, Edda Vuhahula, Charles Massambu, Devanshi Shah, Lee F. Schroeder, Marcia C. Castro, Kenneth Fleming, and John S. Flanigan. "Geographic Variation in Access to Pathology and Laboratory Services in Tanzania: A Cross-Sectional Geospatial Analysis." JCO Global Oncology 6, Supplement_1 (July 2020): 45. http://dx.doi.org/10.1200/go.20.41000.
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PURPOSE Increasing noncommunicable disease burden in sub-Saharan Africa requires the urgent scale-up of pathology and laboratory medicine (PALM) services. To identify service gaps at the district level, we studied geographic variation in the correlation between travel time to health facilities and population density. METHODS We linked geospatial data for Tanzania from multiple sources. Facility locations were extracted from a comprehensive facility list in Africa. Data on geographic factors, demographics, and roads were collected from government and nonprofit databases. We classified facilities assuming increasing PALM service readiness by level: dispensaries, health centers, district hospitals, and regional/referral hospitals. We input these data into the AccessMod 5 algorithm to estimate travel time across Tanzania with 1-km resolution for each PALM classification. We then calculated district-level averages of population and travel time for each PALM category. Associations between these variables were estimated using a bivariable local indicator of spatial autocorrelation, specifying immediate contiguity neighborhood definition. Spatial analysis was restricted to 172 contiguous districts (islands not included). Significance tests were two sided, with an α of .05. RESULTS Analysis included 5,342 dispensaries, 667 health centers, 185 district hospitals, and 34 regional/referral hospitals. Maps revealed clusters of estimated travel time in excess of 6 hours in less populated western and southern districts. More districts reported an average travel time of less than 1 hour to the nearest dispensary (69%) than to regional/referral hospitals (16%). Bivariable local indicators of spatial autocorrelation revealed few significant clusters of spatial correlations; however, significant correlations between low population density and longer travel times in neighboring districts were obtained for 13%, 16%, 15%, and 13% of districts for dispensaries, health centers, district hospitals, and regional/referral hospitals, respectively. CONCLUSION Limited variability of district-level spatial correlations suggests somewhat equitable geographic allocation of PALM services in Tanzania, with small areas of low population density and long travel times that demand additional intervention. Limitations include a lack of ascertainment of specific PALM services.
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Flippot, Ronan, Bradley Alexander McGregor, Abdallah Flaifel, Kathryn P. Gray, Sabina Signoretti, John A. Steinharter, Eliezer Mendel Van Allen, et al. "Atezolizumab plus bevacizumab in non-clear cell renal cell carcinoma (NccRCC) and clear cell renal cell carcinoma with sarcomatoid differentiation (ccRCCsd): Updated results of activity and predictive biomarkers from a phase II study." Journal of Clinical Oncology 37, no. 15_suppl (May 20, 2019): 4583. http://dx.doi.org/10.1200/jco.2019.37.15_suppl.4583.
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4583 Background: NccRCC and ccRCCsd are aggressive tumors associated with poor prognosis and response to therapy. Combination strategies co-targeting VEGF signaling and inhibitory immune checkpoints are highly active in clear-cell renal cell carcinoma, but data is lacking in NccRCC and ccRCCsd. We conducted a multicenter, open-label, single arm phase II trial of atezolizumab plus bevacizumab in NccRCC and ccRCCsd. Methods: Patients with NccRCC and ccRCCsd ( > 20% sarcomatoid differentiation), and ECOG performance status of 0-2 were eligible. Prior systemic treatment was allowed with the exception of prior PD-1/PD-L1-directed therapy. Atezolizumab 1200mg and bevacizumab 15mg/kg were administered every 3 weeks until progression, unacceptable toxicity, or patient withdrawal. Primary endpoint was objective response rate (ORR) per RECIST 1.1. Exploratory biomarker analyses included PD-L1 expression on tumor (TC) and immune cells (IC), and spatial analysis of the immune infiltrate. Results: Sixty patients received at least 1 cycle of treatment, among whom 56 were evaluable for response (17 ccRCCsd and 39 NccRCC). ORR was 34% in the overall population, 53% in ccRCCsd and 26% in NccRCC. Median progression-free survival was 8.4 months (95%CI, 6.9-16.5). Baseline tumor tissue was available for 36 patients. TC PD-L1 expression ≥1% was associated with improved ORR (9/14, 64%) compared to patients with PD-L1 expression < 1% (4/20, 20%). Patients with TC PD-L1 expression ≥1% who experienced progressive disease as best response had shorter average distance between tumor cells and nearest neighboring immune cells at baseline. Further analysis of the immune tumor microenvironment on an expanded cohort, including IC PD-L1 expression and correlation with clinical outcomes, is ongoing and will be updated. Conclusions: The combination of atezolizumab plus bevacizumab is active in NccRCC and ccRCCsd. Candidate predictive biomarkers include PD-L1 expression in TC and topological analysis of the immune infiltrate. Clinical trial information: NCT02724878.
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Paludo, Jonas, Surendra Dasari, Kerstin Wenzl, Shahrzad Jalali, Jordan Krull, Michelle Manske, Esteban Braggio, et al. "Long Non-Coding RNA Expression in Waldenstrom Macroglobulinemia and IgM Monoclonal Gammopathy of Undetermined Significance." Blood 134, Supplement_1 (November 13, 2019): 2774. http://dx.doi.org/10.1182/blood-2019-128912.
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Introduction Waldenstrom macroglobulinemia (WM) is a rare, incurable, indolent non-Hodgkin lymphoma that is preceded by an IgM monoclonal gammopathy of undetermined significance (IgM MGUS). In recent years, better understanding of the pathobiology of WM and IgM MGUS has strengthened the hypothesis that they may represent different stages of the same disease. Studying the relationship between these two conditions is the essential first step to understanding the mechanisms driving the malignant transformation from IgM MGUS to WM. Recently long non-coding RNAs (lncRNAs) have garnered significant recognition for their roles in the regulation of gene expression and cellular function. lncRNA can regulate gene expression of neighboring protein-coding genes through multiple mechanisms such as by recruiting chromatin modifiers and increasing accessibility of genes to transcription proteins, or by directly binding to enhancers or promoters. Here we present the first analysis of the lncRNA expression profile in patients with WM compared to IgM MGUS. Methods Twenty-one patients with untreated WM (n=14) and IgM MGUS (n=7) seen at Mayo Clinic were included in this analysis. Malignant cells were isolated from bone marrow samples using CD19 and CD38 positive selection. Total RNA was extracted and sequencing was performed using the Illumina HiSeq4000 NGS platform. Differentially expressed lncRNAs in WM compared to IgM MGUS were defined as log2 fold change (FC) > 1.0 (upregulated) or < -1.0 (downregulated) and a false discovery rate (FDR) < 0.05. The nearest gene to each lncRNA was also identified and their gene expression was detected. Using the Ingenuity Pathway Analysis software and taking into account the transcriptional expression and known function of these genes, a correlation between cell function and disease analysis was performed. Results A total of 119 lncRNAs were differently expressed between WM and IgM MGUS (17 transcripts were upregulated in WM, while 102 were downregulated in WM). Sixteen unique genes were identified as the nearest gene to 17 upregulated lncRNAs, while 18 genes were identified as the nearest genes to the top 18 downregulated lncRNAs in WM compared to IgM MGUS. Several of the upregulated lncRNA in WM were in proximity to known oncogenes. The well-known oncogenic lncRNA PVT1 was found to be upregulated in WM compared to IgM MGUS (log2 FC 1.35, FRD 0.04). PVT1 is overexpressed in several malignancies and associated with inhibition of apoptosis and increased cell proliferation. PVT1 is in close proximity to MYC and co-expression of PVT1-MYC has been described in multiple cancers. PVT1 also potentiate MYC amplification by increasing MYC protein stability, therefore promoting malignant cell growth. The lncRNA TCB1D27, also upregulated in WM compared to IgM MGUS, is located in close proximity to the oncogene TNFRSF13B (TACI), which was found to be overexpressed in WM patients (log2 FC 1.61, FDR <0.001). TACI is a receptor for BLyS/BAFF, which modulates the development of normal B-cells and plays a role in various B-cell malignancies. In fact, BLyS/BAFF level is known to be increased in WM and to promote survival and proliferation of WM B cells. The upregulated lncRNA RP11-25K21.1 in WM is located in close proximity to the oncogene FCGR2B (CD32B), which was also upregulated in WM compared to IgM MGUS (log2 FC 1.59, FDR 0.001). Overexpression of CD32B was previously shown to be associated with inferior outcomes in DLBCL and FL patients, presumably secondary to an increased internalization and clearance of rituximab due to increased CD32B expression on malignant cells. Several other upregulated lncRNAs in WM compared to IgM MGUS are also in proximity of other oncogenes such as PCED1B, POLD3, CLLU1, and SMAD7. Finally, we considered the expression of the nearest genes and their reported cell function and association with diseases. Several genes that are known to have a role in the cell proliferation and/or neoplasm development were noted to be upregulated in WM (figure 1). Conclusion We present the first report of lncRNAs expression in WM and IgM MGUS in addition to a concurrent analysis of the gene expression of the nearest protein-coding genes. Several known oncogenes are in proximity of overexpressed lncRNAs in WM compared to IgM MGUS that could indicate potential implications in the pathobiology of these diseases and the mechanism driving the malignant transformation to WM. Disclosures Paludo: Celgene: Research Funding; Celgene: Research Funding; Verily Life Sciences: Research Funding; Verily Life Sciences: Research Funding. Dasari:The Binding Site: Patents & Royalties: US Patent Rights on Mass Spectroscopy Licensing agreement with The Binding Site, Research Funding. Kapoor:Celgene: Honoraria; Cellectar: Consultancy; Janssen: Research Funding; Sanofi: Consultancy, Research Funding; Amgen: Research Funding; Takeda: Honoraria, Research Funding; Glaxo Smith Kline: Research Funding. Ailawadhi:Amgen: Consultancy, Research Funding; Pharmacyclics: Research Funding; Takeda: Consultancy; Janssen: Consultancy, Research Funding; Cellectar: Research Funding; Celgene: Consultancy. Gertz:Amyloidosis Foundation: Research Funding; i3Health: Other: Development of educational programs and materials; Medscape: Consultancy, Speakers Bureau; Ionis/Akcea: Consultancy; Celgene: Consultancy; Appellis: Consultancy; Amgen: Consultancy; Teva: Speakers Bureau; Alnylam: Consultancy; Prothena Biosciences Inc: Consultancy; Janssen: Consultancy; Spectrum: Consultancy, Research Funding; Annexon: Consultancy; Physicians Education Resource: Consultancy; Abbvie: Other: personal fees for Data Safety Monitoring board; Research to Practice: Consultancy; Johnson and Johnson: Speakers Bureau; DAVA oncology: Speakers Bureau; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees; Proclara: Membership on an entity's Board of Directors or advisory committees; Springer Publishing: Patents & Royalties; International Waldenstrom Foundation: Research Funding. Novak:Celgene Coorperation: Research Funding. Ansell:Affimed: Research Funding; Regeneron: Research Funding; Mayo Clinic Rochester: Employment; Seattle Genetics: Research Funding; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; Affimed: Research Funding; LAM Therapeutics: Research Funding; Bristol-Myers Squibb: Research Funding; Seattle Genetics: Research Funding; Affimed: Research Funding; Affimed: Research Funding; Mayo Clinic Rochester: Employment; Bristol-Myers Squibb: Research Funding; Mayo Clinic Rochester: Employment; Seattle Genetics: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; Trillium: Research Funding; Regeneron: Research Funding; Seattle Genetics: Research Funding; Mayo Clinic Rochester: Employment; Trillium: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Regeneron: Research Funding; Regeneron: Research Funding; LAM Therapeutics: Research Funding; Mayo Clinic Rochester: Employment; Seattle Genetics: Research Funding; LAM Therapeutics: Research Funding; Seattle Genetics: Research Funding; Mayo Clinic Rochester: Employment; LAM Therapeutics: Research Funding; LAM Therapeutics: Research Funding; Trillium: Research Funding; Affimed: Research Funding; Trillium: Research Funding; Regeneron: Research Funding; Affimed: Research Funding; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; LAM Therapeutics: Research Funding; Trillium: Research Funding; LAM Therapeutics: Research Funding; Regeneron: Research Funding; LAM Therapeutics: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Regeneron: Research Funding; Bristol-Myers Squibb: Research Funding; Affimed: Research Funding; Seattle Genetics: Research Funding; Affimed: Research Funding; Regeneron: Research Funding; Seattle Genetics: Research Funding; Bristol-Myers Squibb: Research Funding; LAM Therapeutics: Research Funding; Regeneron: Research Funding; Seattle Genetics: Research Funding; Trillium: Research Funding.
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Bojovic, Vladimir, Iva Berisavac, and Lukas Rasulic. "Znacaj senzorno evociranih potencijala u visinskoj leziji brahijalnog pleksusa." Acta chirurgica Iugoslavica 50, no. 1 (2003): 15–22. http://dx.doi.org/10.2298/aci0301015b.
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The aim of this work is to show the highlights of electrophysiological diagnostics, i.e. its potentials in level diagnostics of traumatic dysfunction of brachial plexus (BP). In that manner we have analyzed the results of electrophysiological research, made on 53 patients with different levels and grades of traumatic lesion of brachial plexus. We have also analyzed the authors' opinions and points of view who have contributed in solving these problems. Brachilal plexus is a complex, vulnerable nerve structure that is often, in life, exposed to direct or indirect influence of mechanical force. Preserved integrity of bone structures of a shoulder protects BP from longitudinal forces, which are the most common causes of injury of this structure. Traction mechanism of the injury is always up to date in the cases of fractures and dislocation of the skeleton in this region. In the early childhood, mechanical injuries of brachial plexus are caused by distocia in the second delivery period, while in adulthood most common injuries are caused by sudden and intensive forces, which cause dysfunction of plexus by traction mechanism (dislocation of a shoulder and clavicular fracture) and by direct action (stabing and pircing injuries). Slowly progressive, expansive, degenerative and inflammatory processes of neighboring organs are causing the dysfunction of the plexus as well. Traction actions are aimed mostly at radiculars as a vulnerable structure that is placed between relatively mobile shoulder joint and rigid cervical part of vertebral column. Complex anatomical structure and mutual overlapping of radicular motor and sensitive inrevation of muscles and dermatoms, make the diagnostics of dysfunction of this periphery nerve structure very difficult and complicated. Dysfunction of neighboring bone, vascular and muscle elements as well as the nearness of vital organs, which complicates even more the diagnostics. Taking into account the general analysis of all electrophysiological results of the research on 53 patients with an PB injury, we have concluded that none of the functional methods is not sovereign, i.e. the contribution of this research is complementary also with rendgenological results. Clinical data are unavoidable, but they are not enough without good argumentation, especially for the level of lesion, pre- or postganglionary. Electromiography gives reliable results for the phase and the grade of denervation of particular muscle groups, and that way it is possible to conclude, indirectly, which part of the plexus is in difunction. Special attention should be payed to EMG of paraspinal muscles, where the signs of denervation are aleays indicating intradural lesion of the radicular. In the examined group, 52 % of the patients with radicular dysfunction had the signs of denervation in paraspinal muscles. Examination of the sensitive action potentials is another method by which we can see the dysfunction level of the plexus in an anesthetical region. In a group with preganglionary root dysfunction, 48% of the patients had preserved sNAP response. In a group with postganglionary dysfunction, 36% of the patients had no sNAP response. Somatosemsory evoked potentials are addition to EMG and ENG research and they are efficient in the primary phase, when electromyographic and electroneurograhic examinations are not offering relevant data. Checking of the early diagnostics of the pre- and postganglionary lesions with somatosensory evoked potentials wasn't possible in this group because the first examinations of these patients in our laboratory were mostly made couple of months after the injury. Difunction of the ratio amplitudes N9 and P/ N13 in the group with preganglionary lesion was found in 31%, with postganglionary in 42.2 % and with both in 10% of the patients.
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Li, Wen Jie, Tie Hong Wu, and Xiao Jia Li. "The Effects of Tourism Interference on the Soil of Grassland Tourist Spots — A Study of Gold Saddle Tourist Spots of Xilamuren Grassland in Inner Mongolia." Advanced Materials Research 610-613 (December 2012): 3034–41. http://dx.doi.org/10.4028/www.scientific.net/amr.610-613.3034.
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Soil was sampled according to the distance surrounding different activities area and on different levels of travel channel around the gold saddle tourist spots of Xilamuren grassland in Inner Mongolia. Soil samples were collected every 5 cm until down to 30cm in each plot with soil wreath knife. Analyze soil compaction, bulk density, soil moisture content, pH, soil organic matter, available P, total N, total P and total K by experiment. Through data analysis, study the response of tourism interference on soil physical and chemical properties. Study shows that the destruction of tourism activities on the grassland soil is big and the intensity of tourism activities are positively correlated to the degree of interference. The nearer to the concentrated region of tourism spots facilities, the greater intensity of tourist disturbance and the greater damage of soil. The damage of soil in south side was significantly greater than that in the north side. Travel interference changes differently in different tourism activity area. The damage extent of interference in obo district and bok field was greater than that on both sides of horse tract. Different levels of travel channel showed that interference degree was different by the frequency size of vehicle rolling and the destruction of soil conditions was consistent with it. Tourism interference is relatively concentrated. It destructs the local environment. As long as the tourism point, tourism activity area, the layout of tourism road should not be spread too disperse and messy, the impact of grassland landscape pattern will be significantly reduced, and the deterioration trend of ecological environment is expected to be controlled. Through analysis of different type of tourism activities zone, basic cover all aspects of the impact of grassland tourism development on grassland environment. The changes of grassland soil could be explained driven by tourism disturbance. It can provide alternative methods for tourism interference study and provide guidance for the management of grassland tourist areas. Grassland tourist spots tend to be concentrated in the excellent area of pasture landscape resources and environment, but also the fragile area in ecological environment. The interference of human activities are extremely sensitive whether in a desert steppe zone of brown soil as matrix soil or in typical grassland zone of chestnut soil as matrix soil, and even in the meadow steppe of chernozem as matrix soil. Once vegetation is destructed, soil has been the strength trampling, sandy desertification would grow rapidly. It would not only affect pasture tourism, but also would threaten the neighboring regions. Therefore, it is necessary to study the effect of tourism interference on the soil of grassland tourist spots.
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Prasetyo, Pandu Deski, I. Gede Pasek Suta Wijaya, and Ario Yudo Husodo. "Klasifikasi Genre Musik Menggunakan Metode Mel-Frequency Cepstrum Coefficients dan K-Nearest Neighbors Classifier." Jurnal Teknologi Informasi, Komputer, dan Aplikasinya (JTIKA ) 1, no. 2 (September 30, 2019). http://dx.doi.org/10.29303/jtika.v1i2.41.
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In the world of music, music has several types of genre genres that can be grouped, there are music genre pop, rock, blues, slow, jazz, metal, dangdut and many more. And for each person must have a genre favorite that is different from one another, but to distinguish it does not need to play music files one by one especially if the number of music files is a lot. Therefore, computer software is needed to distinguish each of these genres in order to make it easier for users to distinguish and group the types of music according to their wishes automatically. By using the MFCC and KNN methods as a classification solution to classify several types of genre streams can be easily resolved. The results achieved from this study reached 52,4% with a K = 13 as the nearest neighboring point.
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You, Jing-Yang, Bo Gu, and Gang Su. "Flat Band and Hole-induced Ferromagnetism in a Novel Carbon Monolayer." Scientific Reports 9, no. 1 (December 2019). http://dx.doi.org/10.1038/s41598-019-56738-8.
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AbstractIn recent experiments, superconductivity and correlated insulating states were observed in twisted bilayer graphene (TBG) with small magic angles, which highlights the importance of the flat bands near Fermi energy. However, the moiré pattern of TBG consists of more than ten thousand carbon atoms that is not easy to handle with conventional methods. By density functional theory calculations, we obtain a flat band at EF in a novel carbon monolayer coined as cyclicgraphdiyne with the unit cell of eighteen atoms. By doping holes into cyclicgraphdiyne to make the flat band partially occupied, we find that cyclicgraphdiyne with 1/8, 1/4, 3/8 and 1/2 hole doping concentration shows ferromagnetism (half-metal) while the case without doping is nonmagnetic, indicating a hole-induced nonmagnetic-ferromagnetic transition. The calculated conductivity of cyclicgraphdiyne with 1/8, 1/4 and 3/8 hole doping concentration is much higher than that without doping or with 1/2 hole doping. These results make cyclicgraphdiyne really attractive. By studying several carbon monolayers, we find that a perfect flat band may occur in the lattices with both separated or corner-connected triangular motifs with only including nearest-neighboring hopping of electrons, and the dispersion of flat band can be tuned by next-nearest-neighboring hopping. Our results shed insightful light on the formation of flat band in TBG. The present study also poses an alternative way to manipulate magnetism through doping flat band in carbon materials.
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Zhou, Shijie, Amir AbdelWahab, John L. Sapp, Eric Sung, Konstantinos Aronis, James Warren, Paul MacInnis, et al. "Abstract 13184: A New Intraprocedural Automated System for Localizing Idiopathic Ventricular Arrhythmia Origin Sites." Circulation 142, Suppl_3 (November 17, 2020). http://dx.doi.org/10.1161/circ.142.suppl_3.13184.
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Introduction: Few intraprocedural localization systems have been developed to predict idiopathic ventricular arrhythmia (IVA) source sites. However, an accurate and bi-ventricular patient-specific automated site of origin localization system remains elusive. To address this issue, we have developed a new automatic arrhythmia origin localization (AAOL) system that determines the sites of earliest activation in both ventricles and provides superior accuracy. Hypothesis: We hypothesized that the AAOL system can use electroanatomic mapping (EAM) geometry and accurately localize IVA source sites on patient-specific geometry of LV, RV and neighboring vessels using 3-lead ECGs. Methods: Twenty patients undergoing IVA catheter ablation had a 12-lead ECG recorded during clinical arrhythmia and during pacing at various locations identified on EAM geometries. The AAOL system combined 3-lead (III, V2, V6) 120-ms QRS integrals and patient-specific EAM geometry with intracardiac pacing to predict the site of earliest ventricular activation. The predicted site was projected onto the EAM geometry using the EAM triangular-mesh site nearest to the tip of the predicted site. Results: Twenty-three IVA source sites were clinically identified by activation mapping and/or pace mapping (8 RV, 15 LV, including 8 from the posteromedial papillary muscle; 2 from the aortic root; and 1 from the distal coronary sinus). The new system achieved a mean localization accuracy of 3.6 mm for the 23 mapped IVAs (Figure 1D), better than that achieved by previous systems. Conclusions: The new AAOL system offers highly accurate localization of IVA source sites in both ventricles and neighboring vessels, which could facilitate ablation procedures for patients with IVAs.
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Song, Zhaohui, Sanyi Yuan, Zimeng Li, and Shangxu Wang. "kNN-based Gas-bearing Prediction Using Local Waveform Similarity Gas-indication Attribute - An Application to a Tight Sandstone Reservoir." Interpretation, August 24, 2021, 1–45. http://dx.doi.org/10.1190/int-2021-0045.1.
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Gas-bearing prediction of tight sandstone reservoirs is significant but challenging due to the relationship between the gas-bearing property and its seismic response being nonlinear and complex. Although machine learning (ML) methods provide potential for solving the issue, the major challenge of ML applications to gas-bearing prediction is that of generating accurate and interpretable intelligent models with limited training sets. The k Nearest neighbor ( kNN) method is a supervised ML method classifying an unlabeled sample according to its k neighboring labeled samples. We have introduced a kNN-based gas-bearing prediction method. The method can automatically extract a gas-sensitive attribute called the gas-indication local waveform similarity attribute (GLWSA) combining prestack seismic gathers with interpreted gas-bearing curves. GLWSA uses the local waveform similarity among the predicting samples and the gas-bearing training samples to indicate the existence of an exploitable gas reservoir. GLWSA has simple principles and an explicit geophysical meaning. We use a numerical model and field data to test the effectiveness of our method. The result demonstrates that GLWSA is good at characterizing the reservoir morphology and location qualitatively. When the method applies to the field data, we evaluate the performance with a blind well. The prediction result is consistent with the geologic law of the work area and indicates more details compared to the root-mean-square attribute.
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"Machine Learning Based Suspicion of Customer Detention in Banking with Diverse Solver Neighbors and Kernels." International Journal of Recent Technology and Engineering 8, no. 4 (November 30, 2019): 3244–49. http://dx.doi.org/10.35940/ijrte.d8043.118419.
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In the current moving technological business sector, the amount spent for attaching the new customer is highly expensive and time consuming process than adopting some methods to hold and retain the existing customers. So the business sector is in need to make a research on with holding the existing customers by using the current technology. The methods to make the retention of the existing customers with high reliablility are a challenging task. With this view, we focus on predicting the customer churn for the banking application. This paper uses the customer churn bank modeling data set extracted from UCI Machine Learning Repository. The anaconda Navigator IDE along with Spyder is used for implementing the Python code. Our contribution is folded is folded in three ways. First, the data preprocessing is done and the relationship between the attributes are identified. Second, the data set is reduced with the principal component analysis to form the 2 component feature reduced dataset. Third, the raw dataset and 2 component PCA reduced dataset is fitted to various solvers of logistic regression classifiers and the performance is analyzed with the confusion matrix. Fourth, the raw dataset and 2 component PCA reduced dataset is fitted to various neighboring algorithms of K-Nearest Neighbors classifiers and the performance is analyzed with the confusion matrix. Fifth, the raw dataset and 2 component PCA reduced dataset is fitted to various kernels of Support Vector Machine classifiers and the performance is analyzed with the confusion matrix. The implementation is carried out with python code using Anaconda Navigator. Experimental results shows that, the rbf kernel of Support vector machine classifier is effective with the accuracy of 85.8% before applying PCA and accuracy of 80.9% after applying PCA compared to other classifiers.
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Yazel, Eric, Crystal Henderson, and Jessica B. Dennison. "21st Century Approach: Using data and novel technologies to address the opioid crisis." Online Journal of Public Health Informatics 11, no. 1 (May 30, 2019). http://dx.doi.org/10.5210/ojphi.v11i1.9808.
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ObjectiveTo use novel technologies to develop a rapid response framework to reach opioid overdose patients in an area which is challenging from both a geography and population distribution standpoint.IntroductionClark County, Indiana is geographically located in between the urban area of Louisville, Kentucky and Scott County, Indiana. Scott County is the site for the largest HIV outbreak in the history of the United States, directly related to high rates of IV drug abuse. The unique geographic location of Clark County in combination with the recent HIV and Hepatitis C outbreaks in Clark and neighboring counties has greatly informed the development of an effective response to overdoses and the opioid epidemic in general. Furthermore, Clark County has a unique population distribution, with a population of over 125,000 and a land area of over 300 square miles. Despite this large area, over 80% of the population lives within 9 miles of the southern border of the county. This leads to a mix of both urban and rural challenges. There are several areas of the county that have greater than 15 minute emergency response times, which is often the difference between life and death in an overdose situation. These factors led to the development of the Clark County Rapid Response Project. The rapid response project is a community-based, multidisciplinary framework to address the opioid addicted patient, from initial use to successful recovery. The project uses data driven technology to initiate the care of opiate overdose patients and administer lifesaving interventions.MethodsClark County has partnered with the Indiana State Department of Health utilizing the early notification system that monitors statewide overdose activity. Once an alert is sent out, the response involves the use of two early notification systems. Everbridge is a one touch notification system that allows rapid dissemination of information to various community partners to allow them to initiate the appropriate response. Pulse Point is a smart phone application that allows CPR and trained community laypeople to respond to a cardiac arrest or overdose patient in a public place. It provides directions to the patient as well as to the nearest AED. Clark County has also simultaneously instituted a county-wide CPR training initiative and offered Narcan training as well. This is a major paradigm shift, as prior methods of deployment of trained laypeople essentially relied on the chance that an overdose will be reached by a first responder.ResultsEverbridge has allowed for the rapid notification of county entities and deployment of resources to overdose ‘hot spot’ areas. The Pulse Point initiative has dramatically increased the number of CPR and Narcan trained responders and provided means of delivering them to the appropriate patient population in a timely manner. Both these technologies have dramatically increased the delivery of resources to the overdose patient and decreased response times to the delivery of care.ConclusionsUsing data driven technology to inform how Clark County Health Department and first responders collectively address the opioid crisis is a novel approach. Since January 2018, Clark County Health Department has used ESSENCE (Electronic Surveillance System for the Early Notification of Community-based Epidemics) to determine where and when an increase of drug overdose activity is occurring throughout the county. This affords county health officials the ability to inform in “near real-time” first responders, the emergency department and other community stakeholders, relevant information thus allowing for the rapid deployment of county resources to the areas most affected. Our collective efforts to save lives is further enhanced by the county using of novel technologies like Pulse Point which is used to deploy both CPR and Narcan trained laypersons directly to sites in the community where overdoses are occurring. In a community, which is in large part considered rural and, in many places, has a greater than 15 minute emergency response time, using Pulse Point and Everbridge technologies has uniquely positioned Clark County to be on the cutting edge of saving lives as we leverage data and technology to address the opioid epidemic in our communities. This has markedly improved access to treatment and response times to overdose patients in Clark County, Indiana.
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Martirosyan, Danik, Katelynn Gilbert, Benjamin Pitts, and Autumn Allen. "War as a separate and independent factor for rise in COVID-19 cases and death: how to use vitamins, and other bioactive compounds in the absence of vaccine." Bioactive Compounds in Health and Disease 3, no. 12 (December 28, 2020). http://dx.doi.org/10.31989/bchd.v3i12.765.
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Background: COVID-19 is recognized as an acute upper respiratory disease. As the current COVID-19 pandemic nears the anticipated second wave of cases, many countries are struggling with tactics on how to limit the spread of the virus. With the emergence of conflict in the Artsakh region in late September, there has been a sharp rise in COVID cases both in this region and in surrounding countries that appears to be dissimilar from global transmission rates. This trend indicates that war could be acting as an independent and separate factor to COVID-19 spread in this area. With vaccines still in development, alternative methods of curbing the disease and its symptoms are of the utmost importance.Methods: This article examines the historical context of war as a contributing factor in the spread of disease as well as the history of the Artsakh region. Comparing data gathered from the World Health Organization (WHO) on the conflicting region to case counts and death rates in neighboring countries and globally will reveal how transmission rates in this area may be different than others. A review of published literature on functional food ingredients to combat COVID-19 will also be used to frame guidelines and recommendations to reduce the spread of the virus.Results and Conclusions: Based on data from the WHO on the Artsakh region, war appears to act as a separate and independent factor in COVID-19 transmission rates. To control the spread of COVID-19, it is important to eliminate war as a transmission factor by encouraging a ceasefire in areas of conflict and using materials and guidelines from the FFC to help control further spread. FFC guidelines include the use of functional food ingredients to mitigate intestinal and respiratory symptoms, while still promoting social distancing and the use of masks.Keywords: COVID-19, Artsakh region, war, WHO, functional food, bioactive compound, Vitamin C, Vitamin D, zinc, folate, iron, selenium, copper