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https://www.readbyqxmd.com/read/28633857/state-of-the-art-on-ultrasonic-oil-production-technique-for-eor-in-china
#1
Zhenjun Wang, Congbo Yin
Ultrasonic oil production technique for enhanced oil recovery (EOR) attracts more attention due to its high adaptability, simple operation, low cost and zero pollution to the oil reservoir. In this paper, recent new downhole tools used for enhanced oil recovery developed in China are summarized. Furthermore, research advance on some key problems that affect the widespread application of ultrasonic oil production technique in China are also summarized in view of what are the primary factors that influence crude oil paraffin inhibition and viscosity reduction, whether ultrasonic excitation is better than chemical agent for any plugs removal and whether ultrasound-chemical combination plug removal technology has the best plugs removal effect...
September 2017: Ultrasonics Sonochemistry
https://www.readbyqxmd.com/read/28632278/paper-microfluidics-for-nucleic-acid-amplification-testing-naat-of-infectious-diseases
#2
Laura Magro, Camille Escadafal, Pierre Garneret, Béatrice Jacquelin, Aurélia Kwasiborski, Jean-Claude Manuguerra, Fabrice Monti, Anavaj Sakuntabhai, Jessica Vanhomwegen, Pierre Lafaye, Patrick Tabeling
The diagnosis of infectious diseases is entering a new and interesting phase. Technologies based on paper microfluidics, coupled to developments in isothermal amplification of Nucleic Acids (NAs) raise opportunities for bringing the methods of molecular biology in the field, in a low setting environment. A lot of work has been performed in the domain over the last few years and the landscape of contributions is rich and diverse. Most often, the level of sample preparation differs, along with the sample nature, the amplification and detection methods, and the design of the device, among other features...
June 20, 2017: Lab on a Chip
https://www.readbyqxmd.com/read/28632170/current-research-in-lidar-technology-used-for-the-remote-sensing-of-atmospheric-aerosols
#3
REVIEW
Adolfo Comerón, Constantino Muñoz-Porcar, Francesc Rocadenbosch, Alejandro Rodríguez-Gómez, Michaël Sicard
Lidars are active optical remote sensing instruments with unique capabilities for atmospheric sounding. A manifold of atmospheric variables can be profiled using different types of lidar: concentration of species, wind speed, temperature, etc. Among them, measurement of the properties of aerosol particles, whose influence in many atmospheric processes is important but is still poorly stated, stands as one of the main fields of application of current lidar systems. This paper presents a review on fundamentals, technology, methodologies and state-of-the art of the lidar systems used to obtain aerosol information...
June 20, 2017: Sensors
https://www.readbyqxmd.com/read/28627780/from-lithium-ion-to-sodium-ion-batteries-a-materials-perspective
#4
Prasant Kumar Nayak, Liangtao Yang, Wolfgang Brehm, Philipp Adelhelm
Mobile and stationary energy storage by rechargeable batteries is a topic of broad societal and economical relevance. Lithium-ion battery (LIB) technology is at the forefront of the development but a massively growing market will likely put severe pressure on resources and supply chains. Recently, sodium-ion batteries (SIBs) are being reconsidered with the aim of providing a lower-cost alternative that is less susceptible to resource and supply risks. On paper, the replacement of lithium by sodium in a battery seems straightforward at first but unpredictable surprises are often found in practice...
June 19, 2017: Angewandte Chemie
https://www.readbyqxmd.com/read/28625186/how-predictable-are-symptoms-in-psychopathological-networks-a-reanalysis-of-18-published-datasets
#5
J M B Haslbeck, E I Fried
BACKGROUND: Network analyses on psychopathological data focus on the network structure and its derivatives such as node centrality. One conclusion one can draw from centrality measures is that the node with the highest centrality is likely to be the node that is determined most by its neighboring nodes. However, centrality is a relative measure: knowing that a node is highly central gives no information about the extent to which it is determined by its neighbors. Here we provide an absolute measure of determination (or controllability) of a node - its predictability...
June 19, 2017: Psychological Medicine
https://www.readbyqxmd.com/read/28624882/predicting-acute-kidney-injury-current-status-and-future-challenges
#6
REVIEW
Simona Pozzoli, Marco Simonini, Paolo Manunta
Acute kidney injury (AKI) is characterized by an acute decline in renal function and is associated to increased mortality rate, hospitalization time, and total health-related costs. The severity of this 'fearsome' clinical complication might depend on, or even be worsened by, the late detection of AKI, when the diagnosis is based on the elevation of serum creatinine (SCr). For these reasons, in recent years a great number of new tools, biomarkers and predictive models have been proposed to clinicians in order to improve diagnosis and prevent the development of AKI...
June 17, 2017: Journal of Nephrology
https://www.readbyqxmd.com/read/28624881/discriminative-self-representation-sparse-regression-for-neuroimaging-based-alzheimer-s-disease-diagnosis
#7
Xiaofeng Zhu, Heung-Il Suk, Seong-Whan Lee, Dinggang Shen
In this paper, we propose a novel feature selection method by jointly considering (1) 'task-specific' relations between response variables (e.g., clinical labels in this work) and neuroimaging features and (2) 'self-representation' relations among neuroimaging features in a sparse regression framework. Specifically, the task-specific relation is devised to learn the relative importance of features for representation of response variables by a linear combination of the input features in a supervised manner, while the self-representation relation is used to take into account the inherent information among neuroimaging features such that any feature can be represented by a weighted sum of the other features, regardless of the label information, in an unsupervised manner...
June 17, 2017: Brain Imaging and Behavior
https://www.readbyqxmd.com/read/28624821/on-the-social-life-and-motivational-changes-of-aging-monkeys
#8
Julia Fischer
Although nonhuman primates have been used in biomedical research to develop a better understanding of physiological aging processes, their value as models for studying age-related differences in motivation, cognition, and decision-making has only recently been appreciated. This paper reviews the state of the art, with a focus on a recent study on Barbary macaques. A number of studies reported that with increasing age, Old World monkeys spend more time resting, have fewer social partners, and/or spend less time in social interactions, though other studies found no such effects...
June 17, 2017: Gerontology
https://www.readbyqxmd.com/read/28622719/toward-tio2-nanofluids-part-1-preparation-and-properties
#9
REVIEW
Liu Yang, Yuhan Hu
As a new generation of working fluid, nanofluid has long been regarded as a hot research topic in the past three decades. Many review papers have provided comprehensive and systematic summaries on the development and state-of-the-art of nanofluids. As of today, it is becoming increasingly difficult to provide a comprehensive review of all kinds of nanofluids owing to the huge amounts of the related literatures. And many controversies and inconsistencies in the reported arguments have been observed in various nanofluids...
December 2017: Nanoscale Research Letters
https://www.readbyqxmd.com/read/28622675/state-of-the-art-methods-for-brain-tissue-segmentation-a-review
#10
Lingraj Dora, Sanjay Agrawal, Rutuparna Panda, Ajith Abraham
Brain tissue segmentation is one of the most sought after research area in medical image processing. It provides detailed quantitative brain analysis for accurate disease diagnosis, detection and classification of abnormalities. It plays an essential role in discriminating healthy tissues from lesion tissues. Therefore, accurate disease diagnosis and treatment planning depends merely on the performance of segmentation method used. In this paper, we have studied the recent advances in brain tissue segmentation methods and their state-of-the-art in neuroscience research...
June 14, 2017: IEEE Reviews in Biomedical Engineering
https://www.readbyqxmd.com/read/28618719/a-short-progress-report-on-high-efficiency-perovskite-solar-cells
#11
REVIEW
He Tang, Shengsheng He, Chuangwei Peng
Faced with the increasingly serious energy and environmental crisis in the world nowadays, the development of renewable energy has attracted increasingly more attention of all countries. Solar energy as an abundant and cheap energy is one of the most promising renewable energy sources. While high-performance solar cells have been well developed in the last couple of decades, the high module cost largely hinders wide deployment of photovoltaic devices. In the last 10 years, this urgent demand for cost-effective solar cells greatly facilitates the research of solar cells...
December 2017: Nanoscale Research Letters
https://www.readbyqxmd.com/read/28617268/hydrochemical-characterization-of-a-river-affected-by-acid-mine-drainage-in-the-iberian-pyrite-belt
#12
J A Grande, M Santisteban, T Valente, M L de la Torre, P Gomes
This paper addresses the modelling of the processes associated with acid mine drainage affecting the Trimpancho River basin, chosen for this purpose because of its location and paradigmatic hydrological, geological, mining and environmental contexts. By using physical-chemical indicators it is possible to define the contamination degree of the system from the perspective of an entire river basin, due to its reduced dimension. This allows an exhaustive monitoring of the study area, considering the particularity that the stream flows directly into a water dam used for human supply...
June 2017: Water Science and Technology: a Journal of the International Association on Water Pollution Research
https://www.readbyqxmd.com/read/28617222/an-extensive-assessment-of-network-alignment-algorithms-for-comparison-of-brain-connectomes
#13
Marianna Milano, Pietro Hiram Guzzi, Olga Tymofieva, Duan Xu, Christofer Hess, Pierangelo Veltri, Mario Cannataro
BACKGROUND: Recently the study of the complex system of connections in neural systems, i.e. the connectome, has gained a central role in neurosciences. The modeling and analysis of connectomes are therefore a growing area. Here we focus on the representation of connectomes by using graph theory formalisms. Macroscopic human brain connectomes are usually derived from neuroimages; the analyzed brains are co-registered in the image domain and brought to a common anatomical space. An atlas is then applied in order to define anatomically meaningful regions that will serve as the nodes of the network - this process is referred to as parcellation...
June 6, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/28616711/integrating-noe-and-rdc-using-sum-of-squares-relaxation-for-protein-structure-determination
#14
Y Khoo, A Singer, D Cowburn
We revisit the problem of protein structure determination from geometrical restraints from NMR, using convex optimization. It is well-known that the NP-hard distance geometry problem of determining atomic positions from pairwise distance restraints can be relaxed into a convex semidefinite program (SDP). However, often the NOE distance restraints are too imprecise and sparse for accurate structure determination. Residual dipolar coupling (RDC) measurements provide additional geometric information on the angles between atom-pair directions and axes of the principal-axis-frame...
June 14, 2017: Journal of Biomolecular NMR
https://www.readbyqxmd.com/read/28615794/deepinfer-open-source-deep-learning-deployment-toolkit-for-image-guided-therapy
#15
Alireza Mehrtash, Mehran Pesteie, Jorden Hetherington, Peter A Behringer, Tina Kapur, William M Wells, Robert Rohling, Andriy Fedorov, Purang Abolmaesumi
Deep learning models have outperformed some of the previous state-of-the-art approaches in medical image analysis. Instead of using hand-engineered features, deep models attempt to automatically extract hierarchical representations at multiple levels of abstraction from the data. Therefore, deep models are usually considered to be more flexible and robust solutions for image analysis problems compared to conventional computer vision models. They have demonstrated significant improvements in computer-aided diagnosis and automatic medical image analysis applied to such tasks as image segmentation, classification and registration...
February 11, 2017: Proceedings of SPIE
https://www.readbyqxmd.com/read/28613173/large-scale-crowdsourced-study-for-tone-mapped-hdr-pictures
#16
Debarati Kundu, Deepti Ghadiyaram, Alan Bovik, Brian Evans
Measuring digital picture quality, as perceived by human observers, is increasingly important in many applications in which humans are the ultimate consumers of visual information. Standard dynamic range (SDR) images provide 8 bits/color/pixel. High dynamic range (HDR) images, usually created from multiple exposures of the same scene, can provide 16 or 32 bits/color/pixel, but need to be tonemapped to SDR for display on standard monitors. Multi-exposure fusion (MEF) techniques bypass HDR creation by fusing an exposure stack directly to SDR images to achieve aesthetically pleasing luminance and color distributions...
June 8, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28613171/simultaneous-feature-and-dictionary-learning-for-image-set-based-face-recognition
#17
Jiwen Lu, Gang Wang, Jie Zhou
In this paper, we propose a simultaneous feature and dictionary learning (SFDL) method for image set based face recognition, where each training and testing example contains a set of face images which were captured from different variations of pose, illumination, expression, resolution and motion. While a variety of feature learning and dictionary learning methods have been proposed in recent years and some of them have been successfully applied to image set based face recognition, most of them learn features and dictionaries for facial image sets individually, which may not be powerful enough because some discriminative information for dictionary learning may be compromised in the feature learning stage if they are applied sequentially, and vice versa...
June 8, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28613165/lossless-compression-of-medical-images-using-3d-predictors
#18
Luis Lucas, Nuno Rodrigues, Luis Cruz, Sergio Faria
This paper describes a highly efficient method for lossless compression of volumetric sets of medical images, such as CTs or MRIs. The proposed method, referred to as 3D-MRP, is based on the principle of minimum rate predictors (MRP), which is one of the state-of-the-art lossless compression technologies, presented in the data compression literature. The main features of the proposed method include the use of 3D predictors, 3D-block octree partitioning and classification, volume-based optimisation and support for 16 bit-depth images...
June 9, 2017: IEEE Transactions on Medical Imaging
https://www.readbyqxmd.com/read/28613161/deformation-based-curved-shape-representation
#19
Girum G Demisse, Djamila Aouada, Bjorn Ottersten
In this paper, we introduce a deformation based representation space for curved shapes in Rn. Given an ordered set of points sampled from a curved shape, the proposed method represents the set as an element of a finite dimensional matrix Lie group. Variation due to scale and location are filtered in a preprocessing stage, while shapes that vary only in rotation are identified by an equivalence relationship. The use of a finite dimensional matrix Lie group leads to a similarity metric with an explicit geodesic solution...
June 2, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/28611579/stacked-autoencoders-for-the-p300-component-detection
#20
Lukáš Vařeka, Pavel Mautner
Novel neural network training methods (commonly referred to as deep learning) have emerged in recent years. Using a combination of unsupervised pre-training and subsequent fine-tuning, deep neural networks have become one of the most reliable classification methods. Since deep neural networks are especially powerful for high-dimensional and non-linear feature vectors, electroencephalography (EEG) and event-related potentials (ERPs) are one of the promising applications. Furthermore, to the authors' best knowledge, there are very few papers that study deep neural networks for EEG/ERP data...
2017: Frontiers in Neuroscience
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