Emergency Preservation and Resuscitation with Profound Hypothermia, Oxygen, and Glucose Allows Reliable Neurological Recovery after 3 h of Cardiac Arrest from Rapid Exsanguination in Dogs

We have used a rapid induction of profound hypothermia (> 10°C) with delayed resuscitation using cardiopulmonary bypass (CPB) as a novel approach for resuscitation from exsanguination cardiac arrest (ExCA). We have defined this approach as emergency preservation and resuscitation (EPR). We observed that 2 h but not 3 h of preservation could be achieved with favorable outcome using ice-cold normal saline flush to induce profound hypothermia. We tested the hypothesis that adding energy substrates to saline during induction of EPR would allow intact recovery after 3 h CA. Dogs underwent rapid ExCA. Two minutes after CA, EPR was induced with arterial ice-cold flush. Four treatments ( n = 6/group) were defined by a flush solution with or without 2.5% glucose (G + or G–) and with either oxygen or nitrogen (O + or O–) rapidly targeting tympanic temperature of 8°C. At 3 h after CA onset, delayed resuscitation was initiated with CPB, followed by intensive care to 72 h. At 72 h, all dogs in the O + G + group regained consciousness, and the group had better neurological deficit scores and overall performance categories than the O—groups (both P < 0.05). In the O + G—group, four of the six dogs regained consciousness. All but one dog in the O—groups remained comatose. Brain histopathology in the O—G + was worse than the other three groups ( P < 0.05). We conclude that EPR induced with a flush solution containing oxygen and glucose allowed satisfactory recovery of neurological function after a 3 h of CA, suggesting benefit from substrate delivery during induction or maintenance of a profound hypothermic CA.

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An Intuitionistic Fuzzy Based Novel Approach to CPU Scheduler

Current Medical Imaging Formerly Current Medical Imaging Reviews ◽

10.2174/1573405614666180903120708 ◽

2020 ◽

Vol 16 (4) ◽

pp. 316-328

Author(s):

Supriya Raheja

Keyword(s):

Fuzzy Inference ◽

The Other ◽

Simulation Environment ◽

Inference System ◽

Intuitionistic Fuzzy ◽

Novel Approach ◽

Continuous Feedback ◽

Simulation Results ◽

Effectiveness And Efficiency ◽

Unique Capability

Background: The extension of CPU schedulers with fuzzy has been ascertained better because of its unique capability of handling imprecise information. Though, other generalized forms of fuzzy can be used which can further extend the performance of the scheduler. Objectives: This paper introduces a novel approach to design an intuitionistic fuzzy inference system for CPU scheduler. Methods: The proposed inference system is implemented with a priority scheduler. The proposed scheduler has the ability to dynamically handle the impreciseness of both priority and estimated execution time. It also makes the system adaptive based on the continuous feedback. The proposed scheduler is also capable enough to schedule the tasks according to dynamically generated priority. To demonstrate the performance of proposed scheduler, a simulation environment has been implemented and the performance of proposed scheduler is compared with the other three baseline schedulers (conventional priority scheduler, fuzzy based priority scheduler and vague based priority scheduler). Results: Proposed scheduler is also compared with the shortest job first CPU scheduler as it is known to be an optimized solution for the schedulers. Conclusion: Simulation results prove the effectiveness and efficiency of intuitionistic fuzzy based priority scheduler. Moreover, it provides optimised results as its results are comparable to the results of shortest job first.

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Multispecies for multifunctions: combining four complementary species enhances multifunctionality of sown grassland

Scientific Reports ◽

10.1038/s41598-021-82162-y ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Matthias Suter ◽

Olivier Huguenin-Elie ◽

Andreas Lüscher

Keyword(s):

Forage Quality ◽

N Cycling ◽

Forage Production ◽

Mixture Effects ◽

Novel Approach ◽

Overall Performance ◽

The Mean ◽

High Forage ◽

Diversity Effects ◽

Species Mixtures

AbstractAssessing the overall performance of ecosystems requires a quantitative evaluation of multifunctionality. We investigated plant species diversity effects on individual functions and overall multifunctionality in a grassland experiment with sown monocultures and mixtures comprising four key grass and legume species. Nitrogen fertilisation rates were 50, 150, and 450 kg N ha−1 yr−1 (N50, N150, N450). Ten functions were measured representing forage production, N cycling, and forage quality, all being related to either productivity or environmental footprint. Multifunctionality was analysed by a novel approach using the mean log response ratio across functions. Over three experimental years, mixture effects benefited all forage production and N cycling functions, while sustaining high forage quality. Thus, mixture effects did not provoke any trade-off among the analysed functions. High N fertilisation rates generally diminished mixture benefits. Multifunctionality of four-species mixtures was considerably enhanced, and mixture overall performance was up to 1.9 (N50), 1.8 (N150), and 1.6 times (N450) higher than in averaged monocultures. Multifunctionality of four-species mixtures at N50 was at least as high as in grass monocultures at N450. Sown grass–legume mixtures combining few complementary species at low to moderate N fertilisation sustain high multifunctionality and are a ‘ready-to-use’ option for the sustainable intensification of agriculture.

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Surviving cardiac arrest: What happens after admission to the intensive care unit?

Revista Portuguesa de Cardiologia ◽

10.1016/j.repc.2020.07.020 ◽

2021 ◽

Author(s):

Raquel Menezes Fernandes ◽

Daniel Nuñez ◽

Nuno Marques ◽

Cláudia Camila Dias ◽

Cristina Granja

Keyword(s):

Intensive Care Unit ◽

Cardiac Arrest ◽

Intensive Care

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Mining discriminative patches for script identification in natural scene images

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-200260 ◽

2021 ◽

Vol 40 (1) ◽

pp. 551-563

Author(s):

Liqiong Lu ◽

Dong Wu ◽

Ziwei Tang ◽

Yaohua Yi ◽

Faliang Huang

Keyword(s):

Neural Networks ◽

Experimental Results ◽

The Other ◽

Natural Scene ◽

Fixed Size ◽

Script Identification ◽

Aspect Ratios ◽

Novel Approach ◽

Public Datasets ◽

Natural Scene Images

This paper focuses on script identification in natural scene images. Traditional CNNs (Convolution Neural Networks) cannot solve this problem perfectly for two reasons: one is the arbitrary aspect ratios of scene images which bring much difficulty to traditional CNNs with a fixed size image as the input. And the other is that some scripts with minor differences are easily confused because they share a subset of characters with the same shapes. We propose a novel approach combing Score CNN, Attention CNN and patches. Attention CNN is utilized to determine whether a patch is a discriminative patch and calculate the contribution weight of the discriminative patch to script identification of the whole image. Score CNN uses a discriminative patch as input and predict the score of each script type. Firstly patches with the same size are extracted from the scene images. Secondly these patches are used as inputs to Score CNN and Attention CNN to train two patch-level classifiers. Finally, the results of multiple discriminative patches extracted from the same image via the above two classifiers are fused to obtain the script type of this image. Using patches with the same size as inputs to CNN can avoid the problems caused by arbitrary aspect ratios of scene images. The trained classifiers can mine discriminative patches to accurately identify some confusing scripts. The experimental results show the good performance of our approach on four public datasets.

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Density Guarantee on Finding Multiple Subgraphs and Subtensors

ACM Transactions on Knowledge Discovery from Data ◽

10.1145/3446668 ◽

2021 ◽

Vol 15 (5) ◽

pp. 1-32

Author(s):

Quang-huy Duong ◽

Heri Ramampiaro ◽

Kjetil Nørvåg ◽

Thu-lan Dam

Keyword(s):

Lower Bound ◽

State Of The Art ◽

The State ◽

The Other ◽

Exact Methods ◽

Practical Solution ◽

Novel Approach ◽

Wide Range ◽

Real World Datasets ◽

Tensor Data

Dense subregion (subgraph & subtensor) detection is a well-studied area, with a wide range of applications, and numerous efficient approaches and algorithms have been proposed. Approximation approaches are commonly used for detecting dense subregions due to the complexity of the exact methods. Existing algorithms are generally efficient for dense subtensor and subgraph detection, and can perform well in many applications. However, most of the existing works utilize the state-or-the-art greedy 2-approximation algorithm to capably provide solutions with a loose theoretical density guarantee. The main drawback of most of these algorithms is that they can estimate only one subtensor, or subgraph, at a time, with a low guarantee on its density. While some methods can, on the other hand, estimate multiple subtensors, they can give a guarantee on the density with respect to the input tensor for the first estimated subsensor only. We address these drawbacks by providing both theoretical and practical solution for estimating multiple dense subtensors in tensor data and giving a higher lower bound of the density. In particular, we guarantee and prove a higher bound of the lower-bound density of the estimated subgraph and subtensors. We also propose a novel approach to show that there are multiple dense subtensors with a guarantee on its density that is greater than the lower bound used in the state-of-the-art algorithms. We evaluate our approach with extensive experiments on several real-world datasets, which demonstrates its efficiency and feasibility.

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A Novel Approach to Oil Layer Recognition Model Using Whale Optimization Algorithm and Semi-Supervised SVM

Symmetry ◽

10.3390/sym13050757 ◽

2021 ◽

Vol 13 (5) ◽

pp. 757

Author(s):

Yongke Pan ◽

Kewen Xia ◽

Li Wang ◽

Ziping He

Keyword(s):

Optimization Algorithm ◽

Oil And Gas ◽

The Other ◽

Whale Optimization Algorithm ◽

Classification Model ◽

Support Vector ◽

Recognition Model ◽

Novel Approach ◽

Whale Optimization ◽

Oil And Gas Reservoir

The dataset distribution of actual logging is asymmetric, as most logging data are unlabeled. With the traditional classification model, it is hard to predict the oil and gas reservoir accurately. Therefore, a novel approach to the oil layer recognition model using the improved whale swarm algorithm (WOA) and semi-supervised support vector machine (S3VM) is proposed in this paper. At first, in order to overcome the shortcomings of the Whale Optimization Algorithm applied in the parameter-optimization of the S3VM model, such as falling into a local optimization and low convergence precision, an improved WOA was proposed according to the adaptive cloud strategy and the catfish effect. Then, the improved WOA was used to optimize the kernel parameters of S3VM for oil layer recognition. In this paper, the improved WOA is used to test 15 benchmark functions of CEC2005 compared with five other algorithms. The IWOA–S3VM model is used to classify the five kinds of UCI datasets compared with the other two algorithms. Finally, the IWOA–S3VM model is used for oil layer recognition. The result shows that (1) the improved WOA has better convergence speed and optimization ability than the other five algorithms, and (2) the IWOA–S3VM model has better recognition precision when the dataset contains a labeled and unlabeled dataset in oil layer recognition.

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Comparison of three fixation methods for orotracheal intubation in 95 adults

European Journal of Medical Research ◽

10.1186/s40001-020-00446-x ◽

2020 ◽

Vol 25 (1) ◽

Author(s):

Ye Sun ◽

Hua Fan ◽

Xiao-Xia Song ◽

Hua Zhang

Keyword(s):

Intensive Care Unit ◽

Intensive Care ◽

Tracheal Intubation ◽

Older People ◽

Endotracheal Intubation ◽

Orotracheal Intubation ◽

The Other ◽

Fixation Methods ◽

The Difference

Abstract Background The present study aimed to compare three fixation methods for orotracheal intubation. Methods Through literature retrieval, the effects of the adhesive/twill tape method, fixator method, and adhesive/twill tape–fixator alternation method on patients with tracheal intubation in the intensive care unit (ICU) were compared. Results The fixator and alternation methods were more effective in protecting the tongue mucosa and teeth. The alternation method was superior to the other two methods in maintaining the position of the endotracheal intubation. However, the difference in facial and lip injuries between the three methods was not statistically significant. Conclusion The fixator method can significantly reduce intraoral injury and is more suitable for older people with weak tongue mucosa and loose teeth. These are worth popularizing among a wider group.

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