NC Algorithms for Computing a Perfect Matching and a Maximum Flow in One-Crossing-Minor-Free Graphs

Objective: To assess the success of BMG urethroplasty in long segment anterior urethral stricture. Method: From January 2014 to December 2015, twenty male patients with long anterior segment urethral stricture were managed by BMG urethroplasty. After voiding trial they were followed up at 3 month with Uroflowmetry, RGU & MCU and PVR measurement by USG. Patients were further followed up with Uroflowmetry and PVR at 6 months interval.Successful outcome was defined as normal voiding with a maximum flow rate >15ml /sec and PVR<50 ml with consideration of maximum one attempt of OIU after catheter removal. Results: Mean stricture length was 5.2 cm (range 3-9 cm) and mean follow-up was 15.55 months (range 6-23 months). Only two patients developed stricture at proximal anastomotic site during follow-up. One of them voided normally after single attempt of OIU. Other one required second attempt of OIU and was considered as failure (5%). Conclusion: BMG urethroplasty is a simple technique with good surgical outcome. Bangladesh Journal of Urology, Vol. 19, No. 2, July 2016 p.64-68

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Tidal Flow: A Fast and Teachable Maximum Flow Algorithm

OLYMPIADS IN INFORMATICS ◽

10.15388/ioi.2018.03 ◽

2018 ◽

Vol 12 ◽

pp. 25-41

Author(s):

Matthew C. FONTAINE

Keyword(s):

Computer Science ◽

Tidal Flow ◽

Maximum Flow ◽

Efficient Algorithms ◽

Science Students ◽

Flow Algorithm ◽

Maximum Flows

Among the most interesting problems in competitive programming involve maximum flows. However, efficient algorithms for solving these problems are often difficult for students to understand at an intuitive level. One reason for this difficulty may be a lack of suitable metaphors relating these algorithms to concepts that the students already understand. This paper introduces a novel maximum flow algorithm, Tidal Flow, that is designed to be intuitive to undergraduate andpre-university computer science students.

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ASSESSMENT OF HYDROLOGICAL CHANGES IN THE ŠUŠVĖ RIVER

Proceedings of the 7th International Scientific Conference Rural Development 2015 ◽

10.15544/rd.2015.035 ◽

2015 ◽

Author(s):

Gražina ŽIBIENĖ ◽

Alvydas ŽIBAS ◽

Goda BLAŽAITYTĖ

Keyword(s):

Hydrological Regime ◽

Maximum Flow ◽

Environmental Flow ◽

Physical Structure ◽

River Channels ◽

Hydrologic Alteration ◽

Water Conditions ◽

Flow Components ◽

The Impact ◽

Large Floods

The construction of dams in rivers negatively affects ecosystems because dams violate the continuity of rivers, transform the biological and physical structure of the river channels, and the most importantly – alter the hydrological regime. The impact on the hydrology of the river can occur through reducing or increasing flows, altering seasonality of flows, changing the frequency, duration and timing of flow events, etc. In order to determine the extent of the mentioned changes, The Indicators of Hydrologic Alteration (IHA) software was used in this paper. The results showed that after the construction of Angiriai dam, such changes occurred in IHA Parameters group as: the water conditions of April month decreased by 31 %; 1-day, 3-days, 7-days and 30-days maximum flow decreased; the date of minimum flow occurred 21 days later; duration of high and low pulses and the frequency of low pulses decreased, but the frequency of high pulses increased, etc. The analysis of the Environmental Flow Components showed, that the essential differences were recorded in groups of the small and large floods, when, after the establishment of the Šušvė Reservoir, the large floods no longer took place and the probability of frequency of the small floods didn’t exceed 1 time per year.

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Integrating Improved U-Net and Continuous Maximum Flow Algorithm for 3D Brain Tumor Image Segmentation

Journal of Imaging Science and Technology ◽

10.2352/j.imagingsci.technol.2020.64.4.040412 ◽

2020 ◽

Vol 64 (4) ◽

pp. 40412-1-40412-11

Author(s):

Kexin Bai ◽

Qiang Li ◽

Ching-Hsin Wang

Keyword(s):

Brain Tumor ◽

Data Augmentation ◽

A Priori ◽

Class Imbalance ◽

Maximum Flow ◽

Magnetic Resonance Images ◽

Tumor Segmentation ◽

Similarity Coefficients ◽

Segmentation Algorithms ◽

Flow Algorithm

Abstract To address the issues of the relatively small size of brain tumor image datasets, severe class imbalance, and low precision in existing segmentation algorithms for brain tumor images, this study proposes a two-stage segmentation algorithm integrating convolutional neural networks (CNNs) and conventional methods. Four modalities of the original magnetic resonance images were first preprocessed separately. Next, preliminary segmentation was performed using an improved U-Net CNN containing deep monitoring, residual structures, dense connection structures, and dense skip connections. The authors adopted a multiclass Dice loss function to deal with class imbalance and successfully prevented overfitting using data augmentation. The preliminary segmentation results subsequently served as the a priori knowledge for a continuous maximum flow algorithm for fine segmentation of target edges. Experiments revealed that the mean Dice similarity coefficients of the proposed algorithm in whole tumor, tumor core, and enhancing tumor segmentation were 0.9072, 0.8578, and 0.7837, respectively. The proposed algorithm presents higher accuracy and better stability in comparison with some of the more advanced segmentation algorithms for brain tumor images.

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