Theoretical justification of diagnostic intervals for hydraulic components of forest machines equipment

The theoretical justification for the hardening process of the surface layer of machine parts for combined methods of surface hardening with subsequent application of strengthening coatings, as well as reducing or increasing the fatigue limit due to the fretting process is presented.

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Organizational-economic mechanism of regional development of high-tech engineering

Voprosy regionalnoj ekonomiki ◽

10.21499/2078-4023-2018-35-2-3-9 ◽

2018 ◽

Vol 35 (2) ◽

pp. 3-9

Author(s):

M. S. Abrashkin

Keyword(s):

Regional Development ◽

Regional Economy ◽

Machine Building ◽

High Tech ◽

Regional Competitiveness ◽

Theoretical Justification ◽

Economic Mechanism ◽

The Impact

The article presents a study on the assessment of the impact of science-intensive machine building on the development of the regional economy and increasing its competitiveness. Based on the analysis of foreign sources, a theoretical justification was given for increasing the regional competitiveness of the economy. The tools of regional support of enterprises of science-intensive machine building and the model of the organizational and economic mechanism for regional development of science-intensive machine building were proposed. It has been proven that the development of science-intensive machine building influences the competitiveness of the region.

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Mass-conserving diffusion-based dynamics on graphs

European Journal of Applied Mathematics ◽

10.1017/s0956792521000061 ◽

2021 ◽

pp. 1-49

Author(s):

J.M BUDD ◽

Y. VAN GENNIP

Keyword(s):

Multiscale Modeling ◽

Classification Problems ◽

Theoretical Justification ◽

Finite Graphs ◽

Discrete Scheme ◽

Cahn Equation ◽

Separating Flows ◽

Special Case ◽

Appl Math ◽

Convergence Of The Scheme

An emerging technique in image segmentation, semi-supervised learning and general classification problems concerns the use of phase-separating flows defined on finite graphs. This technique was pioneered in Bertozzi and Flenner (2012, Multiscale Modeling and Simulation10(3), 1090–1118), which used the Allen–Cahn flow on a graph, and was then extended in Merkurjev et al. (2013, SIAM J. Imaging Sci.6(4), 1903–1930) using instead the Merriman–Bence–Osher (MBO) scheme on a graph. In previous work by the authors, Budd and Van Gennip (2020, SIAM J. Math. Anal.52(5), 4101–4139), we gave a theoretical justification for this use of the MBO scheme in place of Allen–Cahn flow, showing that the MBO scheme is a special case of a ‘semi-discrete’ numerical scheme for Allen–Cahn flow. In this paper, we extend this earlier work, showing that this link via the semi-discrete scheme is robust to passing to the mass-conserving case. Inspired by Rubinstein and Sternberg (1992, IMA J. Appl. Math.48, 249–264), we define a mass-conserving Allen–Cahn equation on a graph. Then, with the help of the tools of convex optimisation, we show that our earlier machinery can be applied to derive the mass-conserving MBO scheme on a graph as a special case of a semi-discrete scheme for mass-conserving Allen–Cahn. We give a theoretical analysis of this flow and scheme, proving various desired properties like existence and uniqueness of the flow and convergence of the scheme, and also show that the semi-discrete scheme yields a choice function for solutions to the mass-conserving MBO scheme.

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Diagnosed with a Rare Cancer: Experiences of Adult Sarcoma Survivors with the Healthcare System—Results from the SURVSARC Study

Cancers ◽

10.3390/cancers13040679 ◽

2021 ◽

Vol 13 (4) ◽

pp. 679

Author(s):

Cas Drabbe ◽

Dirk J. Grünhagen ◽

Winan J. Van Houdt ◽

Pètra M. Braam ◽

Vicky L. M. N. Soomers ◽

...

Keyword(s):

Supportive Care ◽

Healthcare System ◽

Support Groups ◽

Information Needs ◽

Age Groups ◽

Satisfaction With Care ◽

Rare Cancer ◽

Questionnaire Response ◽

Care Information ◽

Diagnostic Intervals

The aim of this study was to explore the experience of rare cancer patients with the healthcare system and examine differences between age groups (adolescents and young adults (AYA, 18–39 years), older adults (OA, 40–69 years) and elderly (≥70 years)). Dutch sarcoma patients, 2–10 years after diagnosis, completed a questionnaire on their experience with the healthcare system, satisfaction with care, information needs, patient and diagnostic intervals (first symptom to first doctor’s visit and first doctor’s visit to diagnosis, respectively) and received supportive care. In total, 1099 patients completed the questionnaire (response rate 58%): 186 AYAs, 748 OAs and 165 elderly. Many survivors experienced insufficient medical and non-medical guidance (32% and 38%), although satisfaction with care was rated good to excellent by 94%. Both patient and diagnostic intervals were >1 month for over half of the participants and information needs were largely met (97%). AYAs had the longest patient and diagnostic intervals, experienced the greatest lack of (non-)medical guidance, had more desire for patient support groups and used supportive care most often. This nationwide study among sarcoma survivors showed that healthcare experiences differ per age group and identified needs related to the rarity of these tumors, such as improvements concerning (non-)medical guidance and diagnostic intervals.

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Laplacian networks: bounding indicator function smoothness for neural networks robustness

APSIPA Transactions on Signal and Information Processing ◽

10.1017/atsip.2021.2 ◽

2021 ◽

Vol 10 ◽

Author(s):

Carlos Lassance ◽

Vincent Gripon ◽

Antonio Ortega

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Supervised Learning ◽

Indicator Function ◽

Training Data ◽

Theoretical Justification ◽

The Past ◽

Noisy Examples

For the past few years, deep learning (DL) robustness (i.e. the ability to maintain the same decision when inputs are subject to perturbations) has become a question of paramount importance, in particular in settings where misclassification can have dramatic consequences. To address this question, authors have proposed different approaches, such as adding regularizers or training using noisy examples. In this paper we introduce a regularizer based on the Laplacian of similarity graphs obtained from the representation of training data at each layer of the DL architecture. This regularizer penalizes large changes (across consecutive layers in the architecture) in the distance between examples of different classes, and as such enforces smooth variations of the class boundaries. We provide theoretical justification for this regularizer and demonstrate its effectiveness to improve robustness on classical supervised learning vision datasets for various types of perturbations. We also show it can be combined with existing methods to increase overall robustness.

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