Acoustic Tomography of Composite Materials of the Through-Transition Method

2018 ◽  
Vol 938 ◽  
pp. 75-80
Author(s):  
A.I. Soldatov ◽  
A.A. Soldatov ◽  
P.V. Sorokin ◽  
M.A. Kostina ◽  
Y.V. Shulgina
Keyword(s):  
Composite Materials ◽  
Local Area ◽  
Test Area ◽  
Acoustic Tomography ◽  
Back Projection ◽  
Data Set ◽  
Monitor Screen ◽  
Testing Area ◽  
Local Areas ◽  
Acoustic Array

The article presents the image of the testing area for the through-transition method with an acoustic array. Using linear acoustic array in through-transition method, a set of data from different angles is obtained. Using this data set and the back projection method, the test area imaging is obtained, which is represented by a set of small local areas. The number of initial projections passing through each local area is calculated. Furthermore, the density function is determined and the resulting function, that is encoded whether in color or grayscale, is displayed on the monitor screen.

scholarly journals Use of community treatment orders and their outcomes: an observational study

2020 ◽  
Vol 8 (9) ◽  
pp. 1-76
Author(s):  
Scott Weich ◽  
Craig Duncan ◽  
Liz Twigg ◽  
Orla McBride ◽  
Helen Parsons ◽  
...  
Keyword(s):  
Mental Health ◽  
Mental Health Services ◽  
Health Services ◽  
Local Area ◽  
Community Treatment ◽  
Community Treatment Orders ◽  
Data Set ◽  
Local Areas ◽  
Treatment Order ◽  
Community Treatment Order

Background Community treatment orders are widely used in England. It is unclear whether their use varies between patients, places and services, or if they are associated with better patient outcomes. Objectives To examine variation in the use of community treatment orders and their associations with patient outcomes and health-care costs. Design Secondary analysis using multilevel statistical modelling. Setting England, including 61 NHS mental health provider trusts. Participants A total of 69,832 patients eligible to be subject to a community treatment order. Main outcome measures Use of community treatment orders and time subject to community treatment order; re-admission and total time in hospital after the start of a community treatment order; and mortality. Data sources The primary data source was the Mental Health Services Data Set. Mental Health Services Data Set data were linked to mortality records and local area deprivation statistics for England. Results There was significant variation in community treatment order use between patients, provider trusts and local areas. Most variation arose from substantially different practice in a small number of providers. Community treatment order patients were more likely to be in the ‘severe psychotic’ care cluster grouping, male or black. There was also significant variation between service providers and local areas in the time patients remained on community treatment orders. Although slightly more community treatment order patients were re-admitted than non-community treatment order patients during the study period (36.9% vs. 35.6%), there was no significant difference in time to first re-admission (around 32 months on average for both). There was some evidence that the rate of re-admission differed between community treatment order and non-community treatment order patients according to care cluster grouping. Community treatment order patients spent 7.5 days longer, on average, in admission than non-community treatment order patients over the study period. This difference remained when other patient and local area characteristics were taken into account. There was no evidence of significant variation between service providers in the effect of community treatment order on total time in admission. Community treatment order patients were less likely to die than non-community treatment order patients, after taking account of other patient and local area characteristics (odds ratio 0.69, 95% credible interval 0.60 to 0.81). Limitations Confounding by indication and potential bias arising from missing data within the Mental Health Services Data Set. Data quality issues precluded inclusion of patients who were subject to community treatment orders more than once. Conclusions Community treatment order use varied between patients, provider trusts and local areas. Community treatment order use was not associated with shorter time to re-admission or reduced time in hospital to a statistically significant degree. We found no evidence that the effectiveness of community treatment orders varied to a significant degree between provider trusts, nor that community treatment orders were associated with reduced mental health treatment costs. Our findings support the view that community treatment orders in England are not effective in reducing future admissions or time spent in hospital. We provide preliminary evidence of an association between community treatment order use and reduced rate of death. Future work These findings need to be replicated among patients who are subject to community treatment order more than once. The association between community treatment order use and reduced mortality requires further investigation. Study registration The study was approved by the University of Warwick’s Biomedical and Scientific Research Ethics Committee (REGO-2015-1623). Funding This project was funded by the National Institute for Health Research (NIHR) Health Services and Delivery Research programme and will be published in full in Health Services and Delivery Research; Vol. 8, No. 9. See the NIHR Journals Library website for further project information.

scholarly journals High performance logistic regression for privacy-preserving genome analysis

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Martine De Cock ◽  
Rafael Dowsley ◽  
Anderson C. A. Nascimento ◽  
Davis Railsback ◽  
Jianwei Shen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Genome Analysis ◽  
Local Area Network ◽  
Local Area ◽  
Activation Function ◽  
Area Network ◽  
Learning Models ◽  
Data Set ◽  
Machine Learning Models

Abstract Background In biomedical applications, valuable data is often split between owners who cannot openly share the data because of privacy regulations and concerns. Training machine learning models on the joint data without violating privacy is a major technology challenge that can be addressed by combining techniques from machine learning and cryptography. When collaboratively training machine learning models with the cryptographic technique named secure multi-party computation, the price paid for keeping the data of the owners private is an increase in computational cost and runtime. A careful choice of machine learning techniques, algorithmic and implementation optimizations are a necessity to enable practical secure machine learning over distributed data sets. Such optimizations can be tailored to the kind of data and Machine Learning problem at hand. Methods Our setup involves secure two-party computation protocols, along with a trusted initializer that distributes correlated randomness to the two computing parties. We use a gradient descent based algorithm for training a logistic regression like model with a clipped ReLu activation function, and we break down the algorithm into corresponding cryptographic protocols. Our main contributions are a new protocol for computing the activation function that requires neither secure comparison protocols nor Yao’s garbled circuits, and a series of cryptographic engineering optimizations to improve the performance. Results For our largest gene expression data set, we train a model that requires over 7 billion secure multiplications; the training completes in about 26.90 s in a local area network. The implementation in this work is a further optimized version of the implementation with which we won first place in Track 4 of the iDASH 2019 secure genome analysis competition. Conclusions In this paper, we present a secure logistic regression training protocol and its implementation, with a new subprotocol to securely compute the activation function. To the best of our knowledge, we present the fastest existing secure multi-party computation implementation for training logistic regression models on high dimensional genome data distributed across a local area network.

Modeling the Quasigeoid Heights on Local Areas of the Earth Surface by the Results of Expansion into a Generalized Fourier Series

2020 ◽  
Vol 28 (4) ◽  
pp. 82-94
Author(s):  
V.F. Kanushin ◽  
◽  
I.G. Ganagina ◽  
D.N. Goldobin ◽  
◽  
...  
Keyword(s):  
Fourier Series ◽  
Orthonormal System ◽  
Local Area ◽  
Spherical Functions ◽  
Trigonometric Functions ◽  
Computing Technology ◽  
Generalized Fourier Series ◽  
The Earth ◽  
Local Areas ◽  
Discrete Values

The article presents two methods of modeling discrete heights of a quasigeoid on a local area of the earth’s surface using a gen-eralized Fourier series. The first method is based on modeling the characteristics of the earth’s gravitational field on a plane and involves the use of a two-dimensional Fourier transform by an orthonormal system of trigonometric functions. The second method consists in the expansion of the quasigeoid heights in a Fourier series by an orthonormal system of spherical functions on a local area of the earth’s surface. The errors of approxima-tion of the obtained discrete values of the quasigeoid heights on the local territory are analyzed. It is shown that with the modern computing technology, the most accurate and technologically simple way to model the quasigeoid heights on local areas is to expand them into a Fourier series by an orthonormal system of spherical functions.

Local Triads of Sensors to Mine Collocated Acceleration for Structural Health Monitoring of an Aluminum Beam With Bolted Joints

2014 ◽  
Author(s):  
Ioannis T. Georgiou
Keyword(s):  
Aluminum Alloy ◽  
Research Effort ◽  
Complex Structure ◽  
Three Dimensional ◽  
Local Area ◽  
Bolted Joints ◽  
Critical Area ◽  
Local Areas ◽  
Vibration Wave ◽  
Aluminum Beam

Several aerospace, space, and naval platforms process a core structural system assembled wholly or partially by aluminum alloy elementary structures, such as beams, plates, and cylinders, among others. Aluminum alloys are preferred due to light weight and corrosion resistance. The formed complex aluminum alloy structure is characterized by a number of joints where different structural elements are joined together. Areas with joints are prone to damage. Some joints are made by bolts for assembly and reassembly purposes. A loose bolt can be considered as a man-induced damage in the whole complex structure. This damage can result in a catastrophic failure. The vibration and elastic wave propagation-based monitoring of a critical area bearing bolt joints can offer a reliable monitoring. In this work, we present a basic experimental research effort. We consider an aluminum alloy beam composed of two sunbeams joined by means of two bolts. The bolts are healthy in the sense that they are tight to the level of design recommended force. We use three mono-axial sensors in three dimensional arrangement and mine vibration-wave data in the form of collocated signals. These signals are analyzed by means of the proper orthogonal decomposition transform. The local tri-axial arrangement of mono-axial sensors is used to collect collocated acceleration signals in two local areas. One of the local areas contains the bolted joints. The other local area contains the free end of the structure. This is not considered as a critical area but a boundary accessible area. The POD modal structures of collocated vibration signals are quite different. The behavior is compared to that of structure without bolted joints.

scholarly journals Ethnicity at work: the case of British minority workers in the long-term care sector

2014 ◽  
Vol 33 (2) ◽  
pp. 177-192 ◽  
Author(s):  
Shereen Hussein ◽  
Jill Manthorpe ◽  
Mohamed Ismail
Keyword(s):  
Migrant Workers ◽  
Long Term Care ◽  
Social Care ◽  
Local Area ◽  
Care Work ◽  
European Economic Area ◽  
Term Care ◽  
Data Set ◽  
Content Type

Purpose – The aim of this paper is to explore the effect of ethnicity and separate this from the other dynamics associated with migration among members of the long-term care workforce in England focusing on the nature and structure of their jobs. The analysis examines interactions between ethnicity, gender, and age, and their relations with “meso” factors related to job and organizational characteristics and “macro” level factors related to local area characteristics. Design/methodology/approach – The paper analyses new national workforce data, the National Minimum Data Set for Social Care (NMDS-SC), n=357,869. The paper employs descriptive statistical analysis and a set of logistic regression models. Findings – The results indicate that labour participation of British black and minority ethnic (BME) groups in long-term care work is much lower than previously believed. There are variations in nature of work and possibly job security by ethnicity. Research limitations/implications – While the national sample is large, the data were not purposively collected to examine differentials in reasons to work in the care sector by different ethnicity. Practical implications – The analysis highlights the potential to actively promote social care work among British BME groups to meet workforce shortages, especially at a time where immigration policies are restricting the recruitment of non-European Economic Area nationals. Originality/value – The analysis provides a unique insight into the participation of British BME workers in the long-term care sector, separate from that of migrant workers.

Wave‐equation tomography

Geophysics ◽  
1992 ◽  
Vol 57 (1) ◽  
pp. 15-26 ◽  
Author(s):  
Marta Jo Woodward
Keyword(s):  
Wave Equation ◽  
Synthetic Data ◽  
Frequency Dispersion ◽  
Diffraction Tomography ◽  
Back Projection ◽  
Data Set ◽  
Rytov Approximation ◽  
Transmission Geometry ◽  
Ray Trace ◽  
Finite Aperture

The relation between ray‐trace and diffraction tomography is usually obscured by formulation of the two methods in different domains: the former in space, the latter in wavenumber. Here diffraction tomography is reformulated in the space domain, under the title of wave‐equation tomography. With this transformation, wave‐equation tomography projects monochromatic, scattered wavefields back over source‐receiver wavepaths, just as ray‐trace tomography projects traveltime delays back over source‐receiver raypaths. Derived under the Born approximation, these wavepaths are wave‐theoretic back‐projection patterns for reflected energy; derived under the Rytov approximation, they are wave‐theoretic back‐projection patterns for transmitted energy. Differences between ray‐trace and wave‐equation tomography are examined through comparison of wavepaths and raypaths, followed by their application to a transmission‐geometry, synthetic data set. Rytov wave‐equation tomography proves superior to ray‐trace tomography in dealing with geometrical frequency dispersion and finite‐aperture data, but inferior in robustness. Where ray‐trace tomography assumes linear phase delay and inverts the arrival time of one well‐understood event, wave‐equation tomography accommodates scattering and inverts all of the signal and noise on an infinite trace simultaneously. Interpreted through the uncertainty relation, these differences lead to a redefinition of Rytov wavepaths as monochromatic raypaths, and of raypaths as infinite‐bandwidth wavepaths (Rytov wavepaths averaged over an infinite bandwidth). The infinite‐bandwidth and infinite‐time assumptions of ray‐trace and Rytov, wave‐equation tomography are reconciled through the introduction of bandlimited raypaths (Rytov wavepaths averaged over a finite bandwidth). A compromise between rays and waves, bandlimited raypaths are broad back‐projection patterns that account for the uncertainty inherent in picking traveltimes from bandlimited data.

scholarly journals Air Travel in Small Communities: An Econometric Framework and Results

2010 ◽  
Author(s):  
Dipasis Bhadra
Keyword(s):  
United States ◽  
Weighted Least Squares ◽  
Local Area ◽  
The United States ◽  
Air Travel ◽  
United States Bureau ◽  
United States Department ◽  
Small Communities ◽  
Combining Data ◽  
Local Areas

In this paper, we examine the relationship between origin and destination (O&D) travel and local area characteristics for small communities. By combining data from Bureau of Transportation Statistics/United States Department of Transportation (BTS/DOT) on O&D travel with that of local area economic and demographic activities supplied by the United States Bureau of Economic Analyses (BEA), Department of Commerce, we specify a semi-log linear demand relationship for O&D travel in small communities. The resultant dataset covering the period 1999-2000 has more than 4,700 observations; 2,686 for communities without any small hubs, and 2,087 for communities with small hubs. Using a weighted least squares method, we estimate demand for air travel, defined by O&D pairs, for smaller communities. Our results indicate that average fare affects passenger demand negatively for both types of communities. Our results also confirm that local area income affects travel positively in both cases. However, the levels of travel tend to be affected by population differently; origin population affecting traffic negatively for smaller communities without any hub and positively for communities with small hubs. Presence of smaller hubs affects air travel positively; and market concentration of airlines affects O&D travel negatively. We demonstrate in this paper that factors affecting the economic framework are the ultimate factors driving the demand for air travel in the small communities in the long run. We also discuss approaches using our methodology for deriving bottom-up projections. These projections have distinct characteristics that may make them more useful for analyzing flow features, such as passenger and aircraft flows by local areas, determining and prioritizing infrastructure investment requirements by local areas, and determining revenue potential from these travels.

scholarly journals Symptoms Based Multiple Disease Prediction Model using Machine Learning Approach

2021 ◽  
Vol 10 (9) ◽  
pp. 67-72
Author(s):  
Talasila Bhanuteja ◽  
◽  
Kilaru Venkata Narendra Kumar ◽  
Kolli Sai Poornachand ◽  
Chennupati Ashish ◽  
...  
Keyword(s):  
Machine Learning ◽  
Well Being ◽  
Local Area ◽  
Learning Approach ◽  
Disease Prediction ◽  
Decision Tree Classifier ◽  
Data Set ◽  
Tree Classifier ◽  
Machine Learning Approach ◽  
Disease Forecast

The turn of events and misuse of a few noticeable Data mining strategies in various genuine application regions (for example Trade, Medical management and Natural science) has induced the usage of such methods in Machine Learning (ML) constrains, to distinct helpful snippets of information of the predefined information in medical services networks, biomedical fields and so forth The exact examination of clinical data set advantages in early illness expectation, patient consideration and local area administrations. The methodology of Machine Learning (ML) has been effectively utilized in grouped technologies including Disease forecast. The objective of generating classifier framework utilizing Machine Learning (ML) models is to massively assist with addressing the well-being related issues by helping the doctors to foresee and analyze illnesses at a beginning phase. Sample information of 4920 patient’s records determined to have 41 illnesses was chosen for examination. A reliant variable was made out of 41 sicknesses. 95 of 132 autonomous variables (symptoms) firmly identified with infections were chosen and advanced. This examination work completed shows the illness expectation framework created utilizing Machine learning calculations like Random Forest, Decision Tree Classifier and LightGBM. The paper confers the relative investigation of the consequences of the above-mentioned algorithms are utilized efficiently.

scholarly journals Conflation and Integration of Archived Geologic Maps and Associated Uncertainties

2016 ◽  
Vol 8 (1) ◽  
pp. 28
Author(s):  
Tom Shoberg
Keyword(s):  
Confidence Interval ◽  
Test Area ◽  
Geological Survey ◽  
Data Set ◽  
Gis Data ◽  
Geologic Maps ◽  
Data Set Integration ◽  
The U.S

Old, archived geologic maps are often available with little or no associated metadata.  This creates special problems in terms of extracting their data to use with a modern database.  This research focuses on some problems and uncertainties associated with conflating older geologic maps in regions where modern geologic maps are, as yet, non-existent as well as vertically integrating the conflated maps with layers of modern GIS data (in this case, The National Map of the U.S. Geological Survey).   Ste. Genevieve County, Missouri was chosen as the test area.  It is covered by six archived geologic maps constructed in the years between 1928 and 1994. Conflating these maps results in a map that is internally consistent with these six maps, is digitally integrated with hydrography, elevation and orthoimagery data, and has a 95% confidence interval useful for further data set integration.

Sign in / Sign up

Export Citation Format

Share Document