Using GPS to evaluate productivity and performance of forest machine systems

Treesearch

Steven E. Taylor; Timothy P. McDonald; Matthew W. Veal; Ton E. Grift

2001-01-01

This paper reviews recent research and operational applications of using GPS as a tool to help monitor the locations, travel patterns, performance, and productivity of forest machines. The accuracy of dynamic GPS data collected on forest machines under different levels of forest canopy is reviewed first. Then, the paper focuses on the use of GPS for monitoring forest...

77 FR 36251 - Intermountain Region, Boise National Forest; Emmett Ranger District, Idaho; Scriver Creek...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-06-18

... relocated segment of NFS Road 696D would be decommissioned); and (4) road maintenance activities would occur... maintenance activities may include but are not limited to road prism blading, spot aggregate placement...) Permanent national forest system (NFS) roads can increase long term resource impacts and road maintenance...

36 CFR 908.12 - Retention on the List of Qualified Persons.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 36 Parks, Forests, and Public Property 3 2011-07-01 2011-07-01 false Retention on the List of Qualified Persons. 908.12 Section 908.12 Parks, Forests, and Public Property PENNSYLVANIA AVENUE DEVELOPMENT... Qualified Person is relocated into or has a binding lease commitment for Newly Developed Space; (3) The...

36 CFR 908.12 - Retention on the List of Qualified Persons.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 36 Parks, Forests, and Public Property 3 2010-07-01 2010-07-01 false Retention on the List of Qualified Persons. 908.12 Section 908.12 Parks, Forests, and Public Property PENNSYLVANIA AVENUE DEVELOPMENT... Qualified Person is relocated into or has a binding lease commitment for Newly Developed Space; (3) The...

Machine rates for selected forest harvesting machines

Treesearch

R.W. Brinker; J. Kinard; Robert Rummer; B. Lanford

2002-01-01

Very little new literature has been published on the subject of machine rates and machine cost analysis since 1989 when the Alabama Agricultural Experiment Station Circular 296, Machine Rates for Selected Forest Harvesting Machines, was originally published. Many machines discussed in the original publication have undergone substantial changes in various aspects, not...

Global Positioning System (GPS) and Geographic Information System (GIS) analysis of mobile harvesting equipment and sediment delivery to streams during forest harvest operations on steep terrain: Experimental design

Treesearch

Daniel Bowker; Jeff Stringer; Chris Barton; Songlin Fei

2011-01-01

Sediment mobilized by forest harvest machine traffic contributes substantially to the degradation of headwater stream systems. This study monitored forest harvest machine traffic to analyze how it affects sediment delivery to stream channels. Harvest machines were outfitted with global positioning system (GPS) dataloggers, recording machine movements and working status...

The status of accurately locating forest inventory and analysis plots using the Global Positioning System

Treesearch

Michael Hoppus; Andrew Lister

2007-01-01

Historically, field crews used Global Positioning System (GPS) coordinates to establish and relocate plots, as well as document their general location. During the past 5 years, the increase in Geographic Information System (GIS) capabilities and in customer requests to use the spatial relationships between Forest Inventory and Analysis (FIA) plot data and other GIS...

Robust Airborne Networking Extensions (RANGE)

DTIC Science & Technology

2008-02-01

IMUNES [13] project, which provides an entire network stack virtualization and topology control inside a single FreeBSD machine . The emulated topology...Multicast versus broadcast in a manet.” in ADHOC-NOW, 2004, pp. 14–27. [9] J. Mukherjee, R. Atwood , “ Rendezvous point relocation in protocol independent...computer with an Ethernet connection, or a Linux virtual machine on some other (e.g., Windows) operating system, should work. 2.1 Patching the source code

78 FR 25693 - Mt. Baker-Snoqualmie National Forest; Snohomish County, WA; Green Mountain Lookout Removal

Federal Register 2010, 2011, 2012, 2013, 2014

2013-05-02

... Green Mountain and relocate it to Circle Peak, authorizing the use of motorized equipment and mechanical... the Court. 2. Utilize motorized equipment and mechanical transport within Glacier Peak Wilderness for...

Vegetation resurvey is robust to plot location uncertainty

PubMed Central

Kopecký, Martin; Macek, Martin

2017-01-01

Aim Resurveys of historical vegetation plots are increasingly used for the assessment of decadal changes in plant species diversity and composition. However, historical plots are usually relocated only approximately. This potentially inflates temporal changes and undermines results. Location Temperate deciduous forests in Central Europe. Methods To explore if robust conclusions can be drawn from resurvey studies despite location uncertainty, we compared temporal changes in species richness, frequency, composition and compositional heterogeneity between exactly and approximately relocated plots. We hypothesized that compositional changes should be lower and changes in species richness should be less variable on exactly relocated plots, because pseudo-turnover inflates temporal changes on approximately relocated plots. Results Temporal changes in species richness were not more variable and temporal changes in species composition and compositional heterogeneity were not higher on approximately relocated plots. Moreover, the frequency of individual species changed similarly on both plot types. Main conclusions The resurvey of historical vegetation plots is robust to uncertainty in original plot location and, when done properly, provides reliable evidence of decadal changes in plant communities. This provides important background for other resurvey studies and opens up the possibility for large-scale assessments of plant community change. PMID:28503083

An Autonomous Connectivity Restoration Algorithm Based on Finite State Machine for Wireless Sensor-Actor Networks.

PubMed

Zhang, Ying; Wang, Jun; Hao, Guan

2018-01-08

With the development of autonomous unmanned intelligent systems, such as the unmanned boats, unmanned planes and autonomous underwater vehicles, studies on Wireless Sensor-Actor Networks (WSANs) have attracted more attention. Network connectivity algorithms play an important role in data exchange, collaborative detection and information fusion. Due to the harsh application environment, abnormal nodes often appear, and the network connectivity will be prone to be lost. Network self-healing mechanisms have become critical for these systems. In order to decrease the movement overhead of the sensor-actor nodes, an autonomous connectivity restoration algorithm based on finite state machine is proposed. The idea is to identify whether a node is a critical node by using a finite state machine, and update the connected dominating set in a timely way. If an abnormal node is a critical node, the nearest non-critical node will be relocated to replace the abnormal node. In the case of multiple node abnormality, a regional network restoration algorithm is introduced. It is designed to reduce the overhead of node movements while restoration happens. Simulation results indicate the proposed algorithm has better performance on the total moving distance and the number of total relocated nodes compared with some other representative restoration algorithms.

An Autonomous Connectivity Restoration Algorithm Based on Finite State Machine for Wireless Sensor-Actor Networks

PubMed Central

Zhang, Ying; Wang, Jun; Hao, Guan

2018-01-01

With the development of autonomous unmanned intelligent systems, such as the unmanned boats, unmanned planes and autonomous underwater vehicles, studies on Wireless Sensor-Actor Networks (WSANs) have attracted more attention. Network connectivity algorithms play an important role in data exchange, collaborative detection and information fusion. Due to the harsh application environment, abnormal nodes often appear, and the network connectivity will be prone to be lost. Network self-healing mechanisms have become critical for these systems. In order to decrease the movement overhead of the sensor-actor nodes, an autonomous connectivity restoration algorithm based on finite state machine is proposed. The idea is to identify whether a node is a critical node by using a finite state machine, and update the connected dominating set in a timely way. If an abnormal node is a critical node, the nearest non-critical node will be relocated to replace the abnormal node. In the case of multiple node abnormality, a regional network restoration algorithm is introduced. It is designed to reduce the overhead of node movements while restoration happens. Simulation results indicate the proposed algorithm has better performance on the total moving distance and the number of total relocated nodes compared with some other representative restoration algorithms. PMID:29316702

Minimizing soil impacts from forest operations

Treesearch

Emily A. Carter

2011-01-01

Several studies were conducted by Forest Service researchers and University and Industrial collaborators that investigated the potential for lessening soil surface disturbances and compaction in forest operations through modifications of machine components or harvest systems. Specific machine modifications included change in tire size, use of dual tire systems,...

The impact on rural livelihoods and ecosystem services of a major relocation and settlement program: A case in Shaanxi, China.

PubMed

Li, Cong; Li, Shuzhuo; Feldman, Marcus W; Li, Jie; Zheng, Hua; Daily, Gretchen C

2018-03-01

China's largest-ever resettlement program is underway, aiming to restore ecosystems and lift ecosystem service providers out of the poverty trap and into sustainable livelihoods. We examine the impact of the relocation and settlement program (RSP) to date, reporting on an ecosystem services (ES) assessment and a 1400-household survey. The RSP generally achieves the goals of ES increase and livelihood restore. In biophysical terms, the RSP improves water quality, sediment retention, and carbon sequestration. In social terms, resettled households so far report transformation of livelihoods activities from traditional inefficient agricultural and forest production to non-farm activities. Increased income contributes to decrease the poverty rate and improve resettled households' living condition and standard. Meanwhile, the RSP decreases households' dependence on ES in terms of provisioning services. Difficulty and challenge also showed up subsequently after relocation. A major current challenge is to enable poorer households to move, while providing greater follow-up support to relocated households. While the program is unique to China, it illuminates widespread opportunities for addressing environmental and poverty-related concerns in a rapidly changing world.

Studies of worn surfaces by relocation profilometry

NASA Astrophysics Data System (ADS)

Rîpă, M.; Iliuță, V.

2018-01-01

By relocation profilometry, a series of surface profiles can be recorded from the same track on a specimen. These techniques are used for monitoring specific particular points on the surface subjected to wear processes, in a more accurate manner as comparing to those involving average statistical information for surface. The method is providing a much more significant information about the surface, in a more efficient way, assuring that the same unworn investigated surface is studied after wear test. The studied roughness digital profiles were obtained before and after the testing of rolling/sliding line contacts, characteristic for spur gears, which has been simulated on SAE sets, with a two rollers test machine. The acquisition of the relocated profiles is performed on the same generatrix of the roller, before and after wear testing. To correlate the unworn and worn profiles, a spheroconical indentation was created on the circumferential surface of the disk, in the zone of the tested roller that remain unworn during the test. Measuring changes of the profiles by relocation techniques, two methods for wear assessment are presented: linear wear estimation by simulating the profile wearing and estimation of the volume wear.

New developments in operator protection for forest machines

Treesearch

Robert B. Rummer; S. Taylor; M. Veal

2003-01-01

Mechanization of forest operations ha greatly improved saftey of woods work. However, increasing use of machines has introduced new hazards that must be addressed. Two of these hazards are rollover of swing-type forestry machines (currently excluded from standard protection) and the hazard of thrown objects from cutting devices. Ongoing research projects are developing...

Thrown object testing of forest machine operator protective structures

Treesearch

S.E. Taylor; M.W. Veal; R.B. Rummer

2003-01-01

High-speed chains or rotating disks are commonly used to cut and process trees during forest harvesting operations. Mechanical failure or fatigue of these tools can lead to a potentially hazardous situation where fragments of chain or sawteeth are thrown through the operator enclosures on forest machines. This poster presentation discusses the development and...

12 CFR 5.30 - Establishment, acquisition, and relocation of a branch.

Code of Federal Regulations, 2013 CFR

2013-01-01

... deposits are received, checks paid, or money lent. A branch does not include an automated teller machine (ATM) or a remote service unit. (i) A branch established by a national bank includes a mobile facility... purposes of making deposits, paying checks, or borrowing money (e.g., an office established by the bank...

12 CFR 5.30 - Establishment, acquisition, and relocation of a branch.

Code of Federal Regulations, 2012 CFR

2012-01-01

... deposits are received, checks paid, or money lent. A branch does not include an automated teller machine (ATM) or a remote service unit. (i) A branch established by a national bank includes a mobile facility... purposes of making deposits, paying checks, or borrowing money (e.g., an office established by the bank...

12 CFR 5.30 - Establishment, acquisition, and relocation of a branch.

Code of Federal Regulations, 2010 CFR

2010-01-01

... deposits are received, checks paid, or money lent. A branch does not include an automated teller machine (ATM) or a remote service unit. (i) A branch established by a national bank includes a mobile facility... purposes of making deposits, paying checks, or borrowing money (e.g., an office established by the bank...

12 CFR 5.30 - Establishment, acquisition, and relocation of a branch.

Code of Federal Regulations, 2014 CFR

2014-01-01

... deposits are received, checks paid, or money lent. A branch does not include an automated teller machine (ATM) or a remote service unit. (i) A branch established by a national bank includes a mobile facility... purposes of making deposits, paying checks, or borrowing money (e.g., an office established by the bank...

12 CFR 5.30 - Establishment, acquisition, and relocation of a branch.

Code of Federal Regulations, 2011 CFR

2011-01-01

... deposits are received, checks paid, or money lent. A branch does not include an automated teller machine (ATM) or a remote service unit. (i) A branch established by a national bank includes a mobile facility... purposes of making deposits, paying checks, or borrowing money (e.g., an office established by the bank...

Ballistic Missile Propellant Evaluation Test Motor System (Super BATES)

DTIC Science & Technology

1974-11-25

and laminated parts are debulked and 75 cured in a hydroclave at 950 psi (minimum) and 3000 F, and machined to final dimensions. All molded parts are...to pick dlf- ferent lengths of motors simply hy relocating and pinning the cross arms. flatbed truck with forklift will be i adequate for moving

Effect of land-use change on soil organic carbon stocks in the Eastern Usambara Mountain (Tanzania)

NASA Astrophysics Data System (ADS)

Kirsten, Maximilian; Kaaya, Abel; Klinger, Thomas; Feger, Karl-Heinz

2014-05-01

A soil organic carbon (SOC) inventory, covering 10 sites with 5 different land-use systems (primary forest, secondary forest, tea plantation, home garden, and cropland) was conducted in the tropical monsoonal Eastern Usambara Mountains (EUM), NE Tanzania. At all sites the environmental factors such as climate and parent material, for soil formation (gneiss), as well as elevation and slope position are highly comparable. The evergreen submontane primary rain forest, which still exists in vast areas in the EUM and the well-known land-use history there provide nearly optimal conditions for the assessment of land-use change effects on soil properties, notably the SOC stocks. We collected horizon-wise samples from soil pit profiles. In addition, samples from fixed depth-intervals were taken from 8 augering points located systematically around each soil pit. The sampling scheme yielded a unique set of soil information (pedological, chemical, and physical) that favours a reliable assessment of SOC stocks and future analytical work on SOM quality and binding mechanisms. The investigated soils are characterized by high clay contents, which increase with depth. Soil pH varies between 3.5 and 5.4 over all land-use systems and horizons, higher pH values could be detected for the agricultural systems in the topsoil, the differences between agricultural and forest systems decrease in the subsoil. The potential cation exchange capacity is in most cases < 24 cmolc kg-1, furthermore the base saturation is always < 50 % in the subsoil. Thus, based on that analytical data all soils can be classified as Acrisols revealing the high comparability of the investigated sites. This is an excellent prerequisite for the 'false chronosequence' approach applied. Organic carbon (C) stocks in the soils from the investigated land-use systems cover a wide range between 17.1 and 24.2 kg m-2 (0-100 cm). Variability is even high in the subset of the 3 primary forests. Statistically significant differences between the forest and cropland systems occur in the uppermost depth interval 0-10 cm. Furthermore, the primary forests have higher, but not significantly different SOC stocks in the topsoil (0-40 cm) compared with the cropland systems. In all investigated soils the SOC stocks for the entire soil profiles (0-100 cm) are in a narrow range. This may give a hint on SOC relocation from the topsoil to the subsoil when forests were converted to cropland systems. Our results reveal that this land-use change has led to a shift in above- and belowground litter distribution and amount. Also slash and burn practises as well as burning of plant residues in arable farming are common in the EUM. Both phenomena may control SOC relocation as they are associated with a changed C input and/or the formation of C compounds that can be relocated in the profile. In all investigated soils high concentrations of dithionite- and oxalate- extractable iron and aluminum were analyzed. Hence, interaction of SOC with oxides formed by the two metals is here probably one of the main stabilization mechanisms of SOC. The relocation and stabilization processes of SOC are the key functions for the implementation of sustainable agriculture in the EUM, and the conducted study provide a suitable basis for our ongoing research in this region of the wet tropics of Africa.

Design of a hydraulic bending machine

Treesearch

Steven G. Hankel; Marshall Begel

2004-01-01

To keep pace with customer demands while phasing out old and unserviceable test equipment, the staff of the Engineering Mechanics Laboratory (EML) at the USDA Forest Service, Forest Products Laboratory, designed and assembled a hydraulic bending test machine. The EML built this machine to test dimension lumber, nominal 2 in. thick and up to 12 in. deep, at spans up to...

Accuracy of tracking forest machines with GPS

Treesearch

M.W. Veal; S.E. Taylor; T.P. McDonald; D.K. McLemore; M.R. Dunn

2001-01-01

This paper describes the results of a study that measured the accuracy of using GPS to track movement of forest machines. Two different commercially available GPS receivers (Trimble ProXR and GeoExplorer II) were used to track\\r\

Probability machines: consistent probability estimation using nonparametric learning machines.

PubMed

Malley, J D; Kruppa, J; Dasgupta, A; Malley, K G; Ziegler, A

2012-01-01

Most machine learning approaches only provide a classification for binary responses. However, probabilities are required for risk estimation using individual patient characteristics. It has been shown recently that every statistical learning machine known to be consistent for a nonparametric regression problem is a probability machine that is provably consistent for this estimation problem. The aim of this paper is to show how random forests and nearest neighbors can be used for consistent estimation of individual probabilities. Two random forest algorithms and two nearest neighbor algorithms are described in detail for estimation of individual probabilities. We discuss the consistency of random forests, nearest neighbors and other learning machines in detail. We conduct a simulation study to illustrate the validity of the methods. We exemplify the algorithms by analyzing two well-known data sets on the diagnosis of appendicitis and the diagnosis of diabetes in Pima Indians. Simulations demonstrate the validity of the method. With the real data application, we show the accuracy and practicality of this approach. We provide sample code from R packages in which the probability estimation is already available. This means that all calculations can be performed using existing software. Random forest algorithms as well as nearest neighbor approaches are valid machine learning methods for estimating individual probabilities for binary responses. Freely available implementations are available in R and may be used for applications.

23. Photo copy of photograph, (original in Forest Service Office, ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

23. Photo copy of photograph, (original in Forest Service Office, Elkins, WV, photo #248336, 'Tree nurseries-seed bed seeding machine'), D. A. Oliver, 1930. VIEW WEST, SEEDING MACHINE. - Parsons Nursery, South side of U.S. Route 219, Parsons, Tucker County, WV

78 FR 64235 - Emergency Exemption; Issuance of Emergency Permit To Survey for and Relocate Jemez Mountain...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-10-28

... Salamanders Within the Santa Fe National Forest, New Mexico AGENCY: Fish and Wildlife Service, Interior... Jemez Mountain salamander (Plethodon neomexicanus) as endangered throughout its range in New Mexico... individual Jemez Mountain salamanders being needlessly harmed or killed. We, the U.S. Fish and Wildlife...

A techno-economic analysis of using mobile distributed pyrolysis facilities to deliver a forest residue resource.

PubMed

Brown, Duncan; Rowe, Andrew; Wild, Peter

2013-12-01

Distributed mobile conversion facilities using either fast pyrolysis or torrefaction processes can be used to convert forest residues to more energy dense substances (bio-oil, bio-slurry or torrefied wood) that can be transported as feedstock for bio-fuel facilities. Results show that the levelised delivered cost of a forest residue resource using mobile facility networks can be lower than using conventional woodchip delivery methods under appropriate conditions. Torrefied wood is the lowest cost pathway of delivering a forest residue resource when using mobile facilities. Cost savings occur against woodchip delivery for annual forest residue harvests above 2.5 million m(3) or when transport distances greater than 300 km are required. Important parameters that influence levelised delivered costs are transport distances (forest residue spatial density), haul cost factors, and initial moisture content of forest residues. Relocating mobile facilities can be optimised for lowest cost delivery as transport distances of raw biomass are reduced. Copyright © 2013 Elsevier Ltd. All rights reserved.

Novel solutions for an old disease: diagnosis of acute appendicitis with random forest, support vector machines, and artificial neural networks.

PubMed

Hsieh, Chung-Ho; Lu, Ruey-Hwa; Lee, Nai-Hsin; Chiu, Wen-Ta; Hsu, Min-Huei; Li, Yu-Chuan Jack

2011-01-01

Diagnosing acute appendicitis clinically is still difficult. We developed random forests, support vector machines, and artificial neural network models to diagnose acute appendicitis. Between January 2006 and December 2008, patients who had a consultation session with surgeons for suspected acute appendicitis were enrolled. Seventy-five percent of the data set was used to construct models including random forest, support vector machines, artificial neural networks, and logistic regression. Twenty-five percent of the data set was withheld to evaluate model performance. The area under the receiver operating characteristic curve (AUC) was used to evaluate performance, which was compared with that of the Alvarado score. Data from a total of 180 patients were collected, 135 used for training and 45 for testing. The mean age of patients was 39.4 years (range, 16-85). Final diagnosis revealed 115 patients with and 65 without appendicitis. The AUC of random forest, support vector machines, artificial neural networks, logistic regression, and Alvarado was 0.98, 0.96, 0.91, 0.87, and 0.77, respectively. The sensitivity, specificity, positive, and negative predictive values of random forest were 94%, 100%, 100%, and 87%, respectively. Random forest performed better than artificial neural networks, logistic regression, and Alvarado. We demonstrated that random forest can predict acute appendicitis with good accuracy and, deployed appropriately, can be an effective tool in clinical decision making. Copyright © 2011 Mosby, Inc. All rights reserved.

The three R's of roads: redesign, reconstruction, and restoration

Treesearch

Lloyd W. Swift; Richard G. Burns

1999-01-01

All too often, unpaved forest access roads in the Southern Appalachian Mountains were located near streams and rivers, thereby contributing storm flow and sediment to the aquatic ecosystem.Landowners may not have the resources to reconstruct and relocate all these roads to protect water quality.However, simple techniques for redesign of storm water drainage structures...

Stream channel designs for riparian and wet meadow rangelands in the southwestern United States

Treesearch

Roy Jemison; Daniel G. Neary

2000-01-01

Inappropriate land uses have degraded wetland and riparian ecosystems throughout the Southwestern United States. In 1996, the Cibola National Forest in New Mexico implemented a channel relocation project, as part of a road improvement project, to determine the feasibility of restoring wet meadow and riparian ecosystems degraded by inappropriately located roads and...

Utilization and cost for animal logging operations

Treesearch

Suraj P. Shrestha; Bobby L. Lanford

2001-01-01

Forest harvesting with animals is a labor-intensive operation. Due to the development of efficient machines and high volume demands from the forest products industry, mechanization of logging developed very fast, leaving behind the traditional horse and mule logging. It is expensive to use machines on smaller woodlots, which require frequent moves if mechanically...

A Machine Learning and Cross-Validation Approach for the Discrimination of Vegetation Physiognomic Types Using Satellite Based Multispectral and Multitemporal Data.

PubMed

Sharma, Ram C; Hara, Keitarou; Hirayama, Hidetake

2017-01-01

This paper presents the performance and evaluation of a number of machine learning classifiers for the discrimination between the vegetation physiognomic classes using the satellite based time-series of the surface reflectance data. Discrimination of six vegetation physiognomic classes, Evergreen Coniferous Forest, Evergreen Broadleaf Forest, Deciduous Coniferous Forest, Deciduous Broadleaf Forest, Shrubs, and Herbs, was dealt with in the research. Rich-feature data were prepared from time-series of the satellite data for the discrimination and cross-validation of the vegetation physiognomic types using machine learning approach. A set of machine learning experiments comprised of a number of supervised classifiers with different model parameters was conducted to assess how the discrimination of vegetation physiognomic classes varies with classifiers, input features, and ground truth data size. The performance of each experiment was evaluated by using the 10-fold cross-validation method. Experiment using the Random Forests classifier provided highest overall accuracy (0.81) and kappa coefficient (0.78). However, accuracy metrics did not vary much with experiments. Accuracy metrics were found to be very sensitive to input features and size of ground truth data. The results obtained in the research are expected to be useful for improving the vegetation physiognomic mapping in Japan.

Shaping-lathe headrig will stretch shrinking timber supply

Treesearch

J. Gengler; J.D. Saul

1975-01-01

The first commercial version of the shaping lathe headrig, designed to machine short hardwood or softwood logs into cants and flakes, was introduced to forest industry executives in September during a working demonstration at Stetson-Ross Machine Co., Seattle. Based on a concept provided by Dr. Peter Koch, chief wood scientist at the Southern Forest Experiment Station...

Machine Vision Technology for the Forest Products Industry

Treesearch

Richard W. Conners; D.Earl Kline; Philip A. Araman; Thomas T. Drayer

1997-01-01

From forest to finished product, wood is moved from one processing stage to the next, subject to the decisions of individuals along the way. While this process has worked for hundreds of years, the technology exists today to provide more complete information to the decision makers. Virginia Tech has developed this technology, creating a machine vision prototype for...

Use of trees by the Texas ratsnake (Elaphe obsoleta) in eastern Texas

Treesearch

Josh B. Pierce; Robert R. Fleet; Lance McBrayer; D. Craig Rudolph

2008-01-01

We present information on the use of trees by Elaphe obsoleta (Texas Ratsnake) in a mesic pine-hardwood forest in eastern Texas. Using radiotelemetry, seven snakes (3 females, 4 males) were relocated a total of 363 times from April 2004 to May 2005, resulting in 201 unique locations. Snakes selected trees containing cavities and used hardwoods and...

Perceptions of environmental change and use of traditional knowledge to plan riparian forest restoration with relocated communities in Alcântara, Eastern Amazon.

PubMed

Celentano, Danielle; Rousseau, Guillaume Xavier; Engel, Vera Lex; Façanha, Cristiane Lima; Oliveira, Elivaldo Moreira de; Moura, Emanoel Gomes de

2014-01-27

Riparian forests provide ecosystem services that are essential for human well-being. The Pepital River is the main water supply for Alcântara (Brazil) and its forests are disappearing. This is affecting water volume and distribution in the region. Promoting forest restoration is imperative. In deprived regions, restoration success depends on the integration of ecology, livelihoods and traditional knowledge (TEK). In this study, an interdisciplinary research framework is proposed to design riparian forest restoration strategies based on ecological data, TEK and social needs. This study takes place in a region presenting a complex history of human relocation and land tenure. Local populations from seven villages were surveyed to document livelihood (including 'free-listing' of agricultural crops and homegarden tree species). Additionally, their perceptions toward environmental changes were explored through semi-structured interviews (n = 79). Ethnobotanical information on forest species and their uses were assessed by local-specialists (n = 19). Remnants of conserved forests were surveyed to access ecological information on tree species (three plots of 1,000 m2). Results included descriptive statistics, frequency and Smith’s index of salience of the free-list results. The local population depends primarily on slash-and-burn subsistence agriculture to meet their needs. Interviewees showed a strong empirical knowledge about the environmental problems of the river, and of their causes, consequences and potential solutions. Twenty-four tree species (dbh > 10 cm) were found at the reference sites. Tree density averaged 510 individuals per hectare (stdv = 91.6); and 12 species were considered the most abundant (density > 10ind/ha). There was a strong consensus among plant-specialists about the most important trees. The species lists from reference sites and plant-specialists presented an important convergence. Slash-and-burn agriculture is the main source of livelihood but also the main driver of forest degradation. Effective restoration approaches must transform problems into solutions by empowering local people. Successional agroforestry combining annual crops and trees may be a suitable transitional phase for restoration. The model must be designed collectively and include species of ecological, cultural, and socioeconomic value. In deprived communities of the Amazon, forest restoration must be a process that combines environmental and social gains.

Perceptions of environmental change and use of traditional knowledge to plan riparian forest restoration with relocated communities in Alcântara, Eastern Amazon

PubMed Central

2014-01-01

Background Riparian forests provide ecosystem services that are essential for human well-being. The Pepital River is the main water supply for Alcântara (Brazil) and its forests are disappearing. This is affecting water volume and distribution in the region. Promoting forest restoration is imperative. In deprived regions, restoration success depends on the integration of ecology, livelihoods and traditional knowledge (TEK). In this study, an interdisciplinary research framework is proposed to design riparian forest restoration strategies based on ecological data, TEK and social needs. Methods This study takes place in a region presenting a complex history of human relocation and land tenure. Local populations from seven villages were surveyed to document livelihood (including ‘free-listing’ of agricultural crops and homegarden tree species). Additionally, their perceptions toward environmental changes were explored through semi-structured interviews (n = 79). Ethnobotanical information on forest species and their uses were assessed by local-specialists (n = 19). Remnants of conserved forests were surveyed to access ecological information on tree species (three plots of 1,000 m2). Results included descriptive statistics, frequency and Smith’s index of salience of the free-list results. Results The local population depends primarily on slash-and-burn subsistence agriculture to meet their needs. Interviewees showed a strong empirical knowledge about the environmental problems of the river, and of their causes, consequences and potential solutions. Twenty-four tree species (dbh > 10 cm) were found at the reference sites. Tree density averaged 510 individuals per hectare (stdv = 91.6); and 12 species were considered the most abundant (density > 10ind/ha). There was a strong consensus among plant-specialists about the most important trees. The species lists from reference sites and plant-specialists presented an important convergence. Conclusions Slash-and-burn agriculture is the main source of livelihood but also the main driver of forest degradation. Effective restoration approaches must transform problems into solutions by empowering local people. Successional agroforestry combining annual crops and trees may be a suitable transitional phase for restoration. The model must be designed collectively and include species of ecological, cultural, and socioeconomic value. In deprived communities of the Amazon, forest restoration must be a process that combines environmental and social gains. PMID:24468421

36. SOUTHWEST TO BELTPOWERED CIRCA 1900 DROP HAMMER IN NORTHEASTERN ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

36. SOUTHWEST TO BELT-POWERED CIRCA 1900 DROP HAMMER IN NORTHEASTERN QUADRANT OF FACTORY OPPOSITE FROM THE BLACKSMITH SHOP AREA. THIS MACHINE WAS USED TO SHAPE THE STEEL VANE HINGE PART AFTER IT WAS HEATED IN THE FORGE IN THE ADJACENT BLACKSMITH SHOP AREA. USE OF THE ROTATING POWER OF THE PULLEY AT THE TO MADE LIFTING THE HAMMER COMPARATIVELY QUICK AND EASY. AROUND THE MACHINE ARE WHEEL PARTS FOR ELI WINDMILLS. AT THE LEFT FOREGROUND IS A CIRCA 1900 FOUR-SPINDLE PRODUCTION DRILL PRESS WHICH WAS RELOCATED TO THIS AREA APPARENTLY AFTER THE END OF WINDMILL MANUFACTURE. - Kregel Windmill Company Factory, 1416 Central Avenue, Nebraska City, Otoe County, NE

Calibrating random forests for probability estimation.

PubMed

Dankowski, Theresa; Ziegler, Andreas

2016-09-30

Probabilities can be consistently estimated using random forests. It is, however, unclear how random forests should be updated to make predictions for other centers or at different time points. In this work, we present two approaches for updating random forests for probability estimation. The first method has been proposed by Elkan and may be used for updating any machine learning approach yielding consistent probabilities, so-called probability machines. The second approach is a new strategy specifically developed for random forests. Using the terminal nodes, which represent conditional probabilities, the random forest is first translated to logistic regression models. These are, in turn, used for re-calibration. The two updating strategies were compared in a simulation study and are illustrated with data from the German Stroke Study Collaboration. In most simulation scenarios, both methods led to similar improvements. In the simulation scenario in which the stricter assumptions of Elkan's method were not met, the logistic regression-based re-calibration approach for random forests outperformed Elkan's method. It also performed better on the stroke data than Elkan's method. The strength of Elkan's method is its general applicability to any probability machine. However, if the strict assumptions underlying this approach are not met, the logistic regression-based approach is preferable for updating random forests for probability estimation. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

Neck pain combined with arm pain among professional drivers of forest machines and the association with whole-body vibration exposure.

PubMed

Rehn, B; Nilsson, T; Lundström, R; Hagberg, M; Burström, L

2009-10-01

The purpose of this study was to investigate the existence of neck pain and arm pain among professional forest machine drivers and to find out if pain were related to their whole-body vibration (WBV) exposure. A self-administered questionnaire was sent to 529 forest machine drivers in northern Sweden and the response was 63%. Two pain groups were formed; 1) neck pain; 2) neck pain combined with arm pain. From WBV exposure data (recent measurements made according to ISO 2631-1, available information from reports) and from the self-administered questionnaire, 14 various WBV exposure/dose measures were calculated for each driver. The prevalence of neck pain reported both for the previous 12 months and for the previous 7 d was 34% and more than half of them reported neck pain combined with pain in one or both arms. Analysis showed no significant association between neck pain and high WBV exposure; however, cases with neck pain more often experienced shocks and jolts in the vehicle as uncomfortable. There was no significant association between the 14 WBV measures and type of neck pain (neck pain vs. neck pain combined with arm pain). It seems as if characteristics of WBV exposure can explain neither existence nor the type of neck pain amongst professional drivers of forest machines. The logging industry is important for several industrialised countries. Drivers of forest machines frequently report neuromusculoskeletal pain from the neck. The type of neck pain is important for the decision of treatment modality and may be associated with exposure characteristics at work.

Reflections on the Development of a Machine Vision Technology for the Forest Products

Treesearch

Richard W. Conners; D.Earl Kline; Philip A. Araman; Robert L. Brisbon

1992-01-01

The authors have approximately 25 years experience in developing machine vision technology for the forest products industry. Based on this experience this paper will attempt to realistically predict what the future holds for this technology. In particular, this paper will attempt to describe some of the benefits this technology will offer, describe how the technology...

Classification of large-sized hyperspectral imagery using fast machine learning algorithms

NASA Astrophysics Data System (ADS)

Xia, Junshi; Yokoya, Naoto; Iwasaki, Akira

2017-07-01

We present a framework of fast machine learning algorithms in the context of large-sized hyperspectral images classification from the theoretical to a practical viewpoint. In particular, we assess the performance of random forest (RF), rotation forest (RoF), and extreme learning machine (ELM) and the ensembles of RF and ELM. These classifiers are applied to two large-sized hyperspectral images and compared to the support vector machines. To give the quantitative analysis, we pay attention to comparing these methods when working with high input dimensions and a limited/sufficient training set. Moreover, other important issues such as the computational cost and robustness against the noise are also discussed.

Analysis of Machine Learning Techniques for Heart Failure Readmissions.

PubMed

Mortazavi, Bobak J; Downing, Nicholas S; Bucholz, Emily M; Dharmarajan, Kumar; Manhapra, Ajay; Li, Shu-Xia; Negahban, Sahand N; Krumholz, Harlan M

2016-11-01

The current ability to predict readmissions in patients with heart failure is modest at best. It is unclear whether machine learning techniques that address higher dimensional, nonlinear relationships among variables would enhance prediction. We sought to compare the effectiveness of several machine learning algorithms for predicting readmissions. Using data from the Telemonitoring to Improve Heart Failure Outcomes trial, we compared the effectiveness of random forests, boosting, random forests combined hierarchically with support vector machines or logistic regression (LR), and Poisson regression against traditional LR to predict 30- and 180-day all-cause readmissions and readmissions because of heart failure. We randomly selected 50% of patients for a derivation set, and a validation set comprised the remaining patients, validated using 100 bootstrapped iterations. We compared C statistics for discrimination and distributions of observed outcomes in risk deciles for predictive range. In 30-day all-cause readmission prediction, the best performing machine learning model, random forests, provided a 17.8% improvement over LR (mean C statistics, 0.628 and 0.533, respectively). For readmissions because of heart failure, boosting improved the C statistic by 24.9% over LR (mean C statistic 0.678 and 0.543, respectively). For 30-day all-cause readmission, the observed readmission rates in the lowest and highest deciles of predicted risk with random forests (7.8% and 26.2%, respectively) showed a much wider separation than LR (14.2% and 16.4%, respectively). Machine learning methods improved the prediction of readmission after hospitalization for heart failure compared with LR and provided the greatest predictive range in observed readmission rates. © 2016 American Heart Association, Inc.

Environmental Assessment: Relocation of Facilities at Hurlburt Field, Florida

DTIC Science & Technology

2011-11-01

fern (Pteridium aquilinum), and highbush blueberry (Vaccinium corymbosum). The access road route to the NE Area proposed under Alternatives 1 and 2...Beach, in said Okaloosa County, Florida. and that the said newspaper has heretofore been continuously published io said Okaloosa County, Florida...bracken fern (Pteridium aquilinum), and highbush blueberry (Vaccinium corymbosum). The NE Area is surrounded entirely by a large forested wetland

New machine learning tools for predictive vegetation mapping after climate change: Bagging and Random Forest perform better than Regression Tree Analysis

Treesearch

L.R. Iverson; A.M. Prasad; A. Liaw

2004-01-01

More and better machine learning tools are becoming available for landscape ecologists to aid in understanding species-environment relationships and to map probable species occurrence now and potentially into the future. To thal end, we evaluated three statistical models: Regression Tree Analybib (RTA), Bagging Trees (BT) and Random Forest (RF) for their utility in...

Calculating utilization rates for rubber tired grapple skidders in the Southern United States

Treesearch

Jason D. Thompson

2001-01-01

Utilization rate is an important factor in calculating machine rates for forest harvesting machines. Machine rates allow an evaluation of harvesting system costs and facilitate comparisons between different systems and machines. There are many factors that affect utilization rate. These include mechanical delays, non-mechanical delays, operational lost time, and...

Exploring prediction uncertainty of spatial data in geostatistical and machine learning Approaches

NASA Astrophysics Data System (ADS)

Klump, J. F.; Fouedjio, F.

2017-12-01

Geostatistical methods such as kriging with external drift as well as machine learning techniques such as quantile regression forest have been intensively used for modelling spatial data. In addition to providing predictions for target variables, both approaches are able to deliver a quantification of the uncertainty associated with the prediction at a target location. Geostatistical approaches are, by essence, adequate for providing such prediction uncertainties and their behaviour is well understood. However, they often require significant data pre-processing and rely on assumptions that are rarely met in practice. Machine learning algorithms such as random forest regression, on the other hand, require less data pre-processing and are non-parametric. This makes the application of machine learning algorithms to geostatistical problems an attractive proposition. The objective of this study is to compare kriging with external drift and quantile regression forest with respect to their ability to deliver reliable prediction uncertainties of spatial data. In our comparison we use both simulated and real world datasets. Apart from classical performance indicators, comparisons make use of accuracy plots, probability interval width plots, and the visual examinations of the uncertainty maps provided by the two approaches. By comparing random forest regression to kriging we found that both methods produced comparable maps of estimated values for our variables of interest. However, the measure of uncertainty provided by random forest seems to be quite different to the measure of uncertainty provided by kriging. In particular, the lack of spatial context can give misleading results in areas without ground truth data. These preliminary results raise questions about assessing the risks associated with decisions based on the predictions from geostatistical and machine learning algorithms in a spatial context, e.g. mineral exploration.

Does Sentinel multi sensor data offer synergy in Improving Accuracy of Aboveground Biomass Estimate of Dense Tropical Forest? - Utility of Decision Tree Based Machine Learning Algorithms

NASA Astrophysics Data System (ADS)

Ghosh, S. M.; Behera, M. D.

2017-12-01

Forest aboveground biomass (AGB) is an important factor for preparation of global policy making decisions to tackle the impact of climate change. Several previous studies has concluded that remote sensing methods are more suitable for estimating forest biomass on regional scale. Among all available remote sensing data and methods, Synthetic Aperture Radar (SAR) data in combination with decision tree based machine learning algorithms has shown better promise in estimating higher biomass values. There aren't many studies done for biomass estimation of dense Indian tropical forests with high biomass density. In this study aboveground biomass was estimated for two major tree species, Sal (Shorea robusta) and Teak (Tectona grandis), of Katerniaghat Wildlife Sanctuary, a tropical forest situated in northern India. Biomass was estimated by combining C-band SAR data from Sentinel-1A satellite, vegetation indices produced using Sentinel-2A data and ground inventory plots. Along with SAR backscatter value, SAR texture images were also used as input as earlier studies had found that image texture has a correlation with vegetation biomass. Decision tree based nonlinear machine learning algorithms were used in place of parametric regression models for establishing relationship between fields measured values and remotely sensed parameters. Using random forest model with a combination of vegetation indices with SAR backscatter as predictor variables shows best result for Sal forest, with a coefficient of determination value of 0.71 and a RMSE value of 105.027 t/ha. In teak forest also best result can be found in the same combination but for stochastic gradient boosted model with a coefficient of determination value of 0.6 and a RMSE value of 79.45 t/ha. These results are mostly better than the results of other studies done for similar kind of forests. This study shows that Sentinel series satellite data has exceptional capabilities in estimating dense forest AGB and machine learning algorithms are better means to do so than parametric regression models.

Environmental Assessment For the Expansion and Relocation of the Explosive Ordnance Disposal Preliminary Course to Sheppard Air Force Base, Texas

DTIC Science & Technology

2010-11-03

Mechanical Engineering, Mississippi State University, Starkville, MS Tony Ruhlman Natural Consulting Scientist M.S. Biology, Central Michigan...University, 1992 B.S. Biology, Alma College, Alma, Michigan, 1988 Melanie Ruhlman Technical Staff Consultant M.S., Forest Hydrology, University of...29607 ATTN: Tony Ruhlman Phone: (864) 467-0811 truhlman@northwind-inc.com Thank you for your assistance in this matter

Long-term research -- why do we do it?

Treesearch

Bill Leak

2013-01-01

While jotting down some notes for this forword, I’m also thinking about how the day was spent in the upper elevations of the Bartlett Experimental Forest planning out a monitoring scheme for an early upper-slope harvest in an oak stand. The previous several weeks were spent in relocating cruise lines for some 450-500 permanent plots established in 1931-32. And it’s...

Evaluation of the Archaeological Data Base, Coralville Lake, Iowa.

DTIC Science & Technology

1987-05-01

of Ms. Debby Zieglowsky, often provided the precise information necessary to facilitate field relocation. In addition, review of the site forms and...prehistory. Again, Ms. Debby Zieglowsky, Dr. Joseph Tiffany, and Dr. Duane Anderson were quite helpful in securing access to - these collections , providing...drained. Vegetation is deciduous forest of oak, hickory, birch , a few maple and several large cedar trees. The area sampled was _ well above (50’, 15m

Decentralized real-time simulation of forest machines

NASA Astrophysics Data System (ADS)

Freund, Eckhard; Adam, Frank; Hoffmann, Katharina; Rossmann, Juergen; Kraemer, Michael; Schluse, Michael

2000-10-01

To develop realistic forest machine simulators is a demanding task. A useful simulator has to provide a close- to-reality simulation of the forest environment as well as the simulation of the physics of the vehicle. Customers demand a highly realistic three dimensional forestry landscape and the realistic simulation of the complex motion of the vehicle even in rough terrain in order to be able to use the simulator for operator training under close-to- reality conditions. The realistic simulation of the vehicle, especially with the driver's seat mounted on a motion platform, greatly improves the effect of immersion into the virtual reality of a simulated forest and the achievable level of education of the driver. Thus, the connection of the real control devices of forest machines to the simulation system has to be supported, i.e. the real control devices like the joysticks or the board computer system to control the crane, the aggregate etc. Beyond, the fusion of the board computer system and the simulation system is realized by means of sensors, i.e. digital and analog signals. The decentralized system structure allows several virtual reality systems to evaluate and visualize the information of the control devices and the sensors. So, while the driver is practicing, the instructor can immerse into the same virtual forest to monitor the session from his own viewpoint. In this paper, we are describing the realized structure as well as the necessary software and hardware components and application experiences.

Assessing and comparison of different machine learning methods in parent-offspring trios for genotype imputation.

PubMed

Mikhchi, Abbas; Honarvar, Mahmood; Kashan, Nasser Emam Jomeh; Aminafshar, Mehdi

2016-06-21

Genotype imputation is an important tool for prediction of unknown genotypes for both unrelated individuals and parent-offspring trios. Several imputation methods are available and can either employ universal machine learning methods, or deploy algorithms dedicated to infer missing genotypes. In this research the performance of eight machine learning methods: Support Vector Machine, K-Nearest Neighbors, Extreme Learning Machine, Radial Basis Function, Random Forest, AdaBoost, LogitBoost, and TotalBoost compared in terms of the imputation accuracy, computation time and the factors affecting imputation accuracy. The methods employed using real and simulated datasets to impute the un-typed SNPs in parent-offspring trios. The tested methods show that imputation of parent-offspring trios can be accurate. The Random Forest and Support Vector Machine were more accurate than the other machine learning methods. The TotalBoost performed slightly worse than the other methods.The running times were different between methods. The ELM was always most fast algorithm. In case of increasing the sample size, the RBF requires long imputation time.The tested methods in this research can be an alternative for imputation of un-typed SNPs in low missing rate of data. However, it is recommended that other machine learning methods to be used for imputation. Copyright © 2016 Elsevier Ltd. All rights reserved.

Predicting healthcare associated infections using patients' experiences

NASA Astrophysics Data System (ADS)

Pratt, Michael A.; Chu, Henry

2016-05-01

Healthcare associated infections (HAI) are a major threat to patient safety and are costly to health systems. Our goal is to predict the HAI performance of a hospital using the patients' experience responses as input. We use four classifiers, viz. random forest, naive Bayes, artificial feedforward neural networks, and the support vector machine, to perform the prediction of six types of HAI. The six types include blood stream, urinary tract, surgical site, and intestinal infections. Experiments show that the random forest and support vector machine perform well across the six types of HAI.

Mortality risk prediction in burn injury: Comparison of logistic regression with machine learning approaches.

PubMed

Stylianou, Neophytos; Akbarov, Artur; Kontopantelis, Evangelos; Buchan, Iain; Dunn, Ken W

2015-08-01

Predicting mortality from burn injury has traditionally employed logistic regression models. Alternative machine learning methods have been introduced in some areas of clinical prediction as the necessary software and computational facilities have become accessible. Here we compare logistic regression and machine learning predictions of mortality from burn. An established logistic mortality model was compared to machine learning methods (artificial neural network, support vector machine, random forests and naïve Bayes) using a population-based (England & Wales) case-cohort registry. Predictive evaluation used: area under the receiver operating characteristic curve; sensitivity; specificity; positive predictive value and Youden's index. All methods had comparable discriminatory abilities, similar sensitivities, specificities and positive predictive values. Although some machine learning methods performed marginally better than logistic regression the differences were seldom statistically significant and clinically insubstantial. Random forests were marginally better for high positive predictive value and reasonable sensitivity. Neural networks yielded slightly better prediction overall. Logistic regression gives an optimal mix of performance and interpretability. The established logistic regression model of burn mortality performs well against more complex alternatives. Clinical prediction with a small set of strong, stable, independent predictors is unlikely to gain much from machine learning outside specialist research contexts. Copyright © 2015 Elsevier Ltd and ISBI. All rights reserved.

Subpixel urban land cover estimation: comparing cubist, random forests, and support vector regression

Treesearch

Jeffrey T. Walton

2008-01-01

Three machine learning subpixel estimation methods (Cubist, Random Forests, and support vector regression) were applied to estimate urban cover. Urban forest canopy cover and impervious surface cover were estimated from Landsat-7 ETM+ imagery using a higher resolution cover map resampled to 30 m as training and reference data. Three different band combinations (...

The Trail Making test: a study of its ability to predict falls in the acute neurological in-patient population.

PubMed

Mateen, Bilal Akhter; Bussas, Matthias; Doogan, Catherine; Waller, Denise; Saverino, Alessia; Király, Franz J; Playford, E Diane

2018-05-01

To determine whether tests of cognitive function and patient-reported outcome measures of motor function can be used to create a machine learning-based predictive tool for falls. Prospective cohort study. Tertiary neurological and neurosurgical center. In all, 337 in-patients receiving neurosurgical, neurological, or neurorehabilitation-based care. Binary (Y/N) for falling during the in-patient episode, the Trail Making Test (a measure of attention and executive function) and the Walk-12 (a patient-reported measure of physical function). The principal outcome was a fall during the in-patient stay ( n = 54). The Trail test was identified as the best predictor of falls. Moreover, addition of other variables, did not improve the prediction (Wilcoxon signed-rank P < 0.001). Classical linear statistical modeling methods were then compared with more recent machine learning based strategies, for example, random forests, neural networks, support vector machines. The random forest was the best modeling strategy when utilizing just the Trail Making Test data (Wilcoxon signed-rank P < 0.001) with 68% (± 7.7) sensitivity, and 90% (± 2.3) specificity. This study identifies a simple yet powerful machine learning (Random Forest) based predictive model for an in-patient neurological population, utilizing a single neuropsychological test of cognitive function, the Trail Making test.

Computer-Aided Diagnosis for Breast Ultrasound Using Computerized BI-RADS Features and Machine Learning Methods.

PubMed

Shan, Juan; Alam, S Kaisar; Garra, Brian; Zhang, Yingtao; Ahmed, Tahira

2016-04-01

This work identifies effective computable features from the Breast Imaging Reporting and Data System (BI-RADS), to develop a computer-aided diagnosis (CAD) system for breast ultrasound. Computerized features corresponding to ultrasound BI-RADs categories were designed and tested using a database of 283 pathology-proven benign and malignant lesions. Features were selected based on classification performance using a "bottom-up" approach for different machine learning methods, including decision tree, artificial neural network, random forest and support vector machine. Using 10-fold cross-validation on the database of 283 cases, the highest area under the receiver operating characteristic (ROC) curve (AUC) was 0.84 from a support vector machine with 77.7% overall accuracy; the highest overall accuracy, 78.5%, was from a random forest with the AUC 0.83. Lesion margin and orientation were optimum features common to all of the different machine learning methods. These features can be used in CAD systems to help distinguish benign from worrisome lesions. Copyright © 2016 World Federation for Ultrasound in Medicine & Biology. All rights reserved.

Deciphering the Preference and Predicting the Viability of Circular Permutations in Proteins

PubMed Central

Liu, Yen-Yi; Wang, Li-Fen; Hwang, Jenn-Kang; Lyu, Ping-Chiang

2012-01-01

Circular permutation (CP) refers to situations in which the termini of a protein are relocated to other positions in the structure. CP occurs naturally and has been artificially created to study protein function, stability and folding. Recently CP is increasingly applied to engineer enzyme structure and function, and to create bifunctional fusion proteins unachievable by tandem fusion. CP is a complicated and expensive technique. An intrinsic difficulty in its application lies in the fact that not every position in a protein is amenable for creating a viable permutant. To examine the preferences of CP and develop CP viability prediction methods, we carried out comprehensive analyses of the sequence, structural, and dynamical properties of known CP sites using a variety of statistics and simulation methods, such as the bootstrap aggregating, permutation test and molecular dynamics simulations. CP particularly favors Gly, Pro, Asp and Asn. Positions preferred by CP lie within coils, loops, turns, and at residues that are exposed to solvent, weakly hydrogen-bonded, environmentally unpacked, or flexible. Disfavored positions include Cys, bulky hydrophobic residues, and residues located within helices or near the protein's core. These results fostered the development of an effective viable CP site prediction system, which combined four machine learning methods, e.g., artificial neural networks, the support vector machine, a random forest, and a hierarchical feature integration procedure developed in this work. As assessed by using the hydrofolate reductase dataset as the independent evaluation dataset, this prediction system achieved an AUC of 0.9. Large-scale predictions have been performed for nine thousand representative protein structures; several new potential applications of CP were thus identified. Many unreported preferences of CP are revealed in this study. The developed system is the best CP viability prediction method currently available. This work will facilitate the application of CP in research and biotechnology. PMID:22359629

Remeasurement of permanent vegetation plots in the Great Smoky Mountains National Park, Tennessee, USA, and the implications of climatic changes on vegetation. Environmental Sciences Division publication No. 1111

DOE Office of Scientific and Technical Information (OSTI.GOV)

Becking, R. W.; Olson, J. S.

1978-03-01

This report summarizes field work over two summers (1976 and 1977) to relocate, monument and reinventory permanent vegetation plots in the Great Smoky Mountains National Park. These plots were first established by the senior author and R.H. Whittaker in 1959-62. The inventory results are discussed in terms of vegetation changes in high-altitudinal forest ecosystems, in particular the spruce-fir forests, and the factors, climate shift and biotic and abiotic agents, bringing about vegetation change. A second aspect of the report summarizes experience and offers recommendations for establishment of permanent vegetation plots for the purpose of providing a monitoring tool with whichmore » to measure long-term ecological change.« less

Improving the Spatial Prediction of Soil Organic Carbon Stocks in a Complex Tropical Mountain Landscape by Methodological Specifications in Machine Learning Approaches

PubMed Central

Schmidt, Johannes; Glaser, Bruno

2016-01-01

Tropical forests are significant carbon sinks and their soils’ carbon storage potential is immense. However, little is known about the soil organic carbon (SOC) stocks of tropical mountain areas whose complex soil-landscape and difficult accessibility pose a challenge to spatial analysis. The choice of methodology for spatial prediction is of high importance to improve the expected poor model results in case of low predictor-response correlations. Four aspects were considered to improve model performance in predicting SOC stocks of the organic layer of a tropical mountain forest landscape: Different spatial predictor settings, predictor selection strategies, various machine learning algorithms and model tuning. Five machine learning algorithms: random forests, artificial neural networks, multivariate adaptive regression splines, boosted regression trees and support vector machines were trained and tuned to predict SOC stocks from predictors derived from a digital elevation model and satellite image. Topographical predictors were calculated with a GIS search radius of 45 to 615 m. Finally, three predictor selection strategies were applied to the total set of 236 predictors. All machine learning algorithms—including the model tuning and predictor selection—were compared via five repetitions of a tenfold cross-validation. The boosted regression tree algorithm resulted in the overall best model. SOC stocks ranged between 0.2 to 17.7 kg m-2, displaying a huge variability with diffuse insolation and curvatures of different scale guiding the spatial pattern. Predictor selection and model tuning improved the models’ predictive performance in all five machine learning algorithms. The rather low number of selected predictors favours forward compared to backward selection procedures. Choosing predictors due to their indiviual performance was vanquished by the two procedures which accounted for predictor interaction. PMID:27128736

Towards large-scale FAME-based bacterial species identification using machine learning techniques.

PubMed

Slabbinck, Bram; De Baets, Bernard; Dawyndt, Peter; De Vos, Paul

2009-05-01

In the last decade, bacterial taxonomy witnessed a huge expansion. The swift pace of bacterial species (re-)definitions has a serious impact on the accuracy and completeness of first-line identification methods. Consequently, back-end identification libraries need to be synchronized with the List of Prokaryotic names with Standing in Nomenclature. In this study, we focus on bacterial fatty acid methyl ester (FAME) profiling as a broadly used first-line identification method. From the BAME@LMG database, we have selected FAME profiles of individual strains belonging to the genera Bacillus, Paenibacillus and Pseudomonas. Only those profiles resulting from standard growth conditions have been retained. The corresponding data set covers 74, 44 and 95 validly published bacterial species, respectively, represented by 961, 378 and 1673 standard FAME profiles. Through the application of machine learning techniques in a supervised strategy, different computational models have been built for genus and species identification. Three techniques have been considered: artificial neural networks, random forests and support vector machines. Nearly perfect identification has been achieved at genus level. Notwithstanding the known limited discriminative power of FAME analysis for species identification, the computational models have resulted in good species identification results for the three genera. For Bacillus, Paenibacillus and Pseudomonas, random forests have resulted in sensitivity values, respectively, 0.847, 0.901 and 0.708. The random forests models outperform those of the other machine learning techniques. Moreover, our machine learning approach also outperformed the Sherlock MIS (MIDI Inc., Newark, DE, USA). These results show that machine learning proves very useful for FAME-based bacterial species identification. Besides good bacterial identification at species level, speed and ease of taxonomic synchronization are major advantages of this computational species identification strategy.

Improving the Spatial Prediction of Soil Organic Carbon Stocks in a Complex Tropical Mountain Landscape by Methodological Specifications in Machine Learning Approaches.

PubMed

Ließ, Mareike; Schmidt, Johannes; Glaser, Bruno

2016-01-01

Tropical forests are significant carbon sinks and their soils' carbon storage potential is immense. However, little is known about the soil organic carbon (SOC) stocks of tropical mountain areas whose complex soil-landscape and difficult accessibility pose a challenge to spatial analysis. The choice of methodology for spatial prediction is of high importance to improve the expected poor model results in case of low predictor-response correlations. Four aspects were considered to improve model performance in predicting SOC stocks of the organic layer of a tropical mountain forest landscape: Different spatial predictor settings, predictor selection strategies, various machine learning algorithms and model tuning. Five machine learning algorithms: random forests, artificial neural networks, multivariate adaptive regression splines, boosted regression trees and support vector machines were trained and tuned to predict SOC stocks from predictors derived from a digital elevation model and satellite image. Topographical predictors were calculated with a GIS search radius of 45 to 615 m. Finally, three predictor selection strategies were applied to the total set of 236 predictors. All machine learning algorithms-including the model tuning and predictor selection-were compared via five repetitions of a tenfold cross-validation. The boosted regression tree algorithm resulted in the overall best model. SOC stocks ranged between 0.2 to 17.7 kg m-2, displaying a huge variability with diffuse insolation and curvatures of different scale guiding the spatial pattern. Predictor selection and model tuning improved the models' predictive performance in all five machine learning algorithms. The rather low number of selected predictors favours forward compared to backward selection procedures. Choosing predictors due to their indiviual performance was vanquished by the two procedures which accounted for predictor interaction.

Approximating prediction uncertainty for random forest regression models

Treesearch

John W. Coulston; Christine E. Blinn; Valerie A. Thomas; Randolph H. Wynne

2016-01-01

Machine learning approaches such as random forest have increased for the spatial modeling and mapping of continuous variables. Random forest is a non-parametric ensemble approach, and unlike traditional regression approaches there is no direct quantification of prediction error. Understanding prediction uncertainty is important when using model-based continuous maps as...

Predicting surface fuel models and fuel metrics using lidar and CIR imagery in a dense mixed conifer forest

Treesearch

Marek K. Jakubowksi; Qinghua Guo; Brandon Collins; Scott Stephens; Maggi Kelly

2013-01-01

We compared the ability of several classification and regression algorithms to predict forest stand structure metrics and standard surface fuel models. Our study area spans a dense, topographically complex Sierra Nevada mixed-conifer forest. We used clustering, regression trees, and support vector machine algorithms to analyze high density (average 9 pulses/m

Taxi-Out Time Prediction for Departures at Charlotte Airport Using Machine Learning Techniques

NASA Technical Reports Server (NTRS)

Lee, Hanbong; Malik, Waqar; Jung, Yoon C.

2016-01-01

Predicting the taxi-out times of departures accurately is important for improving airport efficiency and takeoff time predictability. In this paper, we attempt to apply machine learning techniques to actual traffic data at Charlotte Douglas International Airport for taxi-out time prediction. To find the key factors affecting aircraft taxi times, surface surveillance data is first analyzed. From this data analysis, several variables, including terminal concourse, spot, runway, departure fix and weight class, are selected for taxi time prediction. Then, various machine learning methods such as linear regression, support vector machines, k-nearest neighbors, random forest, and neural networks model are applied to actual flight data. Different traffic flow and weather conditions at Charlotte airport are also taken into account for more accurate prediction. The taxi-out time prediction results show that linear regression and random forest techniques can provide the most accurate prediction in terms of root-mean-square errors. We also discuss the operational complexity and uncertainties that make it difficult to predict the taxi times accurately.

A tale of two "forests": random forest machine learning AIDS tropical forest carbon mapping.

PubMed

Mascaro, Joseph; Asner, Gregory P; Knapp, David E; Kennedy-Bowdoin, Ty; Martin, Roberta E; Anderson, Christopher; Higgins, Mark; Chadwick, K Dana

2014-01-01

Accurate and spatially-explicit maps of tropical forest carbon stocks are needed to implement carbon offset mechanisms such as REDD+ (Reduced Deforestation and Degradation Plus). The Random Forest machine learning algorithm may aid carbon mapping applications using remotely-sensed data. However, Random Forest has never been compared to traditional and potentially more reliable techniques such as regionally stratified sampling and upscaling, and it has rarely been employed with spatial data. Here, we evaluated the performance of Random Forest in upscaling airborne LiDAR (Light Detection and Ranging)-based carbon estimates compared to the stratification approach over a 16-million hectare focal area of the Western Amazon. We considered two runs of Random Forest, both with and without spatial contextual modeling by including--in the latter case--x, and y position directly in the model. In each case, we set aside 8 million hectares (i.e., half of the focal area) for validation; this rigorous test of Random Forest went above and beyond the internal validation normally compiled by the algorithm (i.e., called "out-of-bag"), which proved insufficient for this spatial application. In this heterogeneous region of Northern Peru, the model with spatial context was the best preforming run of Random Forest, and explained 59% of LiDAR-based carbon estimates within the validation area, compared to 37% for stratification or 43% by Random Forest without spatial context. With the 60% improvement in explained variation, RMSE against validation LiDAR samples improved from 33 to 26 Mg C ha(-1) when using Random Forest with spatial context. Our results suggest that spatial context should be considered when using Random Forest, and that doing so may result in substantially improved carbon stock modeling for purposes of climate change mitigation.

Machine learning to predict the occurrence of bisphosphonate-related osteonecrosis of the jaw associated with dental extraction: A preliminary report.

PubMed

Kim, Dong Wook; Kim, Hwiyoung; Nam, Woong; Kim, Hyung Jun; Cha, In-Ho

2018-04-23

The aim of this study was to build and validate five types of machine learning models that can predict the occurrence of BRONJ associated with dental extraction in patients taking bisphosphonates for the management of osteoporosis. A retrospective review of the medical records was conducted to obtain cases and controls for the study. Total 125 patients consisting of 41 cases and 84 controls were selected for the study. Five machine learning prediction algorithms including multivariable logistic regression model, decision tree, support vector machine, artificial neural network, and random forest were implemented. The outputs of these models were compared with each other and also with conventional methods, such as serum CTX level. Area under the receiver operating characteristic (ROC) curve (AUC) was used to compare the results. The performance of machine learning models was significantly superior to conventional statistical methods and single predictors. The random forest model yielded the best performance (AUC = 0.973), followed by artificial neural network (AUC = 0.915), support vector machine (AUC = 0.882), logistic regression (AUC = 0.844), decision tree (AUC = 0.821), drug holiday alone (AUC = 0.810), and CTX level alone (AUC = 0.630). Machine learning methods showed superior performance in predicting BRONJ associated with dental extraction compared to conventional statistical methods using drug holiday and serum CTX level. Machine learning can thus be applied in a wide range of clinical studies. Copyright © 2017. Published by Elsevier Inc.

Random Forest as an Imputation Method for Education and Psychology Research: Its Impact on Item Fit and Difficulty of the Rasch Model

ERIC Educational Resources Information Center

Golino, Hudson F.; Gomes, Cristiano M. A.

2016-01-01

This paper presents a non-parametric imputation technique, named random forest, from the machine learning field. The random forest procedure has two main tuning parameters: the number of trees grown in the prediction and the number of predictors used. Fifty experimental conditions were created in the imputation procedure, with different…

Development of a Computer Vision Technology for the Forest Products Manufacturing Industry

Treesearch

D. Earl Kline; Richard Conners; Philip A. Araman

1992-01-01

The goal of this research is to create an automated processing/grading system for hardwood lumber that will be of use to the forest products industry. The objective of creating a full scale machine vision prototype for inspecting hardwood lumber will become a reality in calendar year 1992. Space for the full scale prototype has been created at the Brooks Forest...

A Tale of Two “Forests”: Random Forest Machine Learning Aids Tropical Forest Carbon Mapping

PubMed Central

Mascaro, Joseph; Asner, Gregory P.; Knapp, David E.; Kennedy-Bowdoin, Ty; Martin, Roberta E.; Anderson, Christopher; Higgins, Mark; Chadwick, K. Dana

2014-01-01

Accurate and spatially-explicit maps of tropical forest carbon stocks are needed to implement carbon offset mechanisms such as REDD+ (Reduced Deforestation and Degradation Plus). The Random Forest machine learning algorithm may aid carbon mapping applications using remotely-sensed data. However, Random Forest has never been compared to traditional and potentially more reliable techniques such as regionally stratified sampling and upscaling, and it has rarely been employed with spatial data. Here, we evaluated the performance of Random Forest in upscaling airborne LiDAR (Light Detection and Ranging)-based carbon estimates compared to the stratification approach over a 16-million hectare focal area of the Western Amazon. We considered two runs of Random Forest, both with and without spatial contextual modeling by including—in the latter case—x, and y position directly in the model. In each case, we set aside 8 million hectares (i.e., half of the focal area) for validation; this rigorous test of Random Forest went above and beyond the internal validation normally compiled by the algorithm (i.e., called “out-of-bag”), which proved insufficient for this spatial application. In this heterogeneous region of Northern Peru, the model with spatial context was the best preforming run of Random Forest, and explained 59% of LiDAR-based carbon estimates within the validation area, compared to 37% for stratification or 43% by Random Forest without spatial context. With the 60% improvement in explained variation, RMSE against validation LiDAR samples improved from 33 to 26 Mg C ha−1 when using Random Forest with spatial context. Our results suggest that spatial context should be considered when using Random Forest, and that doing so may result in substantially improved carbon stock modeling for purposes of climate change mitigation. PMID:24489686

Machine learning models in breast cancer survival prediction.

PubMed

Montazeri, Mitra; Montazeri, Mohadeseh; Montazeri, Mahdieh; Beigzadeh, Amin

2016-01-01

Breast cancer is one of the most common cancers with a high mortality rate among women. With the early diagnosis of breast cancer survival will increase from 56% to more than 86%. Therefore, an accurate and reliable system is necessary for the early diagnosis of this cancer. The proposed model is the combination of rules and different machine learning techniques. Machine learning models can help physicians to reduce the number of false decisions. They try to exploit patterns and relationships among a large number of cases and predict the outcome of a disease using historical cases stored in datasets. The objective of this study is to propose a rule-based classification method with machine learning techniques for the prediction of different types of Breast cancer survival. We use a dataset with eight attributes that include the records of 900 patients in which 876 patients (97.3%) and 24 (2.7%) patients were females and males respectively. Naive Bayes (NB), Trees Random Forest (TRF), 1-Nearest Neighbor (1NN), AdaBoost (AD), Support Vector Machine (SVM), RBF Network (RBFN), and Multilayer Perceptron (MLP) machine learning techniques with 10-cross fold technique were used with the proposed model for the prediction of breast cancer survival. The performance of machine learning techniques were evaluated with accuracy, precision, sensitivity, specificity, and area under ROC curve. Out of 900 patients, 803 patients and 97 patients were alive and dead, respectively. In this study, Trees Random Forest (TRF) technique showed better results in comparison to other techniques (NB, 1NN, AD, SVM and RBFN, MLP). The accuracy, sensitivity and the area under ROC curve of TRF are 96%, 96%, 93%, respectively. However, 1NN machine learning technique provided poor performance (accuracy 91%, sensitivity 91% and area under ROC curve 78%). This study demonstrates that Trees Random Forest model (TRF) which is a rule-based classification model was the best model with the highest level of accuracy. Therefore, this model is recommended as a useful tool for breast cancer survival prediction as well as medical decision making.

Employing canopy hyperspectral narrowband data and random forest algorithm to differentiate palmer amaranth from colored cotton

USDA-ARS?s Scientific Manuscript database

Palmer amaranth (Amaranthus palmeri S. Wats.) invasion negatively impacts cotton (Gossypium hirsutum L.) production systems throughout the United States. The objective of this study was to evaluate canopy hyperspectral narrowband data as input into the random forest machine learning algorithm to dis...

AUTOCLASSIFICATION OF THE VARIABLE 3XMM SOURCES USING THE RANDOM FOREST MACHINE LEARNING ALGORITHM

DOE Office of Scientific and Technical Information (OSTI.GOV)

Farrell, Sean A.; Murphy, Tara; Lo, Kitty K., E-mail: s.farrell@physics.usyd.edu.au

In the current era of large surveys and massive data sets, autoclassification of astrophysical sources using intelligent algorithms is becoming increasingly important. In this paper we present the catalog of variable sources in the Third XMM-Newton Serendipitous Source catalog (3XMM) autoclassified using the Random Forest machine learning algorithm. We used a sample of manually classified variable sources from the second data release of the XMM-Newton catalogs (2XMMi-DR2) to train the classifier, obtaining an accuracy of ∼92%. We also evaluated the effectiveness of identifying spurious detections using a sample of spurious sources, achieving an accuracy of ∼95%. Manual investigation of amore » random sample of classified sources confirmed these accuracy levels and showed that the Random Forest machine learning algorithm is highly effective at automatically classifying 3XMM sources. Here we present the catalog of classified 3XMM variable sources. We also present three previously unidentified unusual sources that were flagged as outlier sources by the algorithm: a new candidate supergiant fast X-ray transient, a 400 s X-ray pulsar, and an eclipsing 5 hr binary system coincident with a known Cepheid.« less

Data mining methods in the prediction of Dementia: A real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests

PubMed Central

2011-01-01

Background Dementia and cognitive impairment associated with aging are a major medical and social concern. Neuropsychological testing is a key element in the diagnostic procedures of Mild Cognitive Impairment (MCI), but has presently a limited value in the prediction of progression to dementia. We advance the hypothesis that newer statistical classification methods derived from data mining and machine learning methods like Neural Networks, Support Vector Machines and Random Forests can improve accuracy, sensitivity and specificity of predictions obtained from neuropsychological testing. Seven non parametric classifiers derived from data mining methods (Multilayer Perceptrons Neural Networks, Radial Basis Function Neural Networks, Support Vector Machines, CART, CHAID and QUEST Classification Trees and Random Forests) were compared to three traditional classifiers (Linear Discriminant Analysis, Quadratic Discriminant Analysis and Logistic Regression) in terms of overall classification accuracy, specificity, sensitivity, Area under the ROC curve and Press'Q. Model predictors were 10 neuropsychological tests currently used in the diagnosis of dementia. Statistical distributions of classification parameters obtained from a 5-fold cross-validation were compared using the Friedman's nonparametric test. Results Press' Q test showed that all classifiers performed better than chance alone (p < 0.05). Support Vector Machines showed the larger overall classification accuracy (Median (Me) = 0.76) an area under the ROC (Me = 0.90). However this method showed high specificity (Me = 1.0) but low sensitivity (Me = 0.3). Random Forest ranked second in overall accuracy (Me = 0.73) with high area under the ROC (Me = 0.73) specificity (Me = 0.73) and sensitivity (Me = 0.64). Linear Discriminant Analysis also showed acceptable overall accuracy (Me = 0.66), with acceptable area under the ROC (Me = 0.72) specificity (Me = 0.66) and sensitivity (Me = 0.64). The remaining classifiers showed overall classification accuracy above a median value of 0.63, but for most sensitivity was around or even lower than a median value of 0.5. Conclusions When taking into account sensitivity, specificity and overall classification accuracy Random Forests and Linear Discriminant analysis rank first among all the classifiers tested in prediction of dementia using several neuropsychological tests. These methods may be used to improve accuracy, sensitivity and specificity of Dementia predictions from neuropsychological testing. PMID:21849043

Prediction of drug synergy in cancer using ensemble-based machine learning techniques

NASA Astrophysics Data System (ADS)

Singh, Harpreet; Rana, Prashant Singh; Singh, Urvinder

2018-04-01

Drug synergy prediction plays a significant role in the medical field for inhibiting specific cancer agents. It can be developed as a pre-processing tool for therapeutic successes. Examination of different drug-drug interaction can be done by drug synergy score. It needs efficient regression-based machine learning approaches to minimize the prediction errors. Numerous machine learning techniques such as neural networks, support vector machines, random forests, LASSO, Elastic Nets, etc., have been used in the past to realize requirement as mentioned above. However, these techniques individually do not provide significant accuracy in drug synergy score. Therefore, the primary objective of this paper is to design a neuro-fuzzy-based ensembling approach. To achieve this, nine well-known machine learning techniques have been implemented by considering the drug synergy data. Based on the accuracy of each model, four techniques with high accuracy are selected to develop ensemble-based machine learning model. These models are Random forest, Fuzzy Rules Using Genetic Cooperative-Competitive Learning method (GFS.GCCL), Adaptive-Network-Based Fuzzy Inference System (ANFIS) and Dynamic Evolving Neural-Fuzzy Inference System method (DENFIS). Ensembling is achieved by evaluating the biased weighted aggregation (i.e. adding more weights to the model with a higher prediction score) of predicted data by selected models. The proposed and existing machine learning techniques have been evaluated on drug synergy score data. The comparative analysis reveals that the proposed method outperforms others in terms of accuracy, root mean square error and coefficient of correlation.

Lumber Scanning System for Surface Defect Detection

Treesearch

D. Earl Kline; Y. Jason Hou; Richard W. Conners; Daniel L. Schmoldt; Philip A. Araman

1992-01-01

This paper describes research aimed at developing a machine vision technology to drive automated processes in the hardwood forest products manufacturing industry. An industrial-scale machine vision system has been designed to scan variable-size hardwood lumber for detecting important features that influence the grade and value of lumber such as knots, holes, wane,...

Machine Vision Systems for Processing Hardwood Lumber and Logs

Treesearch

Philip A. Araman; Daniel L. Schmoldt; Tai-Hoon Cho; Dongping Zhu; Richard W. Conners; D. Earl Kline

1992-01-01

Machine vision and automated processing systems are under development at Virginia Tech University with support and cooperation from the USDA Forest Service. Our goals are to help U.S. hardwood producers automate, reduce costs, increase product volume and value recovery, and market higher value, more accurately graded and described products. Any vision system is...

Robust Spatial Autoregressive Modeling for Hardwood Log Inspection

Treesearch

Dongping Zhu; A.A. Beex

1994-01-01

We explore the application of a stochastic texture modeling method toward a machine vision system for log inspection in the forest products industry. This machine vision system uses computerized tomography (CT) imaging to locate and identify internal defects in hardwood logs. The application of CT to such industrial vision problems requires efficient and robust image...

MLACP: machine-learning-based prediction of anticancer peptides

PubMed Central

Manavalan, Balachandran; Basith, Shaherin; Shin, Tae Hwan; Choi, Sun; Kim, Myeong Ok; Lee, Gwang

2017-01-01

Cancer is the second leading cause of death globally, and use of therapeutic peptides to target and kill cancer cells has received considerable attention in recent years. Identification of anticancer peptides (ACPs) through wet-lab experimentation is expensive and often time consuming; therefore, development of an efficient computational method is essential to identify potential ACP candidates prior to in vitro experimentation. In this study, we developed support vector machine- and random forest-based machine-learning methods for the prediction of ACPs using the features calculated from the amino acid sequence, including amino acid composition, dipeptide composition, atomic composition, and physicochemical properties. We trained our methods using the Tyagi-B dataset and determined the machine parameters by 10-fold cross-validation. Furthermore, we evaluated the performance of our methods on two benchmarking datasets, with our results showing that the random forest-based method outperformed the existing methods with an average accuracy and Matthews correlation coefficient value of 88.7% and 0.78, respectively. To assist the scientific community, we also developed a publicly accessible web server at www.thegleelab.org/MLACP.html. PMID:29100375

MIP models and hybrid algorithms for simultaneous job splitting and scheduling on unrelated parallel machines.

PubMed

Eroglu, Duygu Yilmaz; Ozmutlu, H Cenk

2014-01-01

We developed mixed integer programming (MIP) models and hybrid genetic-local search algorithms for the scheduling problem of unrelated parallel machines with job sequence and machine-dependent setup times and with job splitting property. The first contribution of this paper is to introduce novel algorithms which make splitting and scheduling simultaneously with variable number of subjobs. We proposed simple chromosome structure which is constituted by random key numbers in hybrid genetic-local search algorithm (GAspLA). Random key numbers are used frequently in genetic algorithms, but it creates additional difficulty when hybrid factors in local search are implemented. We developed algorithms that satisfy the adaptation of results of local search into the genetic algorithms with minimum relocation operation of genes' random key numbers. This is the second contribution of the paper. The third contribution of this paper is three developed new MIP models which are making splitting and scheduling simultaneously. The fourth contribution of this paper is implementation of the GAspLAMIP. This implementation let us verify the optimality of GAspLA for the studied combinations. The proposed methods are tested on a set of problems taken from the literature and the results validate the effectiveness of the proposed algorithms.

Source localization in an ocean waveguide using supervised machine learning.

PubMed

Niu, Haiqiang; Reeves, Emma; Gerstoft, Peter

2017-09-01

Source localization in ocean acoustics is posed as a machine learning problem in which data-driven methods learn source ranges directly from observed acoustic data. The pressure received by a vertical linear array is preprocessed by constructing a normalized sample covariance matrix and used as the input for three machine learning methods: feed-forward neural networks (FNN), support vector machines (SVM), and random forests (RF). The range estimation problem is solved both as a classification problem and as a regression problem by these three machine learning algorithms. The results of range estimation for the Noise09 experiment are compared for FNN, SVM, RF, and conventional matched-field processing and demonstrate the potential of machine learning for underwater source localization.

Foraging and nesting habitat of breeding male northern goshawks in the laurentian mixed forest province, Minnesota

USGS Publications Warehouse

Boal, C.W.; Andersen, D.E.; Kennedy, P.L.

2005-01-01

We used radiotelemetry to examine foraging habitat preferences of 17 breeding, male northern goshawks (Accipiter gentilis) in Minnesota from 1998-2000. We assessed habitat preference using radio relocation points and 50-m radius buffers of radio relocation points. Our data suggested that foraging male goshawks used early-successional upland conifer stands (???25 yrs old), early-successional upland deciduous stands (???50 yrs old), late-successional upland conifer stands (???50 yrs old), and late-successional upland deciduous stands (???50 yrs old) more frequently than expected based on the abundance of these vegetation types in the landscape. The 2 most available stand types, early-successional upland deciduous (<25 yrs old) and all ages of late-successional lowland conifer stands, were used less than expected by foraging goshawks. Late-successional lowland deciduous stands (???50 yrs old) were used in proportion to availability. Although analysis of relocation points suggested early-successional upland deciduous stands (25-49 yrs old) and late-successional upland conifer stands (???50 yrs old) were used in proportion to availability, analysis of buffers around relocation points indicated that these stand types were also used more than expected by foraging goshawks. Regardless of vegetation community type, stands used by goshawks were structurally similar with high canopy and understory stem densities, high canopy closure, substantial shrub cover, and large amounts of woody debris. Nest stands consisted of taller and larger diameter canopy trees and fewer understory trees than foraging stands, but stands were otherwise similar in structural features, suggesting goshawks used similar stands for nesting and foraging but that they tended to select the most mature stands for nesting. A commonality among nesting and foraging stands was the presence of open spaces between the canopy and understory foliage, and between understory and shrub layer foliage. In our study area, these spaces may have served as relatively unobstructed flight paths where foraging and nesting stands possessed stem densities at the upper end of that reported for goshawk habitat.

Thrown object hazards in forest operations

Treesearch

Robert Rummer; John Klepac

2011-01-01

Mechanized equipment for forest operations provide better operator protection in this hazardous work environment. However operators of forestry cutting machines are now exposed to new hazards from the high-energy cutting devices used to cut trees and process logs. Anecdotal reports of thrown objects document a risk of injury and fatality. Two new ISO standards have...

Peter Koch: wizard of wood use

Treesearch

M.E. Lora

1978-01-01

Like his pioneer forefathers, Peter Koch sees opportunity where others see obstacles. And his vision is helping to reshape the wood industry. Since 1963 Koch has directed research on processing southern woods for the U.S. Forest Service's Southern Forest Experiment Station in Pineville, Louisiana. In that time, he has invented six revolutionary machines, developed...

Comparison of machine-learning methods for above-ground biomass estimation based on Landsat imagery

NASA Astrophysics Data System (ADS)

Wu, Chaofan; Shen, Huanhuan; Shen, Aihua; Deng, Jinsong; Gan, Muye; Zhu, Jinxia; Xu, Hongwei; Wang, Ke

2016-07-01

Biomass is one significant biophysical parameter of a forest ecosystem, and accurate biomass estimation on the regional scale provides important information for carbon-cycle investigation and sustainable forest management. In this study, Landsat satellite imagery data combined with field-based measurements were integrated through comparisons of five regression approaches [stepwise linear regression, K-nearest neighbor, support vector regression, random forest (RF), and stochastic gradient boosting] with two different candidate variable strategies to implement the optimal spatial above-ground biomass (AGB) estimation. The results suggested that RF algorithm exhibited the best performance by 10-fold cross-validation with respect to R2 (0.63) and root-mean-square error (26.44 ton/ha). Consequently, the map of estimated AGB was generated with a mean value of 89.34 ton/ha in northwestern Zhejiang Province, China, with a similar pattern to the distribution mode of local forest species. This research indicates that machine-learning approaches associated with Landsat imagery provide an economical way for biomass estimation. Moreover, ensemble methods using all candidate variables, especially for Landsat images, provide an alternative for regional biomass simulation.

Research for the development of best management practices for minimizing horse trail impacts on the Hoosier National Forest

USGS Publications Warehouse

Aust, M.W.; Marion, J.L.; Kyle, K.

2005-01-01

This research investigates horse trail impacts to gain an improved understanding of the relationship between various levels of horse use, horse trail management alternatives, and subsequent horse trail degradation. A survey of existing horse trails on the Hoosier National Forest was used to collect data on use-related, environmental and management factors to model horse trail impacts. Results are analyzed to identify which factors are most easily manipulated by managers to effectively avoid and minimize horse trail impacts. A specific focus includes evaluating the relative effect of trail use level, surfacing, grade, and water control on indices of erosion and trafficability such as trail cross sectional area, estimated erosion, muddiness, and incision. Overall, the Hoosier National Forest horse trails could be significantly improved by relocating or closing inherited trails that directly ascend slope or are excessively steep, reducing the distance between water control structures, and by applying gravel to harden trail surfaces and reduce soil erosion. A set of Best Management Practices for trails are included as a product of this work, with recommendations based on this research.

Patch occupancy and dispersal of spruce grouse on the edge of its range in Maine

USGS Publications Warehouse

Whitcomb, S.A.; Servello, F.A.; O'Connell, A.F.

1996-01-01

We surveyed 18 habitat patches (black spruce (Picea marinana) - tamarack (Larix larcina) wetlands) for spruce grouse (Dendragapus canadensis canadensis) on Mount Desert Island, Maine, during April-May in 1992 and 1993 to determine patch occupancy relative to patch area. We also equipped nine juvenile grouse with radio transmitters to determine movement and habitat use outside of patches during autumn dispersal. The 2 large patches (77 and 269 ha), 5 of 6 medium-sized (11-26 ha) patches, and 1 of 10 small (4-8 ha) patches were occupied. Spruce grouse occupied smaller habitat patches than previously reported, and occupied patches were closer (P < 0.05) to the nearest occupied patch (x = 1.2 km) than were unoccupied patches (x = 2.5 km). Eight of nine juvenile grouse left their natal habitat patch during autumn dispersal, and net dispersal distance (x = 2.3 km) was greater than that reported for grouse in areas with more contiguous habitat. Dispersing juveniles used all major forest types and 33 % of relocations were in deciduous forest. Thus, deciduous forest was not an absolute dispersal barrier.

Accuracy of Tracking Forest Machines with GPS

Treesearch

M.W. Veal; S.E. Taylor; T.P. McDonald; D.K. McLemore; M.R. Dunn

2001-01-01

This paper describes the results of a study that measured the accuracy of using GPS to track movement offorest machines. Two different commercially available GPS receivers (Trimble ProXR and GeoExplorer II) were used to track wheeled skidders under three different canopy conditions at two different vehicle speeds. Dynamic GPS data were compared to position data...

77 FR 76449 - Los Padres National Forest, California; Strategic Community Fuelbreak Improvement Project

Federal Register 2010, 2011, 2012, 2013, 2014

2012-12-28

... unit for a total of 544 acres. Treatments would include the use of machine and/or hand thinning, machine and/or hand piling and pile burning or chipping; and mastication. The project is located in... facilitated and guided by the Fire Learning Network, and a focus on ecological restoration, participants in...

EVALUATION OF DATA OBTAINED ON "MANUFACTURING PROCESS" DEVELOPMENT BUNDLES PD 1 THROUGH 5 PRIOR TO MACHINING OPERATIONS

DOE Office of Scientific and Technical Information (OSTI.GOV)

Frankhouser, W.L.; Eyler, J.H.

1956-07-24

Five reference fuel rod bundles were welded and evaluated dimensionally. Dimensional data are presented for the as-welded condition and for the annealed bundle with spacer strips removed (prior to the final machining operations). The welding sequence developed for Core Manufacturing should provide A'' boundles in respect to rod spacing measurements. It will probably not be possible to meet the same requirements for water channel averages, because the design tolerances are not consistent with some factors inherent to the production process. A method to improve this situation is presented. The data presented were evaluated in a fashion similar to that whichmore » would be used in the proposed scheme. Rods tended to bow resulting in a slightly barrel-shaped'' boundle. It is believed this condition can be overcome by providing special bundle peripheral clamps during annealing. Rod distortion should also be reduced by a redesign and relocation of strip spacers. The new design is proposed. (auth)« less

Correcting Classifiers for Sample Selection Bias in Two-Phase Case-Control Studies

PubMed Central

Theis, Fabian J.

2017-01-01

Epidemiological studies often utilize stratified data in which rare outcomes or exposures are artificially enriched. This design can increase precision in association tests but distorts predictions when applying classifiers on nonstratified data. Several methods correct for this so-called sample selection bias, but their performance remains unclear especially for machine learning classifiers. With an emphasis on two-phase case-control studies, we aim to assess which corrections to perform in which setting and to obtain methods suitable for machine learning techniques, especially the random forest. We propose two new resampling-based methods to resemble the original data and covariance structure: stochastic inverse-probability oversampling and parametric inverse-probability bagging. We compare all techniques for the random forest and other classifiers, both theoretically and on simulated and real data. Empirical results show that the random forest profits from only the parametric inverse-probability bagging proposed by us. For other classifiers, correction is mostly advantageous, and methods perform uniformly. We discuss consequences of inappropriate distribution assumptions and reason for different behaviors between the random forest and other classifiers. In conclusion, we provide guidance for choosing correction methods when training classifiers on biased samples. For random forests, our method outperforms state-of-the-art procedures if distribution assumptions are roughly fulfilled. We provide our implementation in the R package sambia. PMID:29312464

Applications of random forest feature selection for fine-scale genetic population assignment.

PubMed

Sylvester, Emma V A; Bentzen, Paul; Bradbury, Ian R; Clément, Marie; Pearce, Jon; Horne, John; Beiko, Robert G

2018-02-01

Genetic population assignment used to inform wildlife management and conservation efforts requires panels of highly informative genetic markers and sensitive assignment tests. We explored the utility of machine-learning algorithms (random forest, regularized random forest and guided regularized random forest) compared with F ST ranking for selection of single nucleotide polymorphisms (SNP) for fine-scale population assignment. We applied these methods to an unpublished SNP data set for Atlantic salmon ( Salmo salar ) and a published SNP data set for Alaskan Chinook salmon ( Oncorhynchus tshawytscha ). In each species, we identified the minimum panel size required to obtain a self-assignment accuracy of at least 90% using each method to create panels of 50-700 markers Panels of SNPs identified using random forest-based methods performed up to 7.8 and 11.2 percentage points better than F ST -selected panels of similar size for the Atlantic salmon and Chinook salmon data, respectively. Self-assignment accuracy ≥90% was obtained with panels of 670 and 384 SNPs for each data set, respectively, a level of accuracy never reached for these species using F ST -selected panels. Our results demonstrate a role for machine-learning approaches in marker selection across large genomic data sets to improve assignment for management and conservation of exploited populations.

Identifying tropical dry forests extent and succession via the use of machine learning techniques

NASA Astrophysics Data System (ADS)

Li, Wei; Cao, Sen; Campos-Vargas, Carlos; Sanchez-Azofeifa, Arturo

2017-12-01

Information on ecosystem services as a function of the successional stage for secondary tropical dry forests (TDFs) is scarce and limited. Secondary TDFs succession is defined as regrowth following a complete forest clearance for cattle growth or agriculture activities. In the context of large conservation initiatives, the identification of the extent, structure and composition of secondary TDFs can serve as key elements to estimate the effectiveness of such activities. As such, in this study we evaluate the use of a Hyperspectral MAPper (HyMap) dataset and a waveform LIDAR dataset for characterization of different levels of intra-secondary forests stages at the Santa Rosa National Park (SRNP) Environmental Monitoring Super Site located in Costa Rica. Specifically, a multi-task learning based machine learning classifier (MLC-MTL) is employed on the first shortwave infrared (SWIR1) of HyMap in order to identify the variability of aboveground biomass of secondary TDFs along a successional gradient. Our paper recognizes that the process of ecological succession is not deterministic but a combination of transitional forests types along a stochastic path that depends on ecological, edaphic, land use, and micro-meteorological conditions, and our results provide a new way to obtain the spatial distribution of three main types of TDFs successional stages.

Automated hardwood lumber grading utilizing a multiple sensor machine vision technology

Treesearch

D. Earl Kline; Chris Surak; Philip A. Araman

2003-01-01

Over the last 10 years, scientists at the Thomas M. Brooks Forest Products Center, the Bradley Department of Electrical and Computer Engineering, and the USDA Forest Service have been working on lumber scanning systems that can accurately locate and identify defects in hardwood lumber. Current R&D efforts are targeted toward developing automated lumber grading...

Removing Chinese privet from riparian forests still benefits pollinators five years later

Treesearch

Jacob R. Hudson; James Hanula; Scott Horn

2014-01-01

Chinese privet (Ligustrum sinense) is an invasive shrub of the Southeastern U.S. that forms dense stands and limits biodiversity. It was removed from heavily infested riparian forests of the Georgia Piedmont in 2005 by mulching machine or chainsaw felling and subsequent herbicide application. Abundance and species richness of bees and butterflies...

A 3D stand generator for central Appalachian hardwood forests

Treesearch

Jingxin Wang; Yaoxiang Li; Gary W. Miller

2002-01-01

A 3-dimensional (3D) stand generator was developed for central Appalachian hardwood forests. It was designed for a harvesting simulator to examine the interactions of stand, harvest, and machine. The Component Object Model (COM) was used to design and implement the program. Input to the generator includes species composition, stand density, and spatial pattern. Output...

Chinese Privet (Ligustrum sinense) removal and its effect on native plant communities of Riparian Forests

Treesearch

James Hanula; Scott Horn; John W. Taylor

2010-01-01

Chinese privet is a major invasive shrub within riparian zones throughout the southeastern United States. Weremoved privet shrubs from four riparian forests in October 2005 with a GyrotracH mulching machine or by handfelling with chainsaws and machetes to determine how well these treatments controlled privet and how they affected plant...

Deriving Forest Harvesting Machine Productivity from Positional Data

Treesearch

T.P. McDonald; S.E. Taylor; R.B. Rummer

2000-01-01

Automated production study systems will provide researchers a valuable tool for developing cost and impact models of forest operations under a wide range of conditions, making the development of true planning tools for tailoring logging systems to a particular site a reality. An automated time study system for skidders was developed, and in this study application of...

47 CFR 27.1182 - Reimbursement under the Cost-Sharing Plan.

Code of Federal Regulations, 2011 CFR

2011-10-01

... reimbursement, an AWS relocator must submit documentation of the relocation agreement to the clearinghouse... involuntary relocation, an AWS relocator must submit documentation of the relocated system within 30 calendar... above ground level height of the system's receiving antenna centerline. (3) The AWS relocator must also...

47 CFR 27.1182 - Reimbursement under the Cost-Sharing Plan.

Code of Federal Regulations, 2010 CFR

2010-10-01

... reimbursement, an AWS relocator must submit documentation of the relocation agreement to the clearinghouse... involuntary relocation, an AWS relocator must submit documentation of the relocated system within 30 calendar... above ground level height of the system's receiving antenna centerline. (3) The AWS relocator must also...

25 CFR 700.93 - Relocation plan.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 25 Indians 2 2010-04-01 2010-04-01 false Relocation plan. 700.93 Section 700.93 Indians THE OFFICE OF NAVAJO AND HOPI INDIAN RELOCATION COMMISSION OPERATIONS AND RELOCATION PROCEDURES General Policies and Instructions Definitions § 700.93 Relocation plan. The relocation plan shall be the plan prepared...

Accurate Diabetes Risk Stratification Using Machine Learning: Role of Missing Value and Outliers.

PubMed

Maniruzzaman, Md; Rahman, Md Jahanur; Al-MehediHasan, Md; Suri, Harman S; Abedin, Md Menhazul; El-Baz, Ayman; Suri, Jasjit S

2018-04-10

Diabetes mellitus is a group of metabolic diseases in which blood sugar levels are too high. About 8.8% of the world was diabetic in 2017. It is projected that this will reach nearly 10% by 2045. The major challenge is that when machine learning-based classifiers are applied to such data sets for risk stratification, leads to lower performance. Thus, our objective is to develop an optimized and robust machine learning (ML) system under the assumption that missing values or outliers if replaced by a median configuration will yield higher risk stratification accuracy. This ML-based risk stratification is designed, optimized and evaluated, where: (i) the features are extracted and optimized from the six feature selection techniques (random forest, logistic regression, mutual information, principal component analysis, analysis of variance, and Fisher discriminant ratio) and combined with ten different types of classifiers (linear discriminant analysis, quadratic discriminant analysis, naïve Bayes, Gaussian process classification, support vector machine, artificial neural network, Adaboost, logistic regression, decision tree, and random forest) under the hypothesis that both missing values and outliers when replaced by computed medians will improve the risk stratification accuracy. Pima Indian diabetic dataset (768 patients: 268 diabetic and 500 controls) was used. Our results demonstrate that on replacing the missing values and outliers by group median and median values, respectively and further using the combination of random forest feature selection and random forest classification technique yields an accuracy, sensitivity, specificity, positive predictive value, negative predictive value and area under the curve as: 92.26%, 95.96%, 79.72%, 91.14%, 91.20%, and 0.93, respectively. This is an improvement of 10% over previously developed techniques published in literature. The system was validated for its stability and reliability. RF-based model showed the best performance when outliers are replaced by median values.

Prediction of Return-to-original-work after an Industrial Accident Using Machine Learning and Comparison of Techniques

PubMed Central

2018-01-01

Background Many studies have tried to develop predictors for return-to-work (RTW). However, since complex factors have been demonstrated to predict RTW, it is difficult to use them practically. This study investigated whether factors used in previous studies could predict whether an individual had returned to his/her original work by four years after termination of the worker's recovery period. Methods An initial logistic regression analysis of 1,567 participants of the fourth Panel Study of Worker's Compensation Insurance yielded odds ratios. The participants were divided into two subsets, a training dataset and a test dataset. Using the training dataset, logistic regression, decision tree, random forest, and support vector machine models were established, and important variables of each model were identified. The predictive abilities of the different models were compared. Results The analysis showed that only earned income and company-related factors significantly affected return-to-original-work (RTOW). The random forest model showed the best accuracy among the tested machine learning models; however, the difference was not prominent. Conclusion It is possible to predict a worker's probability of RTOW using machine learning techniques with moderate accuracy. PMID:29736160

Identifying Active Travel Behaviors in Challenging Environments Using GPS, Accelerometers, and Machine Learning Algorithms.

PubMed

Ellis, Katherine; Godbole, Suneeta; Marshall, Simon; Lanckriet, Gert; Staudenmayer, John; Kerr, Jacqueline

2014-01-01

Active travel is an important area in physical activity research, but objective measurement of active travel is still difficult. Automated methods to measure travel behaviors will improve research in this area. In this paper, we present a supervised machine learning method for transportation mode prediction from global positioning system (GPS) and accelerometer data. We collected a dataset of about 150 h of GPS and accelerometer data from two research assistants following a protocol of prescribed trips consisting of five activities: bicycling, riding in a vehicle, walking, sitting, and standing. We extracted 49 features from 1-min windows of this data. We compared the performance of several machine learning algorithms and chose a random forest algorithm to classify the transportation mode. We used a moving average output filter to smooth the output predictions over time. The random forest algorithm achieved 89.8% cross-validated accuracy on this dataset. Adding the moving average filter to smooth output predictions increased the cross-validated accuracy to 91.9%. Machine learning methods are a viable approach for automating measurement of active travel, particularly for measuring travel activities that traditional accelerometer data processing methods misclassify, such as bicycling and vehicle travel.

A machine learning system to improve heart failure patient assistance.

PubMed

Guidi, Gabriele; Pettenati, Maria Chiara; Melillo, Paolo; Iadanza, Ernesto

2014-11-01

In this paper, we present a clinical decision support system (CDSS) for the analysis of heart failure (HF) patients, providing various outputs such as an HF severity evaluation, HF-type prediction, as well as a management interface that compares the different patients' follow-ups. The whole system is composed of a part of intelligent core and of an HF special-purpose management tool also providing the function to act as interface for the artificial intelligence training and use. To implement the smart intelligent functions, we adopted a machine learning approach. In this paper, we compare the performance of a neural network (NN), a support vector machine, a system with fuzzy rules genetically produced, and a classification and regression tree and its direct evolution, which is the random forest, in analyzing our database. Best performances in both HF severity evaluation and HF-type prediction functions are obtained by using the random forest algorithm. The management tool allows the cardiologist to populate a "supervised database" suitable for machine learning during his or her regular outpatient consultations. The idea comes from the fact that in literature there are a few databases of this type, and they are not scalable to our case.

Decree No. 849/988 of 14 December 1988 setting forth regulations on the prevention and combat of forest fires.

PubMed

1989-01-01

This Uruguayan Decree sets forth regulations on the prevention and fighting of forest fires. Among other things, it does the following: 1) requires all public and private organizations, as well as all persons, to assist personally in and provide vehicles, machines, and tools for the fighting of forest fires; 2) requires the owners of property containing forests to maintain instruction in fighting fires for an adequate number of employees; 3) requires all forests to be kept cleared of vegetation capable of spreading fires and to have fire walls; 4) requires owners of forests larger than 30 hectares in size to present to the Forest Directorate an annual plan for forest fire defense; and 5) requires owners of forests larger than 30 hectares in size to maintain specified equipment for fighting fires. Persons violating the provisions of this Decree are subject to fines.

Three-dimensional machining of carbon nanotube forests using water-assisted scanning electron microscope processing

NASA Astrophysics Data System (ADS)

Rajabifar, Bahram; Kim, Sanha; Slinker, Keith; Ehlert, Gregory J.; Hart, A. John; Maschmann, Matthew R.

2015-10-01

We demonstrate that vertically aligned carbon nanotubes (CNTs) can be precisely machined in a low pressure water vapor ambient using the electron beam of an environmental scanning electron microscope. The electron beam locally damages the irradiated regions of the CNT forest and also dissociates the water vapor molecules into reactive species including hydroxyl radicals. These species then locally oxidize the damaged region of the CNTs. The technique offers material removal capabilities ranging from selected CNTs to hundreds of cubic microns. We study how the material removal rate is influenced by the acceleration voltage, beam current, dwell time, operating pressure, and CNT orientation. Milled cuts with depths between 0-100 microns are generated, corresponding to a material removal rate of up to 20.1 μm3/min. The technique produces little carbon residue and does not disturb the native morphology of the CNT network. Finally, we demonstrate direct machining of pyramidal surfaces and re-entrant cuts to create freestanding geometries.

Three-dimensional machining of carbon nanotube forests using water-assisted scanning electron microscope processing

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rajabifar, Bahram; Maschmann, Matthew R., E-mail: MaschmannM@missouri.edu; Kim, Sanha

2015-10-05

We demonstrate that vertically aligned carbon nanotubes (CNTs) can be precisely machined in a low pressure water vapor ambient using the electron beam of an environmental scanning electron microscope. The electron beam locally damages the irradiated regions of the CNT forest and also dissociates the water vapor molecules into reactive species including hydroxyl radicals. These species then locally oxidize the damaged region of the CNTs. The technique offers material removal capabilities ranging from selected CNTs to hundreds of cubic microns. We study how the material removal rate is influenced by the acceleration voltage, beam current, dwell time, operating pressure, andmore » CNT orientation. Milled cuts with depths between 0–100 microns are generated, corresponding to a material removal rate of up to 20.1 μm{sup 3}/min. The technique produces little carbon residue and does not disturb the native morphology of the CNT network. Finally, we demonstrate direct machining of pyramidal surfaces and re-entrant cuts to create freestanding geometries.« less

41 CFR 302-12.101 - May we enter into a contract with a relocation services company for the company to provide...

Code of Federal Regulations, 2010 CFR

2010-07-01

... contract with a relocation services company for the company to provide relocation services? 302-12.101... the company to provide relocation services? Yes, you may enter into a contract with a relocation services company for the company to provide relocation services. ...

Uncertainty in Random Forests: What does it mean in a spatial context?

NASA Astrophysics Data System (ADS)

Klump, Jens; Fouedjio, Francky

2017-04-01

Geochemical surveys are an important part of exploration for mineral resources and in environmental studies. The samples and chemical analyses are often laborious and difficult to obtain and therefore come at a high cost. As a consequence, these surveys are characterised by datasets with large numbers of variables but relatively few data points when compared to conventional big data problems. With more remote sensing platforms and sensor networks being deployed, large volumes of auxiliary data of the surveyed areas are becoming available. The use of these auxiliary data has the potential to improve the prediction of chemical element concentrations over the whole study area. Kriging is a well established geostatistical method for the prediction of spatial data but requires significant pre-processing and makes some basic assumptions about the underlying distribution of the data. Some machine learning algorithms, on the other hand, may require less data pre-processing and are non-parametric. In this study we used a dataset provided by Kirkwood et al. [1] to explore the potential use of Random Forest in geochemical mapping. We chose Random Forest because it is a well understood machine learning method and has the advantage that it provides us with a measure of uncertainty. By comparing Random Forest to Kriging we found that both methods produced comparable maps of estimated values for our variables of interest. Kriging outperformed Random Forest for variables of interest with relatively strong spatial correlation. The measure of uncertainty provided by Random Forest seems to be quite different to the measure of uncertainty provided by Kriging. In particular, the lack of spatial context can give misleading results in areas without ground truth data. In conclusion, our preliminary results show that the model driven approach in geostatistics gives us more reliable estimates for our target variables than Random Forest for variables with relatively strong spatial correlation. However, in cases of weak spatial correlation Random Forest, as a nonparametric method, may give the better results once we have a better understanding of the meaning of its uncertainty measures in a spatial context. References [1] Kirkwood, C., M. Cave, D. Beamish, S. Grebby, and A. Ferreira (2016), A machine learning approach to geochemical mapping, Journal of Geochemical Exploration, 163, 28-40, doi:10.1016/j.gexplo.2016.05.003.

A machine learning model with human cognitive biases capable of learning from small and biased datasets.

PubMed

Taniguchi, Hidetaka; Sato, Hiroshi; Shirakawa, Tomohiro

2018-05-09

Human learners can generalize a new concept from a small number of samples. In contrast, conventional machine learning methods require large amounts of data to address the same types of problems. Humans have cognitive biases that promote fast learning. Here, we developed a method to reduce the gap between human beings and machines in this type of inference by utilizing cognitive biases. We implemented a human cognitive model into machine learning algorithms and compared their performance with the currently most popular methods, naïve Bayes, support vector machine, neural networks, logistic regression and random forests. We focused on the task of spam classification, which has been studied for a long time in the field of machine learning and often requires a large amount of data to obtain high accuracy. Our models achieved superior performance with small and biased samples in comparison with other representative machine learning methods.

Support vector machine in machine condition monitoring and fault diagnosis

NASA Astrophysics Data System (ADS)

Widodo, Achmad; Yang, Bo-Suk

2007-08-01

Recently, the issue of machine condition monitoring and fault diagnosis as a part of maintenance system became global due to the potential advantages to be gained from reduced maintenance costs, improved productivity and increased machine availability. This paper presents a survey of machine condition monitoring and fault diagnosis using support vector machine (SVM). It attempts to summarize and review the recent research and developments of SVM in machine condition monitoring and diagnosis. Numerous methods have been developed based on intelligent systems such as artificial neural network, fuzzy expert system, condition-based reasoning, random forest, etc. However, the use of SVM for machine condition monitoring and fault diagnosis is still rare. SVM has excellent performance in generalization so it can produce high accuracy in classification for machine condition monitoring and diagnosis. Until 2006, the use of SVM in machine condition monitoring and fault diagnosis is tending to develop towards expertise orientation and problem-oriented domain. Finally, the ability to continually change and obtain a novel idea for machine condition monitoring and fault diagnosis using SVM will be future works.

Clustering Single-Cell Expression Data Using Random Forest Graphs.

PubMed

Pouyan, Maziyar Baran; Nourani, Mehrdad

2017-07-01

Complex tissues such as brain and bone marrow are made up of multiple cell types. As the study of biological tissue structure progresses, the role of cell-type-specific research becomes increasingly important. Novel sequencing technology such as single-cell cytometry provides researchers access to valuable biological data. Applying machine-learning techniques to these high-throughput datasets provides deep insights into the cellular landscape of the tissue where those cells are a part of. In this paper, we propose the use of random-forest-based single-cell profiling, a new machine-learning-based technique, to profile different cell types of intricate tissues using single-cell cytometry data. Our technique utilizes random forests to capture cell marker dependences and model the cellular populations using the cell network concept. This cellular network helps us discover what cell types are in the tissue. Our experimental results on public-domain datasets indicate promising performance and accuracy of our technique in extracting cell populations of complex tissues.

Classifier ensemble construction with rotation forest to improve medical diagnosis performance of machine learning algorithms.

PubMed

Ozcift, Akin; Gulten, Arif

2011-12-01

Improving accuracies of machine learning algorithms is vital in designing high performance computer-aided diagnosis (CADx) systems. Researches have shown that a base classifier performance might be enhanced by ensemble classification strategies. In this study, we construct rotation forest (RF) ensemble classifiers of 30 machine learning algorithms to evaluate their classification performances using Parkinson's, diabetes and heart diseases from literature. While making experiments, first the feature dimension of three datasets is reduced using correlation based feature selection (CFS) algorithm. Second, classification performances of 30 machine learning algorithms are calculated for three datasets. Third, 30 classifier ensembles are constructed based on RF algorithm to assess performances of respective classifiers with the same disease data. All the experiments are carried out with leave-one-out validation strategy and the performances of the 60 algorithms are evaluated using three metrics; classification accuracy (ACC), kappa error (KE) and area under the receiver operating characteristic (ROC) curve (AUC). Base classifiers succeeded 72.15%, 77.52% and 84.43% average accuracies for diabetes, heart and Parkinson's datasets, respectively. As for RF classifier ensembles, they produced average accuracies of 74.47%, 80.49% and 87.13% for respective diseases. RF, a newly proposed classifier ensemble algorithm, might be used to improve accuracy of miscellaneous machine learning algorithms to design advanced CADx systems. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Movements and use of space by Mangrove Cuckoos (Coccyzus minor) in Florida, USA.

PubMed

Lloyd, John David

2017-01-01

I used radio-telemetry to track the movements of Mangrove Cuckoos ( Coccyzus minor ) captured in southwest Florida. Relatively little is known about the natural history of Mangrove Cuckoos, and my goal was to provide an initial description of how individuals use space, with a focus on the size and placement of home ranges. I captured and affixed VHF radio-transmitters to 32 individuals between 2012 and 2015, and obtained a sufficient number of relocations from 16 of them to estimate home-range boundaries and describe patterns of movement. Home-range area varied widely among individuals, but in general was roughly four times larger than expected based on the body size of Mangrove Cuckoos. The median core area (50% isopleth) of a home range was 42 ha (range: 9-91 ha), and the median overall home range (90% isopleth) was 128 ha (range: 28-319 ha). The median distance between estimated locations recorded on subsequent days was 298 m (95% CI [187 m-409 m]), but variation within and among individuals was substantial, and it was not uncommon to relocate individuals >1 km from their location on the previous day. Site fidelity by individual birds was low; although Mangrove Cuckoos were present year-round within the study area, I did not observe any individuals that remained on a single home range throughout the year. Although individual birds showed no evidence of avoiding anthropogenic edges, they did not incorporate developed areas into their daily movements and home ranges consisted almost entirely of mangrove forest. The persistence of the species in the study area depended on a network of conserved lands-mostly public, but some privately conserved land as well-because large patches of mangrove forest did not occur on tracts left unprotected from development.

New tree puller increases yield 20%

Treesearch

E. Kerr

1977-01-01

A new tree harvester that extracts both taproot and stem makes a pine tree 20 percent more useful. The machine shears the lateral roots close to the taprrot and then plucks the entire tree from the ground like a carrot. The concept was developed by Dr. Peter Koch at the U.S. Forest Service's Southern Forest Experiment Station in Pineville, La. The shearing...

Board-foot and Cubic-foot Volume Computing Equations for Southeastern Tree Species

Treesearch

Mackay B. Bryan; Joe P. McClure

1962-01-01

Wide acceptance of Bitterlich's (2) method of sampling, popularized in this country by Grosenbaugh (3), with adaptations such as the variable plot used by Forest Survey in the Southeast, has opened a new era in forest surveying. The efficiency of these sampling methods, accompanied by the timely availability of electronic computing machines, has made it feasible...

Before and after comparisons of tree height in successive loblolly pine plantations with intervening machine, whole-tree harvesting

Treesearch

Thomas J. Dean; D. Andrew Scott; A. Gordon Holley

2013-01-01

In 1993, several forest industries, the U.S. Forest Service Southern Research Station, Louisiana Tech University, and the School of Renewable Natural Resources in the Louisiana State University Agricultural Center formed a cooperative that came to be called Cooperative Research in Sustainable Silviculture and Soil Productivity. One of the objectives of the cooperative...

Impacts of removing Chinese privet from riparian forests on plant communities and tree growth five years later

Treesearch

Jacob R. Hudson; James L. Hanula; Scott Horn

2014-01-01

An invasive shrub, Chinese privet (Ligustrum sinense Lour.), was removed from heavily infested riparian forests in the Georgia Piedmont in 2005 by mulching machine or chainsaw felling. Subsequent herbicide treatment eliminated almost all privet by 2007. Recovery of plant communities, return of Chinese privet, and canopy tree growth were measured on...

Evaluation of a multi-sensor machine vision system for automated hardwood lumber grading

Treesearch

D. Earl Kline; Chris Surak; Philip A. Araman

2000-01-01

Over the last 10 years, scientists at the Thomas M. Brooks Forest Products Center, the Bradley Department of Electrical Engineering, and the USDA Forest Service have been working on lumber scanning systems that can accurately locate and identify defects in hardwood lumber. Current R&D efforts are targeted toward developing automated lumber grading technologies. The...

76 FR 46830 - Notice of Proposed Information Collection: Comment Request; Application for Displacement...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-08-03

... Information Collection: Comment Request; Application for Displacement/Relocation/Temporary Relocation... Proposal: Application for Displacement/Relocation/ Temporary Relocation Assistance for Person. OMB Control...: Application for displacement/relocation assistance for persons (families, individuals, businesses, nonprofit...

Can machine-learning improve cardiovascular risk prediction using routine clinical data?

PubMed Central

Kai, Joe; Garibaldi, Jonathan M.; Qureshi, Nadeem

2017-01-01

Background Current approaches to predict cardiovascular risk fail to identify many people who would benefit from preventive treatment, while others receive unnecessary intervention. Machine-learning offers opportunity to improve accuracy by exploiting complex interactions between risk factors. We assessed whether machine-learning can improve cardiovascular risk prediction. Methods Prospective cohort study using routine clinical data of 378,256 patients from UK family practices, free from cardiovascular disease at outset. Four machine-learning algorithms (random forest, logistic regression, gradient boosting machines, neural networks) were compared to an established algorithm (American College of Cardiology guidelines) to predict first cardiovascular event over 10-years. Predictive accuracy was assessed by area under the ‘receiver operating curve’ (AUC); and sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) to predict 7.5% cardiovascular risk (threshold for initiating statins). Findings 24,970 incident cardiovascular events (6.6%) occurred. Compared to the established risk prediction algorithm (AUC 0.728, 95% CI 0.723–0.735), machine-learning algorithms improved prediction: random forest +1.7% (AUC 0.745, 95% CI 0.739–0.750), logistic regression +3.2% (AUC 0.760, 95% CI 0.755–0.766), gradient boosting +3.3% (AUC 0.761, 95% CI 0.755–0.766), neural networks +3.6% (AUC 0.764, 95% CI 0.759–0.769). The highest achieving (neural networks) algorithm predicted 4,998/7,404 cases (sensitivity 67.5%, PPV 18.4%) and 53,458/75,585 non-cases (specificity 70.7%, NPV 95.7%), correctly predicting 355 (+7.6%) more patients who developed cardiovascular disease compared to the established algorithm. Conclusions Machine-learning significantly improves accuracy of cardiovascular risk prediction, increasing the number of patients identified who could benefit from preventive treatment, while avoiding unnecessary treatment of others. PMID:28376093

Can machine-learning improve cardiovascular risk prediction using routine clinical data?

PubMed

Weng, Stephen F; Reps, Jenna; Kai, Joe; Garibaldi, Jonathan M; Qureshi, Nadeem

2017-01-01

Current approaches to predict cardiovascular risk fail to identify many people who would benefit from preventive treatment, while others receive unnecessary intervention. Machine-learning offers opportunity to improve accuracy by exploiting complex interactions between risk factors. We assessed whether machine-learning can improve cardiovascular risk prediction. Prospective cohort study using routine clinical data of 378,256 patients from UK family practices, free from cardiovascular disease at outset. Four machine-learning algorithms (random forest, logistic regression, gradient boosting machines, neural networks) were compared to an established algorithm (American College of Cardiology guidelines) to predict first cardiovascular event over 10-years. Predictive accuracy was assessed by area under the 'receiver operating curve' (AUC); and sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) to predict 7.5% cardiovascular risk (threshold for initiating statins). 24,970 incident cardiovascular events (6.6%) occurred. Compared to the established risk prediction algorithm (AUC 0.728, 95% CI 0.723-0.735), machine-learning algorithms improved prediction: random forest +1.7% (AUC 0.745, 95% CI 0.739-0.750), logistic regression +3.2% (AUC 0.760, 95% CI 0.755-0.766), gradient boosting +3.3% (AUC 0.761, 95% CI 0.755-0.766), neural networks +3.6% (AUC 0.764, 95% CI 0.759-0.769). The highest achieving (neural networks) algorithm predicted 4,998/7,404 cases (sensitivity 67.5%, PPV 18.4%) and 53,458/75,585 non-cases (specificity 70.7%, NPV 95.7%), correctly predicting 355 (+7.6%) more patients who developed cardiovascular disease compared to the established algorithm. Machine-learning significantly improves accuracy of cardiovascular risk prediction, increasing the number of patients identified who could benefit from preventive treatment, while avoiding unnecessary treatment of others.

Machine learning methods for the classification of gliomas: Initial results using features extracted from MR spectroscopy.

PubMed

Ranjith, G; Parvathy, R; Vikas, V; Chandrasekharan, Kesavadas; Nair, Suresh

2015-04-01

With the advent of new imaging modalities, radiologists are faced with handling increasing volumes of data for diagnosis and treatment planning. The use of automated and intelligent systems is becoming essential in such a scenario. Machine learning, a branch of artificial intelligence, is increasingly being used in medical image analysis applications such as image segmentation, registration and computer-aided diagnosis and detection. Histopathological analysis is currently the gold standard for classification of brain tumors. The use of machine learning algorithms along with extraction of relevant features from magnetic resonance imaging (MRI) holds promise of replacing conventional invasive methods of tumor classification. The aim of the study is to classify gliomas into benign and malignant types using MRI data. Retrospective data from 28 patients who were diagnosed with glioma were used for the analysis. WHO Grade II (low-grade astrocytoma) was classified as benign while Grade III (anaplastic astrocytoma) and Grade IV (glioblastoma multiforme) were classified as malignant. Features were extracted from MR spectroscopy. The classification was done using four machine learning algorithms: multilayer perceptrons, support vector machine, random forest and locally weighted learning. Three of the four machine learning algorithms gave an area under ROC curve in excess of 0.80. Random forest gave the best performance in terms of AUC (0.911) while sensitivity was best for locally weighted learning (86.1%). The performance of different machine learning algorithms in the classification of gliomas is promising. An even better performance may be expected by integrating features extracted from other MR sequences. © The Author(s) 2015 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

Effects of Machine Traffic on the Physical Properties of Ash-Cap Soils

Treesearch

Leonard R. Johnson; Debbie Page-Dumroese; Han-Sup Han

2007-01-01

With pressure and vibration on a soil, air spaces between soil particles can be reduced by displaced soil particles. Activity associated with heavy machine traffic increases the density of the soil and can also increase the resistance of the soil to penetration. This paper reviews research related to disturbance of forest soils with a primary focus on compaction in ash...

ChargeOut! : determining machine and capital equipment charge-out rates using discounted cash-flow analysis

Treesearch

E.M. (Ted) Bilek

2007-01-01

The model ChargeOut! was developed to determine charge-out rates or rates of return for machines and capital equipment. This paper introduces a costing methodology and applies it to a piece of capital equipment. Although designed for the forest industry, the methodology is readily transferable to other sectors. Based on discounted cash-flow analysis, ChargeOut!...

Incorporation of tracers and dazomet by rotary tillers and a spading machine

Treesearch

J. Juzwik; D. L. Stenlund; R.R. Allmaras; S. M. Copeland; R. E. McRoberts

1997-01-01

Soil fumigant efficacy in forest-tree and ornamental nurseries depends on the tillage tool used for incorporation. Maximum depth and uniformity of incorporation of surface applied materials by three rotary tillers and a spading machine were compared in a loamy sand nursery using ceramic-sphere tracers (1-3 mm diameter) and dazomet (tetrahydro-3,5,dimethyl-2H-1,3,5-...

Development of SNS Stream Analysis Based on Forest Disaster Warning Information Service System

NASA Astrophysics Data System (ADS)

Oh, J.; KIM, D.; Kang, M.; Woo, C.; Kim, D.; Seo, J.; Lee, C.; Yoon, H.; Heon, S.

2017-12-01

Forest disasters, such as landslides and wildfires, cause huge economic losses and casualties, and the cost of recovery is increasing every year. While forest disaster mitigation technologies have been focused on the development of prevention and response technologies, they are now required to evolve into evacuation and border evacuation, and to develop technologies fused with ICT. In this study, we analyze the SNS (Social Network Service) stream and implement a system to detect the message that the forest disaster occurred or the forest disaster, and search the keyword related to the forest disaster in advance in real time. It is possible to detect more accurate forest disaster messages by repeatedly learning the retrieved results using machine learning techniques. To do this, we designed and implemented a system based on Hadoop and Spark, a distributed parallel processing platform, to handle Twitter stream messages that open SNS. In order to develop the technology to notify the information of forest disaster risk, a linkage of technology such as CBS (Cell Broadcasting System) based on mobile communication, internet-based civil defense siren, SNS and the legal and institutional issues for applying these technologies are examined. And the protocol of the forest disaster warning information service system that can deliver the SNS analysis result was developed. As a result, it was possible to grasp real-time forest disaster situation by real-time big data analysis of SNS that occurred during forest disasters. In addition, we confirmed that it is possible to rapidly propagate alarm or warning according to the disaster situation by using the function of the forest disaster warning information notification service. However, the limitation of system application due to the restriction of opening and sharing of SNS data currently in service and the disclosure of personal information remains a problem to be solved in the future. Keyword : SNS stream, Big data, Machine learning techniques, CBS, Forest disaster warning information service system Acknowledgement : This research was supported by the Forestry Technology 2015 Forestry Technology Research and Development Project (Planning project).

A machine learning method to estimate PM2.5 concentrations across China with remote sensing, meteorological and land use information.

PubMed

Chen, Gongbo; Li, Shanshan; Knibbs, Luke D; Hamm, N A S; Cao, Wei; Li, Tiantian; Guo, Jianping; Ren, Hongyan; Abramson, Michael J; Guo, Yuming

2018-09-15

Machine learning algorithms have very high predictive ability. However, no study has used machine learning to estimate historical concentrations of PM 2.5 (particulate matter with aerodynamic diameter ≤ 2.5 μm) at daily time scale in China at a national level. To estimate daily concentrations of PM 2.5 across China during 2005-2016. Daily ground-level PM 2.5 data were obtained from 1479 stations across China during 2014-2016. Data on aerosol optical depth (AOD), meteorological conditions and other predictors were downloaded. A random forests model (non-parametric machine learning algorithms) and two traditional regression models were developed to estimate ground-level PM 2.5 concentrations. The best-fit model was then utilized to estimate the daily concentrations of PM 2.5 across China with a resolution of 0.1° (≈10 km) during 2005-2016. The daily random forests model showed much higher predictive accuracy than the other two traditional regression models, explaining the majority of spatial variability in daily PM 2.5 [10-fold cross-validation (CV) R 2  = 83%, root mean squared prediction error (RMSE) = 28.1 μg/m 3 ]. At the monthly and annual time-scale, the explained variability of average PM 2.5 increased up to 86% (RMSE = 10.7 μg/m 3 and 6.9 μg/m 3 , respectively). Taking advantage of a novel application of modeling framework and the most recent ground-level PM 2.5 observations, the machine learning method showed higher predictive ability than previous studies. Random forests approach can be used to estimate historical exposure to PM 2.5 in China with high accuracy. Copyright © 2018 Elsevier B.V. All rights reserved.

Functions of perch relocations in a communal night roost of wintering bald eagles

USGS Publications Warehouse

Yackel Adams, A.A.; Skagen, S.K.; Knight, R.L.

2000-01-01

We investigated the functions of perch relocations within a communal night roost of wintering bald eagles (Haliaeetus leucocephalus) along the Nooksack River, Washington, during two winters. We tested seven predictions of two nonexclusive hypotheses: (1) bald eagles relocate within roosts to assess foraging success of conspecifics and (2) bald eagles relocate to obtain thermoregulatory benefits from an improved microclimate. Additionally, we gathered descriptive information to allow refinement of further alternative hypotheses. We rejected the hypothesis that relocations are a means of assessing foraging success. Contrary to our expectations, immature eagles did not relocate to be closer to adults, and relocations were less frequent when food was less abundant. Our data support the hypothesis that eagles relocate within night roosts to obtain a favorable microclimate during winters when they are subjected to cold stress and food stress. In both winters, relocations were more frequent in the evening than in the morning. In both winters, most evening relocations were to the center of the roost rather than to its edge, and the frequency of relocation to the center was greater when temperatures were low. The microclimate hypothesis, however, explains only a limited number of relocations. Based on our findings, it is likely that relocation has multiple functions, including establishing and (or) maintaining foraging associations, establishing and (or) maintaining social-dominance hierarchies when food is less abundant, and nonsocial activities.

Towards a tipping point in responding to change: rising costs, fewer options for Arctic and global societies.

PubMed

Huntington, Henry P; Goodstein, Eban; Euskirchen, Eugénie

2012-02-01

Climate change incurs costs, but government adaptation budgets are limited. Beyond a certain point, individuals must bear the costs or adapt to new circumstances, creating political-economic tipping points that we explore in three examples. First, many Alaska Native villages are threatened by erosion, but relocation is expensive. To date, critically threatened villages have not yet been relocated, suggesting that we may already have reached a political-economic tipping point. Second, forest fires shape landscape and ecological characteristics in interior Alaska. Climate-driven changes in fire regime require increased fire-fighting resources to maintain current patterns of vegetation and land use, but these resources appear to be less and less available, indicating an approaching tipping point. Third, rapid sea level rise, for example from accelerated melting of the Greenland ice sheet, will create a choice between protection and abandonment for coastal regions throughout the world, a potential global tipping point comparable to those now faced by Arctic communities. The examples illustrate the basic idea that if costs of response increase more quickly than available resources, then society has fewer and fewer options as time passes.

Detection and Monitoring of Small-Scale Mining Operations in the Eastern Democratic Republic of the Congo (DRC) Using Multi-Temporal, Multi-Sensor Remote Sensing Data

NASA Astrophysics Data System (ADS)

Walther, Christian; Frei, Michaela

2017-04-01

Mining of so-called "conflict minerals" is often related with small-scale mining activities. The here discussed activities are located in forested areas in the eastern DRC, which are often remote, difficult to access and insecure for traditional geological field inspection. In order to accelerate their CTC (Certified Trading Chain)-certification process, remote sensing data are used for detection and monitoring of these small-scale mining operations. This requires a high image acquisition frequency due to mining site relocations and for compensation of year-round high cloud coverage, especially for optical data evaluation. Freely available medium resolution optical data of Sentinel-2 and Landsat-8 as well as SAR data of Sentinel-1 are used for detecting small mining targets with a minimum size of approximately 0.5 km2. The developed method enables a robust multi-temporal detection of mining sites, monitoring of mining site spatio-temporal relocations and environmental changes. Since qualitative and quantitative comparable results are generated, the followed change detection approach is objective and transparent and may push the certification process forward.

System-Level Virtualization for High Performance Computing

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vallee, Geoffroy R; Naughton, III, Thomas J; Engelmann, Christian

2008-01-01

System-level virtualization has been a research topic since the 70's but regained popularity during the past few years because of the availability of efficient solution such as Xen and the implementation of hardware support in commodity processors (e.g. Intel-VT, AMD-V). However, a majority of system-level virtualization projects is guided by the server consolidation market. As a result, current virtualization solutions appear to not be suitable for high performance computing (HPC) which is typically based on large-scale systems. On another hand there is significant interest in exploiting virtual machines (VMs) within HPC for a number of other reasons. By virtualizing themore » machine, one is able to run a variety of operating systems and environments as needed by the applications. Virtualization allows users to isolate workloads, improving security and reliability. It is also possible to support non-native environments and/or legacy operating environments through virtualization. In addition, it is possible to balance work loads, use migration techniques to relocate applications from failing machines, and isolate fault systems for repair. This document presents the challenges for the implementation of a system-level virtualization solution for HPC. It also presents a brief survey of the different approaches and techniques to address these challenges.« less

MIP Models and Hybrid Algorithms for Simultaneous Job Splitting and Scheduling on Unrelated Parallel Machines

PubMed Central

Ozmutlu, H. Cenk

2014-01-01

We developed mixed integer programming (MIP) models and hybrid genetic-local search algorithms for the scheduling problem of unrelated parallel machines with job sequence and machine-dependent setup times and with job splitting property. The first contribution of this paper is to introduce novel algorithms which make splitting and scheduling simultaneously with variable number of subjobs. We proposed simple chromosome structure which is constituted by random key numbers in hybrid genetic-local search algorithm (GAspLA). Random key numbers are used frequently in genetic algorithms, but it creates additional difficulty when hybrid factors in local search are implemented. We developed algorithms that satisfy the adaptation of results of local search into the genetic algorithms with minimum relocation operation of genes' random key numbers. This is the second contribution of the paper. The third contribution of this paper is three developed new MIP models which are making splitting and scheduling simultaneously. The fourth contribution of this paper is implementation of the GAspLAMIP. This implementation let us verify the optimality of GAspLA for the studied combinations. The proposed methods are tested on a set of problems taken from the literature and the results validate the effectiveness of the proposed algorithms. PMID:24977204

Discriminant forest classification method and system

DOEpatents

Chen, Barry Y.; Hanley, William G.; Lemmond, Tracy D.; Hiller, Lawrence J.; Knapp, David A.; Mugge, Marshall J.

2012-11-06

A hybrid machine learning methodology and system for classification that combines classical random forest (RF) methodology with discriminant analysis (DA) techniques to provide enhanced classification capability. A DA technique which uses feature measurements of an object to predict its class membership, such as linear discriminant analysis (LDA) or Andersen-Bahadur linear discriminant technique (AB), is used to split the data at each node in each of its classification trees to train and grow the trees and the forest. When training is finished, a set of n DA-based decision trees of a discriminant forest is produced for use in predicting the classification of new samples of unknown class.

Ornamental Planting Restoration at Jefferson's Poplar Forest Through XRF and ICP-OES Analysis of Disturbed Soils

NASA Astrophysics Data System (ADS)

Hatfield, M.; Low, P. C.; Devlin, S.

2011-12-01

Thomas Jefferson's Poplar Forest estate near Lynchburg, VA is currently attempting to restore the property to its Jeffersonian condition. Subsequent modifications to the property following its sale by Jefferson's heirs included the removal of the original trees in order to facilitate agricultural activity. One key facet of the restoration involves determining the precise location of the sixty-four paper mulberry trees that Jefferson reportedly had transplanted in 1815 from his on-site nursery to near the main house. At Monticello, it is well-documented that Jefferson used contextually innovative fertilizing techniques, including the addition of gypsum and lime "to restore the exhaustion of a single crop from the soil." Whether he used these methods in the nursery at Poplar Forest to the degree that decades of subsequent leaching, weathering, and other disturbances would not erase remains historically and analytically unclear. Since the transplantation process requires that large amounts of soil be moved with the trees, small areas of compositionally distinct soils in the suspected planting area could be used to establish the exact location of each tree through differentiating between nursery and in situ soils. Through X-ray fluorescence spectroscopy (XRF) and intercoupled plasma optical emission spectroscopy (ICP-OES) geochemical analysis, the specific composition of soil can be determined. Preliminary analysis shows slight differences in phosphorus and sulfur between the nursery and in situ soil; however, the property lies on three different distinct geological units: actinolite schist and feldspathic metagreywacke units of the Alligator Back formation, and biotite gneiss of the Ashe Formation (biotite gneiss). The location of the nursery where the sixty-four paper mulberry trees were originally grown lies on the feldspathic metagreywacke unit; whereas the relocation site where Jefferson had them planted rests on the actinolite schist unit. Percursory study identifies significant differences in major elements such as silicon, aluminum, calcium, and manganese. Locating the presence of the nursery soil within the relocation site soil will allow staff archaeologists to replant the paper mulberry trees in order to reconstruct Jefferson's plantation.

The influence of negative training set size on machine learning-based virtual screening.

PubMed

Kurczab, Rafał; Smusz, Sabina; Bojarski, Andrzej J

2014-01-01

The paper presents a thorough analysis of the influence of the number of negative training examples on the performance of machine learning methods. The impact of this rather neglected aspect of machine learning methods application was examined for sets containing a fixed number of positive and a varying number of negative examples randomly selected from the ZINC database. An increase in the ratio of positive to negative training instances was found to greatly influence most of the investigated evaluating parameters of ML methods in simulated virtual screening experiments. In a majority of cases, substantial increases in precision and MCC were observed in conjunction with some decreases in hit recall. The analysis of dynamics of those variations let us recommend an optimal composition of training data. The study was performed on several protein targets, 5 machine learning algorithms (SMO, Naïve Bayes, Ibk, J48 and Random Forest) and 2 types of molecular fingerprints (MACCS and CDK FP). The most effective classification was provided by the combination of CDK FP with SMO or Random Forest algorithms. The Naïve Bayes models appeared to be hardly sensitive to changes in the number of negative instances in the training set. In conclusion, the ratio of positive to negative training instances should be taken into account during the preparation of machine learning experiments, as it might significantly influence the performance of particular classifier. What is more, the optimization of negative training set size can be applied as a boosting-like approach in machine learning-based virtual screening.

The influence of negative training set size on machine learning-based virtual screening

PubMed Central

2014-01-01

Background The paper presents a thorough analysis of the influence of the number of negative training examples on the performance of machine learning methods. Results The impact of this rather neglected aspect of machine learning methods application was examined for sets containing a fixed number of positive and a varying number of negative examples randomly selected from the ZINC database. An increase in the ratio of positive to negative training instances was found to greatly influence most of the investigated evaluating parameters of ML methods in simulated virtual screening experiments. In a majority of cases, substantial increases in precision and MCC were observed in conjunction with some decreases in hit recall. The analysis of dynamics of those variations let us recommend an optimal composition of training data. The study was performed on several protein targets, 5 machine learning algorithms (SMO, Naïve Bayes, Ibk, J48 and Random Forest) and 2 types of molecular fingerprints (MACCS and CDK FP). The most effective classification was provided by the combination of CDK FP with SMO or Random Forest algorithms. The Naïve Bayes models appeared to be hardly sensitive to changes in the number of negative instances in the training set. Conclusions In conclusion, the ratio of positive to negative training instances should be taken into account during the preparation of machine learning experiments, as it might significantly influence the performance of particular classifier. What is more, the optimization of negative training set size can be applied as a boosting-like approach in machine learning-based virtual screening. PMID:24976867

76 FR 39117 - Notice of Proposed Information Collection: Relocation and Real Property Acquisition...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-07-05

... Information Collection: Relocation and Real Property Acquisition, Recordkeeping Requirements Under the Uniform Relocation Assistance and Real Property Acquisition Policies Act of 1970, as Amended (URA) Comment Request..., DC 20410. FOR FURTHER INFORMATION CONTACT: Bryan O'Neill, Relocation Specialist, Relocation and Real...

Lathe creates hardwood flakes for manufacture of "super strong" flakeboard

Treesearch

P. Koch

1973-01-01

Most industry members got their first look at a prototype of the Koch lathe at this year's Southern Forest Products Assn. Machinery Exhibition held in Atlanta. With the residue from this machine, Dr. Peter Koch, project leader at the Southern Forest Experiment Station in Pineville, LA thinks it will be possible to create a flake that can be used for making a...

A new slash bundling concept for use in a Southern U.S

Treesearch

Steven Meadows; Tom Gallagher; Dana Mitchell

2011-01-01

John Deere’s biomass bundler unit is an effective machine for harvesting forest residues, which can be used as a source of fuelwood and/or a feedstock for biofuel production. This project explored an avenue that could supply a promising source of readily available energy in southeastern forested lands. Typical southern harvesting operations consist of whole-tree...

Development of machine learning models for diagnosis of glaucoma.

PubMed

Kim, Seong Jae; Cho, Kyong Jin; Oh, Sejong

2017-01-01

The study aimed to develop machine learning models that have strong prediction power and interpretability for diagnosis of glaucoma based on retinal nerve fiber layer (RNFL) thickness and visual field (VF). We collected various candidate features from the examination of retinal nerve fiber layer (RNFL) thickness and visual field (VF). We also developed synthesized features from original features. We then selected the best features proper for classification (diagnosis) through feature evaluation. We used 100 cases of data as a test dataset and 399 cases of data as a training and validation dataset. To develop the glaucoma prediction model, we considered four machine learning algorithms: C5.0, random forest (RF), support vector machine (SVM), and k-nearest neighbor (KNN). We repeatedly composed a learning model using the training dataset and evaluated it by using the validation dataset. Finally, we got the best learning model that produces the highest validation accuracy. We analyzed quality of the models using several measures. The random forest model shows best performance and C5.0, SVM, and KNN models show similar accuracy. In the random forest model, the classification accuracy is 0.98, sensitivity is 0.983, specificity is 0.975, and AUC is 0.979. The developed prediction models show high accuracy, sensitivity, specificity, and AUC in classifying among glaucoma and healthy eyes. It will be used for predicting glaucoma against unknown examination records. Clinicians may reference the prediction results and be able to make better decisions. We may combine multiple learning models to increase prediction accuracy. The C5.0 model includes decision rules for prediction. It can be used to explain the reasons for specific predictions.

41 CFR 302-2.6 - May I be reimbursed for relocation expenses if I relocate to a new official station that does not...

Code of Federal Regulations, 2014 CFR

2014-07-01

... relocation expenses if I relocate to a new official station that does not meet the 50-mile distance test? 302... reimbursed for relocation expenses if I relocate to a new official station that does not meet the 50-mile... official station that does not meet the 50-mile distance test. (a) The distance test is met when the new...

41 CFR 302-2.6 - May I be reimbursed for relocation expenses if I relocate to a new official station that does not...

Code of Federal Regulations, 2012 CFR

2012-07-01

... relocation expenses if I relocate to a new official station that does not meet the 50-mile distance test? 302... reimbursed for relocation expenses if I relocate to a new official station that does not meet the 50-mile... official station that does not meet the 50-mile distance test. (a) The distance test is met when the new...

41 CFR 302-2.6 - May I be reimbursed for relocation expenses if I relocate to a new official station that is less...

Code of Federal Regulations, 2011 CFR

2011-07-01

... relocation expenses if I relocate to a new official station that is less than 50 miles from my old official... reimbursed for relocation expenses if I relocate to a new official station that is less than 50 miles from my... to a new official station that is less than 50 miles from your old official station, unless the head...

41 CFR 302-2.6 - May I be reimbursed for relocation expenses if I relocate to a new official station that does not...

Code of Federal Regulations, 2013 CFR

2013-07-01

... relocation expenses if I relocate to a new official station that does not meet the 50-mile distance test? 302... reimbursed for relocation expenses if I relocate to a new official station that does not meet the 50-mile... official station that does not meet the 50-mile distance test. (a) The distance test is met when the new...

Evaluation of freshwater mussel relocation as a conservation and management strategy

USGS Publications Warehouse

Cope, W. Gregory; Waller, Diane L.

1995-01-01

The relocation of unionacean mussels is commonly used as a conservation and management tool in large rivers and streams. Relocation has been used to recolonize areas where mussel populations have been eliminated by prior pollution events, to remove mussels from construction zones and to re-establish populations of endangered species. More recently, relocation has been used to protect native freshwater mussels from colonization by the exotic zebra mussel Dreissena polymorpha. We conducted a literature review of mussel relocations and evaluated their relative success as a conservation and management strategy. We found that 43% of all relocations were conducted because of construction projects that were forced to comply with the Endangered Species Act 1973 and that only 16% were monitored for five or more consecutive years. Most (43%) relocation projects were conducted from July to September, presumably a period when reproductive stress is relatively low for most species and the metabolic rate is sufficient for reburrowing in the substrate. The mortality of relocated mussels was unreported in 27% of projects; reported mortality varied widely among projects and species and was difficult to assess. The mean mortality of relocated mussels was 49% based on an average recovery rate of 43%. There is little guidance on the methods for relocation or for monitoring the subsequent long-term status of relocated mussels. Based on this evaluation, research is needed to develop criteria for selecting a suitable relocation site and to establish appropriate methods and guidelines for conducting relocation projects.

Identifying Active Travel Behaviors in Challenging Environments Using GPS, Accelerometers, and Machine Learning Algorithms

PubMed Central

Ellis, Katherine; Godbole, Suneeta; Marshall, Simon; Lanckriet, Gert; Staudenmayer, John; Kerr, Jacqueline

2014-01-01

Background: Active travel is an important area in physical activity research, but objective measurement of active travel is still difficult. Automated methods to measure travel behaviors will improve research in this area. In this paper, we present a supervised machine learning method for transportation mode prediction from global positioning system (GPS) and accelerometer data. Methods: We collected a dataset of about 150 h of GPS and accelerometer data from two research assistants following a protocol of prescribed trips consisting of five activities: bicycling, riding in a vehicle, walking, sitting, and standing. We extracted 49 features from 1-min windows of this data. We compared the performance of several machine learning algorithms and chose a random forest algorithm to classify the transportation mode. We used a moving average output filter to smooth the output predictions over time. Results: The random forest algorithm achieved 89.8% cross-validated accuracy on this dataset. Adding the moving average filter to smooth output predictions increased the cross-validated accuracy to 91.9%. Conclusion: Machine learning methods are a viable approach for automating measurement of active travel, particularly for measuring travel activities that traditional accelerometer data processing methods misclassify, such as bicycling and vehicle travel. PMID:24795875

Application of Machine Learning Approaches for Classifying Sitting Posture Based on Force and Acceleration Sensors.

PubMed

Zemp, Roland; Tanadini, Matteo; Plüss, Stefan; Schnüriger, Karin; Singh, Navrag B; Taylor, William R; Lorenzetti, Silvio

2016-01-01

Occupational musculoskeletal disorders, particularly chronic low back pain (LBP), are ubiquitous due to prolonged static sitting or nonergonomic sitting positions. Therefore, the aim of this study was to develop an instrumented chair with force and acceleration sensors to determine the accuracy of automatically identifying the user's sitting position by applying five different machine learning methods (Support Vector Machines, Multinomial Regression, Boosting, Neural Networks, and Random Forest). Forty-one subjects were requested to sit four times in seven different prescribed sitting positions (total 1148 samples). Sixteen force sensor values and the backrest angle were used as the explanatory variables (features) for the classification. The different classification methods were compared by means of a Leave-One-Out cross-validation approach. The best performance was achieved using the Random Forest classification algorithm, producing a mean classification accuracy of 90.9% for subjects with which the algorithm was not familiar. The classification accuracy varied between 81% and 98% for the seven different sitting positions. The present study showed the possibility of accurately classifying different sitting positions by means of the introduced instrumented office chair combined with machine learning analyses. The use of such novel approaches for the accurate assessment of chair usage could offer insights into the relationships between sitting position, sitting behaviour, and the occurrence of musculoskeletal disorders.

41 CFR 302-3.508 - What relocation expenses are not authorized for new appointees or student trainees?

Code of Federal Regulations, 2012 CFR

2012-07-01

... 41 Public Contracts and Property Management 4 2012-07-01 2012-07-01 false What relocation expenses... Contracts and Property Management Federal Travel Regulation System RELOCATION ALLOWANCES RELOCATION ALLOWANCES 3-RELOCATION ALLOWANCE BY SPECIFIC TYPE Agency Responsibilities New Appointees § 302-3.508 What...

41 CFR 302-3.508 - What relocation expenses are not authorized for new appointees or student trainees?

Code of Federal Regulations, 2014 CFR

2014-07-01

... 41 Public Contracts and Property Management 4 2014-07-01 2014-07-01 false What relocation expenses... Contracts and Property Management Federal Travel Regulation System RELOCATION ALLOWANCES RELOCATION ALLOWANCES 3-RELOCATION ALLOWANCE BY SPECIFIC TYPE Agency Responsibilities New Appointees § 302-3.508 What...

41 CFR 302-3.508 - What relocation expenses are not authorized for new appointees or student trainees?

Code of Federal Regulations, 2011 CFR

2011-07-01

... 41 Public Contracts and Property Management 4 2011-07-01 2011-07-01 false What relocation expenses... Contracts and Property Management Federal Travel Regulation System RELOCATION ALLOWANCES RELOCATION ALLOWANCES 3-RELOCATION ALLOWANCE BY SPECIFIC TYPE Agency Responsibilities New Appointees § 302-3.508 What...

41 CFR 302-3.508 - What relocation expenses are not authorized for new appointees or student trainees?

Code of Federal Regulations, 2013 CFR

2013-07-01

... 41 Public Contracts and Property Management 4 2013-07-01 2012-07-01 true What relocation expenses... Contracts and Property Management Federal Travel Regulation System RELOCATION ALLOWANCES RELOCATION ALLOWANCES 3-RELOCATION ALLOWANCE BY SPECIFIC TYPE Agency Responsibilities New Appointees § 302-3.508 What...

41 CFR 302-3.207 - Am I eligible to receive relocation allowances for overseas assignment and return travel?

Code of Federal Regulations, 2011 CFR

2011-07-01

... relocation allowances for overseas assignment and return travel? 302-3.207 Section 302-3.207 Public Contracts and Property Management Federal Travel Regulation System RELOCATION ALLOWANCES RELOCATION ALLOWANCES 3... eligible to receive relocation allowances for overseas assignment and return travel? You may be eligible to...

41 CFR 302-3.207 - Am I eligible to receive relocation allowances for overseas assignment and return travel?

Code of Federal Regulations, 2013 CFR

2013-07-01

... relocation allowances for overseas assignment and return travel? 302-3.207 Section 302-3.207 Public Contracts and Property Management Federal Travel Regulation System RELOCATION ALLOWANCES RELOCATION ALLOWANCES 3... eligible to receive relocation allowances for overseas assignment and return travel? You may be eligible to...

41 CFR 302-3.207 - Am I eligible to receive relocation allowances for overseas assignment and return travel?

Code of Federal Regulations, 2012 CFR

2012-07-01

... relocation allowances for overseas assignment and return travel? 302-3.207 Section 302-3.207 Public Contracts and Property Management Federal Travel Regulation System RELOCATION ALLOWANCES RELOCATION ALLOWANCES 3... eligible to receive relocation allowances for overseas assignment and return travel? You may be eligible to...

41 CFR 302-3.207 - Am I eligible to receive relocation allowances for overseas assignment and return travel?

Code of Federal Regulations, 2010 CFR

2010-07-01

... relocation allowances for overseas assignment and return travel? 302-3.207 Section 302-3.207 Public Contracts and Property Management Federal Travel Regulation System RELOCATION ALLOWANCES RELOCATION ALLOWANCES 3... eligible to receive relocation allowances for overseas assignment and return travel? You may be eligible to...

47 CFR 27.1111 - Relocation of fixed microwave service licensees in the 2110-2150 MHz band.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 47 Telecommunication 2 2010-10-01 2010-10-01 false Relocation of fixed microwave service licensees..., 2110-2155 MHz, 2160-2180 MHz Bands Relocation of Incumbents § 27.1111 Relocation of fixed microwave... contain provisions governing the relocation of incumbent fixed microwave service licensees in the 2110...

47 CFR 27.1111 - Relocation of fixed microwave service licensees in the 2110-2150 MHz band.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 47 Telecommunication 2 2011-10-01 2011-10-01 false Relocation of fixed microwave service licensees..., 2110-2155 MHz, 2160-2180 MHz Bands Relocation of Incumbents § 27.1111 Relocation of fixed microwave... contain provisions governing the relocation of incumbent fixed microwave service licensees in the 2110...

47 CFR 27.1111 - Relocation of fixed microwave service licensees in the 2110-2150 MHz band.

Code of Federal Regulations, 2012 CFR

2012-10-01

... 47 Telecommunication 2 2012-10-01 2012-10-01 false Relocation of fixed microwave service licensees..., 2110-2155 MHz, 2160-2180 MHz Bands Relocation of Incumbents § 27.1111 Relocation of fixed microwave... contain provisions governing the relocation of incumbent fixed microwave service licensees in the 2110...

41 CFR 302-12.100 - What are “relocation services”?

Code of Federal Regulations, 2013 CFR

2013-07-01

... 41 Public Contracts and Property Management 4 2013-07-01 2012-07-01 true What are ârelocation servicesâ? 302-12.100 Section 302-12.100 Public Contracts and Property Management Federal Travel Regulation... Agency's Use of a Relocation Services Company § 302-12.100 What are “relocation services”? “Relocation...

A tailored relocation stress intervention programme for family caregivers of patients transferred from a surgical intensive care unit to a general ward.

PubMed

Lee, Seul; Oh, HyunSoo; Suh, YeonOk; Seo, WhaSook

2017-03-01

To develop and examine a relocation stress intervention programme tailored for the family caregivers of patients scheduled for transfer from a surgical intensive care unit to a general ward. Family relocation stress syndrome has been reported to be similar to that exhibited by patients, and investigators have emphasised that nurses should make special efforts to relieve family relocation stress to maximise positive contributions to the well-being of patients by family caregivers. A nonequivalent control group, nonsynchronised pretest-post-test design was adopted. The study subjects were 60 family caregivers of patients with neurosurgical or general surgical conditions in the surgical intensive care unit of a university hospital located in Incheon, South Korea. Relocation stress and family burden were evaluated at three times, that is before intervention, immediately after transfer and four to five days after transfer. This relocation stress intervention programme was developed for the family caregivers based on disease characteristics and relocation-related needs. In the experimental group, relocation stress levels significantly and continuously decreased after intervention, whereas in the control group, a slight nonsignificant trend was observed. Family burden levels in the control group increased significantly after transfer, whereas burden levels in the experimental group increased only marginally and nonsignificantly. No significant between-group differences in relocation stress or family burden levels were observed after intervention. Relocation stress levels of family caregivers were significantly decreased after intervention in the experimental group, which indicates that the devised family relocation stress intervention programme effectively alleviated family relocation stress. The devised intervention programme, which was tailored to disease characteristics and relocation-related needs, may enhance the practicality and efficacy of relocation stress management and make meaningful contribution to the relief of family relocation stress, promote patient recovery and enhance the well-being of patients and family caregivers. © 2016 John Wiley & Sons Ltd.

Assessment of various supervised learning algorithms using different performance metrics

NASA Astrophysics Data System (ADS)

Susheel Kumar, S. M.; Laxkar, Deepak; Adhikari, Sourav; Vijayarajan, V.

2017-11-01

Our work brings out comparison based on the performance of supervised machine learning algorithms on a binary classification task. The supervised machine learning algorithms which are taken into consideration in the following work are namely Support Vector Machine(SVM), Decision Tree(DT), K Nearest Neighbour (KNN), Naïve Bayes(NB) and Random Forest(RF). This paper mostly focuses on comparing the performance of above mentioned algorithms on one binary classification task by analysing the Metrics such as Accuracy, F-Measure, G-Measure, Precision, Misclassification Rate, False Positive Rate, True Positive Rate, Specificity, Prevalence.

Performance of thigh-mounted triaxial accelerometer algorithms in objective quantification of sedentary behaviour and physical activity in older adults

PubMed Central

Verschueren, Sabine M. P.; Degens, Hans; Morse, Christopher I.; Onambélé, Gladys L.

2017-01-01

Accurate monitoring of sedentary behaviour and physical activity is key to investigate their exact role in healthy ageing. To date, accelerometers using cut-off point models are most preferred for this, however, machine learning seems a highly promising future alternative. Hence, the current study compared between cut-off point and machine learning algorithms, for optimal quantification of sedentary behaviour and physical activity intensities in the elderly. Thus, in a heterogeneous sample of forty participants (aged ≥60 years, 50% female) energy expenditure during laboratory-based activities (ranging from sedentary behaviour through to moderate-to-vigorous physical activity) was estimated by indirect calorimetry, whilst wearing triaxial thigh-mounted accelerometers. Three cut-off point algorithms and a Random Forest machine learning model were developed and cross-validated using the collected data. Detailed analyses were performed to check algorithm robustness, and examine and benchmark both overall and participant-specific balanced accuracies. This revealed that the four models can at least be used to confidently monitor sedentary behaviour and moderate-to-vigorous physical activity. Nevertheless, the machine learning algorithm outperformed the cut-off point models by being robust for all individual’s physiological and non-physiological characteristics and showing more performance of an acceptable level over the whole range of physical activity intensities. Therefore, we propose that Random Forest machine learning may be optimal for objective assessment of sedentary behaviour and physical activity in older adults using thigh-mounted triaxial accelerometry. PMID:29155839

Performance of thigh-mounted triaxial accelerometer algorithms in objective quantification of sedentary behaviour and physical activity in older adults.

PubMed

Wullems, Jorgen A; Verschueren, Sabine M P; Degens, Hans; Morse, Christopher I; Onambélé, Gladys L

2017-01-01

Accurate monitoring of sedentary behaviour and physical activity is key to investigate their exact role in healthy ageing. To date, accelerometers using cut-off point models are most preferred for this, however, machine learning seems a highly promising future alternative. Hence, the current study compared between cut-off point and machine learning algorithms, for optimal quantification of sedentary behaviour and physical activity intensities in the elderly. Thus, in a heterogeneous sample of forty participants (aged ≥60 years, 50% female) energy expenditure during laboratory-based activities (ranging from sedentary behaviour through to moderate-to-vigorous physical activity) was estimated by indirect calorimetry, whilst wearing triaxial thigh-mounted accelerometers. Three cut-off point algorithms and a Random Forest machine learning model were developed and cross-validated using the collected data. Detailed analyses were performed to check algorithm robustness, and examine and benchmark both overall and participant-specific balanced accuracies. This revealed that the four models can at least be used to confidently monitor sedentary behaviour and moderate-to-vigorous physical activity. Nevertheless, the machine learning algorithm outperformed the cut-off point models by being robust for all individual's physiological and non-physiological characteristics and showing more performance of an acceptable level over the whole range of physical activity intensities. Therefore, we propose that Random Forest machine learning may be optimal for objective assessment of sedentary behaviour and physical activity in older adults using thigh-mounted triaxial accelerometry.

Using machine learning for sequence-level automated MRI protocol selection in neuroradiology.

PubMed

Brown, Andrew D; Marotta, Thomas R

2018-05-01

Incorrect imaging protocol selection can lead to important clinical findings being missed, contributing to both wasted health care resources and patient harm. We present a machine learning method for analyzing the unstructured text of clinical indications and patient demographics from magnetic resonance imaging (MRI) orders to automatically protocol MRI procedures at the sequence level. We compared 3 machine learning models - support vector machine, gradient boosting machine, and random forest - to a baseline model that predicted the most common protocol for all observations in our test set. The gradient boosting machine model significantly outperformed the baseline and demonstrated the best performance of the 3 models in terms of accuracy (95%), precision (86%), recall (80%), and Hamming loss (0.0487). This demonstrates the feasibility of automating sequence selection by applying machine learning to MRI orders. Automated sequence selection has important safety, quality, and financial implications and may facilitate improvements in the quality and safety of medical imaging service delivery.

41 CFR 302-3.208 - What relocation expenses will my agency pay for my overseas assignment and return?

Code of Federal Regulations, 2011 CFR

2011-07-01

... 41 Public Contracts and Property Management 4 2011-07-01 2011-07-01 false What relocation expenses... and Property Management Federal Travel Regulation System RELOCATION ALLOWANCES RELOCATION ALLOWANCES 3-RELOCATION ALLOWANCE BY SPECIFIC TYPE Types of Transfers Overseas Assignment and Return § 302-3.208 What...

41 CFR 302-3.208 - What relocation expenses will my agency pay for my overseas assignment and return?

Code of Federal Regulations, 2014 CFR

2014-07-01

... 41 Public Contracts and Property Management 4 2014-07-01 2014-07-01 false What relocation expenses... and Property Management Federal Travel Regulation System RELOCATION ALLOWANCES RELOCATION ALLOWANCES 3-RELOCATION ALLOWANCE BY SPECIFIC TYPE Types of Transfers Overseas Assignment and Return § 302-3.208 What...

41 CFR 302-3.208 - What relocation expenses will my agency pay for my overseas assignment and return?

Code of Federal Regulations, 2013 CFR

2013-07-01

... 41 Public Contracts and Property Management 4 2013-07-01 2012-07-01 true What relocation expenses... and Property Management Federal Travel Regulation System RELOCATION ALLOWANCES RELOCATION ALLOWANCES 3-RELOCATION ALLOWANCE BY SPECIFIC TYPE Types of Transfers Overseas Assignment and Return § 302-3.208 What...

41 CFR 302-3.208 - What relocation expenses will my agency pay for my overseas assignment and return?

Code of Federal Regulations, 2012 CFR

2012-07-01

... 41 Public Contracts and Property Management 4 2012-07-01 2012-07-01 false What relocation expenses... and Property Management Federal Travel Regulation System RELOCATION ALLOWANCES RELOCATION ALLOWANCES 3-RELOCATION ALLOWANCE BY SPECIFIC TYPE Types of Transfers Overseas Assignment and Return § 302-3.208 What...

41 CFR 302-3.506 - May we pay relocation expenses if the employee violates his/her service agreement?

Code of Federal Regulations, 2011 CFR

2011-07-01

... 41 Public Contracts and Property Management 4 2011-07-01 2011-07-01 false May we pay relocation... and Property Management Federal Travel Regulation System RELOCATION ALLOWANCES RELOCATION ALLOWANCES 3-RELOCATION ALLOWANCE BY SPECIFIC TYPE Agency Responsibilities Service Agreements § 302-3.506 May we pay...

41 CFR 302-3.506 - May we pay relocation expenses if the employee violates his/her service agreement?

Code of Federal Regulations, 2012 CFR

2012-07-01

... 41 Public Contracts and Property Management 4 2012-07-01 2012-07-01 false May we pay relocation... and Property Management Federal Travel Regulation System RELOCATION ALLOWANCES RELOCATION ALLOWANCES 3-RELOCATION ALLOWANCE BY SPECIFIC TYPE Agency Responsibilities Service Agreements § 302-3.506 May we pay...

41 CFR 302-3.506 - May we pay relocation expenses if the employee violates his/her service agreement?

Code of Federal Regulations, 2014 CFR

2014-07-01

... 41 Public Contracts and Property Management 4 2014-07-01 2014-07-01 false May we pay relocation... and Property Management Federal Travel Regulation System RELOCATION ALLOWANCES RELOCATION ALLOWANCES 3-RELOCATION ALLOWANCE BY SPECIFIC TYPE Agency Responsibilities Service Agreements § 302-3.506 May we pay...

41 CFR 302-3.506 - May we pay relocation expenses if the employee violates his/her service agreement?

Code of Federal Regulations, 2013 CFR

2013-07-01

... 41 Public Contracts and Property Management 4 2013-07-01 2012-07-01 true May we pay relocation... and Property Management Federal Travel Regulation System RELOCATION ALLOWANCES RELOCATION ALLOWANCES 3-RELOCATION ALLOWANCE BY SPECIFIC TYPE Agency Responsibilities Service Agreements § 302-3.506 May we pay...

41 CFR 302-3.506 - May we pay relocation expenses if the employee violates his/her service agreement?

Code of Federal Regulations, 2010 CFR

2010-07-01

... 41 Public Contracts and Property Management 4 2010-07-01 2010-07-01 false May we pay relocation... and Property Management Federal Travel Regulation System RELOCATION ALLOWANCES RELOCATION ALLOWANCES 3-RELOCATION ALLOWANCE BY SPECIFIC TYPE Agency Responsibilities Service Agreements § 302-3.506 May we pay...

41 CFR 302-3.208 - What relocation expenses will my agency pay for my overseas assignment and return?

Code of Federal Regulations, 2010 CFR

2010-07-01

... 41 Public Contracts and Property Management 4 2010-07-01 2010-07-01 false What relocation expenses... and Property Management Federal Travel Regulation System RELOCATION ALLOWANCES RELOCATION ALLOWANCES 3-RELOCATION ALLOWANCE BY SPECIFIC TYPE Types of Transfers Overseas Assignment and Return § 302-3.208 What...

41 CFR 302-12.103 - May we separately contract for each type of relocation service?

Code of Federal Regulations, 2010 CFR

2010-07-01

... contract for each type of relocation service? 302-12.103 Section 302-12.103 Public Contracts and Property... A RELOCATION SERVICES COMPANY Agency's Use of a Relocation Services Company § 302-12.103 May we separately contract for each type of relocation service? Yes, you may separately contract for each type of...

Removing an invasive shrub (Chinese privet) increases native bee diversity and abundance in riparian forests of the southeastern United States

Treesearch

James L. Hanula; Scott Horn

2011-01-01

1. Chinese privet (Ligustrum sinense Lour.) was removed from riparian forests in the Piedmont of Georgia in November 2005 by mulching with a track-mounted mulching machine or by chainsaw felling. The remaining privet in the herbaceous layer was killed with herbicide in December 2006. 2. Bee (Hymentoptera: Apoidea) abundance, diversity and community similarity in the...

41 CFR 302-2.5 - May I relocate from a location other than the location specified in my relocation travel...

Code of Federal Regulations, 2010 CFR

2010-07-01

... 41 Public Contracts and Property Management 4 2010-07-01 2010-07-01 false May I relocate from a... Public Contracts and Property Management Federal Travel Regulation System RELOCATION ALLOWANCES INTRODUCTION 2-EMPLOYEES ELIGIBILITY REQUIREMENTS General Rules § 302-2.5 May I relocate from a location other...

Clinical validity of a relocation stress scale for the families of patients transferred from intensive care units.

PubMed

Oh, HyunSoo; Lee, Seul; Kim, JiSun; Lee, EunJu; Min, HyoNam; Cho, OkJa; Seo, WhaSook

2015-07-01

This study was conducted to develop a family relocation stress scale by modifying the Son's Relocation Stress Syndrome Scale, to examine its clinical validity and reliability and to confirm its suitability for measuring family relocation stress. The transfer of ICU patients to general wards is a significant anxiety-producing event for family members. However, no relocation stress scale has been developed specifically for families. A nonexperimental, correlation design was adopted. The study subjects were 95 family members of 95 ICU patients at a university hospital located in Incheon, South Korea. Face and construct validities of the devised family relocation stress scale were examined. Construct validity was examined using factor analysis and by using a nomological validity test. Reliability was also examined. Face and content validity of the scale were verified by confirming that its items adequately measured family relocation stress. Factor analysis yielded four components, and the total variance explained by these four components was 63·0%, which is acceptable. Nomological validity was well supported by significant relationships between relocation stress and degree of preparation for relocation, patient self-care ability, family burden and satisfaction with the relocation process. The devised scale was also found to have good reliability. The family relocation stress scale devised in this study was found to have good validity and reliability, and thus, is believed to offer a means of assessing family relocation stress. The findings of this study provide a reliable and valid assessment tool when nurses prepare families for patient transfer from an ICU to a ward setting, and may also provide useful information to those developing an intervention programme for family relocation stress management. © 2015 John Wiley & Sons Ltd.

Surgical relocation of the papillary muscles in functional ischemic mitral regurgitation: what are the forces of the relocation stitches acting on the myocardium?

PubMed

Jensen, Henrik; Jensen, Morten O; Vind-Kezunovic, Stefan; Vestergaard, Rikke; Ringgaard, Steffen; Smerup, Morten H; Hønge, Jesper L; Hasenkam, J Michael; Nielsen, Sten L

2013-07-01

In patients with chronic functional ischemic mitral regurgitation (FIMR), papillary muscle relocation has the potential to induce reverse left ventricular remodeling. However, in order to optimize function and durability, the forces imposed on the left ventricular myocardium by papillary muscle relocation should be assessed. Eight pigs with FIMR were subjected to down-sized ring annuloplasty in combination with relocation of the anterior (5 mm) and posterior (15 mm) papillary muscles towards the respective trigone. Papillary muscle relocation was obtained by a 2-0 expanded polytetrafluoroethylene stitch fixed to the trigone, exteriorized through the myocardium overlying the papillary muscle, and fixed to an epicardial disc. Tension in these stitches was measured at a systolic blood pressure > 80 mmHg using a custom-made sliding caliper with a strain gauge mounted in line. This allowed assessment of the cyclic change from minimal diastolic to maximum systolic papillary muscle relocation stitch tension. Maximum cyclic change in the posterior papillary muscle (PPM) stitch tension was 1.1 N at 15 mm relocation. In comparison, the anterior papillary muscle (APM) tension was increased to a maximum of 1.4 N with only 5 mm relocation. Surprisingly, during each step of isolated PPM relocation, the APM stitch tension increased concomitantly, but in contrast APM relocation did not influence the magnitude of PPM stitch tension. There was no statistically significant difference between cyclic changes in APM and PPM stitch tension at any step of relocation. Papillary muscle relocation using stitches attached between epicardial discs and respective trigones induced a cyclic change in papillary muscle relocation stitch tension of 1.1-1.4 N. These values were in the range of normal tension in the mitral valve apparatus, and equivalent to only 19-24% of the total papillary muscle forces. Therefore, this technique does not appear to induce a non-physiologically high cyclic load on the mitral valve complex.

FOCIS: A forest classification and inventory system using LANDSAT and digital terrain data

NASA Technical Reports Server (NTRS)

Strahler, A. H.; Franklin, J.; Woodcook, C. E.; Logan, T. L.

1981-01-01

Accurate, cost-effective stratification of forest vegetation and timber inventory is the primary goal of a Forest Classification and Inventory System (FOCIS). Conventional timber stratification using photointerpretation can be time-consuming, costly, and inconsistent from analyst to analyst. FOCIS was designed to overcome these problems by using machine processing techniques to extract and process tonal, textural, and terrain information from registered LANDSAT multispectral and digital terrain data. Comparison of samples from timber strata identified by conventional procedures showed that both have about the same potential to reduce the variance of timber volume estimates over simple random sampling.

47 CFR 24.245 - Reimbursement under the Cost-Sharing Plan.

Code of Federal Regulations, 2013 CFR

2013-10-01

... SERVICES PERSONAL COMMUNICATIONS SERVICES Broadband PCS Policies Governing Microwave Relocation from the... incumbent. (2) To obtain reimbursement, a voluntarily relocating microwave incumbent must submit... PCS relocator or the voluntarily relocating microwave incumbent, must submit documentation itemizing...

47 CFR 24.245 - Reimbursement under the Cost-Sharing Plan.

Code of Federal Regulations, 2011 CFR

2011-10-01

... SERVICES PERSONAL COMMUNICATIONS SERVICES Broadband PCS Policies Governing Microwave Relocation from the... incumbent. (2) To obtain reimbursement, a voluntarily relocating microwave incumbent must submit... PCS relocator or the voluntarily relocating microwave incumbent, must submit documentation itemizing...

47 CFR 24.245 - Reimbursement under the Cost-Sharing Plan.

Code of Federal Regulations, 2014 CFR

2014-10-01

... SERVICES PERSONAL COMMUNICATIONS SERVICES Broadband PCS Policies Governing Microwave Relocation from the... incumbent. (2) To obtain reimbursement, a voluntarily relocating microwave incumbent must submit... PCS relocator or the voluntarily relocating microwave incumbent, must submit documentation itemizing...

47 CFR 24.245 - Reimbursement under the Cost-Sharing Plan.

Code of Federal Regulations, 2010 CFR

2010-10-01

... SERVICES PERSONAL COMMUNICATIONS SERVICES Broadband PCS Policies Governing Microwave Relocation from the... incumbent. (2) To obtain reimbursement, a voluntarily relocating microwave incumbent must submit... PCS relocator or the voluntarily relocating microwave incumbent, must submit documentation itemizing...

Seeing the wood for the trees: a forest of methods for optimization and omic-network integration in metabolic modelling.

PubMed

Vijayakumar, Supreeta; Conway, Max; Lió, Pietro; Angione, Claudio

2017-05-30

Metabolic modelling has entered a mature phase with dozens of methods and software implementations available to the practitioner and the theoretician. It is not easy for a modeller to be able to see the wood (or the forest) for the trees. Driven by this analogy, we here present a 'forest' of principal methods used for constraint-based modelling in systems biology. This provides a tree-based view of methods available to prospective modellers, also available in interactive version at http://modellingmetabolism.net, where it will be kept updated with new methods after the publication of the present manuscript. Our updated classification of existing methods and tools highlights the most promising in the different branches, with the aim to develop a vision of how existing methods could hybridize and become more complex. We then provide the first hands-on tutorial for multi-objective optimization of metabolic models in R. We finally discuss the implementation of multi-view machine learning approaches in poly-omic integration. Throughout this work, we demonstrate the optimization of trade-offs between multiple metabolic objectives, with a focus on omic data integration through machine learning. We anticipate that the combination of a survey, a perspective on multi-view machine learning and a step-by-step R tutorial should be of interest for both the beginner and the advanced user. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Assessment of multi-wildfire occurrence data for machine learning based risk modelling

NASA Astrophysics Data System (ADS)

Lim, C. H.; Kim, M.; Kim, S. J.; Yoo, S.; Lee, W. K.

2017-12-01

The occurrence of East Asian wildfires is mainly caused by human-activities, but the extreme drought increased due to the climate change caused wildfires and they spread to large-scale fires. Accurate occurrence location data is required for modelling wildfire probability and risk. In South Korea, occurrence data surveyed through KFS (Korea Forest Service) and MODIS (MODerate-resolution Imaging Spectroradiometer) satellite-based active fire data can be utilized. In this study, two sorts of wildfire occurrence data were applied to select suitable occurrence data for machine learning based wildfire risk modelling. MaxEnt (Maximum Entropy) model based on machine learning is used for wildfire risk modelling, and two types of occurrence data and socio-economic and climate-environment data are applied to modelling. In the results with KFS survey based data, the low relationship was shown with climate-environmental factors, and the uncertainty of coordinate information appeared. The MODIS-based active fire data were found outside the forests, and there were a lot of spots that did not match the actual wildfires. In order to utilize MODIS-based active fire data, it was necessary to extract forest area and utilize only high-confidence level data. In KFS data, it was necessary to separate the analysis according to the damage scale to improve the modelling accuracy. Ultimately, it is considered to be the best way to simulate the wildfire risk by constructing more accurate information by combining two sorts of wildfire occurrence data.

Resourcefulness, positive cognitions, relocation controllability and relocation adjustment among older people: a cross-sectional study of cultural differences.

PubMed

Bekhet, Abir K; Zauszniewski, Jaclene A

2013-09-01

The population of older people in both the United States and Egypt is expected to double by the year 2030. With ageing, chronic illnesses increase and many older people need to relocate to retirement communities. Research has shown that positive cognitions and resourcefulness are positively correlated with adaptive functioning and better adjustment. The purpose of this study was to examine and compare relocation controllability, positive cognitions, resourcefulness and relocation adjustment between American and Egyptian older people living in retirement communities. The purpose of this cultural comparison is to gain insight into influencing factors in each culture that might lead to interventions to help relocated older adults in both cultures adjust to their new surroundings. A cross-sectional, descriptive design was used to compare relocation controllability, positive cognitions, resourcefulness and relocation adjustment of a convenience sample of American older people (n = 104) and a convenience sample of Egyptian older people (n = 94). The study was a secondary analysis of two studies of older people residing in six retirement communities in Northeast Ohio and in five retirement communities in Alexandria, Egypt. Examination of mean scores and standard deviations on the measure of positive cognitions using independent sample t-tests indicated that on average, the American older people reported more positive cognitions (t (131.16) = 11.29, P < 0.001), more relocation controllability (t (196) = -6.78, P < 0.001) and more relocation adjustment (t (196) = 9.42, P < 0.001) than the Egyptian older people. However, there was no significant difference between Egyptians and Americans in resourcefulness (t (174.16) = -0.97, P > 0.05). The results provide direction for the development of positive cognition interventions and engaging older people in the decision-making process to help them to adjust to relocation. Implications for practice.  Positive thinking and resourcefulness training interventions can be used by nurses to help relocated older people to adjust to the stress of relocation to retirement communities. These interventions can be used on primary, secondary, and tertiary levels. Primary interventions can help to prevent the stress of relocation before happening by helping older people to use their positive thinking and their resources and work with them before relocating to retirement communities. Secondary prevention can be used by nurses to help older people who have already relocated to retirement communities and have already experienced stress of relocation to help them out by decreasing the stress that they are suffering. Tertiary prevention can be used to prevent further stress and deterioration for those who have suffered physical and psychological symptoms as a result of relocation. © 2012 Blackwell Publishing Ltd.

Response of beetles (Coleoptera) at three heights to the experimental removal of an invasive shrub, Chinese privet (Lingustrum sinense), from floodplain forests

Treesearch

Michael D. Ulyshen; Scott Horn; James L. Hanula

2010-01-01

Chinese privet (Ligustrum sinense Lour.), an invasive shrub from Asia, is well established in the southeastern United States where it dominates many floodplain forests. We used flight intercept traps to sample beetles at three heights (0.5, 5 and 15 m) in *2 ha plots in which L. sinense had (by chainsaws or mulching machine) or had not been removed...

Cellular Manufacturing System with Dynamic Lot Size Material Handling

NASA Astrophysics Data System (ADS)

Khannan, M. S. A.; Maruf, A.; Wangsaputra, R.; Sutrisno, S.; Wibawa, T.

2016-02-01

Material Handling take as important role in Cellular Manufacturing System (CMS) design. In several study at CMS design material handling was assumed per pieces or with constant lot size. In real industrial practice, lot size may change during rolling period to cope with demand changes. This study develops CMS Model with Dynamic Lot Size Material Handling. Integer Linear Programming is used to solve the problem. Objective function of this model is minimizing total expected cost consisting machinery depreciation cost, operating costs, inter-cell material handling cost, intra-cell material handling cost, machine relocation costs, setup costs, and production planning cost. This model determines optimum cell formation and optimum lot size. Numerical examples are elaborated in the paper to ilustrate the characterictic of the model.

75 FR 4822 - 2010 Travel and Relocation Excellence Award

Federal Register 2010, 2011, 2012, 2013, 2014

2010-01-29

... GENERAL SERVICES ADMINISTRATION 2010 Travel and Relocation Excellence Award AGENCY: Office of... Administration (GSA) is seeking candidates for the biennial 2010 Travel and Relocation Excellence Award, which honors excellence in federal travel and relocation policy. FOR FURTHER INFORMATION CONTACT: Go to GSA's...

47 CFR 27.1252 - Involuntary Relocation Procedures.

Code of Federal Regulations, 2011 CFR

2011-10-01

... agreement is reached during the mandatory negotiation period, an AWS licensee may initiate involuntary relocation procedures under the Commission's rules. AWS licensees are obligated to pay to relocate BRS systems to which the AWS system poses an interference problem. Under involuntary relocation, the BRS...

Comparison of four machine learning methods for object-oriented change detection in high-resolution satellite imagery

NASA Astrophysics Data System (ADS)

Bai, Ting; Sun, Kaimin; Deng, Shiquan; Chen, Yan

2018-03-01

High resolution image change detection is one of the key technologies of remote sensing application, which is of great significance for resource survey, environmental monitoring, fine agriculture, military mapping and battlefield environment detection. In this paper, for high-resolution satellite imagery, Random Forest (RF), Support Vector Machine (SVM), Deep belief network (DBN), and Adaboost models were established to verify the possibility of different machine learning applications in change detection. In order to compare detection accuracy of four machine learning Method, we applied these four machine learning methods for two high-resolution images. The results shows that SVM has higher overall accuracy at small samples compared to RF, Adaboost, and DBN for binary and from-to change detection. With the increase in the number of samples, RF has higher overall accuracy compared to Adaboost, SVM and DBN.

Predicting the dissolution kinetics of silicate glasses using machine learning

NASA Astrophysics Data System (ADS)

Anoop Krishnan, N. M.; Mangalathu, Sujith; Smedskjaer, Morten M.; Tandia, Adama; Burton, Henry; Bauchy, Mathieu

2018-05-01

Predicting the dissolution rates of silicate glasses in aqueous conditions is a complex task as the underlying mechanism(s) remain poorly understood and the dissolution kinetics can depend on a large number of intrinsic and extrinsic factors. Here, we assess the potential of data-driven models based on machine learning to predict the dissolution rates of various aluminosilicate glasses exposed to a wide range of solution pH values, from acidic to caustic conditions. Four classes of machine learning methods are investigated, namely, linear regression, support vector machine regression, random forest, and artificial neural network. We observe that, although linear methods all fail to describe the dissolution kinetics, the artificial neural network approach offers excellent predictions, thanks to its inherent ability to handle non-linear data. Overall, we suggest that a more extensive use of machine learning approaches could significantly accelerate the design of novel glasses with tailored properties.

Recent advances in environmental data mining

NASA Astrophysics Data System (ADS)

Leuenberger, Michael; Kanevski, Mikhail

2016-04-01

Due to the large amount and complexity of data available nowadays in geo- and environmental sciences, we face the need to develop and incorporate more robust and efficient methods for their analysis, modelling and visualization. An important part of these developments deals with an elaboration and application of a contemporary and coherent methodology following the process from data collection to the justification and communication of the results. Recent fundamental progress in machine learning (ML) can considerably contribute to the development of the emerging field - environmental data science. The present research highlights and investigates the different issues that can occur when dealing with environmental data mining using cutting-edge machine learning algorithms. In particular, the main attention is paid to the description of the self-consistent methodology and two efficient algorithms - Random Forest (RF, Breiman, 2001) and Extreme Learning Machines (ELM, Huang et al., 2006), which recently gained a great popularity. Despite the fact that they are based on two different concepts, i.e. decision trees vs artificial neural networks, they both propose promising results for complex, high dimensional and non-linear data modelling. In addition, the study discusses several important issues of data driven modelling, including feature selection and uncertainties. The approach considered is accompanied by simulated and real data case studies from renewable resources assessment and natural hazards tasks. In conclusion, the current challenges and future developments in statistical environmental data learning are discussed. References - Breiman, L., 2001. Random Forests. Machine Learning 45 (1), 5-32. - Huang, G.-B., Zhu, Q.-Y., Siew, C.-K., 2006. Extreme learning machine: theory and applications. Neurocomputing 70 (1-3), 489-501. - Kanevski, M., Pozdnoukhov, A., Timonin, V., 2009. Machine Learning for Spatial Environmental Data. EPFL Press; Lausanne, Switzerland, p.392. - Leuenberger, M., Kanevski, M., 2015. Extreme Learning Machines for spatial environmental data. Computers and Geosciences 85, 64-73.

25 CFR 700.133 - Notice of displacement.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 25 Indians 2 2011-04-01 2011-04-01 false Notice of displacement. 700.133 Section 700.133 Indians THE OFFICE OF NAVAJO AND HOPI INDIAN RELOCATION COMMISSION OPERATIONS AND RELOCATION PROCEDURES General Relocation Requirements § 700.133 Notice of displacement. After the Commission's Relocation Report...

25 CFR 700.133 - Notice of displacement.

Code of Federal Regulations, 2013 CFR

2013-04-01

... 25 Indians 2 2013-04-01 2013-04-01 false Notice of displacement. 700.133 Section 700.133 Indians THE OFFICE OF NAVAJO AND HOPI INDIAN RELOCATION COMMISSION OPERATIONS AND RELOCATION PROCEDURES General Relocation Requirements § 700.133 Notice of displacement. After the Commission's Relocation Report...

24 CFR 570.457 - Displacement, relocation, acquisition, and replacement of housing.

Code of Federal Regulations, 2012 CFR

2012-04-01

... 24 Housing and Urban Development 3 2012-04-01 2012-04-01 false Displacement, relocation... DEVELOPMENT BLOCK GRANTS Urban Development Action Grants § 570.457 Displacement, relocation, acquisition, and replacement of housing. The displacement, relocation, acquisition, and replacement of housing requirements of...

24 CFR 570.457 - Displacement, relocation, acquisition, and replacement of housing.

Code of Federal Regulations, 2013 CFR

2013-04-01

... 24 Housing and Urban Development 3 2013-04-01 2013-04-01 false Displacement, relocation... DEVELOPMENT BLOCK GRANTS Urban Development Action Grants § 570.457 Displacement, relocation, acquisition, and replacement of housing. The displacement, relocation, acquisition, and replacement of housing requirements of...

7 CFR 1944.667 - Relocation and displacement.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 13 2010-01-01 2009-01-01 true Relocation and displacement. 1944.667 Section 1944.667...) PROGRAM REGULATIONS (CONTINUED) HOUSING Housing Preservation Grants § 1944.667 Relocation and displacement... maximum amount of temporary or permanent relocation costs proposed to be allowed. (b) Displacement. The...

7 CFR 1944.667 - Relocation and displacement.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 7 Agriculture 13 2011-01-01 2009-01-01 true Relocation and displacement. 1944.667 Section 1944.667...) PROGRAM REGULATIONS (CONTINUED) HOUSING Housing Preservation Grants § 1944.667 Relocation and displacement... maximum amount of temporary or permanent relocation costs proposed to be allowed. (b) Displacement. The...

25 CFR 700.133 - Notice of displacement.

Code of Federal Regulations, 2012 CFR

2012-04-01

... 25 Indians 2 2012-04-01 2012-04-01 false Notice of displacement. 700.133 Section 700.133 Indians THE OFFICE OF NAVAJO AND HOPI INDIAN RELOCATION COMMISSION OPERATIONS AND RELOCATION PROCEDURES General Relocation Requirements § 700.133 Notice of displacement. After the Commission's Relocation Report...

24 CFR 570.457 - Displacement, relocation, acquisition, and replacement of housing.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 24 Housing and Urban Development 3 2010-04-01 2010-04-01 false Displacement, relocation... DEVELOPMENT BLOCK GRANTS Urban Development Action Grants § 570.457 Displacement, relocation, acquisition, and replacement of housing. The displacement, relocation, acquisition, and replacement of housing requirements of...

25 CFR 700.133 - Notice of displacement.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 25 Indians 2 2010-04-01 2010-04-01 false Notice of displacement. 700.133 Section 700.133 Indians THE OFFICE OF NAVAJO AND HOPI INDIAN RELOCATION COMMISSION OPERATIONS AND RELOCATION PROCEDURES General Relocation Requirements § 700.133 Notice of displacement. After the Commission's Relocation Report...

24 CFR 570.457 - Displacement, relocation, acquisition, and replacement of housing.

Code of Federal Regulations, 2014 CFR

2014-04-01

... 24 Housing and Urban Development 3 2014-04-01 2013-04-01 true Displacement, relocation... DEVELOPMENT BLOCK GRANTS Urban Development Action Grants § 570.457 Displacement, relocation, acquisition, and replacement of housing. The displacement, relocation, acquisition, and replacement of housing requirements of...

25 CFR 700.133 - Notice of displacement.

Code of Federal Regulations, 2014 CFR

2014-04-01

... 25 Indians 2 2014-04-01 2014-04-01 false Notice of displacement. 700.133 Section 700.133 Indians THE OFFICE OF NAVAJO AND HOPI INDIAN RELOCATION COMMISSION OPERATIONS AND RELOCATION PROCEDURES General Relocation Requirements § 700.133 Notice of displacement. After the Commission's Relocation Report...

24 CFR 570.457 - Displacement, relocation, acquisition, and replacement of housing.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 24 Housing and Urban Development 3 2011-04-01 2010-04-01 true Displacement, relocation... DEVELOPMENT BLOCK GRANTS Urban Development Action Grants § 570.457 Displacement, relocation, acquisition, and replacement of housing. The displacement, relocation, acquisition, and replacement of housing requirements of...

7 CFR 1944.667 - Relocation and displacement.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 7 Agriculture 13 2014-01-01 2013-01-01 true Relocation and displacement. 1944.667 Section 1944.667...) PROGRAM REGULATIONS (CONTINUED) HOUSING Housing Preservation Grants § 1944.667 Relocation and displacement... maximum amount of temporary or permanent relocation costs proposed to be allowed. (b) Displacement. The...

Combining macula clinical signs and patient characteristics for age-related macular degeneration diagnosis: a machine learning approach.

PubMed

Fraccaro, Paolo; Nicolo, Massimo; Bonetto, Monica; Giacomini, Mauro; Weller, Peter; Traverso, Carlo Enrico; Prosperi, Mattia; OSullivan, Dympna

2015-01-27

To investigate machine learning methods, ranging from simpler interpretable techniques to complex (non-linear) "black-box" approaches, for automated diagnosis of Age-related Macular Degeneration (AMD). Data from healthy subjects and patients diagnosed with AMD or other retinal diseases were collected during routine visits via an Electronic Health Record (EHR) system. Patients' attributes included demographics and, for each eye, presence/absence of major AMD-related clinical signs (soft drusen, retinal pigment epitelium, defects/pigment mottling, depigmentation area, subretinal haemorrhage, subretinal fluid, macula thickness, macular scar, subretinal fibrosis). Interpretable techniques known as white box methods including logistic regression and decision trees as well as less interpreitable techniques known as black box methods, such as support vector machines (SVM), random forests and AdaBoost, were used to develop models (trained and validated on unseen data) to diagnose AMD. The gold standard was confirmed diagnosis of AMD by physicians. Sensitivity, specificity and area under the receiver operating characteristic (AUC) were used to assess performance. Study population included 487 patients (912 eyes). In terms of AUC, random forests, logistic regression and adaboost showed a mean performance of (0.92), followed by SVM and decision trees (0.90). All machine learning models identified soft drusen and age as the most discriminating variables in clinicians' decision pathways to diagnose AMD. Both black-box and white box methods performed well in identifying diagnoses of AMD and their decision pathways. Machine learning models developed through the proposed approach, relying on clinical signs identified by retinal specialists, could be embedded into EHR to provide physicians with real time (interpretable) support.

Coniferous forest classification and inventory using Landsat and digital terrain data

NASA Technical Reports Server (NTRS)

Franklin, J.; Logan, T. L.; Woodcock, C. E.; Strahler, A. H.

1986-01-01

Machine-processing techniques were used in a Forest Classification and Inventory System (FOCIS) procedure to extract and process tonal, textural, and terrain information from registered Landsat multispectral and digital terrain data. Using FOCIS as a basis for stratified sampling, the softwood timber volumes of the Klamath National Forest and Eldorado National Forest were estimated within standard errors of 4.8 and 4.0 percent, respectively. The accuracy of these large-area inventories is comparable to the accuracy yielded by use of conventional timber inventory methods, but, because of automation, the FOCIS inventories are more rapid (9-12 months compared to 2-3 years for conventional manual photointerpretation, map compilation and drafting, field sampling, and data processing) and are less costly.

47 CFR 101.91 - Involuntary relocation procedures.

Code of Federal Regulations, 2013 CFR

2013-10-01

... SERVICES FIXED MICROWAVE SERVICES Applications and Licenses Policies Governing Fixed Service Relocation from the 18.58-19.30 Ghz Band § 101.91 Involuntary relocation procedures. (a) If no agreement is... Commission's rules. FSS licensees are obligated to pay to relocate only the specific microwave links from...

47 CFR 101.91 - Involuntary relocation procedures.

Code of Federal Regulations, 2014 CFR

2014-10-01

... SERVICES FIXED MICROWAVE SERVICES Applications and Licenses Policies Governing Fixed Service Relocation from the 18.58-19.30 Ghz Band § 101.91 Involuntary relocation procedures. (a) If no agreement is... Commission's rules. FSS licensees are obligated to pay to relocate only the specific microwave links from...

47 CFR 101.91 - Involuntary relocation procedures.

Code of Federal Regulations, 2012 CFR

2012-10-01

... SERVICES FIXED MICROWAVE SERVICES Applications and Licenses Policies Governing Fixed Service Relocation from the 18.58-19.30 Ghz Band § 101.91 Involuntary relocation procedures. (a) If no agreement is... Commission's rules. FSS licensees are obligated to pay to relocate only the specific microwave links from...

47 CFR 101.91 - Involuntary relocation procedures.

Code of Federal Regulations, 2011 CFR

2011-10-01

... SERVICES FIXED MICROWAVE SERVICES Applications and Licenses Policies Governing Fixed Service Relocation from the 18.58-19.30 Ghz Band § 101.91 Involuntary relocation procedures. (a) If no agreement is... Commission's rules. FSS licensees are obligated to pay to relocate only the specific microwave links from...

7 CFR 1944.667 - Relocation and displacement.

Code of Federal Regulations, 2013 CFR

2013-01-01

... 7 Agriculture 13 2013-01-01 2013-01-01 false Relocation and displacement. 1944.667 Section 1944... displacement. (a) Relocation. Public bodies and agencies must comply with the requirements of the Uniform... maximum amount of temporary or permanent relocation costs proposed to be allowed. (b) Displacement. The...

7 CFR 1944.667 - Relocation and displacement.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 7 Agriculture 13 2012-01-01 2012-01-01 false Relocation and displacement. 1944.667 Section 1944... displacement. (a) Relocation. Public bodies and agencies must comply with the requirements of the Uniform... maximum amount of temporary or permanent relocation costs proposed to be allowed. (b) Displacement. The...

47 CFR 24.247 - Triggering a reimbursement obligation.

Code of Federal Regulations, 2014 CFR

2014-10-01

... PERSONAL COMMUNICATIONS SERVICES Broadband PCS Policies Governing Microwave Relocation from the 1850-1990... relocator or a voluntarily relocating microwave incumbent in accordance with the formula detailed in § 24.243: (1) All or part of the relocated microwave link was initially co-channel with the licensed PCS...

47 CFR 24.247 - Triggering a reimbursement obligation.

Code of Federal Regulations, 2011 CFR

2011-10-01

... PERSONAL COMMUNICATIONS SERVICES Broadband PCS Policies Governing Microwave Relocation from the 1850-1990... relocator or a voluntarily relocating microwave incumbent in accordance with the formula detailed in § 24.243: (1) All or part of the relocated microwave link was initially co-channel with the licensed PCS...

47 CFR 24.247 - Triggering a reimbursement obligation.

Code of Federal Regulations, 2010 CFR

2010-10-01

... PERSONAL COMMUNICATIONS SERVICES Broadband PCS Policies Governing Microwave Relocation from the 1850-1990... relocator or a voluntarily relocating microwave incumbent in accordance with the formula detailed in § 24.243: (1) All or part of the relocated microwave link was initially co-channel with the licensed PCS...

47 CFR 24.247 - Triggering a reimbursement obligation.

Code of Federal Regulations, 2013 CFR

2013-10-01

... PERSONAL COMMUNICATIONS SERVICES Broadband PCS Policies Governing Microwave Relocation from the 1850-1990... relocator or a voluntarily relocating microwave incumbent in accordance with the formula detailed in § 24.243: (1) All or part of the relocated microwave link was initially co-channel with the licensed PCS...

Environmental Costs of Government-Sponsored Agrarian Settlements in Brazilian Amazonia.

PubMed

Schneider, Maurício; Peres, Carlos A

2015-01-01

Brazil has presided over the most comprehensive agrarian reform frontier colonization program on Earth, in which ~1.2 million settlers have been translocated by successive governments since the 1970's, mostly into forested hinterlands of Brazilian Amazonia. These settlements encompass 5.3% of this ~5 million km2 region, but have contributed with 13.5% of all land conversion into agropastoral land uses. The Brazilian Federal Agrarian Agency (INCRA) has repeatedly claimed that deforestation in these areas largely predates the sanctioned arrival of new settlers. Here, we quantify rates of natural vegetation conversion across 1911 agrarian settlements allocated to 568 Amazonian counties and compare fire incidence and deforestation rates before and after the official occupation of settlements by migrant farmers. The timing and spatial distribution of deforestation and fires in our analysis provides irrefutable chronological and spatially explicit evidence of agropastoral conversion both inside and immediately outside agrarian settlements over the last decade. Deforestation rates are strongly related to local human population density and road access to regional markets. Agrarian settlements consistently accelerated rates of deforestation and fires, compared to neighboring areas outside settlements, but within the same counties. Relocated smallholders allocated to forest areas undoubtedly operate as pivotal agents of deforestation, and most of the forest clearance occurs in the aftermath of government-induced migration.

Environmental Costs of Government-Sponsored Agrarian Settlements in Brazilian Amazonia

PubMed Central

2015-01-01

Brazil has presided over the most comprehensive agrarian reform frontier colonization program on Earth, in which ~1.2 million settlers have been translocated by successive governments since the 1970’s, mostly into forested hinterlands of Brazilian Amazonia. These settlements encompass 5.3% of this ~5 million km2 region, but have contributed with 13.5% of all land conversion into agropastoral land uses. The Brazilian Federal Agrarian Agency (INCRA) has repeatedly claimed that deforestation in these areas largely predates the sanctioned arrival of new settlers. Here, we quantify rates of natural vegetation conversion across 1911 agrarian settlements allocated to 568 Amazonian counties and compare fire incidence and deforestation rates before and after the official occupation of settlements by migrant farmers. The timing and spatial distribution of deforestation and fires in our analysis provides irrefutable chronological and spatially explicit evidence of agropastoral conversion both inside and immediately outside agrarian settlements over the last decade. Deforestation rates are strongly related to local human population density and road access to regional markets. Agrarian settlements consistently accelerated rates of deforestation and fires, compared to neighboring areas outside settlements, but within the same counties. Relocated smallholders allocated to forest areas undoubtedly operate as pivotal agents of deforestation, and most of the forest clearance occurs in the aftermath of government-induced migration. PMID:26247467

76 FR 47223 - Notice of Submission of Proposed Information Collection to OMB Relocation and Real Property...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-08-04

... Proposed Information Collection to OMB Relocation and Real Property Acquisition, Recordkeeping Requirements Under the Uniform Relocation Assistance and Real Property Acquisition Policies Act of 1970, as Amended... acquisition of property are subject to the Uniform Relocation Assistance and Real Property Acquisition...

78 FR 26637 - Federal Travel Regulation (FTR); Relocation Allowance-Relocation Income Tax (RIT) Allowable Tables

Federal Register 2010, 2011, 2012, 2013, 2014

2013-05-07

... GENERAL SERVICES ADMINISTRATION [Notice-FTR 2013-02; Docket 2013-0002; Sequence 14] Federal Travel Regulation (FTR); Relocation Allowance--Relocation Income Tax (RIT) Allowable Tables AGENCY: Office of Governmentwide Policy (OGP), General Services Administration (GSA). ACTION: Notice of bulletin 13-05. SUMMARY...

24 CFR 570.488 - Displacement, relocation, acquisition, and replacement of housing.

Code of Federal Regulations, 2014 CFR

2014-04-01

... 24 Housing and Urban Development 3 2014-04-01 2013-04-01 true Displacement, relocation... DEVELOPMENT BLOCK GRANTS State Community Development Block Grant Program § 570.488 Displacement, relocation... displacement, relocation, acquisition, and replacement of housing are in § 570.606 and 24 CFR part 42. [61 FR...

24 CFR 570.488 - Displacement, relocation, acquisition, and replacement of housing.

Code of Federal Regulations, 2013 CFR

2013-04-01

... 24 Housing and Urban Development 3 2013-04-01 2013-04-01 false Displacement, relocation... DEVELOPMENT BLOCK GRANTS State Community Development Block Grant Program § 570.488 Displacement, relocation... displacement, relocation, acquisition, and replacement of housing are in § 570.606 and 24 CFR part 42. [61 FR...

24 CFR 570.488 - Displacement, relocation, acquisition, and replacement of housing.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 24 Housing and Urban Development 3 2010-04-01 2010-04-01 false Displacement, relocation... DEVELOPMENT BLOCK GRANTS State Community Development Block Grant Program § 570.488 Displacement, relocation... displacement, relocation, acquisition, and replacement of housing are in § 570.606 and 24 CFR part 42. [61 FR...

24 CFR 570.488 - Displacement, relocation, acquisition, and replacement of housing.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 24 Housing and Urban Development 3 2011-04-01 2010-04-01 true Displacement, relocation... DEVELOPMENT BLOCK GRANTS State Community Development Block Grant Program § 570.488 Displacement, relocation... displacement, relocation, acquisition, and replacement of housing are in § 570.606 and 24 CFR part 42. [61 FR...

24 CFR 570.488 - Displacement, relocation, acquisition, and replacement of housing.

Code of Federal Regulations, 2012 CFR

2012-04-01

... 24 Housing and Urban Development 3 2012-04-01 2012-04-01 false Displacement, relocation... DEVELOPMENT BLOCK GRANTS State Community Development Block Grant Program § 570.488 Displacement, relocation... displacement, relocation, acquisition, and replacement of housing are in § 570.606 and 24 CFR part 42. [61 FR...

25 CFR 700.91 - Relocation report.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 25 Indians 2 2011-04-01 2011-04-01 false Relocation report. 700.91 Section 700.91 Indians THE... Policies and Instructions Definitions § 700.91 Relocation report. The relocation report shall be the report prepared by the Commission and submitted to Congress pursuant to section 13(a) of the Act. ...

25 CFR 700.91 - Relocation report.

Code of Federal Regulations, 2012 CFR

2012-04-01

... 25 Indians 2 2012-04-01 2012-04-01 false Relocation report. 700.91 Section 700.91 Indians THE... Policies and Instructions Definitions § 700.91 Relocation report. The relocation report shall be the report prepared by the Commission and submitted to Congress pursuant to section 13(a) of the Act. ...

25 CFR 700.91 - Relocation report.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 25 Indians 2 2010-04-01 2010-04-01 false Relocation report. 700.91 Section 700.91 Indians THE... Policies and Instructions Definitions § 700.91 Relocation report. The relocation report shall be the report prepared by the Commission and submitted to Congress pursuant to section 13(a) of the Act. ...

25 CFR 700.91 - Relocation report.

Code of Federal Regulations, 2014 CFR

2014-04-01

... 25 Indians 2 2014-04-01 2014-04-01 false Relocation report. 700.91 Section 700.91 Indians THE... Policies and Instructions Definitions § 700.91 Relocation report. The relocation report shall be the report prepared by the Commission and submitted to Congress pursuant to section 13(a) of the Act. ...

25 CFR 700.91 - Relocation report.

Code of Federal Regulations, 2013 CFR

2013-04-01

... 25 Indians 2 2013-04-01 2013-04-01 false Relocation report. 700.91 Section 700.91 Indians THE... Policies and Instructions Definitions § 700.91 Relocation report. The relocation report shall be the report prepared by the Commission and submitted to Congress pursuant to section 13(a) of the Act. ...

Noise induced hearing loss of forest workers in Turkey.

PubMed

Tunay, M; Melemez, K

2008-09-01

In this study, a total number of 114 workers who were in 3 different groups in terms of age and work underwent audiometric analysis. In order to determine whether there was a statistically significant difference between the hearing loss levels of the workers who were included in the study, variance analysis was applied with the help of the data obtained as a result of the evaluation. Correlation and regression analysis were applied in order to determine the relations between hearing loss and their age and their time of work. As a result of the variance analysis, statistically significant differences were found at 500, 2000 and 4000 Hz frequencies. The most specific difference was observed among chainsaw machine operators at 4000 Hz frequency, which was determined by the variance analysis. As a result of the correlation analysis, significant relations were found between time of work and hearing loss in 0.01 confidence level and between age and hearing loss in 0.05 confidence level. Forest workers using chainsaw machines should be informed, they should wear or use protective materials and less noising chainsaw machines should be used if possible and workers should undergo audiometric tests when they start work and once a year.

Ergonomic factors and production target evaluation in eucalyptus timber harvesting operations in mountainous terrains.

PubMed

de Souza, Amaury Paulo; Minette, Luciano José; Sanches, André Luis Petean; da Silva, Emília Pio; Rodrigues, Valéria Antônia Justino; de Oliveira, Luciana Aparecida

2012-01-01

There are several forest operations involved in Eucalyptus timber harvesting. This study was carried out during brush-cutting; tree felling, bucking, delimbing, piling and manual extraction operations, with the following objectives: a) analyzing, ergonomically, two systems of brush-cutting: one manual and the other semi-mechanized, using two different machines; b) ergonomically evaluating three different brands of pruner machines used in delimbing felled trees. c) determining the feasible target of productivity as a function of ergonomic factors relevant to establish the time of resting pauses for workers in manual and semi-mechanized timber harvesting systems in mountainous terrain. Brush-cutting, either manual or semimechanized, is an activity carried out prior to timber harvesting. It is usually a hard work, with low productivity when compared with mechanized systems. Pruner machines have been used by forest companies, due to the great possibilities to improve productivity, quality and the health of workers. Ergonomics is a discipline that promotes the adequacy of work to the physical and mental characteristics of human beings, seeking to design production systems and products considering relevant aspects, including social, organizational and environmental factors. Companies should consider the ergonomic factor in the determination of daily worker production targets.

Machine Learning Predictions of a Multiresolution Climate Model Ensemble

NASA Astrophysics Data System (ADS)

Anderson, Gemma J.; Lucas, Donald D.

2018-05-01

Statistical models of high-resolution climate models are useful for many purposes, including sensitivity and uncertainty analyses, but building them can be computationally prohibitive. We generated a unique multiresolution perturbed parameter ensemble of a global climate model. We use a novel application of a machine learning technique known as random forests to train a statistical model on the ensemble to make high-resolution model predictions of two important quantities: global mean top-of-atmosphere energy flux and precipitation. The random forests leverage cheaper low-resolution simulations, greatly reducing the number of high-resolution simulations required to train the statistical model. We demonstrate that high-resolution predictions of these quantities can be obtained by training on an ensemble that includes only a small number of high-resolution simulations. We also find that global annually averaged precipitation is more sensitive to resolution changes than to any of the model parameters considered.

Machine Learning Methods for Production Cases Analysis

NASA Astrophysics Data System (ADS)

Mokrova, Nataliya V.; Mokrov, Alexander M.; Safonova, Alexandra V.; Vishnyakov, Igor V.

2018-03-01

Approach to analysis of events occurring during the production process were proposed. Described machine learning system is able to solve classification tasks related to production control and hazard identification at an early stage. Descriptors of the internal production network data were used for training and testing of applied models. k-Nearest Neighbors and Random forest methods were used to illustrate and analyze proposed solution. The quality of the developed classifiers was estimated using standard statistical metrics, such as precision, recall and accuracy.

Forest above ground biomass estimation and forest/non-forest classification for Odisha, India, using L-band Synthetic Aperture Radar (SAR) data

NASA Astrophysics Data System (ADS)

Suresh, M.; Kiran Chand, T. R.; Fararoda, R.; Jha, C. S.; Dadhwal, V. K.

2014-11-01

Tropical forests contribute to approximately 40 % of the total carbon found in terrestrial biomass. In this context, forest/non-forest classification and estimation of forest above ground biomass over tropical regions are very important and relevant in understanding the contribution of tropical forests in global biogeochemical cycles, especially in terms of carbon pools and fluxes. Information on the spatio-temporal biomass distribution acts as a key input to Reducing Emissions from Deforestation and forest Degradation Plus (REDD+) action plans. This necessitates precise and reliable methods to estimate forest biomass and to reduce uncertainties in existing biomass quantification scenarios. The use of backscatter information from a host of allweather capable Synthetic Aperture Radar (SAR) systems during the recent past has demonstrated the potential of SAR data in forest above ground biomass estimation and forest / nonforest classification. In the present study, Advanced Land Observing Satellite (ALOS) / Phased Array L-band Synthetic Aperture Radar (PALSAR) data along with field inventory data have been used in forest above ground biomass estimation and forest / non-forest classification over Odisha state, India. The ALOSPALSAR 50 m spatial resolution orthorectified and radiometrically corrected HH/HV dual polarization data (digital numbers) for the year 2010 were converted to backscattering coefficient images (Schimada et al., 2009). The tree level measurements collected during field inventory (2009-'10) on Girth at Breast Height (GBH at 1.3 m above ground) and height of all individual trees at plot (plot size 0.1 ha) level were converted to biomass density using species specific allometric equations and wood densities. The field inventory based biomass estimations were empirically integrated with ALOS-PALSAR backscatter coefficients to derive spatial forest above ground biomass estimates for the study area. Further, The Support Vector Machines (SVM) based Radial Basis Function classification technique was employed to carry out binary (forest-non forest) classification using ALOSPALSAR HH and HV backscatter coefficient images and field inventory data. The textural Haralick's Grey Level Cooccurrence Matrix (GLCM) texture measures are determined on HV backscatter image for Odisha, for the year 2010. PALSAR HH, HV backscatter coefficient images, their difference (HHHV) and HV backscatter coefficient based eight textural parameters (Mean, Variance, Dissimilarity, Contrast, Angular second moment, Homogeneity, Correlation and Contrast) are used as input parameters for Support Vector Machines (SVM) tool. Ground based inputs for forest / non-forest were taken from field inventory data and high resolution Google maps. Results suggested significant relationship between HV backscatter coefficient and field based biomass (R2 = 0.508, p = 0.55) compared to HH with biomass values ranging from 5 to 365 t/ha. The spatial variability of biomass with reference to different forest types is in good agreement. The forest / nonforest classified map suggested a total forest cover of 50214 km2 with an overall accuracy of 92.54 %. The forest / non-forest information derived from the present study showed a good spatial agreement with the standard forest cover map of Forest Survey of India (FSI) and corresponding published area of 50575 km2. Results are discussed in the paper.

41 CFR 302-3.508 - What relocation expenses are not authorized for new appointees or student trainees?

Code of Federal Regulations, 2010 CFR

2010-07-01

... are not authorized for new appointees or student trainees? 302-3.508 Section 302-3.508 Public Contracts and Property Management Federal Travel Regulation System RELOCATION ALLOWANCES RELOCATION... relocation expenses are not authorized for new appointees or student trainees? You must not pay any expenses...

41 CFR 302-3.507 - Once we authorize relocation expenses for new appointees or student trainees what expenses must...

Code of Federal Regulations, 2014 CFR

2014-07-01

... 41 Public Contracts and Property Management 4 2014-07-01 2014-07-01 false Once we authorize....507 Public Contracts and Property Management Federal Travel Regulation System RELOCATION ALLOWANCES RELOCATION ALLOWANCES 3-RELOCATION ALLOWANCE BY SPECIFIC TYPE Agency Responsibilities New Appointees § 302-3...

41 CFR 302-3.507 - Once we authorize relocation expenses for new appointees or student trainees what expenses must...

Code of Federal Regulations, 2011 CFR

2011-07-01

... 41 Public Contracts and Property Management 4 2011-07-01 2011-07-01 false Once we authorize....507 Public Contracts and Property Management Federal Travel Regulation System RELOCATION ALLOWANCES RELOCATION ALLOWANCES 3-RELOCATION ALLOWANCE BY SPECIFIC TYPE Agency Responsibilities New Appointees § 302-3...

41 CFR 302-3.507 - Once we authorize relocation expenses for new appointees or student trainees what expenses must...

Code of Federal Regulations, 2012 CFR

2012-07-01

... 41 Public Contracts and Property Management 4 2012-07-01 2012-07-01 false Once we authorize....507 Public Contracts and Property Management Federal Travel Regulation System RELOCATION ALLOWANCES RELOCATION ALLOWANCES 3-RELOCATION ALLOWANCE BY SPECIFIC TYPE Agency Responsibilities New Appointees § 302-3...

41 CFR 302-3.507 - Once we authorize relocation expenses for new appointees or student trainees what expenses must...

Code of Federal Regulations, 2013 CFR

2013-07-01

... 41 Public Contracts and Property Management 4 2013-07-01 2012-07-01 true Once we authorize....507 Public Contracts and Property Management Federal Travel Regulation System RELOCATION ALLOWANCES RELOCATION ALLOWANCES 3-RELOCATION ALLOWANCE BY SPECIFIC TYPE Agency Responsibilities New Appointees § 302-3...

33 CFR 150.515 - What are the requirements for weight testing of newly installed or relocated craft?

Code of Federal Regulations, 2010 CFR

2010-07-01

... weight testing of newly installed or relocated craft? 150.515 Section 150.515 Navigation and Navigable... testing of newly installed or relocated craft? (a) The operator must perform installation weight testing... (a) of this section, when survival crafts are relocated to another deepwater port. ...

Mississippi Labor Mobility Demonstration Project--Relocating the Unemployed: Dimensions of Success.

ERIC Educational Resources Information Center

Speight, John F.; And Others

The document provides an analysis of relocation stability of individuals relocated during the March, 1970-November, 1971 contract period. Data bases were 1,244 applicants with screening information and 401 individuals with follow-up interview information. Approximately one half were in new areas six months after being relocated. Reasons for…

13 CFR 123.102 - What circumstances would justify my relocating?

Code of Federal Regulations, 2013 CFR

2013-01-01

... my relocating? 123.102 Section 123.102 Business Credit and Assistance SMALL BUSINESS ADMINISTRATION DISASTER LOAN PROGRAM Home Disaster Loans § 123.102 What circumstances would justify my relocating? SBA may approve a loan if you intend to relocate outside the business area in which the disaster has occurred if...

13 CFR 123.102 - What circumstances would justify my relocating?

Code of Federal Regulations, 2011 CFR

2011-01-01

... my relocating? 123.102 Section 123.102 Business Credit and Assistance SMALL BUSINESS ADMINISTRATION DISASTER LOAN PROGRAM Home Disaster Loans § 123.102 What circumstances would justify my relocating? SBA may approve a loan if you intend to relocate outside the business area in which the disaster has occurred if...

13 CFR 123.102 - What circumstances would justify my relocating?

Code of Federal Regulations, 2014 CFR

2014-01-01

... my relocating? 123.102 Section 123.102 Business Credit and Assistance SMALL BUSINESS ADMINISTRATION DISASTER LOAN PROGRAM Home Disaster Loans § 123.102 What circumstances would justify my relocating? SBA may approve a loan if you intend to relocate outside the business area in which the disaster has occurred if...

13 CFR 123.102 - What circumstances would justify my relocating?

Code of Federal Regulations, 2012 CFR

2012-01-01

... my relocating? 123.102 Section 123.102 Business Credit and Assistance SMALL BUSINESS ADMINISTRATION DISASTER LOAN PROGRAM Home Disaster Loans § 123.102 What circumstances would justify my relocating? SBA may approve a loan if you intend to relocate outside the business area in which the disaster has occurred if...

13 CFR 123.102 - What circumstances would justify my relocating?

Code of Federal Regulations, 2010 CFR

2010-01-01

... my relocating? 123.102 Section 123.102 Business Credit and Assistance SMALL BUSINESS ADMINISTRATION DISASTER LOAN PROGRAM Home Disaster Loans § 123.102 What circumstances would justify my relocating? SBA may approve a loan if you intend to relocate outside the business area in which the disaster has occurred if...

Large-Scale Gene Relocations following an Ancient Genome Triplication Associated with the Diversification of Core Eudicots.

PubMed

Wang, Yupeng; Ficklin, Stephen P; Wang, Xiyin; Feltus, F Alex; Paterson, Andrew H

2016-01-01

Different modes of gene duplication including whole-genome duplication (WGD), and tandem, proximal and dispersed duplications are widespread in angiosperm genomes. Small-scale, stochastic gene relocations and transposed gene duplications are widely accepted to be the primary mechanisms for the creation of dispersed duplicates. However, here we show that most surviving ancient dispersed duplicates in core eudicots originated from large-scale gene relocations within a narrow window of time following a genome triplication (γ) event that occurred in the stem lineage of core eudicots. We name these surviving ancient dispersed duplicates as relocated γ duplicates. In Arabidopsis thaliana, relocated γ, WGD and single-gene duplicates have distinct features with regard to gene functions, essentiality, and protein interactions. Relative to γ duplicates, relocated γ duplicates have higher non-synonymous substitution rates, but comparable levels of expression and regulation divergence. Thus, relocated γ duplicates should be distinguished from WGD and single-gene duplicates for evolutionary investigations. Our results suggest large-scale gene relocations following the γ event were associated with the diversification of core eudicots.

Large-Scale Gene Relocations following an Ancient Genome Triplication Associated with the Diversification of Core Eudicots

PubMed Central

Wang, Yupeng; Ficklin, Stephen P.; Wang, Xiyin; Feltus, F. Alex; Paterson, Andrew H.

2016-01-01

Different modes of gene duplication including whole-genome duplication (WGD), and tandem, proximal and dispersed duplications are widespread in angiosperm genomes. Small-scale, stochastic gene relocations and transposed gene duplications are widely accepted to be the primary mechanisms for the creation of dispersed duplicates. However, here we show that most surviving ancient dispersed duplicates in core eudicots originated from large-scale gene relocations within a narrow window of time following a genome triplication (γ) event that occurred in the stem lineage of core eudicots. We name these surviving ancient dispersed duplicates as relocated γ duplicates. In Arabidopsis thaliana, relocated γ, WGD and single-gene duplicates have distinct features with regard to gene functions, essentiality, and protein interactions. Relative to γ duplicates, relocated γ duplicates have higher non-synonymous substitution rates, but comparable levels of expression and regulation divergence. Thus, relocated γ duplicates should be distinguished from WGD and single-gene duplicates for evolutionary investigations. Our results suggest large-scale gene relocations following the γ event were associated with the diversification of core eudicots. PMID:27195960

The effect of long-term relocation on child and adolescent survivors of Hurricane Katrina.

PubMed

Hansel, Tonya C; Osofsky, Joy D; Osofsky, Howard J; Friedrich, Patricia

2013-10-01

The current study is designed to increase knowledge of the effects of relocation and its association with longer-term psychological symptoms following disaster. Following clinical observations and in discussions held with school officials expressing concerns about relocated students, it was hypothesized that students who relocated to a different city following Hurricane Katrina in 2005 would have more symptoms of posttraumatic stress compared to students who returned to New Orleans. The effect of Hurricane Katrina relocation was assessed on a sample of child and adolescent survivors in 5th through 12th grades (N = 795). Students with Orleans Parish zip codes prior to Hurricane Katrina were categorized into relocation groupings: (a) relocated to Baton Rouge, (b) returned to prior zip code, and (c) moved to a different zip code within Orleans Parish. Overall results revealed more trauma symptoms for relocated students. Results also revealed that younger relocated students had fewer symptoms compared to older students. The opposite was found for students who returned to their same zip code, with older students having fewer symptoms. This study supports the need for school-based services not only in disaster areas, but also in schools where survivors tend to migrate. Copyright © 2013 International Society for Traumatic Stress Studies.

[Exploring relocation stress and diabetes control in the elderly residents of long-term care facilities].

PubMed

Yang, Yueh-Ying; Chen, Shu-Ming; Kuo, Chien-Lin; Lee, Hsin-Ju

2014-12-01

Stress and glycemic control have a significant and positive relationship. However, elderly diabetic patients who are relocated involuntarily to an institution often exhibit poor control of blood sugar. Few studies have addressed the relationship between relocation stress and diabetes control. This study explores the relationship between relocation stress and glycemic control in seniors with diabetes in nursing homes. This study used a cross-sectional descriptive correlation design with a convenience sampling method to recruit 88 elderly diabetes patients who had relocated to a nursing home within the past 1 year. The structural questionnaires used in this study adopted a personal and disease characteristics datasheet and the modified Chinese-version Relocation Appraisal Scale (RAS). SPSS (Window 18.0 version) was used for statistical analyses. Those participants with diabetes who relocated involuntary, had low functional independence, lived with their family prior to admission, had poor health, or were diagnosed with depression faced a significantly higher risk of poor diabetes control. The significant predictors for diabetes control were: low functional independence and relocation stress, which accounted for 45.7% of the total variance for diabetes control. The result of this study may be referenced to help reduce relocation stress and help improve glycemic control in recently institutionalized seniors with diabetes.

Heavy-machinery traffic impacts methane emissions as well as methanogen abundance and community structure in oxic forest soils.

PubMed

Frey, Beat; Niklaus, Pascal A; Kremer, Johann; Lüscher, Peter; Zimmermann, Stephan

2011-09-01

Temperate forest soils are usually efficient sinks for the greenhouse gas methane, at least in the absence of significant amounts of methanogens. We demonstrate here that trafficking with heavy harvesting machines caused a large reduction in CH(4) consumption and even turned well-aerated forest soils into net methane sources. In addition to studying methane fluxes, we investigated the responses of methanogens after trafficking in two different forest sites. Trafficking generated wheel tracks with different impact (low, moderate, severe, and unaffected). We found that machine passes decreased the soils' macropore space and lowered hydraulic conductivities in wheel tracks. Severely compacted soils yielded high methanogenic abundance, as demonstrated by quantitative PCR analyses of methyl coenzyme M reductase (mcrA) genes, whereas these sequences were undetectable in unaffected soils. Even after a year after traffic compression, methanogen abundance in compacted soils did not decline, indicating a stability of methanogens here over time. Compacted wheel tracks exhibited a relatively constant community structure, since we found several persisting mcrA sequence types continuously present at all sampling times. Phylogenetic analysis revealed a rather large methanogen diversity in the compacted soil, and most mcrA gene sequences were mostly similar to known sequences from wetlands. The majority of mcrA gene sequences belonged either to the order Methanosarcinales or Methanomicrobiales, whereas both sites were dominated by members of the families Methanomicrobiaceae Fencluster, with similar sequences obtained from peatland environments. The results show that compacting wet forest soils by heavy machinery causes increases in methane production and release.

Robust synthesis and continuous manufacturing of carbon nanotube forests and graphene films

NASA Astrophysics Data System (ADS)

Polsen, Erik S.

Successful translation of the outstanding properties of carbon nanotubes (CNTs) and graphene to commercial applications requires highly consistent methods of synthesis, using scalable and cost-effective machines. This thesis presents robust process conditions and a series of process operations that will enable integrated roll-to-roll (R2R) CNT and graphene growth on flexible substrates. First, a comprehensive study was undertaken to establish the sources of variation in laboratory CVD growth of CNT forests. Statistical analysis identified factors that contribute to variation in forest height and density including ambient humidity, sample position in the reactor, and barometric pressure. Implementation of system modifications and user procedures reduced the variation in height and density by 50% and 54% respectively. With improved growth, two new methods for continuous deposition and patterning of catalyst nanoparticles for CNT forest growth were developed, enabling the diameter, density and pattern geometry to be tailored through the control of process parameters. Convective assembly of catalyst nanoparticles in solution enables growth of CNT forests with density 3-fold higher than using sputtered catalyst films with the same growth parameters. Additionally, laser printing of magnetic ink character recognition toner provides a large scale patterning method, with digital control of the pattern density and tunable CNT density via laser intensity. A concentric tube CVD reactor was conceptualized, designed and built for R2R growth of CNT forests and graphene on flexible substrates helically fed through the annular gap. The design enables downstream injection of the hydrocarbon source, and gas consumption is reduced 90% compared to a standard tube furnace. Multi-wall CNT forests are grown continuously on metallic and ceramic fiber substrates at 33 mm/min. High quality, uniform bi- and multi-layer graphene is grown on Cu and Ni foils at 25 - 495 mm/min. A second machine for continuous forest growth and delamination was developed; and forest-substrate adhesion strength was controlled through CVD parameters. Taken together, these methods enable uniform R2R processing of CNT forests and graphene with engineered properties. Last, it is projected that foreseeable improvements in CNT forest quality and density using these methods will result in electrical and thermal properties that exceed state-of-the-art bulk materials.

Studies of the DIII-D disruption database using Machine Learning algorithms

NASA Astrophysics Data System (ADS)

Rea, Cristina; Granetz, Robert; Meneghini, Orso

2017-10-01

A Random Forests Machine Learning algorithm, trained on a large database of both disruptive and non-disruptive DIII-D discharges, predicts disruptive behavior in DIII-D with about 90% of accuracy. Several algorithms have been tested and Random Forests was found superior in performances for this particular task. Over 40 plasma parameters are included in the database, with data for each of the parameters taken from 500k time slices. We focused on a subset of non-dimensional plasma parameters, deemed to be good predictors based on physics considerations. Both binary (disruptive/non-disruptive) and multi-label (label based on the elapsed time before disruption) classification problems are investigated. The Random Forests algorithm provides insight on the available dataset by ranking the relative importance of the input features. It is found that q95 and Greenwald density fraction (n/nG) are the most relevant parameters for discriminating between DIII-D disruptive and non-disruptive discharges. A comparison with the Gradient Boosted Trees algorithm is shown and the first results coming from the application of regression algorithms are presented. Work supported by the US Department of Energy under DE-FC02-04ER54698, DE-SC0014264 and DE-FG02-95ER54309.

Human ecology, land use and biomass burning in DRC, Central Africa, using GIS and remote-sensing

NASA Astrophysics Data System (ADS)

Kazadi, S.; Kobayashi, S.

2007-05-01

Four major vegetation types are shown to be the dominant ecosystems over Kayamba County in the Congo (DRC). Covering about 76.6% of the County total area, savanna is the largest land cover type, and the marshlands (grass formations over waterlogged soils) the second (12.9% of the area). This amounts to 89.5% of the County lands being covered with herbaceous vegetations, compared to a very weak proportion of forests cover (10.5%). Open water bodies are rare, covering only 1.1 km2 (0.04%) of the County territory. They consist mostly of small ponds in the vast marshlands along the main rivers. Kayamba is thus shown to be a savanna area, with large expanse of wetlands and scattered patches of various types of tropical rainforests (natural or man-made forests, and sparse woodlands). Rain fed agriculture (slash and burn in the forests, or shifting cultivation in the savanna) is shown to be the main life-sustaining human activity among the Luba of Kayamba County. Its full dependence on the natural elements (especially the rainfall) makes it easily affected by any variability in the climatic regimes. Hunting, fishing and gathering provide a supplement to the daily food intake. This lifestyle compares to that of other tropical rainforest dwellers (e.g. the Kayapo Indian in Brazil or the Karen in Thailand). A strong village dynamics (permanent relocations in the North and the Center, new villages built at important crossways, or splitting followed by relocation along main arteries in the South), more likely in response to the need for a new economy-oriented way of life in the County is also observed, pointing to the need for more investigation in relation with the possible development of this area. Biomass burning in Kayamba is either planned (bushfire hunting) or accidental (uncontrolled fires from field debris burning), occurring exclusively during the peak of the dry season (June-July). The seasonal bushfires regime is analyzed and discussed. It is shown that of the annual GHG emissions into the atmosphere, 615,000 tCO2⋯ (99.6%) are from bush fires, and the remaining 3,206 tCO2⋯ from fuel wood burning. This amounts to about 13,612 tCO2⋯ for every one of the 45,000 inhabitants of the County.

Comparison of Models for the Prediction of Medical Costs of Spinal Fusion in Taiwan Diagnosis-Related Groups by Machine Learning Algorithms.

PubMed

Kuo, Ching-Yen; Yu, Liang-Chin; Chen, Hou-Chaung; Chan, Chien-Lung

2018-01-01

The aims of this study were to compare the performance of machine learning methods for the prediction of the medical costs associated with spinal fusion in terms of profit or loss in Taiwan Diagnosis-Related Groups (Tw-DRGs) and to apply these methods to explore the important factors associated with the medical costs of spinal fusion. A data set was obtained from a regional hospital in Taoyuan city in Taiwan, which contained data from 2010 to 2013 on patients of Tw-DRG49702 (posterior and other spinal fusion without complications or comorbidities). Naïve-Bayesian, support vector machines, logistic regression, C4.5 decision tree, and random forest methods were employed for prediction using WEKA 3.8.1. Five hundred thirty-two cases were categorized as belonging to the Tw-DRG49702 group. The mean medical cost was US $4,549.7, and the mean age of the patients was 62.4 years. The mean length of stay was 9.3 days. The length of stay was an important variable in terms of determining medical costs for patients undergoing spinal fusion. The random forest method had the best predictive performance in comparison to the other methods, achieving an accuracy of 84.30%, a sensitivity of 71.4%, a specificity of 92.2%, and an AUC of 0.904. Our study demonstrated that the random forest model can be employed to predict the medical costs of Tw-DRG49702, and could inform hospital strategy in terms of increasing the financial management efficiency of this operation.

41 CFR 302-3.507 - Once we authorize relocation expenses for new appointees or student trainees what expenses must...

Code of Federal Regulations, 2010 CFR

2010-07-01

... relocation expenses for new appointees or student trainees what expenses must we pay? 302-3.507 Section 302-3.507 Public Contracts and Property Management Federal Travel Regulation System RELOCATION ALLOWANCES....507 Once we authorize relocation expenses for new appointees or student trainees what expenses must we...

41 CFR 302-3.500 - What governing policies and procedures must we establish for paying a relocation allowance under...

Code of Federal Regulations, 2010 CFR

2010-07-01

... 41 Public Contracts and Property Management 4 2010-07-01 2010-07-01 false What governing policies... Section 302-3.500 Public Contracts and Property Management Federal Travel Regulation System RELOCATION ALLOWANCES RELOCATION ALLOWANCES 3-RELOCATION ALLOWANCE BY SPECIFIC TYPE Agency Responsibilities § 302-3.500...

41 CFR 302-3.500 - What governing policies and procedures must we establish for paying a relocation allowance under...

Code of Federal Regulations, 2014 CFR

2014-07-01

... 41 Public Contracts and Property Management 4 2014-07-01 2014-07-01 false What governing policies... Section 302-3.500 Public Contracts and Property Management Federal Travel Regulation System RELOCATION ALLOWANCES RELOCATION ALLOWANCES 3-RELOCATION ALLOWANCE BY SPECIFIC TYPE Agency Responsibilities § 302-3.500...

41 CFR 302-3.501 - Must we establish any specific procedures for paying a relocation allowance to new appointees?

Code of Federal Regulations, 2014 CFR

2014-07-01

... 41 Public Contracts and Property Management 4 2014-07-01 2014-07-01 false Must we establish any... Contracts and Property Management Federal Travel Regulation System RELOCATION ALLOWANCES RELOCATION ALLOWANCES 3-RELOCATION ALLOWANCE BY SPECIFIC TYPE Agency Responsibilities § 302-3.501 Must we establish any...

41 CFR 302-3.501 - Must we establish any specific procedures for paying a relocation allowance to new appointees?

Code of Federal Regulations, 2012 CFR

2012-07-01

... 41 Public Contracts and Property Management 4 2012-07-01 2012-07-01 false Must we establish any... Contracts and Property Management Federal Travel Regulation System RELOCATION ALLOWANCES RELOCATION ALLOWANCES 3-RELOCATION ALLOWANCE BY SPECIFIC TYPE Agency Responsibilities § 302-3.501 Must we establish any...

41 CFR 302-3.501 - Must we establish any specific procedures for paying a relocation allowance to new appointees?

Code of Federal Regulations, 2010 CFR

2010-07-01

... 41 Public Contracts and Property Management 4 2010-07-01 2010-07-01 false Must we establish any... Contracts and Property Management Federal Travel Regulation System RELOCATION ALLOWANCES RELOCATION ALLOWANCES 3-RELOCATION ALLOWANCE BY SPECIFIC TYPE Agency Responsibilities § 302-3.501 Must we establish any...

41 CFR 302-3.101 - As a transferred employee what relocation allowances must my agency pay or reimburse me for...

Code of Federal Regulations, 2011 CFR

2011-07-01

... 41 Public Contracts and Property Management 4 2011-07-01 2011-07-01 false As a transferred... of station? 302-3.101 Section 302-3.101 Public Contracts and Property Management Federal Travel Regulation System RELOCATION ALLOWANCES RELOCATION ALLOWANCES 3-RELOCATION ALLOWANCE BY SPECIFIC TYPE...

41 CFR 302-3.500 - What governing policies and procedures must we establish for paying a relocation allowance under...

Code of Federal Regulations, 2012 CFR

2012-07-01

... 41 Public Contracts and Property Management 4 2012-07-01 2012-07-01 false What governing policies... Section 302-3.500 Public Contracts and Property Management Federal Travel Regulation System RELOCATION ALLOWANCES RELOCATION ALLOWANCES 3-RELOCATION ALLOWANCE BY SPECIFIC TYPE Agency Responsibilities § 302-3.500...

41 CFR 302-3.101 - As a transferred employee what relocation allowances must my agency pay or reimburse to me?

Code of Federal Regulations, 2014 CFR

2014-07-01

... 41 Public Contracts and Property Management 4 2014-07-01 2014-07-01 false As a transferred... Public Contracts and Property Management Federal Travel Regulation System RELOCATION ALLOWANCES RELOCATION ALLOWANCES 3-RELOCATION ALLOWANCE BY SPECIFIC TYPE Transferred Employees § 302-3.101 As a...

41 CFR 302-3.501 - Must we establish any specific procedures for paying a relocation allowance to new appointees?

Code of Federal Regulations, 2013 CFR

2013-07-01

... 41 Public Contracts and Property Management 4 2013-07-01 2012-07-01 true Must we establish any... Contracts and Property Management Federal Travel Regulation System RELOCATION ALLOWANCES RELOCATION ALLOWANCES 3-RELOCATION ALLOWANCE BY SPECIFIC TYPE Agency Responsibilities § 302-3.501 Must we establish any...

41 CFR 302-3.501 - Must we establish any specific procedures for paying a relocation allowance to new appointees?

Code of Federal Regulations, 2011 CFR

2011-07-01

... 41 Public Contracts and Property Management 4 2011-07-01 2011-07-01 false Must we establish any... Contracts and Property Management Federal Travel Regulation System RELOCATION ALLOWANCES RELOCATION ALLOWANCES 3-RELOCATION ALLOWANCE BY SPECIFIC TYPE Agency Responsibilities § 302-3.501 Must we establish any...

41 CFR 302-3.101 - As a transferred employee what relocation allowances must my agency pay or reimburse to me?

Code of Federal Regulations, 2013 CFR

2013-07-01

... 41 Public Contracts and Property Management 4 2013-07-01 2012-07-01 true As a transferred employee... Contracts and Property Management Federal Travel Regulation System RELOCATION ALLOWANCES RELOCATION ALLOWANCES 3-RELOCATION ALLOWANCE BY SPECIFIC TYPE Transferred Employees § 302-3.101 As a transferred...

41 CFR 302-3.207 - Am I eligible to receive relocation allowances for overseas assignment and return travel?

Code of Federal Regulations, 2014 CFR

2014-07-01

... 41 Public Contracts and Property Management 4 2014-07-01 2014-07-01 false Am I eligible to receive... and Property Management Federal Travel Regulation System RELOCATION ALLOWANCES RELOCATION ALLOWANCES 3-RELOCATION ALLOWANCE BY SPECIFIC TYPE Types of Transfers Overseas Assignment and Return § 302-3.207 Am I...

41 CFR 302-3.101 - As a transferred employee what relocation allowances must my agency pay or reimburse to me?

Code of Federal Regulations, 2012 CFR

2012-07-01

... 41 Public Contracts and Property Management 4 2012-07-01 2012-07-01 false As a transferred... Public Contracts and Property Management Federal Travel Regulation System RELOCATION ALLOWANCES RELOCATION ALLOWANCES 3-RELOCATION ALLOWANCE BY SPECIFIC TYPE Transferred Employees § 302-3.101 As a...

41 CFR 302-3.500 - What governing policies and procedures must we establish for paying a relocation allowance under...

Code of Federal Regulations, 2011 CFR

2011-07-01

... 41 Public Contracts and Property Management 4 2011-07-01 2011-07-01 false What governing policies... Section 302-3.500 Public Contracts and Property Management Federal Travel Regulation System RELOCATION ALLOWANCES RELOCATION ALLOWANCES 3-RELOCATION ALLOWANCE BY SPECIFIC TYPE Agency Responsibilities § 302-3.500...

41 CFR 302-3.500 - What governing policies and procedures must we establish for paying a relocation allowance under...

Code of Federal Regulations, 2013 CFR

2013-07-01

... 41 Public Contracts and Property Management 4 2013-07-01 2012-07-01 true What governing policies... Section 302-3.500 Public Contracts and Property Management Federal Travel Regulation System RELOCATION ALLOWANCES RELOCATION ALLOWANCES 3-RELOCATION ALLOWANCE BY SPECIFIC TYPE Agency Responsibilities § 302-3.500...

Statistical-learning strategies generate only modestly performing predictive models for urinary symptoms following external beam radiotherapy of the prostate: A comparison of conventional and machine-learning methods

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yahya, Noorazrul, E-mail: noorazrul.yahya@research.uwa.edu.au; Ebert, Martin A.; Bulsara, Max

Purpose: Given the paucity of available data concerning radiotherapy-induced urinary toxicity, it is important to ensure derivation of the most robust models with superior predictive performance. This work explores multiple statistical-learning strategies for prediction of urinary symptoms following external beam radiotherapy of the prostate. Methods: The performance of logistic regression, elastic-net, support-vector machine, random forest, neural network, and multivariate adaptive regression splines (MARS) to predict urinary symptoms was analyzed using data from 754 participants accrued by TROG03.04-RADAR. Predictive features included dose-surface data, comorbidities, and medication-intake. Four symptoms were analyzed: dysuria, haematuria, incontinence, and frequency, each with three definitions (grade ≥more » 1, grade ≥ 2 and longitudinal) with event rate between 2.3% and 76.1%. Repeated cross-validations producing matched models were implemented. A synthetic minority oversampling technique was utilized in endpoints with rare events. Parameter optimization was performed on the training data. Area under the receiver operating characteristic curve (AUROC) was used to compare performance using sample size to detect differences of ≥0.05 at the 95% confidence level. Results: Logistic regression, elastic-net, random forest, MARS, and support-vector machine were the highest-performing statistical-learning strategies in 3, 3, 3, 2, and 1 endpoints, respectively. Logistic regression, MARS, elastic-net, random forest, neural network, and support-vector machine were the best, or were not significantly worse than the best, in 7, 7, 5, 5, 3, and 1 endpoints. The best-performing statistical model was for dysuria grade ≥ 1 with AUROC ± standard deviation of 0.649 ± 0.074 using MARS. For longitudinal frequency and dysuria grade ≥ 1, all strategies produced AUROC>0.6 while all haematuria endpoints and longitudinal incontinence models produced AUROC<0.6. Conclusions: Logistic regression and MARS were most likely to be the best-performing strategy for the prediction of urinary symptoms with elastic-net and random forest producing competitive results. The predictive power of the models was modest and endpoint-dependent. New features, including spatial dose maps, may be necessary to achieve better models.« less

CTUIR Grande Ronde River Basin Watershed Restoration Program McCoy Creek/McIntyre Creek Road Crossing, 1996-1998 Progress Report.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Childs, Allen B.

1999-07-01

This Annual Report provides a detailed overview of watershed restoration accomplishments achieved by the Confederated Tribes of the Umatilla Indian Reservation (CTUIR) and project partners in the Upper Grande Ronde River Basin under contract with the Bonneville Power Administration (BPA) during the period July 1, 1997 through June 30, 1998. The Contract Agreement entitled McCoy Meadows Watershed Restoration Project (Project No.96-83-01) includes habitat restoration planning, design, and implementation in two project areas--the McCoy Meadows Ranch located in the Meadow, McCoy, and McIntyre Creek subbasins on private land and the Mainstem Grande Ronde River Habitat Enhancement Project located on private andmore » National Forest System lands near Bird Tract Springs along the Grande Ronde River. During the contract period, the CTUIR and partners (Mark and Lorna Tipperman, landowners), Oregon Department of Environmental Quality (ODEQ), U.S. Environmental Protection Agency (EPA), Oregon Department of Fish and Wildlife (ODFW), and Natural Resource Conservation Service (NRCS) initiated phase 1 construction of the McCoy Meadows Restoration Project. Phase 1 involved reintroduction of a segment of McCoy Creek from its existing channelized configuration into a historic meander channel. Project efforts included bioengineering and tree/shrub planting and protection, transporting salvaged cottonwood tree boles and limbs from offsite source to the project area for utilization by resident beaver populations for forage and dam construction materials, relocation of existing BPA/ODFW riparian corridor fencing to outer edges of meadow floodplain, establishment of pre-project photo points, and coordination of other monitoring and evaluation efforts being led by other project partners including groundwater monitoring wells, channel cross sections, water quality monitoring stations, juvenile population sampling index sites, redd surveys, and habitat surveys. Project activities also included coordination with the U.S. Forest Service, Wallowa-Whitman National Forest, LaGrande Ranger District (USFS) on the Forest Road 2137 (McIntyre Road) Relocation and Obliteration Project and the McCoy Creek crossing. The USFS completed engineering designs under the cooperative effort for the McCoy Creek crossing. Project activities accomplished on the Upper Mainstem Large Wood Addition Project included placement of approximately 120 whole trees to enhance instream structural diversity, pool habitat quality, streambank stability, and improved floodplain morphology. Project activities accomplished on the Mainstem Grande Ronde Habitat Enhancement Project included coordination with landowners (Shauna Musgrove of Cuhna Ranches, Dean Stone, and the Wallowa-Whitman National Forest, LaGrande Ranger District) to develop a habitat enhancement/restoration project opportunity along a 3 mile section of the mainstem Grande Ronde River and major tributaries including the lower reaches of Bear Creek and Jordan Creek. Upon securing an agreement with the landowners, project partners including the CTUIR, ODFW, NRCS, and USFS initiated development of project objectives and site-specific designs. By June 1998, project designs were completed and preparations nearly complete to initiate onsite project construction.« less

Mapping leaf nitrogen and carbon concentrations of intact and fragmented indigenous forest ecosystems using empirical modeling techniques and WorldView-2 data

NASA Astrophysics Data System (ADS)

Omer, Galal; Mutanga, Onisimo; Abdel-Rahman, Elfatih M.; Peerbhay, Kabir; Adam, Elhadi

2017-09-01

Forest nitrogen (N) and carbon (C) are among the most important biochemical components of tree organic matter, and the estimation of their concentrations can help to monitor the nutrient uptake processes and health of forest trees. Traditionally, these tree biochemical components are estimated using costly, labour intensive, time-consuming and subjective analytical protocols. The use of very high spatial resolution multispectral data and advanced machine learning regression algorithms such as support vector machines (SVM) and artificial neural networks (ANN) provide an opportunity to accurately estimate foliar N and C concentrations over intact and fragmented forest ecosystems. In the present study, the utility of spectral vegetation indices calculated from WorldView-2 (WV-2) imagery for mapping leaf N and C concentrations of fragmented and intact indigenous forest ecosystems was explored. We collected leaf samples from six tree species in the fragmented as well as intact Dukuduku indigenous forest ecosystems. Leaf samples (n = 85 for each of the fragmented and intact forests) were subjected to chemical analysis for estimating the concentrations of N and C. We used 70% of samples for training our models and 30% for validating the accuracy of our predictive empirical models. The study showed that the N concentration was significantly higher (p = 0.03) in the intact forests than in the fragmented forest. There was no significant difference (p = 0.55) in the C concentration between the intact and fragmented forest strata. The results further showed that the foliar N and C concentrations could be more accurately estimated using the fragmented stratum data compared with the intact stratum data. Further, SVM achieved relatively more accurate N (maximum R2 Val = 0.78 and minimum RMSEVal = 1.07% of the mean) and C (maximum R2 Val = 0.67 and minimum RMSEVal = 1.64% of the mean) estimates compared with ANN (maximum R2Val = 0.70 for N and 0.51 for C and minimum RMSEVal = 5.40% of the mean for N and 2.21% of the mean for C). Overall, SVM regressions achieved more accurate models for estimating forest foliar N and C concentrations in the fragmented and intact indigenous forests compared to the ANN regression method. It is concluded that the successful application of the WV-2 data integrated with SVM can provide an accurate framework for mapping the concentrations of biochemical elements in two indigenous forest ecosystems.

Random Bits Forest: a Strong Classifier/Regressor for Big Data

NASA Astrophysics Data System (ADS)

Wang, Yi; Li, Yi; Pu, Weilin; Wen, Kathryn; Shugart, Yin Yao; Xiong, Momiao; Jin, Li

2016-07-01

Efficiency, memory consumption, and robustness are common problems with many popular methods for data analysis. As a solution, we present Random Bits Forest (RBF), a classification and regression algorithm that integrates neural networks (for depth), boosting (for width), and random forests (for prediction accuracy). Through a gradient boosting scheme, it first generates and selects ~10,000 small, 3-layer random neural networks. These networks are then fed into a modified random forest algorithm to obtain predictions. Testing with datasets from the UCI (University of California, Irvine) Machine Learning Repository shows that RBF outperforms other popular methods in both accuracy and robustness, especially with large datasets (N > 1000). The algorithm also performed highly in testing with an independent data set, a real psoriasis genome-wide association study (GWAS).

High-Risk Breast Lesions: A Machine Learning Model to Predict Pathologic Upgrade and Reduce Unnecessary Surgical Excision.

PubMed

Bahl, Manisha; Barzilay, Regina; Yedidia, Adam B; Locascio, Nicholas J; Yu, Lili; Lehman, Constance D

2018-03-01

Purpose To develop a machine learning model that allows high-risk breast lesions (HRLs) diagnosed with image-guided needle biopsy that require surgical excision to be distinguished from HRLs that are at low risk for upgrade to cancer at surgery and thus could be surveilled. Materials and Methods Consecutive patients with biopsy-proven HRLs who underwent surgery or at least 2 years of imaging follow-up from June 2006 to April 2015 were identified. A random forest machine learning model was developed to identify HRLs at low risk for upgrade to cancer. Traditional features such as age and HRL histologic results were used in the model, as were text features from the biopsy pathologic report. Results One thousand six HRLs were identified, with a cancer upgrade rate of 11.4% (115 of 1006). A machine learning random forest model was developed with 671 HRLs and tested with an independent set of 335 HRLs. Among the most important traditional features were age and HRL histologic results (eg, atypical ductal hyperplasia). An important text feature from the pathologic reports was "severely atypical." Instead of surgical excision of all HRLs, if those categorized with the model to be at low risk for upgrade were surveilled and the remainder were excised, then 97.4% (37 of 38) of malignancies would have been diagnosed at surgery, and 30.6% (91 of 297) of surgeries of benign lesions could have been avoided. Conclusion This study provides proof of concept that a machine learning model can be applied to predict the risk of upgrade of HRLs to cancer. Use of this model could decrease unnecessary surgery by nearly one-third and could help guide clinical decision making with regard to surveillance versus surgical excision of HRLs. © RSNA, 2017.

ADMET Evaluation in Drug Discovery. 18. Reliable Prediction of Chemical-Induced Urinary Tract Toxicity by Boosting Machine Learning Approaches.

PubMed

Lei, Tailong; Sun, Huiyong; Kang, Yu; Zhu, Feng; Liu, Hui; Zhou, Wenfang; Wang, Zhe; Li, Dan; Li, Youyong; Hou, Tingjun

2017-11-06

Xenobiotic chemicals and their metabolites are mainly excreted out of our bodies by the urinary tract through the urine. Chemical-induced urinary tract toxicity is one of the main reasons that cause failure during drug development, and it is a common adverse event for medications, natural supplements, and environmental chemicals. Despite its importance, there are only a few in silico models for assessing urinary tract toxicity for a large number of compounds with diverse chemical structures. Here, we developed a series of qualitative and quantitative structure-activity relationship (QSAR) models for predicting urinary tract toxicity. In our study, the recursive feature elimination method incorporated with random forests (RFE-RF) was used for dimension reduction, and then eight machine learning approaches were used for QSAR modeling, i.e., relevance vector machine (RVM), support vector machine (SVM), regularized random forest (RRF), C5.0 trees, eXtreme gradient boosting (XGBoost), AdaBoost.M1, SVM boosting (SVMBoost), and RVM boosting (RVMBoost). For building classification models, the synthetic minority oversampling technique was used to handle the imbalance data set problem. Among all the machine learning approaches, SVMBoost based on the RBF kernel achieves both the best quantitative (q ext 2 = 0.845) and qualitative predictions for the test set (MCC of 0.787, AUC of 0.893, sensitivity of 89.6%, specificity of 94.1%, and global accuracy of 90.8%). The application domains were then analyzed, and all of the tested chemicals fall within the application domain coverage. We also examined the structure features of the chemicals with large prediction errors. In brief, both the regression and classification models developed by the SVMBoost approach have reliable prediction capability for assessing chemical-induced urinary tract toxicity.

[A diachronic study on resettlers' perceptions toward ecological relocation in world heritage site: A case study of Wulingyuan Scenic Area, China].

PubMed

Wang, Kai; Chen, Qin Chang; Li, Zhi Miao

2018-03-01

Perceptions of residents in ecological resettlement area are important for evaluating the implementation effect of ecological relocation and sustainable development of world heritage site. With the residents from three different resettlement communities in Wulingyuan Scenic Area as the research object, we carried out a diachronic study on changes of the resettlers' perceptions of ecological relocation at different times and the main driving factors based on systematic survey data in 2010 and 2016. The results showed that in the year 2010 and 2016, resettlers reacted negatively to the indicators such as "enhancement of employment opportunity", "improvement of education and training opportunity", "enhanced environment in scenic area", "recognizing the identity change 'from rural to non-rural' after relocation". They favored the indicators such as "undermining traditional value", "lack of supervision during the implementation of policies". In 2016, resettlers of different gender, age and average monthly income had substantial different opinions on the economic and psychological impacts of ecological relocation. Education and income level had great impacts on their opinions of ecological relocation policies. Resettlers relocated by the way of investment for developing perceived were more sensitive to the economic impacts. Economic and policy impacts became the dominant driving factors for their general perception of ecological relocation. They pay more attention to employment, children's education opportunity as well as social security system for relocation.

44 CFR 206.161 - Relocation assistance.

Code of Federal Regulations, 2010 CFR

2010-10-01

... § 206.161 Relocation assistance. Notwithstanding any other provision of law, no person otherwise eligible for any kind of replacement housing payment under the Uniform Relocation Assistance and Real...

41 CFR 302-3.4 - If my agency authorizes me allowances for relocation, must it pay all of the expenses listed in...

Code of Federal Regulations, 2013 CFR

2013-07-01

... 41 Public Contracts and Property Management 4 2013-07-01 2012-07-01 true If my agency authorizes... 302-3.4 Public Contracts and Property Management Federal Travel Regulation System RELOCATION ALLOWANCES RELOCATION ALLOWANCES 3-RELOCATION ALLOWANCE BY SPECIFIC TYPE New Appointee § 302-3.4 If my agency...

41 CFR 302-3.203 - If I am transferring in the interest of the Government and my employed immediate family member(s...

Code of Federal Regulations, 2010 CFR

2010-07-01

... 41 Public Contracts and Property Management 4 2010-07-01 2010-07-01 false If I am transferring in... Contracts and Property Management Federal Travel Regulation System RELOCATION ALLOWANCES RELOCATION ALLOWANCES 3-RELOCATION ALLOWANCE BY SPECIFIC TYPE Types of Transfers Relocation of Two Or More Employed...

41 CFR 302-3.4 - If my agency authorizes me allowances for relocation, must it pay all of the expenses listed in...

Code of Federal Regulations, 2012 CFR

2012-07-01

... 41 Public Contracts and Property Management 4 2012-07-01 2012-07-01 false If my agency authorizes... 302-3.4 Public Contracts and Property Management Federal Travel Regulation System RELOCATION ALLOWANCES RELOCATION ALLOWANCES 3-RELOCATION ALLOWANCE BY SPECIFIC TYPE New Appointee § 302-3.4 If my agency...

41 CFR 302-3.4 - If my agency authorizes me allowances for relocation, must it pay all of the expenses listed in...

Code of Federal Regulations, 2014 CFR

2014-07-01

... 41 Public Contracts and Property Management 4 2014-07-01 2014-07-01 false If my agency authorizes... 302-3.4 Public Contracts and Property Management Federal Travel Regulation System RELOCATION ALLOWANCES RELOCATION ALLOWANCES 3-RELOCATION ALLOWANCE BY SPECIFIC TYPE New Appointee § 302-3.4 If my agency...

41 CFR 302-3.203 - If I am transferring in the interest of the Government and my employed immediate family member(s...

Code of Federal Regulations, 2013 CFR

2013-07-01

... 41 Public Contracts and Property Management 4 2013-07-01 2012-07-01 true If I am transferring in... Contracts and Property Management Federal Travel Regulation System RELOCATION ALLOWANCES RELOCATION ALLOWANCES 3-RELOCATION ALLOWANCE BY SPECIFIC TYPE Types of Transfers Relocation of Two Or More Employed...

41 CFR 302-3.203 - If I am transferring in the interest of the Government and my employed immediate family member(s...

Code of Federal Regulations, 2014 CFR

2014-07-01

... 41 Public Contracts and Property Management 4 2014-07-01 2014-07-01 false If I am transferring in... Contracts and Property Management Federal Travel Regulation System RELOCATION ALLOWANCES RELOCATION ALLOWANCES 3-RELOCATION ALLOWANCE BY SPECIFIC TYPE Types of Transfers Relocation of Two Or More Employed...

41 CFR 302-3.4 - If my agency authorizes me allowances for relocation, must it pay all of the expenses listed in...

Code of Federal Regulations, 2010 CFR

2010-07-01

... 41 Public Contracts and Property Management 4 2010-07-01 2010-07-01 false If my agency authorizes... 302-3.4 Public Contracts and Property Management Federal Travel Regulation System RELOCATION ALLOWANCES RELOCATION ALLOWANCES 3-RELOCATION ALLOWANCE BY SPECIFIC TYPE New Appointee § 302-3.4 If my agency...

41 CFR 302-3.4 - If my agency authorizes me allowances for relocation, must it pay all of the expenses listed in...

Code of Federal Regulations, 2011 CFR

2011-07-01

... 41 Public Contracts and Property Management 4 2011-07-01 2011-07-01 false If my agency authorizes... 302-3.4 Public Contracts and Property Management Federal Travel Regulation System RELOCATION ALLOWANCES RELOCATION ALLOWANCES 3-RELOCATION ALLOWANCE BY SPECIFIC TYPE New Appointee § 302-3.4 If my agency...

41 CFR 302-3.203 - If I am transferring in the interest of the Government and my employed immediate family member(s...

Code of Federal Regulations, 2011 CFR

2011-07-01

... 41 Public Contracts and Property Management 4 2011-07-01 2011-07-01 false If I am transferring in... Contracts and Property Management Federal Travel Regulation System RELOCATION ALLOWANCES RELOCATION ALLOWANCES 3-RELOCATION ALLOWANCE BY SPECIFIC TYPE Types of Transfers Relocation of Two Or More Employed...

41 CFR 302-3.203 - If I am transferring in the interest of the Government and my employed immediate family member(s...

Code of Federal Regulations, 2012 CFR

2012-07-01

... 41 Public Contracts and Property Management 4 2012-07-01 2012-07-01 false If I am transferring in... Contracts and Property Management Federal Travel Regulation System RELOCATION ALLOWANCES RELOCATION ALLOWANCES 3-RELOCATION ALLOWANCE BY SPECIFIC TYPE Types of Transfers Relocation of Two Or More Employed...

Academic Unit Relocation: Students' Pre- and Post-Move Responses

ERIC Educational Resources Information Center

Snir, Raphael

2017-01-01

Relocation of an academic unit affects not only the staff, but also the students. A pre- and post-move study examined the responses of undergraduate students to the relocation to a new and spacious campus carried out during the break between two semesters. The distance between the old and the new site did not require home relocation. However, it…

Machine vision and the OMV

NASA Technical Reports Server (NTRS)

Mcanulty, M. A.

1986-01-01

The orbital Maneuvering Vehicle (OMV) is intended to close with orbiting targets for relocation or servicing. It will be controlled via video signals and thruster activation based upon Earth or space station directives. A human operator is squarely in the middle of the control loop for close work. Without directly addressing future, more autonomous versions of a remote servicer, several techniques that will doubtless be important in a future increase of autonomy also have some direct application to the current situation, particularly in the area of image enhancement and predictive analysis. Several techniques are presentet, and some few have been implemented, which support a machine vision capability proposed to be adequate for detection, recognition, and tracking. Once feasibly implemented, they must then be further modified to operate together in real time. This may be achieved by two courses, the use of an array processor and some initial steps toward data reduction. The methodology or adapting to a vector architecture is discussed in preliminary form, and a highly tentative rationale for data reduction at the front end is also discussed. As a by-product, a working implementation of the most advanced graphic display technique, ray-casting, is described.

Mathematical model for dynamic cell formation in fast fashion apparel manufacturing stage

NASA Astrophysics Data System (ADS)

Perera, Gayathri; Ratnayake, Vijitha

2018-05-01

This paper presents a mathematical programming model for dynamic cell formation to minimize changeover-related costs (i.e., machine relocation costs and machine setup cost) and inter-cell material handling cost to cope with the volatile production environments in apparel manufacturing industry. The model is formulated through findings of a comprehensive literature review. Developed model is validated based on data collected from three different factories in apparel industry, manufacturing fast fashion products. A program code is developed using Lingo 16.0 software package to generate optimal cells for developed model and to determine the possible cost-saving percentage when the existing layouts used in three factories are replaced by generated optimal cells. The optimal cells generated by developed mathematical model result in significant cost saving when compared with existing product layouts used in production/assembly department of selected factories in apparel industry. The developed model can be considered as effective in minimizing the considered cost terms in dynamic production environment of fast fashion apparel manufacturing industry. Findings of this paper can be used for further researches on minimizing the changeover-related costs in fast fashion apparel production stage.

24 CFR 42.350 - Relocation assistance for displaced persons.

Code of Federal Regulations, 2014 CFR

2014-04-01

... Housing and Urban Development DISPLACEMENT, RELOCATION ASSISTANCE, AND REAL PROPERTY ACQUISITION FOR HUD... displacement, including moving expenses and increased housing costs, if: (1) The person must relocate...

24 CFR 42.350 - Relocation assistance for displaced persons.

Code of Federal Regulations, 2012 CFR

2012-04-01

... Housing and Urban Development DISPLACEMENT, RELOCATION ASSISTANCE, AND REAL PROPERTY ACQUISITION FOR HUD... displacement, including moving expenses and increased housing costs, if: (1) The person must relocate...

24 CFR 42.350 - Relocation assistance for displaced persons.

Code of Federal Regulations, 2011 CFR

2011-04-01

... Housing and Urban Development DISPLACEMENT, RELOCATION ASSISTANCE, AND REAL PROPERTY ACQUISITION FOR HUD... displacement, including moving expenses and increased housing costs, if: (1) The person must relocate...

24 CFR 42.350 - Relocation assistance for displaced persons.

Code of Federal Regulations, 2010 CFR

2010-04-01

... Housing and Urban Development DISPLACEMENT, RELOCATION ASSISTANCE, AND REAL PROPERTY ACQUISITION FOR HUD... displacement, including moving expenses and increased housing costs, if: (1) The person must relocate...

24 CFR 42.350 - Relocation assistance for displaced persons.

Code of Federal Regulations, 2013 CFR

2013-04-01

... Housing and Urban Development DISPLACEMENT, RELOCATION ASSISTANCE, AND REAL PROPERTY ACQUISITION FOR HUD... displacement, including moving expenses and increased housing costs, if: (1) The person must relocate...

The assessment of environmentally sensitive forest road construction in Calabrian pine forest areas of Turkey.

PubMed

Tunay, Metin

2006-07-01

Forest road construction by bulldozers in Calabrian Pine (Pinus brutia Ten.) forests on mountainous terrain of Turkey causes considerable damage to the environment and the forest standing alongside the road. This situation obliges a study of environmentally sound road construction in Turkey. This study was carried out in 4 sample sites of Antalya Forest Directorate in steep (34-50% gradient) and very steep terrain (51-70% gradient) conditions with bulldozer and excavator machine and direct damages to forest during road construction was determined, including forest area losses and damages to downhill trees in mountainous areas. It was determined that in steep terrain when excavators were used, less forest area (22.16%) was destroyed compared to bulldozers and 26.54% less area in very steep terrain. The proportion of damage on trees where bulldozer worked was nearly twofold higher than excavator was used. The results of this research show that the environmentally sensitive techniques applied for the road construction projects are considerably superior to the traditional use of bulldozers on steep slopes. The environmentally sound forest road construction by use of excavator must be considered an appropriate and reliable solution for mountainous terrain where areas of sensitive forest ecosystems are to be opened up.

Environmental impacts of forest road construction on mountainous terrain.

PubMed

Caliskan, Erhan

2013-03-15

Forest roads are the base infrastructure foundation of forestry operations. These roads entail a complex engineering effort because they can cause substantial environmental damage to forests and include a high-cost construction. This study was carried out in four sample sites of Giresun, Trabzon(2) and Artvin Forest Directorate, which is in the Black Sea region of Turkey. The areas have both steep terrain (30-50% gradient) and very steep terrain (51-80% gradient). Bulldozers and hydraulic excavators were determined to be the main machines for forest road construction, causing environmental damage and cross sections in mountainous areas.As a result of this study, the percent damage to forests was determined as follows: on steep terrain, 21% of trees were damaged by excavators and 33% of trees were damaged by bulldozers during forest road construction, and on very steep terrain, 27% of trees were damaged by excavators and 44% of trees were damaged by bulldozers during forest road construction. It was also determined that on steep terrain, when excavators were used, 12.23% less forest area was destroyed compared with when bulldozers were used and 16.13% less area was destroyed by excavators on very steep terrain. In order to reduce the environmental damage on the forest ecosystem, especially in steep terrains, hydraulic excavators should replace bulldozers in forest road construction activities.

Superfund Relocation Information

EPA Pesticide Factsheets

Superfund’s relocation policies and guidance provide EPA staff with tools on when to consider permanent relocation of residents and businesses living near or on NPL sites as part of a Superfund remedial action.

Learning About Climate and Atmospheric Models Through Machine Learning

NASA Astrophysics Data System (ADS)

Lucas, D. D.

2017-12-01

From the analysis of ensemble variability to improving simulation performance, machine learning algorithms can play a powerful role in understanding the behavior of atmospheric and climate models. To learn about model behavior, we create training and testing data sets through ensemble techniques that sample different model configurations and values of input parameters, and then use supervised machine learning to map the relationships between the inputs and outputs. Following this procedure, we have used support vector machines, random forests, gradient boosting and other methods to investigate a variety of atmospheric and climate model phenomena. We have used machine learning to predict simulation crashes, estimate the probability density function of climate sensitivity, optimize simulations of the Madden Julian oscillation, assess the impacts of weather and emissions uncertainty on atmospheric dispersion, and quantify the effects of model resolution changes on precipitation. This presentation highlights recent examples of our applications of machine learning to improve the understanding of climate and atmospheric models. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.

Application of Machine Learning Approaches for Protein-protein Interactions Prediction.

PubMed

Zhang, Mengying; Su, Qiang; Lu, Yi; Zhao, Manman; Niu, Bing

2017-01-01

Proteomics endeavors to study the structures, functions and interactions of proteins. Information of the protein-protein interactions (PPIs) helps to improve our knowledge of the functions and the 3D structures of proteins. Thus determining the PPIs is essential for the study of the proteomics. In this review, in order to study the application of machine learning in predicting PPI, some machine learning approaches such as support vector machine (SVM), artificial neural networks (ANNs) and random forest (RF) were selected, and the examples of its applications in PPIs were listed. SVM and RF are two commonly used methods. Nowadays, more researchers predict PPIs by combining more than two methods. This review presents the application of machine learning approaches in predicting PPI. Many examples of success in identification and prediction in the area of PPI prediction have been discussed, and the PPIs research is still in progress. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

47 CFR 24.249 - Payment issues.

Code of Federal Regulations, 2013 CFR

2013-10-01

... SERVICES Broadband PCS Policies Governing Microwave Relocation from the 1850-1990 Mhz Band § 24.249 Payment... directly to the PCS relocator or the voluntarily relocating microwave incumbent the amount owed within...

47 CFR 24.249 - Payment issues.

Code of Federal Regulations, 2014 CFR

2014-10-01

... SERVICES Broadband PCS Policies Governing Microwave Relocation from the 1850-1990 Mhz Band § 24.249 Payment... directly to the PCS relocator or the voluntarily relocating microwave incumbent the amount owed within...

47 CFR 24.249 - Payment issues.

Code of Federal Regulations, 2011 CFR

2011-10-01

... SERVICES Broadband PCS Policies Governing Microwave Relocation from the 1850-1990 Mhz Band § 24.249 Payment... directly to the PCS relocator or the voluntarily relocating microwave incumbent the amount owed within...

47 CFR 24.249 - Payment issues.

Code of Federal Regulations, 2010 CFR

2010-10-01

... SERVICES Broadband PCS Policies Governing Microwave Relocation from the 1850-1990 Mhz Band § 24.249 Payment... directly to the PCS relocator or the voluntarily relocating microwave incumbent the amount owed within...

Retrieval of aerosol optical depth from surface solar radiation measurements using machine learning algorithms, non-linear regression and a radiative transfer-based look-up table

NASA Astrophysics Data System (ADS)

Huttunen, Jani; Kokkola, Harri; Mielonen, Tero; Esa Juhani Mononen, Mika; Lipponen, Antti; Reunanen, Juha; Vilhelm Lindfors, Anders; Mikkonen, Santtu; Erkki Juhani Lehtinen, Kari; Kouremeti, Natalia; Bais, Alkiviadis; Niska, Harri; Arola, Antti

2016-07-01

In order to have a good estimate of the current forcing by anthropogenic aerosols, knowledge on past aerosol levels is needed. Aerosol optical depth (AOD) is a good measure for aerosol loading. However, dedicated measurements of AOD are only available from the 1990s onward. One option to lengthen the AOD time series beyond the 1990s is to retrieve AOD from surface solar radiation (SSR) measurements taken with pyranometers. In this work, we have evaluated several inversion methods designed for this task. We compared a look-up table method based on radiative transfer modelling, a non-linear regression method and four machine learning methods (Gaussian process, neural network, random forest and support vector machine) with AOD observations carried out with a sun photometer at an Aerosol Robotic Network (AERONET) site in Thessaloniki, Greece. Our results show that most of the machine learning methods produce AOD estimates comparable to the look-up table and non-linear regression methods. All of the applied methods produced AOD values that corresponded well to the AERONET observations with the lowest correlation coefficient value being 0.87 for the random forest method. While many of the methods tended to slightly overestimate low AODs and underestimate high AODs, neural network and support vector machine showed overall better correspondence for the whole AOD range. The differences in producing both ends of the AOD range seem to be caused by differences in the aerosol composition. High AODs were in most cases those with high water vapour content which might affect the aerosol single scattering albedo (SSA) through uptake of water into aerosols. Our study indicates that machine learning methods benefit from the fact that they do not constrain the aerosol SSA in the retrieval, whereas the LUT method assumes a constant value for it. This would also mean that machine learning methods could have potential in reproducing AOD from SSR even though SSA would have changed during the observation period.

Fertility Intentions and Residential Relocations.

PubMed

Vidal, Sergi; Huinink, Johannes; Feldhaus, Michael

2017-08-01

This research addresses the question of whether fertility intentions (before conception) are associated with residential relocations and the distance of the relocation. We empirically tested this using data from two birth cohorts (aged 24-28 and 34-38 in the first survey wave) of the German Family Panel (pairfam) and event history analysis. Bivariate analyses showed that coupled individuals relocated at a higher rate if they intended to have a(nother) child. We found substantial heterogeneity according to individuals' age and parental status, particularly for outside-town relocations. Childless individuals of average age at family formation-a highly mobile group-relocated at a lower rate if they intended to have a child. In contrast, older individuals who already had children-the least-mobile group-relocated at a higher rate if they intended to have another child. Multivariate analyses show that these associations are largely due to adjustments in housing and other living conditions. Our results suggest that anticipatory relocations (before conception) to adapt to growing household size are importantly nuanced by the opportunities and rationales of couples to adjust their living conditions over the life course. Our research contributes to the understanding of residential mobility as a by-product of fertility decisions and, more broadly, evidences that intentions matter and need to be considered in the analysis of family life courses.

7 CFR 1469.3 - Definitions.

Code of Federal Regulations, 2011 CFR

2011-01-01

... grasses and legumes; summer fallow; typically cropped wet areas, such as rice fields, rotated to wildlife... machine harvested. The crop may be grasses, legumes, or a combination of both. Incidental forest land... mixture, or a grass-legume mixture. Management usually consists of cultural treatments: fertilization...

Equations for estimating stand establishment, release, and thinning costs in the Lake States.

Treesearch

Jeffrey T. Olson; Allen L. Lundgren; Dietmar Rose

1978-01-01

Equations for estimating project costs for certain silvicultural treatments in the Lake States have been developed from project records of public forests. Treatments include machine site preparation, hand planting, aerial spraying, prescribed burning, manual release, and thinning.

Woody species susceptibility to forest herbicides applied by ground machines

Treesearch

James H. Miller; M. Boyd Edwards

1996-01-01

Abstract. This study used a simple approach of post-treatment observations to colleot data on hexbicide effectiveness for common southeastern hardwoods and shrub species, and for loblolly pine. Both site preparation and release herbicides labeled for loblolly pine were examiued.

Machine learning methods in chemoinformatics

PubMed Central

Mitchell, John B O

2014-01-01

Machine learning algorithms are generally developed in computer science or adjacent disciplines and find their way into chemical modeling by a process of diffusion. Though particular machine learning methods are popular in chemoinformatics and quantitative structure–activity relationships (QSAR), many others exist in the technical literature. This discussion is methods-based and focused on some algorithms that chemoinformatics researchers frequently use. It makes no claim to be exhaustive. We concentrate on methods for supervised learning, predicting the unknown property values of a test set of instances, usually molecules, based on the known values for a training set. Particularly relevant approaches include Artificial Neural Networks, Random Forest, Support Vector Machine, k-Nearest Neighbors and naïve Bayes classifiers. WIREs Comput Mol Sci 2014, 4:468–481. How to cite this article: WIREs Comput Mol Sci 2014, 4:468–481. doi:10.1002/wcms.1183 PMID:25285160

A Comparison of Machine Learning Approaches for Corn Yield Estimation

NASA Astrophysics Data System (ADS)

Kim, N.; Lee, Y. W.

2017-12-01

Machine learning is an efficient empirical method for classification and prediction, and it is another approach to crop yield estimation. The objective of this study is to estimate corn yield in the Midwestern United States by employing the machine learning approaches such as the support vector machine (SVM), random forest (RF), and deep neural networks (DNN), and to perform the comprehensive comparison for their results. We constructed the database using satellite images from MODIS, the climate data of PRISM climate group, and GLDAS soil moisture data. In addition, to examine the seasonal sensitivities of corn yields, two period groups were set up: May to September (MJJAS) and July and August (JA). In overall, the DNN showed the highest accuracies in term of the correlation coefficient for the two period groups. The differences between our predictions and USDA yield statistics were about 10-11 %.

Fiber tractography using machine learning.

PubMed

Neher, Peter F; Côté, Marc-Alexandre; Houde, Jean-Christophe; Descoteaux, Maxime; Maier-Hein, Klaus H

2017-09-01

We present a fiber tractography approach based on a random forest classification and voting process, guiding each step of the streamline progression by directly processing raw diffusion-weighted signal intensities. For comparison to the state-of-the-art, i.e. tractography pipelines that rely on mathematical modeling, we performed a quantitative and qualitative evaluation with multiple phantom and in vivo experiments, including a comparison to the 96 submissions of the ISMRM tractography challenge 2015. The results demonstrate the vast potential of machine learning for fiber tractography. Copyright © 2017 Elsevier Inc. All rights reserved.

Epidermis area detection for immunofluorescence microscopy

NASA Astrophysics Data System (ADS)

Dovganich, Andrey; Krylov, Andrey; Nasonov, Andrey; Makhneva, Natalia

2018-04-01

We propose a novel image segmentation method for immunofluorescence microscopy images of skin tissue for the diagnosis of various skin diseases. The segmentation is based on machine learning algorithms. The feature vector is filled by three groups of features: statistical features, Laws' texture energy measures and local binary patterns. The images are preprocessed for better learning. Different machine learning algorithms have been used and the best results have been obtained with random forest algorithm. We use the proposed method to detect the epidermis region as a part of pemphigus diagnosis system.

Integration of spectral, spatial and morphometric data into lithological mapping: A comparison of different Machine Learning Algorithms in the Kurdistan Region, NE Iraq

NASA Astrophysics Data System (ADS)

Othman, Arsalan A.; Gloaguen, Richard

2017-09-01

Lithological mapping in mountainous regions is often impeded by limited accessibility due to relief. This study aims to evaluate (1) the performance of different supervised classification approaches using remote sensing data and (2) the use of additional information such as geomorphology. We exemplify the methodology in the Bardi-Zard area in NE Iraq, a part of the Zagros Fold - Thrust Belt, known for its chromite deposits. We highlighted the improvement of remote sensing geological classification by integrating geomorphic features and spatial information in the classification scheme. We performed a Maximum Likelihood (ML) classification method besides two Machine Learning Algorithms (MLA): Support Vector Machine (SVM) and Random Forest (RF) to allow the joint use of geomorphic features, Band Ratio (BR), Principal Component Analysis (PCA), spatial information (spatial coordinates) and multispectral data of the Advanced Space-borne Thermal Emission and Reflection radiometer (ASTER) satellite. The RF algorithm showed reliable results and discriminated serpentinite, talus and terrace deposits, red argillites with conglomerates and limestone, limy conglomerates and limestone conglomerates, tuffites interbedded with basic lavas, limestone and Metamorphosed limestone and reddish green shales. The best overall accuracy (∼80%) was achieved by Random Forest (RF) algorithms in the majority of the sixteen tested combination datasets.

Predicting human liver microsomal stability with machine learning techniques.

PubMed

Sakiyama, Yojiro; Yuki, Hitomi; Moriya, Takashi; Hattori, Kazunari; Suzuki, Misaki; Shimada, Kaoru; Honma, Teruki

2008-02-01

To ensure a continuing pipeline in pharmaceutical research, lead candidates must possess appropriate metabolic stability in the drug discovery process. In vitro ADMET (absorption, distribution, metabolism, elimination, and toxicity) screening provides us with useful information regarding the metabolic stability of compounds. However, before the synthesis stage, an efficient process is required in order to deal with the vast quantity of data from large compound libraries and high-throughput screening. Here we have derived a relationship between the chemical structure and its metabolic stability for a data set of in-house compounds by means of various in silico machine learning such as random forest, support vector machine (SVM), logistic regression, and recursive partitioning. For model building, 1952 proprietary compounds comprising two classes (stable/unstable) were used with 193 descriptors calculated by Molecular Operating Environment. The results using test compounds have demonstrated that all classifiers yielded satisfactory results (accuracy > 0.8, sensitivity > 0.9, specificity > 0.6, and precision > 0.8). Above all, classification by random forest as well as SVM yielded kappa values of approximately 0.7 in an independent validation set, slightly higher than other classification tools. These results suggest that nonlinear/ensemble-based classification methods might prove useful in the area of in silico ADME modeling.

Exploiting machine learning algorithms for tree species classification in a semiarid woodland using RapidEye image

NASA Astrophysics Data System (ADS)

Adelabu, Samuel; Mutanga, Onisimo; Adam, Elhadi; Cho, Moses Azong

2013-01-01

Classification of different tree species in semiarid areas can be challenging as a result of the change in leaf structure and orientation due to soil moisture constraints. Tree species mapping is, however, a key parameter for forest management in semiarid environments. In this study, we examined the suitability of 5-band RapidEye satellite data for the classification of five tree species in mopane woodland of Botswana using machine leaning algorithms with limited training samples.We performed classification using random forest (RF) and support vector machines (SVM) based on EnMap box. The overall accuracies for classifying the five tree species was 88.75 and 85% for both SVM and RF, respectively. We also demonstrated that the new red-edge band in the RapidEye sensor has the potential for classifying tree species in semiarid environments when integrated with other standard bands. Similarly, we observed that where there are limited training samples, SVM is preferred over RF. Finally, we demonstrated that the two accuracy measures of quantity and allocation disagreement are simpler and more helpful for the vast majority of remote sensing classification process than the kappa coefficient. Overall, high species classification can be achieved using strategically located RapidEye bands integrated with advanced processing algorithms.

Mortality risk score prediction in an elderly population using machine learning.

PubMed

Rose, Sherri

2013-03-01

Standard practice for prediction often relies on parametric regression methods. Interesting new methods from the machine learning literature have been introduced in epidemiologic studies, such as random forest and neural networks. However, a priori, an investigator will not know which algorithm to select and may wish to try several. Here I apply the super learner, an ensembling machine learning approach that combines multiple algorithms into a single algorithm and returns a prediction function with the best cross-validated mean squared error. Super learning is a generalization of stacking methods. I used super learning in the Study of Physical Performance and Age-Related Changes in Sonomans (SPPARCS) to predict death among 2,066 residents of Sonoma, California, aged 54 years or more during the period 1993-1999. The super learner for predicting death (risk score) improved upon all single algorithms in the collection of algorithms, although its performance was similar to that of several algorithms. Super learner outperformed the worst algorithm (neural networks) by 44% with respect to estimated cross-validated mean squared error and had an R2 value of 0.201. The improvement of super learner over random forest with respect to R2 was approximately 2-fold. Alternatives for risk score prediction include the super learner, which can provide improved performance.

Adaptive governance and institutional strategies for climate-induced community relocations in Alaska.

PubMed

Bronen, Robin; Chapin, F Stuart

2013-06-04

This article presents governance and institutional strategies for climate-induced community relocations. In Alaska, repeated extreme weather events coupled with climate change-induced coastal erosion impact the habitability of entire communities. Community residents and government agencies concur that relocation is the only adaptation strategy that can protect lives and infrastructure. Community relocation stretches the financial and institutional capacity of existing governance institutions. Based on a comparative analysis of three Alaskan communities, Kivalina, Newtok, and Shishmaref, which have chosen to relocate, we examine the institutional constraints to relocation in the United States. We identify policy changes and components of a toolkit that can facilitate community-based adaptation when environmental events threaten people's lives and protection in place is not possible. Policy changes include amendment of the Stafford Act to include gradual geophysical processes, such as erosion, in the statutory definition of disaster and the creation of an adaptive governance framework to allow communities a continuum of responses from protection in place to community relocation. Key components of the toolkit are local leadership and integration of social and ecological well-being into adaptation planning.

Forest Cover Estimation in Ireland Using Radar Remote Sensing: A Comparative Analysis of Forest Cover Assessment Methodologies.

PubMed

Devaney, John; Barrett, Brian; Barrett, Frank; Redmond, John; O Halloran, John

2015-01-01

Quantification of spatial and temporal changes in forest cover is an essential component of forest monitoring programs. Due to its cloud free capability, Synthetic Aperture Radar (SAR) is an ideal source of information on forest dynamics in countries with near-constant cloud-cover. However, few studies have investigated the use of SAR for forest cover estimation in landscapes with highly sparse and fragmented forest cover. In this study, the potential use of L-band SAR for forest cover estimation in two regions (Longford and Sligo) in Ireland is investigated and compared to forest cover estimates derived from three national (Forestry2010, Prime2, National Forest Inventory), one pan-European (Forest Map 2006) and one global forest cover (Global Forest Change) product. Two machine-learning approaches (Random Forests and Extremely Randomised Trees) are evaluated. Both Random Forests and Extremely Randomised Trees classification accuracies were high (98.1-98.5%), with differences between the two classifiers being minimal (<0.5%). Increasing levels of post classification filtering led to a decrease in estimated forest area and an increase in overall accuracy of SAR-derived forest cover maps. All forest cover products were evaluated using an independent validation dataset. For the Longford region, the highest overall accuracy was recorded with the Forestry2010 dataset (97.42%) whereas in Sligo, highest overall accuracy was obtained for the Prime2 dataset (97.43%), although accuracies of SAR-derived forest maps were comparable. Our findings indicate that spaceborne radar could aid inventories in regions with low levels of forest cover in fragmented landscapes. The reduced accuracies observed for the global and pan-continental forest cover maps in comparison to national and SAR-derived forest maps indicate that caution should be exercised when applying these datasets for national reporting.

Forest Cover Estimation in Ireland Using Radar Remote Sensing: A Comparative Analysis of Forest Cover Assessment Methodologies

PubMed Central

Devaney, John; Barrett, Brian; Barrett, Frank; Redmond, John; O`Halloran, John

2015-01-01

Quantification of spatial and temporal changes in forest cover is an essential component of forest monitoring programs. Due to its cloud free capability, Synthetic Aperture Radar (SAR) is an ideal source of information on forest dynamics in countries with near-constant cloud-cover. However, few studies have investigated the use of SAR for forest cover estimation in landscapes with highly sparse and fragmented forest cover. In this study, the potential use of L-band SAR for forest cover estimation in two regions (Longford and Sligo) in Ireland is investigated and compared to forest cover estimates derived from three national (Forestry2010, Prime2, National Forest Inventory), one pan-European (Forest Map 2006) and one global forest cover (Global Forest Change) product. Two machine-learning approaches (Random Forests and Extremely Randomised Trees) are evaluated. Both Random Forests and Extremely Randomised Trees classification accuracies were high (98.1–98.5%), with differences between the two classifiers being minimal (<0.5%). Increasing levels of post classification filtering led to a decrease in estimated forest area and an increase in overall accuracy of SAR-derived forest cover maps. All forest cover products were evaluated using an independent validation dataset. For the Longford region, the highest overall accuracy was recorded with the Forestry2010 dataset (97.42%) whereas in Sligo, highest overall accuracy was obtained for the Prime2 dataset (97.43%), although accuracies of SAR-derived forest maps were comparable. Our findings indicate that spaceborne radar could aid inventories in regions with low levels of forest cover in fragmented landscapes. The reduced accuracies observed for the global and pan-continental forest cover maps in comparison to national and SAR-derived forest maps indicate that caution should be exercised when applying these datasets for national reporting. PMID:26262681

Practical Guide for Emergency Crime Prevention and Penal System Alternatives in Crisis Relocation Planning.

DTIC Science & Technology

1982-09-01

93117____________ 11. CONTROLLING OFFICE NAMIE AND ADDRESS 12. REPORT DATE Federal Emergency Management Agency 21 September 1982 13. NUMBER OF...relocation is the controlled , orderly evacuation of a community that is a possible target for attack by a foreign power. The concept of crisis...SI s Relocation? Crisis relocation is the controlled , orderly evacuation of a comunity which is considered a possible target for foreign attack

Modeling long-term suspended-sediment export from an undisturbed forest catchment

NASA Astrophysics Data System (ADS)

Zimmermann, Alexander; Francke, Till; Elsenbeer, Helmut

2013-04-01

Most estimates of suspended sediment yields from humid, undisturbed, and geologically stable forest environments fall within a range of 5 - 30 t km-2 a-1. These low natural erosion rates in small headwater catchments (≤ 1 km2) support the common impression that a well-developed forest cover prevents surface erosion. Interestingly, those estimates originate exclusively from areas with prevailing vertical hydrological flow paths. Forest environments dominated by (near-) surface flow paths (overland flow, pipe flow, and return flow) and a fast response to rainfall, however, are not an exceptional phenomenon, yet only very few sediment yields have been estimated for these areas. Not surprisingly, even fewer long-term (≥ 10 years) records exist. In this contribution we present our latest research which aims at quantifying long-term suspended-sediment export from an undisturbed rainforest catchment prone to frequent overland flow. A key aspect of our approach is the application of machine-learning techniques (Random Forest, Quantile Regression Forest) which allows not only the handling of non-Gaussian data, non-linear relations between predictors and response, and correlations between predictors, but also the assessment of prediction uncertainty. For the current study we provided the machine-learning algorithms exclusively with information from a high-resolution rainfall time series to reconstruct discharge and suspended sediment dynamics for a 21-year period. The significance of our results is threefold. First, our estimates clearly show that forest cover does not necessarily prevent erosion if wet antecedent conditions and large rainfalls coincide. During these situations, overland flow is widespread and sediment fluxes increase in a non-linear fashion due to the mobilization of new sediment sources. Second, our estimates indicate that annual suspended sediment yields of the undisturbed forest catchment show large fluctuations. Depending on the frequency of large events, annual suspended-sediment yield varies between 74 - 416 t km-2 a-1. Third, the estimated sediment yields exceed former benchmark values by an order of magnitude and provide evidence that the erosion footprint of undisturbed, forested catchments can be undistinguishable from that of sustainably managed, but hydrologically less responsive areas. Because of the susceptibility to soil loss we argue that any land use should be avoided in natural erosion hotspots.

Effects of Unit Design on Acute Care Nurses' Walking Distances, Energy Expenditure, and Job Satisfaction: A Pre-Post Relocation Study.

PubMed

Copeland, Darcy; Chambers, Misty

2017-07-01

The purpose of this study was to determine what differences occurred in steps taken and energy expenditure among acute care nurses when their work environment moved from a hospital with centralized nurses' stations to a hospital with decentralized nurses' stations. Additional goals were to determine design features nurses perceived as contributing to or deterring from their work activities and what changes occurred in reported job satisfaction. Since design features can also affect patient outcomes, patient falls were monitored. The construction of a replacement facility for a 224-bed Level 1 trauma center provided the opportunity to compare the effects of centralized versus decentralized nurses' stations on nurses' experiences of their work environments. A pre-post quasi-experimental design was used. RN participants completed an open-ended questionnaire and recorded pedometer data at the end of each shift, working for 3-month pre-relocation and for 3-month post-relocation. Nine months passed between the move and post-relocation data collection. There were significant reductions in nurses' energy expenditure ( p < .001) and steps taken ( p = .041) post-relocation. Overall, nurses' job satisfaction was high and improved post-relocation, and patient falls decreased by 55%. Post-relocation, a number of the dissatisfiers associated with the physical environment were eliminated, and nurses identified more satisfiers (in general and related to the physical environment). Patients are safer post-relocation as indicated by a decrease in falls. This decrease is even more noteworthy when considering that the numbers of patient beds on each unit is higher post-relocation.

Residential relocation and change in social capital: A natural experiment from the 2011 Great East Japan Earthquake and Tsunami.

PubMed

Hikichi, Hiroyuki; Sawada, Yasuyuki; Tsuboya, Toru; Aida, Jun; Kondo, Katsunori; Koyama, Shihoko; Kawachi, Ichiro

2017-07-01

Social connections in the community ("social capital") represent an important source of resilience in the aftermath of major disasters. However, little is known about how residential relocation due to housing destruction affects survivors' social capital. We examined changes in social capital among survivors of the 2011 Great East Japan Earthquake and Tsunami. People who lost their homes were resettled to new locations by two primary means: (i) group relocation to public temporary trailer housing or (ii) individual relocation, in which victims moved into government-provided housing by lottery or arranged for their own accommodation (market rental housing or private purchase/new construction). The baseline for our natural experiment was established 7 months before the 11 March 2011 disaster, when we conducted a survey of older community-dwelling adults who lived 80-km west of the earthquake epicenter. Approximately 2.5 years after the disaster, the follow-up survey gathered information about personal experiences of disaster as well as health status and social capital. Among 3421 people in our study, 79 people moved via group relocation to public temporary trailer housing, whereas 96 people moved on their own. The individual fixed-effects model showed that group relocation was associated with improved informal socializing and social participation (β coefficient = 0.053, 95% confidence interval: 0.011 to 0.095). In contrast, individual relocation was associated with declining informal socializing and social participation (β coefficient = -0.039, 95% confidence interval: -0.074 to -0.003). Group relocation, as compared to individual relocation, appeared to preserve social participation and informal socializing in the community.

Integrating Archaeological Modeling in DoD Cultural Resource Compliance

DTIC Science & Technology

2012-10-26

Leo 2001 Random Forests. Machine Learning 45:5–32. Briuer, Frederick, Clifford Brown, Alan Gillespie, Fredrick Limp, Michael Trimble, and Len...glaciolacustrine clays on glacial lake plains Inceptisols Very-fine, mixed, active, nonacid, mesic Mollic Endoaquepts Low to none LoC Lowville silt

Combination of support vector machine, artificial neural network and random forest for improving the classification of convective and stratiform rain using spectral features of SEVIRI data

NASA Astrophysics Data System (ADS)

Lazri, Mourad; Ameur, Soltane

2018-05-01

A model combining three classifiers, namely Support vector machine, Artificial neural network and Random forest (SAR) is designed for improving the classification of convective and stratiform rain. This model (SAR model) has been trained and then tested on a datasets derived from MSG-SEVIRI (Meteosat Second Generation-Spinning Enhanced Visible and Infrared Imager). Well-classified, mid-classified and misclassified pixels are determined from the combination of three classifiers. Mid-classified and misclassified pixels that are considered unreliable pixels are reclassified by using a novel training of the developed scheme. In this novel training, only the input data corresponding to the pixels in question to are used. This whole process is repeated a second time and applied to mid-classified and misclassified pixels separately. Learning and validation of the developed scheme are realized against co-located data observed by ground radar. The developed scheme outperformed different classifiers used separately and reached 97.40% of overall accuracy of classification.

A review of the Virginia Department of Transportation's business relocation process.

DOT National Transportation Integrated Search

2001-01-01

This report details a study that reviewed the Virginia Department of Transportation's (VDOT) business relocation program, with a focus on the relocation difficulties of retail gasoline service stations operating on leased property, as mandated by Hou...

Environmental impacts of forest road construction on mountainous terrain

PubMed Central

2013-01-01

Forest roads are the base infrastructure foundation of forestry operations. These roads entail a complex engineering effort because they can cause substantial environmental damage to forests and include a high-cost construction. This study was carried out in four sample sites of Giresun, Trabzon(2) and Artvin Forest Directorate, which is in the Black Sea region of Turkey. The areas have both steep terrain (30-50% gradient) and very steep terrain (51-80% gradient). Bulldozers and hydraulic excavators were determined to be the main machines for forest road construction, causing environmental damage and cross sections in mountainous areas. As a result of this study, the percent damage to forests was determined as follows: on steep terrain, 21% of trees were damaged by excavators and 33% of trees were damaged by bulldozers during forest road construction, and on very steep terrain, 27% of trees were damaged by excavators and 44% of trees were damaged by bulldozers during forest road construction. It was also determined that on steep terrain, when excavators were used, 12.23% less forest area was destroyed compared with when bulldozers were used and 16.13% less area was destroyed by excavators on very steep terrain. In order to reduce the environmental damage on the forest ecosystem, especially in steep terrains, hydraulic excavators should replace bulldozers in forest road construction activities. PMID:23497078

Demography and movement in a relocated population of Oregon Spotted Frogs (Rana pretiosa): Influence of season and gender

USGS Publications Warehouse

Chelgren, N.D.; Pearl, C.A.; Adams, M.J.; Bowerman, J.

2008-01-01

We used five years of recapture data and Bayesian estimation to assess seasonal survival, movement, and growth of Oregon Spotted Frogs (Rana pretiosa) relocated into created ponds at Dilman Meadow in Oregon, USA. We evaluate hypotheses specific to the relocation and elucidate aspects of R. pretiosa life history that are poorly known. The odds of survival of relocated individuals during the first year following relocation were 0.36 times the survival odds of relocated and non-relocated frogs after one year since the relocation. Survival rate was higher for large frogs. After accounting for frog size, we found little variation in survival between ponds at Dilman Meadow. Survival was lowest for males during the breeding/post-breeding redistribution period, suggesting a high cost of breeding for males. The highest survival rates occurred during winter for both genders, and one small spring was used heavily during winter but was used rarely during the rest of the year. Individual growth was higher in ponds that were not used for breeding, and increased with increasing pond age. Our study supports other evidence that R. pretiosa use different habitats seasonally and are specific in their overwintering habitat requirements. Because frogs were concentrated during winter, predator-free overwintering springs are likely to be of particular value for R. pretiosa populations. ?? 2008 by the American Society of Ichthyologists and Herpetologists.

Hubble Tarantula Treasury Project - VI. Identification of Pre-Main-Sequence Stars using Machine Learning techniques

NASA Astrophysics Data System (ADS)

Ksoll, Victor F.; Gouliermis, Dimitrios A.; Klessen, Ralf S.; Grebel, Eva K.; Sabbi, Elena; Anderson, Jay; Lennon, Daniel J.; Cignoni, Michele; de Marchi, Guido; Smith, Linda J.; Tosi, Monica; van der Marel, Roeland P.

2018-05-01

The Hubble Tarantula Treasury Project (HTTP) has provided an unprecedented photometric coverage of the entire star-burst region of 30 Doradus down to the half Solar mass limit. We use the deep stellar catalogue of HTTP to identify all the pre-main-sequence (PMS) stars of the region, i.e., stars that have not started their lives on the main-sequence yet. The photometric distinction of these stars from the more evolved populations is not a trivial task due to several factors that alter their colour-magnitude diagram positions. The identification of PMS stars requires, thus, sophisticated statistical methods. We employ Machine Learning Classification techniques on the HTTP survey of more than 800,000 sources to identify the PMS stellar content of the observed field. Our methodology consists of 1) carefully selecting the most probable low-mass PMS stellar population of the star-forming cluster NGC2070, 2) using this sample to train classification algorithms to build a predictive model for PMS stars, and 3) applying this model in order to identify the most probable PMS content across the entire Tarantula Nebula. We employ Decision Tree, Random Forest and Support Vector Machine classifiers to categorise the stars as PMS and Non-PMS. The Random Forest and Support Vector Machine provided the most accurate models, predicting about 20,000 sources with a candidateship probability higher than 50 percent, and almost 10,000 PMS candidates with a probability higher than 95 percent. This is the richest and most accurate photometric catalogue of extragalactic PMS candidates across the extent of a whole star-forming complex.

Supervised machine learning for analysing spectra of exoplanetary atmospheres

NASA Astrophysics Data System (ADS)

Márquez-Neila, Pablo; Fisher, Chloe; Sznitman, Raphael; Heng, Kevin

2018-06-01

The use of machine learning is becoming ubiquitous in astronomy1-3, but remains rare in the study of the atmospheres of exoplanets. Given the spectrum of an exoplanetary atmosphere, a multi-parameter space is swept through in real time to find the best-fit model4-6. Known as atmospheric retrieval, this technique originates in the Earth and planetary sciences7. Such methods are very time-consuming, and by necessity there is a compromise between physical and chemical realism and computational feasibility. Machine learning has previously been used to determine which molecules to include in the model, but the retrieval itself was still performed using standard methods8. Here, we report an adaptation of the `random forest' method of supervised machine learning9,10, trained on a precomputed grid of atmospheric models, which retrieves full posterior distributions of the abundances of molecules and the cloud opacity. The use of a precomputed grid allows a large part of the computational burden to be shifted offline. We demonstrate our technique on a transmission spectrum of the hot gas-giant exoplanet WASP-12b using a five-parameter model (temperature, a constant cloud opacity and the volume mixing ratios or relative abundances of molecules of water, ammonia and hydrogen cyanide)11. We obtain results consistent with the standard nested-sampling retrieval method. We also estimate the sensitivity of the measured spectrum to the model parameters, and we are able to quantify the information content of the spectrum. Our method can be straightforwardly applied using more sophisticated atmospheric models to interpret an ensemble of spectra without having to retrain the random forest.

Machine-learning-based classification of real-time tissue elastography for hepatic fibrosis in patients with chronic hepatitis B.

PubMed

Chen, Yang; Luo, Yan; Huang, Wei; Hu, Die; Zheng, Rong-Qin; Cong, Shu-Zhen; Meng, Fan-Kun; Yang, Hong; Lin, Hong-Jun; Sun, Yan; Wang, Xiu-Yan; Wu, Tao; Ren, Jie; Pei, Shu-Fang; Zheng, Ying; He, Yun; Hu, Yu; Yang, Na; Yan, Hongmei

2017-10-01

Hepatic fibrosis is a common middle stage of the pathological processes of chronic liver diseases. Clinical intervention during the early stages of hepatic fibrosis can slow the development of liver cirrhosis and reduce the risk of developing liver cancer. Performing a liver biopsy, the gold standard for viral liver disease management, has drawbacks such as invasiveness and a relatively high sampling error rate. Real-time tissue elastography (RTE), one of the most recently developed technologies, might be promising imaging technology because it is both noninvasive and provides accurate assessments of hepatic fibrosis. However, determining the stage of liver fibrosis from RTE images in a clinic is a challenging task. In this study, in contrast to the previous liver fibrosis index (LFI) method, which predicts the stage of diagnosis using RTE images and multiple regression analysis, we employed four classical classifiers (i.e., Support Vector Machine, Naïve Bayes, Random Forest and K-Nearest Neighbor) to build a decision-support system to improve the hepatitis B stage diagnosis performance. Eleven RTE image features were obtained from 513 subjects who underwent liver biopsies in this multicenter collaborative research. The experimental results showed that the adopted classifiers significantly outperformed the LFI method and that the Random Forest(RF) classifier provided the highest average accuracy among the four machine algorithms. This result suggests that sophisticated machine-learning methods can be powerful tools for evaluating the stage of hepatic fibrosis and show promise for clinical applications. Copyright © 2017 Elsevier Ltd. All rights reserved.

Integrating human and machine intelligence in galaxy morphology classification tasks

NASA Astrophysics Data System (ADS)

Beck, Melanie R.; Scarlata, Claudia; Fortson, Lucy F.; Lintott, Chris J.; Simmons, B. D.; Galloway, Melanie A.; Willett, Kyle W.; Dickinson, Hugh; Masters, Karen L.; Marshall, Philip J.; Wright, Darryl

2018-06-01

Quantifying galaxy morphology is a challenging yet scientifically rewarding task. As the scale of data continues to increase with upcoming surveys, traditional classification methods will struggle to handle the load. We present a solution through an integration of visual and automated classifications, preserving the best features of both human and machine. We demonstrate the effectiveness of such a system through a re-analysis of visual galaxy morphology classifications collected during the Galaxy Zoo 2 (GZ2) project. We reprocess the top-level question of the GZ2 decision tree with a Bayesian classification aggregation algorithm dubbed SWAP, originally developed for the Space Warps gravitational lens project. Through a simple binary classification scheme, we increase the classification rate nearly 5-fold classifying 226 124 galaxies in 92 d of GZ2 project time while reproducing labels derived from GZ2 classification data with 95.7 per cent accuracy. We next combine this with a Random Forest machine learning algorithm that learns on a suite of non-parametric morphology indicators widely used for automated morphologies. We develop a decision engine that delegates tasks between human and machine and demonstrate that the combined system provides at least a factor of 8 increase in the classification rate, classifying 210 803 galaxies in just 32 d of GZ2 project time with 93.1 per cent accuracy. As the Random Forest algorithm requires a minimal amount of computational cost, this result has important implications for galaxy morphology identification tasks in the era of Euclid and other large-scale surveys.

Predicting diabetes mellitus using SMOTE and ensemble machine learning approach: The Henry Ford ExercIse Testing (FIT) project.

PubMed

Alghamdi, Manal; Al-Mallah, Mouaz; Keteyian, Steven; Brawner, Clinton; Ehrman, Jonathan; Sakr, Sherif

2017-01-01

Machine learning is becoming a popular and important approach in the field of medical research. In this study, we investigate the relative performance of various machine learning methods such as Decision Tree, Naïve Bayes, Logistic Regression, Logistic Model Tree and Random Forests for predicting incident diabetes using medical records of cardiorespiratory fitness. In addition, we apply different techniques to uncover potential predictors of diabetes. This FIT project study used data of 32,555 patients who are free of any known coronary artery disease or heart failure who underwent clinician-referred exercise treadmill stress testing at Henry Ford Health Systems between 1991 and 2009 and had a complete 5-year follow-up. At the completion of the fifth year, 5,099 of those patients have developed diabetes. The dataset contained 62 attributes classified into four categories: demographic characteristics, disease history, medication use history, and stress test vital signs. We developed an Ensembling-based predictive model using 13 attributes that were selected based on their clinical importance, Multiple Linear Regression, and Information Gain Ranking methods. The negative effect of the imbalance class of the constructed model was handled by Synthetic Minority Oversampling Technique (SMOTE). The overall performance of the predictive model classifier was improved by the Ensemble machine learning approach using the Vote method with three Decision Trees (Naïve Bayes Tree, Random Forest, and Logistic Model Tree) and achieved high accuracy of prediction (AUC = 0.92). The study shows the potential of ensembling and SMOTE approaches for predicting incident diabetes using cardiorespiratory fitness data.

24 CFR 583.310 - Displacement, relocation, and acquisition.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 24 Housing and Urban Development 3 2010-04-01 2010-04-01 false Displacement, relocation, and....310 Displacement, relocation, and acquisition. (a) Minimizing displacement. Consistent with the other... minimize the displacement of persons (families, individuals, businesses, nonprofit organizations, and farms...

24 CFR 578.83 - Displacement, relocation, and acquisition.

Code of Federal Regulations, 2014 CFR

2014-04-01

... 24 Housing and Urban Development 3 2014-04-01 2013-04-01 true Displacement, relocation, and... Displacement, relocation, and acquisition. (a) Minimizing displacement. Consistent with the other goals and... to minimize the displacement of persons (families, individuals, businesses, nonprofit organizations...

24 CFR 236.1001 - Displacement, relocation, and acquisition.

Code of Federal Regulations, 2012 CFR

2012-04-01

... 24 Housing and Urban Development 2 2012-04-01 2012-04-01 false Displacement, relocation, and... Assistance § 236.1001 Displacement, relocation, and acquisition. (a) Minimizing displacement. Consistent with... reasonable steps to minimize the displacement of persons (households, businesses, nonprofit organizations...

24 CFR 92.353 - Displacement, relocation, and acquisition.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 24 Housing and Urban Development 1 2011-04-01 2011-04-01 false Displacement, relocation, and... Displacement, relocation, and acquisition. (a) Minimizing displacement. Consistent with the other goals and... minimize the displacement of persons (families, individuals, businesses, nonprofit organizations, and farms...

24 CFR 236.1001 - Displacement, relocation, and acquisition.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 24 Housing and Urban Development 2 2011-04-01 2011-04-01 false Displacement, relocation, and... Assistance § 236.1001 Displacement, relocation, and acquisition. (a) Minimizing displacement. Consistent with... reasonable steps to minimize the displacement of persons (households, businesses, nonprofit organizations...

24 CFR 583.310 - Displacement, relocation, and acquisition.

Code of Federal Regulations, 2012 CFR

2012-04-01

... 24 Housing and Urban Development 3 2012-04-01 2012-04-01 false Displacement, relocation, and....310 Displacement, relocation, and acquisition. (a) Minimizing displacement. Consistent with the other... minimize the displacement of persons (families, individuals, businesses, nonprofit organizations, and farms...

24 CFR 92.353 - Displacement, relocation, and acquisition.

Code of Federal Regulations, 2013 CFR

2013-04-01

... 24 Housing and Urban Development 1 2013-04-01 2013-04-01 false Displacement, relocation, and... Displacement, relocation, and acquisition. (a) Minimizing displacement. Consistent with the other goals and... minimize the displacement of persons (families, individuals, businesses, nonprofit organizations, and farms...

24 CFR 92.353 - Displacement, relocation, and acquisition.

Code of Federal Regulations, 2012 CFR

2012-04-01

... 24 Housing and Urban Development 1 2012-04-01 2012-04-01 false Displacement, relocation, and... Displacement, relocation, and acquisition. (a) Minimizing displacement. Consistent with the other goals and... minimize the displacement of persons (families, individuals, businesses, nonprofit organizations, and farms...

24 CFR 578.83 - Displacement, relocation, and acquisition.

Code of Federal Regulations, 2013 CFR

2013-04-01

... 24 Housing and Urban Development 3 2013-04-01 2013-04-01 false Displacement, relocation, and... Displacement, relocation, and acquisition. (a) Minimizing displacement. Consistent with the other goals and... to minimize the displacement of persons (families, individuals, businesses, nonprofit organizations...

24 CFR 92.353 - Displacement, relocation, and acquisition.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 24 Housing and Urban Development 1 2010-04-01 2010-04-01 false Displacement, relocation, and... Displacement, relocation, and acquisition. (a) Minimizing displacement. Consistent with the other goals and... minimize the displacement of persons (families, individuals, businesses, nonprofit organizations, and farms...

24 CFR 236.1001 - Displacement, relocation, and acquisition.

Code of Federal Regulations, 2014 CFR

2014-04-01

... 24 Housing and Urban Development 2 2014-04-01 2014-04-01 false Displacement, relocation, and... Assistance § 236.1001 Displacement, relocation, and acquisition. (a) Minimizing displacement. Consistent with... reasonable steps to minimize the displacement of persons (households, businesses, nonprofit organizations...

24 CFR 583.310 - Displacement, relocation, and acquisition.

Code of Federal Regulations, 2013 CFR

2013-04-01

... 24 Housing and Urban Development 3 2013-04-01 2013-04-01 false Displacement, relocation, and....310 Displacement, relocation, and acquisition. (a) Minimizing displacement. Consistent with the other... minimize the displacement of persons (families, individuals, businesses, nonprofit organizations, and farms...

24 CFR 583.310 - Displacement, relocation, and acquisition.

Code of Federal Regulations, 2014 CFR

2014-04-01

... 24 Housing and Urban Development 3 2014-04-01 2013-04-01 true Displacement, relocation, and....310 Displacement, relocation, and acquisition. (a) Minimizing displacement. Consistent with the other... minimize the displacement of persons (families, individuals, businesses, nonprofit organizations, and farms...

24 CFR 236.1001 - Displacement, relocation, and acquisition.

Code of Federal Regulations, 2013 CFR

2013-04-01

... 24 Housing and Urban Development 2 2013-04-01 2013-04-01 false Displacement, relocation, and... Assistance § 236.1001 Displacement, relocation, and acquisition. (a) Minimizing displacement. Consistent with... reasonable steps to minimize the displacement of persons (households, businesses, nonprofit organizations...

24 CFR 583.310 - Displacement, relocation, and acquisition.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 24 Housing and Urban Development 3 2011-04-01 2010-04-01 true Displacement, relocation, and....310 Displacement, relocation, and acquisition. (a) Minimizing displacement. Consistent with the other... minimize the displacement of persons (families, individuals, businesses, nonprofit organizations, and farms...

24 CFR 236.1001 - Displacement, relocation, and acquisition.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 24 Housing and Urban Development 2 2010-04-01 2010-04-01 false Displacement, relocation, and... Assistance § 236.1001 Displacement, relocation, and acquisition. (a) Minimizing displacement. Consistent with... reasonable steps to minimize the displacement of persons (households, businesses, nonprofit organizations...

24 CFR 92.353 - Displacement, relocation, and acquisition.

Code of Federal Regulations, 2014 CFR

2014-04-01

... 24 Housing and Urban Development 1 2014-04-01 2014-04-01 false Displacement, relocation, and... Displacement, relocation, and acquisition. (a) Minimizing displacement. Consistent with the other goals and... minimize the displacement of persons (families, individuals, businesses, nonprofit organizations, and farms...

MODIS Based Estimation of Forest Aboveground Biomass in China.

PubMed

Yin, Guodong; Zhang, Yuan; Sun, Yan; Wang, Tao; Zeng, Zhenzhong; Piao, Shilong

2015-01-01

Accurate estimation of forest biomass C stock is essential to understand carbon cycles. However, current estimates of Chinese forest biomass are mostly based on inventory-based timber volumes and empirical conversion factors at the provincial scale, which could introduce large uncertainties in forest biomass estimation. Here we provide a data-driven estimate of Chinese forest aboveground biomass from 2001 to 2013 at a spatial resolution of 1 km by integrating a recently reviewed plot-level ground-measured forest aboveground biomass database with geospatial information from 1-km Moderate-Resolution Imaging Spectroradiometer (MODIS) dataset in a machine learning algorithm (the model tree ensemble, MTE). We show that Chinese forest aboveground biomass is 8.56 Pg C, which is mainly contributed by evergreen needle-leaf forests and deciduous broadleaf forests. The mean forest aboveground biomass density is 56.1 Mg C ha-1, with high values observed in temperate humid regions. The responses of forest aboveground biomass density to mean annual temperature are closely tied to water conditions; that is, negative responses dominate regions with mean annual precipitation less than 1300 mm y-1 and positive responses prevail in regions with mean annual precipitation higher than 2800 mm y-1. During the 2000s, the forests in China sequestered C by 61.9 Tg C y-1, and this C sink is mainly distributed in north China and may be attributed to warming climate, rising CO2 concentration, N deposition, and growth of young forests.

MODIS Based Estimation of Forest Aboveground Biomass in China

PubMed Central

Sun, Yan; Wang, Tao; Zeng, Zhenzhong; Piao, Shilong

2015-01-01

Accurate estimation of forest biomass C stock is essential to understand carbon cycles. However, current estimates of Chinese forest biomass are mostly based on inventory-based timber volumes and empirical conversion factors at the provincial scale, which could introduce large uncertainties in forest biomass estimation. Here we provide a data-driven estimate of Chinese forest aboveground biomass from 2001 to 2013 at a spatial resolution of 1 km by integrating a recently reviewed plot-level ground-measured forest aboveground biomass database with geospatial information from 1-km Moderate-Resolution Imaging Spectroradiometer (MODIS) dataset in a machine learning algorithm (the model tree ensemble, MTE). We show that Chinese forest aboveground biomass is 8.56 Pg C, which is mainly contributed by evergreen needle-leaf forests and deciduous broadleaf forests. The mean forest aboveground biomass density is 56.1 Mg C ha−1, with high values observed in temperate humid regions. The responses of forest aboveground biomass density to mean annual temperature are closely tied to water conditions; that is, negative responses dominate regions with mean annual precipitation less than 1300 mm y−1 and positive responses prevail in regions with mean annual precipitation higher than 2800 mm y−1. During the 2000s, the forests in China sequestered C by 61.9 Tg C y−1, and this C sink is mainly distributed in north China and may be attributed to warming climate, rising CO2 concentration, N deposition, and growth of young forests. PMID:26115195

Secondary Migration and Relocation Among African Refugee Families in the United States

PubMed Central

Weine, Stevan Merrill; Hoffman, Yael; Ware, Norma; Tugenberg, Toni; Hakizimana, Leonce; Dahnweigh, Gonwo; Currie, Madeleine; Wagner, Maureen

2014-01-01

The purpose of this study was to understand the secondary migration and relocation of African refugees resettled in the United States. Secondary migration refers to moves out of state, while relocation refers to moves within state. Of 73 recently resettled refugee families from Burundi and Liberia followed for 1 year through ethnographic interviews and observations, 13 instances of secondary migration and 9 instances of relocation were identified. A family ecodevelopmental framework was applied to address: Who moved again, why, and with what consequences? How did moving again impact family risk and protective factors? How might policies, researchers, and practitioners better manage refugees moving again? Findings indicated that families undertook secondary migration principally for employment, affordable housing, family reunification, and to feel more at home. Families relocated primarily for affordable housing. Parents reported that secondary migration and relocation enhanced family stability. Youth reported disruption to both schooling and attachments with peers and community. In conclusion, secondary migration and relocation were family efforts to enhance family and community protective resources and to mitigate shortcomings in resettlement conditions. Policymakers could provide newly resettled refugees jobs, better housing and family reunification. Practitioners could devise ways to better engage and support those families who consider moving. PMID:21361922

TH-CD-206-05: Machine-Learning Based Segmentation of Organs at Risks for Head and Neck Radiotherapy Planning

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ibragimov, B; Pernus, F; Strojan, P

Purpose: Accurate and efficient delineation of tumor target and organs-at-risks is essential for the success of radiotherapy. In reality, despite of decades of intense research efforts, auto-segmentation has not yet become clinical practice. In this study, we present, for the first time, a deep learning-based classification algorithm for autonomous segmentation in head and neck (HaN) treatment planning. Methods: Fifteen HN datasets of CT, MR and PET images with manual annotation of organs-at-risk (OARs) including spinal cord, brainstem, optic nerves, chiasm, eyes, mandible, tongue, parotid glands were collected and saved in a library of plans. We also have ten super-resolution MRmore » images of the tongue area, where the genioglossus and inferior longitudinalis tongue muscles are defined as organs of interest. We applied the concepts of random forest- and deep learning-based object classification for automated image annotation with the aim of using machine learning to facilitate head and neck radiotherapy planning process. In this new paradigm of segmentation, random forests were used for landmark-assisted segmentation of super-resolution MR images. Alternatively to auto-segmentation with random forest-based landmark detection, deep convolutional neural networks were developed for voxel-wise segmentation of OARs in single and multi-modal images. The network consisted of three pairs of convolution and pooing layer, one RuLU layer and a softmax layer. Results: We present a comprehensive study on using machine learning concepts for auto-segmentation of OARs and tongue muscles for the HaN radiotherapy planning. An accuracy of 81.8% in terms of Dice coefficient was achieved for segmentation of genioglossus and inferior longitudinalis tongue muscles. Preliminary results of OARs regimentation also indicate that deep-learning afforded an unprecedented opportunities to improve the accuracy and robustness of radiotherapy planning. Conclusion: A novel machine learning framework has been developed for image annotation and structure segmentation. Our results indicate the great potential of deep learning in radiotherapy treatment planning.« less

Cardiovascular Event Prediction by Machine Learning: The Multi-Ethnic Study of Atherosclerosis.

PubMed

Ambale-Venkatesh, Bharath; Yang, Xiaoying; Wu, Colin O; Liu, Kiang; Hundley, W Gregory; McClelland, Robyn; Gomes, Antoinette S; Folsom, Aaron R; Shea, Steven; Guallar, Eliseo; Bluemke, David A; Lima, João A C

2017-10-13

Machine learning may be useful to characterize cardiovascular risk, predict outcomes, and identify biomarkers in population studies. To test the ability of random survival forests, a machine learning technique, to predict 6 cardiovascular outcomes in comparison to standard cardiovascular risk scores. We included participants from the MESA (Multi-Ethnic Study of Atherosclerosis). Baseline measurements were used to predict cardiovascular outcomes over 12 years of follow-up. MESA was designed to study progression of subclinical disease to cardiovascular events where participants were initially free of cardiovascular disease. All 6814 participants from MESA, aged 45 to 84 years, from 4 ethnicities, and 6 centers across the United States were included. Seven-hundred thirty-five variables from imaging and noninvasive tests, questionnaires, and biomarker panels were obtained. We used the random survival forests technique to identify the top-20 predictors of each outcome. Imaging, electrocardiography, and serum biomarkers featured heavily on the top-20 lists as opposed to traditional cardiovascular risk factors. Age was the most important predictor for all-cause mortality. Fasting glucose levels and carotid ultrasonography measures were important predictors of stroke. Coronary Artery Calcium score was the most important predictor of coronary heart disease and all atherosclerotic cardiovascular disease combined outcomes. Left ventricular structure and function and cardiac troponin-T were among the top predictors for incident heart failure. Creatinine, age, and ankle-brachial index were among the top predictors of atrial fibrillation. TNF-α (tissue necrosis factor-α) and IL (interleukin)-2 soluble receptors and NT-proBNP (N-Terminal Pro-B-Type Natriuretic Peptide) levels were important across all outcomes. The random survival forests technique performed better than established risk scores with increased prediction accuracy (decreased Brier score by 10%-25%). Machine learning in conjunction with deep phenotyping improves prediction accuracy in cardiovascular event prediction in an initially asymptomatic population. These methods may lead to greater insights on subclinical disease markers without apriori assumptions of causality. URL: http://www.clinicaltrials.gov. Unique identifier: NCT00005487. © 2017 American Heart Association, Inc.

Can Simple Biophysical Principles Yield Complicated Biological Functions?

NASA Astrophysics Data System (ADS)

Liphardt, Jan

2011-03-01

About once a year, a new regulatory paradigm is discovered in cell biology. As of last count, eukaryotic cells have more than 40 distinct ways of regulating protein concentration and function. Regulatory possibilities include site-specific phosphorylation, epigenetics, alternative splicing, mRNA (re)localization, and modulation of nucleo-cytoplasmic transport. This raises a simple question. Do all the remarkable things cells do, require an intricately choreographed supporting cast of hundreds of molecular machines and associated signaling networks? Alternatively, are there a few simple biophysical principles that can generate apparently very complicated cellular behaviors and functions? I'll discuss two problems, spatial organization of the bacterial chemotaxis system and nucleo-cytoplasmic transport, where the latter might be true. In both cases, the ability to precisely quantify biological organization and function, at the single-molecule level, helped to find signatures of basic biological organizing principles.

24 CFR 582.335 - Displacement, relocation, and real property acquisition.

Code of Federal Regulations, 2012 CFR

2012-04-01

... 24 Housing and Urban Development 3 2012-04-01 2012-04-01 false Displacement, relocation, and real....335 Displacement, relocation, and real property acquisition. (a) Minimizing displacement. Consistent... reasonable steps to minimize the displacement of persons (families, individuals, businesses, nonprofit...

24 CFR 582.335 - Displacement, relocation, and real property acquisition.

Code of Federal Regulations, 2014 CFR

2014-04-01

... 24 Housing and Urban Development 3 2014-04-01 2013-04-01 true Displacement, relocation, and real....335 Displacement, relocation, and real property acquisition. (a) Minimizing displacement. Consistent... reasonable steps to minimize the displacement of persons (families, individuals, businesses, nonprofit...

24 CFR 582.335 - Displacement, relocation, and real property acquisition.

Code of Federal Regulations, 2013 CFR

2013-04-01

... 24 Housing and Urban Development 3 2013-04-01 2013-04-01 false Displacement, relocation, and real....335 Displacement, relocation, and real property acquisition. (a) Minimizing displacement. Consistent... reasonable steps to minimize the displacement of persons (families, individuals, businesses, nonprofit...

24 CFR 582.335 - Displacement, relocation, and real property acquisition.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 24 Housing and Urban Development 3 2011-04-01 2010-04-01 true Displacement, relocation, and real....335 Displacement, relocation, and real property acquisition. (a) Minimizing displacement. Consistent... reasonable steps to minimize the displacement of persons (families, individuals, businesses, nonprofit...

24 CFR 582.335 - Displacement, relocation, and real property acquisition.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 24 Housing and Urban Development 3 2010-04-01 2010-04-01 false Displacement, relocation, and real....335 Displacement, relocation, and real property acquisition. (a) Minimizing displacement. Consistent... reasonable steps to minimize the displacement of persons (families, individuals, businesses, nonprofit...

47 CFR 24.239 - Cost-sharing requirements for broadband PCS.

Code of Federal Regulations, 2013 CFR

2013-10-01

... SERVICES PERSONAL COMMUNICATIONS SERVICES Broadband PCS Policies Governing Microwave Relocation from the...) are required to relocate the existing Fixed Microwave Services (FMS) licensees in these bands if... by other PCS entities or a voluntarily relocating microwave incumbent, must contribute to such...

47 CFR 24.239 - Cost-sharing requirements for broadband PCS.

Code of Federal Regulations, 2014 CFR

2014-10-01

... SERVICES PERSONAL COMMUNICATIONS SERVICES Broadband PCS Policies Governing Microwave Relocation from the...) are required to relocate the existing Fixed Microwave Services (FMS) licensees in these bands if... by other PCS entities or a voluntarily relocating microwave incumbent, must contribute to such...

47 CFR 24.239 - Cost-sharing requirements for broadband PCS.

Code of Federal Regulations, 2010 CFR

2010-10-01

... SERVICES PERSONAL COMMUNICATIONS SERVICES Broadband PCS Policies Governing Microwave Relocation from the...) are required to relocate the existing Fixed Microwave Services (FMS) licensees in these bands if... by other PCS entities or a voluntarily relocating microwave incumbent, must contribute to such...

47 CFR 24.239 - Cost-sharing requirements for broadband PCS.

Code of Federal Regulations, 2011 CFR

2011-10-01

... SERVICES PERSONAL COMMUNICATIONS SERVICES Broadband PCS Policies Governing Microwave Relocation from the...) are required to relocate the existing Fixed Microwave Services (FMS) licensees in these bands if... by other PCS entities or a voluntarily relocating microwave incumbent, must contribute to such...

Residential relocation and change in social capital: A natural experiment from the 2011 Great East Japan Earthquake and Tsunami

PubMed Central

Hikichi, Hiroyuki; Sawada, Yasuyuki; Tsuboya, Toru; Aida, Jun; Kondo, Katsunori; Koyama, Shihoko; Kawachi, Ichiro

2017-01-01

Social connections in the community (“social capital”) represent an important source of resilience in the aftermath of major disasters. However, little is known about how residential relocation due to housing destruction affects survivors’ social capital. We examined changes in social capital among survivors of the 2011 Great East Japan Earthquake and Tsunami. People who lost their homes were resettled to new locations by two primary means: (i) group relocation to public temporary trailer housing or (ii) individual relocation, in which victims moved into government-provided housing by lottery or arranged for their own accommodation (market rental housing or private purchase/new construction). The baseline for our natural experiment was established 7 months before the 11 March 2011 disaster, when we conducted a survey of older community-dwelling adults who lived 80-km west of the earthquake epicenter. Approximately 2.5 years after the disaster, the follow-up survey gathered information about personal experiences of disaster as well as health status and social capital. Among 3421 people in our study, 79 people moved via group relocation to public temporary trailer housing, whereas 96 people moved on their own. The individual fixed-effects model showed that group relocation was associated with improved informal socializing and social participation (β coefficient = 0.053, 95% confidence interval: 0.011 to 0.095). In contrast, individual relocation was associated with declining informal socializing and social participation (β coefficient = −0.039, 95% confidence interval: −0.074 to −0.003). Group relocation, as compared to individual relocation, appeared to preserve social participation and informal socializing in the community. PMID:28782024

An Updated Earthquake Relocation Catalog for the Island of Hawaíi from 2009 to 2016

NASA Astrophysics Data System (ADS)

Lin, G.; Okubo, P.; Shearer, P. M.; Matoza, R. S.

2017-12-01

We present an updated catalog of Hawaiian seismicity, systematically relocated from a starting catalog compiled by the Hawaiian Volcano Observatory (HVO). This is a continuation of our collaboration that began with relocating Hawaiian seismicity from 1992 through April 2009 and subsequently added 1986 through 1991, all initially processed with HVO's Caltech-USGS Seismic Processing systems. Our current efforts are initially focused on extending waveform cross-correlation analyses to significantly greater numbers of candidate event pairs of earthquakes recorded since 2009, after HVO migrated to its ANSS Quake Management Software (AQMS) systems. In its roughly 8 years of AQMS processing, HVO has cataloged over 170,000 events. Particular challenges with this more recent dataset relate to field network upgrades that introduced numerous broadband sensors to replace short-period instruments and significantly increased numbers of event triggers. A relatively low percentage of interactively-reviewed events compared to the pre-2009 catalogs also presents a significant challenge to our analysis. We start by ray tracing through a previously developed three-dimensional (3-D) seismic velocity model to relocate all the earthquakes with phase arrivals. We then use these 3-D relocated events, with improved absolute locations, as reference events to perform similar-event cluster analysis and differential-time relative relocation to all the available events in the data set. The resulting catalog of relocated, well-constrained hypocenters is an extension of our previous studies. Combined with earlier products of our systematic catalog relocations, the increased numbers of relocated earthquakes from more than 30 years of seismic monitoring offer enhanced opportunities for study and interpretation of seismic and volcanic processes spanning the entire 1986-2016 interval.

Prediction of antiepileptic drug treatment outcomes using machine learning.

PubMed

Colic, Sinisa; Wither, Robert G; Lang, Min; Zhang, Liang; Eubanks, James H; Bardakjian, Berj L

2017-02-01

Antiepileptic drug (AED) treatments produce inconsistent outcomes, often necessitating patients to go through several drug trials until a successful treatment can be found. This study proposes the use of machine learning techniques to predict epilepsy treatment outcomes of commonly used AEDs. Machine learning algorithms were trained and evaluated using features obtained from intracranial electroencephalogram (iEEG) recordings of the epileptiform discharges observed in Mecp2-deficient mouse model of the Rett Syndrome. Previous work have linked the presence of cross-frequency coupling (I CFC ) of the delta (2-5 Hz) rhythm with the fast ripple (400-600 Hz) rhythm in epileptiform discharges. Using the I CFC to label post-treatment outcomes we compared support vector machines (SVMs) and random forest (RF) machine learning classifiers for providing likelihood scores of successful treatment outcomes. (a) There was heterogeneity in AED treatment outcomes, (b) machine learning techniques could be used to rank the efficacy of AEDs by estimating likelihood scores for successful treatment outcome, (c) I CFC features yielded the most effective a priori identification of appropriate AED treatment, and (d) both classifiers performed comparably. Machine learning approaches yielded predictions of successful drug treatment outcomes which in turn could reduce the burdens of drug trials and lead to substantial improvements in patient quality of life.

Prediction of antiepileptic drug treatment outcomes using machine learning

NASA Astrophysics Data System (ADS)

Colic, Sinisa; Wither, Robert G.; Lang, Min; Zhang, Liang; Eubanks, James H.; Bardakjian, Berj L.

2017-02-01

Objective. Antiepileptic drug (AED) treatments produce inconsistent outcomes, often necessitating patients to go through several drug trials until a successful treatment can be found. This study proposes the use of machine learning techniques to predict epilepsy treatment outcomes of commonly used AEDs. Approach. Machine learning algorithms were trained and evaluated using features obtained from intracranial electroencephalogram (iEEG) recordings of the epileptiform discharges observed in Mecp2-deficient mouse model of the Rett Syndrome. Previous work have linked the presence of cross-frequency coupling (I CFC) of the delta (2-5 Hz) rhythm with the fast ripple (400-600 Hz) rhythm in epileptiform discharges. Using the I CFC to label post-treatment outcomes we compared support vector machines (SVMs) and random forest (RF) machine learning classifiers for providing likelihood scores of successful treatment outcomes. Main results. (a) There was heterogeneity in AED treatment outcomes, (b) machine learning techniques could be used to rank the efficacy of AEDs by estimating likelihood scores for successful treatment outcome, (c) I CFC features yielded the most effective a priori identification of appropriate AED treatment, and (d) both classifiers performed comparably. Significance. Machine learning approaches yielded predictions of successful drug treatment outcomes which in turn could reduce the burdens of drug trials and lead to substantial improvements in patient quality of life.

Disruption prediction investigations using Machine Learning tools on DIII-D and Alcator C-Mod

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rea, C.; Granetz, R. S.; Montes, K.

Using data-driven methodology, we exploit the time series of relevant plasma parameters for a large set of disrupted and non-disrupted discharges to develop a classification algorithm for detecting disruptive phases in shots that eventually disrupt. Comparing the same methodology on different devices is crucial in order to have information on the portability of the developed algorithm and the possible extrapolation to ITER. Therefore, we use data from two very different tokamaks, DIII-D and Alcator C-Mod. We then focus on a subset of disruption predictors, most of which are dimensionless and/or machine-independent parameters, coming from both plasma diagnostics and equilibrium reconstructions,more » such as the normalized plasma internal inductance ℓ and the n = 1 mode amplitude normalized to the toroidal magnetic field. Using such dimensionless indicators facilitates a more direct comparison between DIII-D and C-Mod. We then choose a shallow Machine Learning technique, called Random Forests, to explore the databases available for the two devices. We show results from the classification task, where we introduce a time dependency through the definition of class labels on the basis of the elapsed time before the disruption (i.e. ‘far from a disruption’ and ‘close to a disruption’). The performances of the different Random Forest classifiers are discussed in terms of several metrics, by showing the number of successfully detected samples, as well as the misclassifications. The overall model accuracies are above 97% when identifying a ‘far from disruption’ and a ‘disruptive’ phase for disrupted discharges. Nevertheless, the Forests are intrinsically different in their capability of predicting a disruptive behavior, with C-Mod predictions comparable to random guesses. Indeed, we show that C-Mod recall index, i.e. the sensitivity to a disruptive behavior, is as low as 0.47, while DIII-D recall is ~0.72. The portability of the developed algorithm is also tested across the two devices, by using DIII-D data for training the forests and C-Mod for testing and vice versa.« less

Fangorn Forest (F2): a machine learning approach to classify genes and genera in the family Geminiviridae.

PubMed

Silva, José Cleydson F; Carvalho, Thales F M; Fontes, Elizabeth P B; Cerqueira, Fabio R

2017-09-30

Geminiviruses infect a broad range of cultivated and non-cultivated plants, causing significant economic losses worldwide. The studies of the diversity of species, taxonomy, mechanisms of evolution, geographic distribution, and mechanisms of interaction of these pathogens with the host have greatly increased in recent years. Furthermore, the use of rolling circle amplification (RCA) and advanced metagenomics approaches have enabled the elucidation of viromes and the identification of many viral agents in a large number of plant species. As a result, determining the nomenclature and taxonomically classifying geminiviruses turned into complex tasks. In addition, the gene responsible for viral replication (particularly, the viruses belonging to the genus Mastrevirus) may be spliced due to the use of the transcriptional/splicing machinery in the host cells. However, the current tools have limitations concerning the identification of introns. This study proposes a new method, designated Fangorn Forest (F2), based on machine learning approaches to classify genera using an ab initio approach, i.e., using only the genomic sequence, as well as to predict and classify genes in the family Geminiviridae. In this investigation, nine genera of the family Geminiviridae and their related satellite DNAs were selected. We obtained two training sets, one for genus classification, containing attributes extracted from the complete genome of geminiviruses, while the other was made up to classify geminivirus genes, containing attributes extracted from ORFs taken from the complete genomes cited above. Three ML algorithms were applied on those datasets to build the predictive models: support vector machines, using the sequential minimal optimization training approach, random forest (RF), and multilayer perceptron. RF demonstrated a very high predictive power, achieving 0.966, 0.964, and 0.995 of precision, recall, and area under the curve (AUC), respectively, for genus classification. For gene classification, RF could reach 0.983, 0.983, and 0.998 of precision, recall, and AUC, respectively. Therefore, Fangorn Forest is proven to be an efficient method for classifying genera of the family Geminiviridae with high precision and effective gene prediction and classification. The method is freely accessible at www.geminivirus.org:8080/geminivirusdw/discoveryGeminivirus.jsp .

Disruption prediction investigations using Machine Learning tools on DIII-D and Alcator C-Mod

DOE PAGES

Rea, C.; Granetz, R. S.; Montes, K.; ...

2018-06-18

Using data-driven methodology, we exploit the time series of relevant plasma parameters for a large set of disrupted and non-disrupted discharges to develop a classification algorithm for detecting disruptive phases in shots that eventually disrupt. Comparing the same methodology on different devices is crucial in order to have information on the portability of the developed algorithm and the possible extrapolation to ITER. Therefore, we use data from two very different tokamaks, DIII-D and Alcator C-Mod. We then focus on a subset of disruption predictors, most of which are dimensionless and/or machine-independent parameters, coming from both plasma diagnostics and equilibrium reconstructions,more » such as the normalized plasma internal inductance ℓ and the n = 1 mode amplitude normalized to the toroidal magnetic field. Using such dimensionless indicators facilitates a more direct comparison between DIII-D and C-Mod. We then choose a shallow Machine Learning technique, called Random Forests, to explore the databases available for the two devices. We show results from the classification task, where we introduce a time dependency through the definition of class labels on the basis of the elapsed time before the disruption (i.e. ‘far from a disruption’ and ‘close to a disruption’). The performances of the different Random Forest classifiers are discussed in terms of several metrics, by showing the number of successfully detected samples, as well as the misclassifications. The overall model accuracies are above 97% when identifying a ‘far from disruption’ and a ‘disruptive’ phase for disrupted discharges. Nevertheless, the Forests are intrinsically different in their capability of predicting a disruptive behavior, with C-Mod predictions comparable to random guesses. Indeed, we show that C-Mod recall index, i.e. the sensitivity to a disruptive behavior, is as low as 0.47, while DIII-D recall is ~0.72. The portability of the developed algorithm is also tested across the two devices, by using DIII-D data for training the forests and C-Mod for testing and vice versa.« less

47 CFR 0.383 - Emergency Relocation Board, authority delegated.

Code of Federal Regulations, 2010 CFR

2010-10-01

... ORGANIZATION Delegations of Authority National Security and Emergency Preparedness Delegations § 0.383 Emergency Relocation Board, authority delegated. (a) During any period in which the Commission is unable to... 47 Telecommunication 1 2010-10-01 2010-10-01 false Emergency Relocation Board, authority delegated...

25 CFR 700.1 - Purpose.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 25 Indians 2 2010-04-01 2010-04-01 false Purpose. 700.1 Section 700.1 Indians THE OFFICE OF NAVAJO AND HOPI INDIAN RELOCATION COMMISSION OPERATIONS AND RELOCATION PROCEDURES General Policies and... the disproportionate adverse, social, economic, cultural and other impacts of relocation. (b) To set...

24 CFR 576.408 - Displacement, relocation, and acquisition.

Code of Federal Regulations, 2014 CFR

2014-04-01

... 24 Housing and Urban Development 3 2014-04-01 2013-04-01 true Displacement, relocation, and... § 576.408 Displacement, relocation, and acquisition. (a) Minimizing displacement. Consistent with the... assure that they have taken all reasonable steps to minimize the displacement of persons (families...

24 CFR 941.207 - Displacement, relocation, and acquisition.

Code of Federal Regulations, 2013 CFR

2013-04-01

... 24 Housing and Urban Development 4 2013-04-01 2013-04-01 false Displacement, relocation, and... Displacement, relocation, and acquisition. (a) Minimizing displacement. Consistent with the other goals and... displacement of persons (households, businesses, nonprofit organizations, and farms) as a result of a project...

24 CFR 882.810 - Displacement, relocation, and acquisition.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 24 Housing and Urban Development 4 2011-04-01 2011-04-01 false Displacement, relocation, and... Rehabilitation Single Room Occupancy Program for Homeless Individuals § 882.810 Displacement, relocation, and acquisition. (a) Minimizing displacement. (1) Consistent with the other goals and objectives of this part...

24 CFR 941.207 - Displacement, relocation, and acquisition.

Code of Federal Regulations, 2012 CFR

2012-04-01

... 24 Housing and Urban Development 4 2012-04-01 2012-04-01 false Displacement, relocation, and... Displacement, relocation, and acquisition. (a) Minimizing displacement. Consistent with the other goals and... displacement of persons (households, businesses, nonprofit organizations, and farms) as a result of a project...

24 CFR 882.810 - Displacement, relocation, and acquisition.

Code of Federal Regulations, 2014 CFR

2014-04-01

... 24 Housing and Urban Development 4 2014-04-01 2014-04-01 false Displacement, relocation, and... Rehabilitation Single Room Occupancy Program for Homeless Individuals § 882.810 Displacement, relocation, and acquisition. (a) Minimizing displacement. (1) Consistent with the other goals and objectives of this part...

24 CFR 576.408 - Displacement, relocation, and acquisition.

Code of Federal Regulations, 2012 CFR

2012-04-01

... 24 Housing and Urban Development 3 2012-04-01 2012-04-01 false Displacement, relocation, and... § 576.408 Displacement, relocation, and acquisition. (a) Minimizing displacement. Consistent with the... assure that they have taken all reasonable steps to minimize the displacement of persons (families...

24 CFR 882.810 - Displacement, relocation, and acquisition.

Code of Federal Regulations, 2012 CFR

2012-04-01

... 24 Housing and Urban Development 4 2012-04-01 2012-04-01 false Displacement, relocation, and... Rehabilitation Single Room Occupancy Program for Homeless Individuals § 882.810 Displacement, relocation, and acquisition. (a) Minimizing displacement. (1) Consistent with the other goals and objectives of this part...

24 CFR 941.207 - Displacement, relocation, and acquisition.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 24 Housing and Urban Development 4 2011-04-01 2011-04-01 false Displacement, relocation, and... Displacement, relocation, and acquisition. (a) Minimizing displacement. Consistent with the other goals and... displacement of persons (households, businesses, nonprofit organizations, and farms) as a result of a project...

24 CFR 882.810 - Displacement, relocation, and acquisition.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 24 Housing and Urban Development 4 2010-04-01 2010-04-01 false Displacement, relocation, and... Rehabilitation Single Room Occupancy Program for Homeless Individuals § 882.810 Displacement, relocation, and acquisition. (a) Minimizing displacement. (1) Consistent with the other goals and objectives of this part...

24 CFR 576.408 - Displacement, relocation, and acquisition.

Code of Federal Regulations, 2013 CFR

2013-04-01

... 24 Housing and Urban Development 3 2013-04-01 2013-04-01 false Displacement, relocation, and... § 576.408 Displacement, relocation, and acquisition. (a) Minimizing displacement. Consistent with the... assure that they have taken all reasonable steps to minimize the displacement of persons (families...

24 CFR 882.810 - Displacement, relocation, and acquisition.

Code of Federal Regulations, 2013 CFR

2013-04-01

... 24 Housing and Urban Development 4 2013-04-01 2013-04-01 false Displacement, relocation, and... Rehabilitation Single Room Occupancy Program for Homeless Individuals § 882.810 Displacement, relocation, and acquisition. (a) Minimizing displacement. (1) Consistent with the other goals and objectives of this part...

24 CFR 941.207 - Displacement, relocation, and acquisition.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 24 Housing and Urban Development 4 2010-04-01 2010-04-01 false Displacement, relocation, and... Displacement, relocation, and acquisition. (a) Minimizing displacement. Consistent with the other goals and... displacement of persons (households, businesses, nonprofit organizations, and farms) as a result of a project...

Individual relocation decisions after tornadoes: a multi-level analysis.

PubMed

Cong, Zhen; Nejat, Ali; Liang, Daan; Pei, Yaolin; Javid, Roxana J

2018-04-01

This study examines how multi-level factors affected individuals' relocation decisions after EF4 and EF5 (Enhanced Fujita Tornado Intensity Scale) tornadoes struck the United States in 2013. A telephone survey was conducted with 536 respondents, including oversampled older adults, one year after these two disaster events. Respondents' addresses were used to associate individual information with block group-level variables recorded by the American Community Survey. Logistic regression revealed that residential damage and homeownership are important predictors of relocation. There was also significant interaction between these two variables, indicating less difference between homeowners and renters at higher damage levels. Homeownership diminished the likelihood of relocation among younger respondents. Random effects logistic regression found that the percentage of homeownership and of higher income households in the community buffered the effect of damage on relocation; the percentage of older adults reduced the likelihood of this group relocating. The findings are assessed from the standpoint of age difference, policy implications, and social capital and vulnerability. © 2018 The Author(s). Disasters © Overseas Development Institute, 2018.

Introducing the Japan Unified HIgh-Resolution Relocated Catalog for Earthquakes (JUICE) Project

NASA Astrophysics Data System (ADS)

Yano, T. E.; Takeda, T.; Shiomi, K.

2013-12-01

To understand the tectonic processes, seismogenic zones, and active fault evaluations, the precise location of earthquake hypocenters is necessary. Routinely determined hypocenters typically have uncertainties that can make seismically active areas appear more diffuse. These uncertainties influence the interpretation of what are active faults. Objective of this Japan Unified HIgh-resolution Relocated Catalog for Earthquakes (JUICE) project is to create a high-resolution earthquake relocated catalog for all of Japan. To initiate the project, we relocate hypocenters around Kanto-Tokai region. The network geometry, available phases, arrival-time reading accuracy, and knowledge of crustal structure control the accuracy of absolute hypocenter locations (Pavlis, 1986; Gomberg et al., 1990). We take advantage of having an excellent network operated by NIED Hi-net team. We use the high-quality data from this network for events from 2001 to the present. To initiate the JUICE project, we utilize more than 5,500,000 and 5,300,000 P and S phase arrival-time readings (catalog data) and waveforms for about 120,000 events between M0 and M6.5 from 2001 through 2012 in the Kanto and Tokai region. To reduce uncertainties, we apply the double-difference algorithm (hypoDD) by Waldhauser and Ellsworth (2000) to the data. To obtain the travel time differences for the pairs of earthquakes, we cross correlate the seismograms at the stations, which produces another data set -- cross-correlation data. In addition to the catalog phase data, we add 800,000 and 1,000,000 of P and S phase cross-correlations that are used to relocate hypocenters. We use Hi-net routine velocity structure (Ukawa et al., 1984) to estimate theoretical differential travel times. The newly relocated hypocenters show tighter clusters and lineaments compared to the routinely generated hypocenters. Figure 1 (a) shows the hypocenters in the Shizuoka region before relocation and (b) shows the hypocenters after relocation. Particularly, more compact clusters and lineaments clearly appear in the Shizuoka region after relocation. Significant changes are indicated in red circles and arrows for clusters and lineaments, respectively This relocated catalog will contribute to a better understanding of the depth of seismogenic zone and the mechanism for earthquakes. Because relocated hypocenters reflect the thickness of the seismogenic zone more accurately (Hauksson et al., 2012), they are more easily related to other data sets, such as geodetic, geological, gravity, and stress field measurements. We will continue expanding the area of study to relocate events all over Japan. We will apply 3D velocity model in future updated JUICE catalog to complete this project. Figure 1 (a): Map of hypocenters routinely determined by NIED Hi-net. (b): An example of hypocenters after relocations. Significant changes are indicated in red circles/arrows for clusters/lineaments.

Forest tree species clssification based on airborne hyper-spectral imagery

NASA Astrophysics Data System (ADS)

Dian, Yuanyong; Li, Zengyuan; Pang, Yong

2013-10-01

Forest precision classification products were the basic data for surveying of forest resource, updating forest subplot information, logging and design of forest. However, due to the diversity of stand structure, complexity of the forest growth environment, it's difficult to discriminate forest tree species using multi-spectral image. The airborne hyperspectral images can achieve the high spatial and spectral resolution imagery of forest canopy, so it will good for tree species level classification. The aim of this paper was to test the effective of combining spatial and spectral features in airborne hyper-spectral image classification. The CASI hyper spectral image data were acquired from Liangshui natural reserves area. Firstly, we use the MNF (minimum noise fraction) transform method for to reduce the hyperspectral image dimensionality and highlighting variation. And secondly, we use the grey level co-occurrence matrix (GLCM) to extract the texture features of forest tree canopy from the hyper-spectral image, and thirdly we fused the texture and the spectral features of forest canopy to classify the trees species using support vector machine (SVM) with different kernel functions. The results showed that when using the SVM classifier, MNF and texture-based features combined with linear kernel function can achieve the best overall accuracy which was 85.92%. It was also confirm that combine the spatial and spectral information can improve the accuracy of tree species classification.

Relocation: Its Effect on Health, Functioning and Mortality.

ERIC Educational Resources Information Center

Borup, Jerry H.; And Others

1980-01-01

Relocation of older patients had a positive effect on hypochondria, stamina, hygiene, and daily functioning but no effect on health status. Self-health assessments, stamina, hypochondria, and hygiene had no effect on the mortality rate of relocated patients, but daily functioning did effect the mortality rate. (Author)

48 CFR 31.205-35 - Relocation costs.

Code of Federal Regulations, 2010 CFR

2010-10-01

... paragraphs (b) and (f) of this subsection: (1) Costs of travel of the employee and members of the employee's... residences times the current balance of the old mortgage times 3 years. (ii) When mortgage differential... actual time of the relocation. (8) Rental differential payments covering situations where relocated...

24 CFR 574.630 - Displacement, relocation and real property acquisition.

Code of Federal Regulations, 2013 CFR

2013-04-01

... 24 Housing and Urban Development 3 2013-04-01 2013-04-01 false Displacement, relocation and real... Other Federal Requirements § 574.630 Displacement, relocation and real property acquisition. (a) Minimizing displacement. Consistent with the other goals and objectives of this part, grantees and project...

24 CFR 570.606 - Displacement, relocation, acquisition, and replacement of housing.

Code of Federal Regulations, 2012 CFR

2012-04-01

... 24 Housing and Urban Development 3 2012-04-01 2012-04-01 false Displacement, relocation... DEVELOPMENT BLOCK GRANTS Other Program Requirements § 570.606 Displacement, relocation, acquisition, and replacement of housing. (a) General policy for minimizing displacement. Consistent with the other goals and...

24 CFR 574.630 - Displacement, relocation and real property acquisition.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 24 Housing and Urban Development 3 2010-04-01 2010-04-01 false Displacement, relocation and real... Other Federal Requirements § 574.630 Displacement, relocation and real property acquisition. (a) Minimizing displacement. Consistent with the other goals and objectives of this part, grantees and project...

24 CFR 886.138 - Displacement, relocation, and acquisition.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 24 Housing and Urban Development 4 2011-04-01 2011-04-01 false Displacement, relocation, and... Additional Assistance Program for Projects With HUD-Insured and HUD-Held Mortgages § 886.138 Displacement, relocation, and acquisition. (a) Minimizing displacement. Consistent with the other goals and objectives of...

24 CFR 886.138 - Displacement, relocation, and acquisition.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 24 Housing and Urban Development 4 2010-04-01 2010-04-01 false Displacement, relocation, and... Additional Assistance Program for Projects With HUD-Insured and HUD-Held Mortgages § 886.138 Displacement, relocation, and acquisition. (a) Minimizing displacement. Consistent with the other goals and objectives of...

24 CFR 968.108 - Displacement, relocation, and real property acquisition.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 24 Housing and Urban Development 4 2010-04-01 2010-04-01 false Displacement, relocation, and real..., DEPARTMENT OF HOUSING AND URBAN DEVELOPMENT PUBLIC HOUSING MODERNIZATION General § 968.108 Displacement, relocation, and real property acquisition. (a) Minimizing displacement. Consistent with the other goals and...

24 CFR 886.338 - Displacement, relocation, and acquisition.

Code of Federal Regulations, 2013 CFR

2013-04-01

... 24 Housing and Urban Development 4 2013-04-01 2013-04-01 false Displacement, relocation, and... Section 8 Housing Assistance Program for the Disposition of HUD-Owned Projects § 886.338 Displacement, relocation, and acquisition. (a) Minimizing displacement. Consistent with the other goals and objectives of...

24 CFR 886.338 - Displacement, relocation, and acquisition.

Code of Federal Regulations, 2014 CFR

2014-04-01

... 24 Housing and Urban Development 4 2014-04-01 2014-04-01 false Displacement, relocation, and... Section 8 Housing Assistance Program for the Disposition of HUD-Owned Projects § 886.338 Displacement, relocation, and acquisition. (a) Minimizing displacement. Consistent with the other goals and objectives of...

24 CFR 886.338 - Displacement, relocation, and acquisition.

Code of Federal Regulations, 2012 CFR

2012-04-01

... 24 Housing and Urban Development 4 2012-04-01 2012-04-01 false Displacement, relocation, and... Section 8 Housing Assistance Program for the Disposition of HUD-Owned Projects § 886.338 Displacement, relocation, and acquisition. (a) Minimizing displacement. Consistent with the other goals and objectives of...

24 CFR 570.606 - Displacement, relocation, acquisition, and replacement of housing.

Code of Federal Regulations, 2014 CFR

2014-04-01

... 24 Housing and Urban Development 3 2014-04-01 2013-04-01 true Displacement, relocation... DEVELOPMENT BLOCK GRANTS Other Program Requirements § 570.606 Displacement, relocation, acquisition, and replacement of housing. (a) General policy for minimizing displacement. Consistent with the other goals and...

24 CFR 886.138 - Displacement, relocation, and acquisition.

Code of Federal Regulations, 2014 CFR

2014-04-01

... 24 Housing and Urban Development 4 2014-04-01 2014-04-01 false Displacement, relocation, and... Additional Assistance Program for Projects With HUD-Insured and HUD-Held Mortgages § 886.138 Displacement, relocation, and acquisition. (a) Minimizing displacement. Consistent with the other goals and objectives of...

24 CFR 570.606 - Displacement, relocation, acquisition, and replacement of housing.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 24 Housing and Urban Development 3 2010-04-01 2010-04-01 false Displacement, relocation... DEVELOPMENT BLOCK GRANTS Other Program Requirements § 570.606 Displacement, relocation, acquisition, and replacement of housing. (a) General policy for minimizing displacement. Consistent with the other goals and...

24 CFR 570.606 - Displacement, relocation, acquisition, and replacement of housing.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 24 Housing and Urban Development 3 2011-04-01 2010-04-01 true Displacement, relocation... DEVELOPMENT BLOCK GRANTS Other Program Requirements § 570.606 Displacement, relocation, acquisition, and replacement of housing. (a) General policy for minimizing displacement. Consistent with the other goals and...

24 CFR 886.338 - Displacement, relocation, and acquisition.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 24 Housing and Urban Development 4 2010-04-01 2010-04-01 false Displacement, relocation, and... Section 8 Housing Assistance Program for the Disposition of HUD-Owned Projects § 886.338 Displacement, relocation, and acquisition. (a) Minimizing displacement. Consistent with the other goals and objectives of...

24 CFR 570.606 - Displacement, relocation, acquisition, and replacement of housing.

Code of Federal Regulations, 2013 CFR

2013-04-01

... 24 Housing and Urban Development 3 2013-04-01 2013-04-01 false Displacement, relocation... DEVELOPMENT BLOCK GRANTS Other Program Requirements § 570.606 Displacement, relocation, acquisition, and replacement of housing. (a) General policy for minimizing displacement. Consistent with the other goals and...

24 CFR 886.338 - Displacement, relocation, and acquisition.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 24 Housing and Urban Development 4 2011-04-01 2011-04-01 false Displacement, relocation, and... Section 8 Housing Assistance Program for the Disposition of HUD-Owned Projects § 886.338 Displacement, relocation, and acquisition. (a) Minimizing displacement. Consistent with the other goals and objectives of...

24 CFR 968.108 - Displacement, relocation, and real property acquisition.

Code of Federal Regulations, 2012 CFR

2012-04-01

... 24 Housing and Urban Development 4 2012-04-01 2012-04-01 false Displacement, relocation, and real..., DEPARTMENT OF HOUSING AND URBAN DEVELOPMENT PUBLIC HOUSING MODERNIZATION General § 968.108 Displacement, relocation, and real property acquisition. (a) Minimizing displacement. Consistent with the other goals and...

24 CFR 574.630 - Displacement, relocation and real property acquisition.

Code of Federal Regulations, 2012 CFR

2012-04-01

... 24 Housing and Urban Development 3 2012-04-01 2012-04-01 false Displacement, relocation and real... Other Federal Requirements § 574.630 Displacement, relocation and real property acquisition. (a) Minimizing displacement. Consistent with the other goals and objectives of this part, grantees and project...

24 CFR 968.108 - Displacement, relocation, and real property acquisition.

Code of Federal Regulations, 2013 CFR

2013-04-01

... 24 Housing and Urban Development 4 2013-04-01 2013-04-01 false Displacement, relocation, and real..., DEPARTMENT OF HOUSING AND URBAN DEVELOPMENT PUBLIC HOUSING MODERNIZATION General § 968.108 Displacement, relocation, and real property acquisition. (a) Minimizing displacement. Consistent with the other goals and...

24 CFR 574.630 - Displacement, relocation and real property acquisition.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 24 Housing and Urban Development 3 2011-04-01 2010-04-01 true Displacement, relocation and real... Other Federal Requirements § 574.630 Displacement, relocation and real property acquisition. (a) Minimizing displacement. Consistent with the other goals and objectives of this part, grantees and project...

24 CFR 886.138 - Displacement, relocation, and acquisition.

Code of Federal Regulations, 2012 CFR

2012-04-01

... 24 Housing and Urban Development 4 2012-04-01 2012-04-01 false Displacement, relocation, and... Additional Assistance Program for Projects With HUD-Insured and HUD-Held Mortgages § 886.138 Displacement, relocation, and acquisition. (a) Minimizing displacement. Consistent with the other goals and objectives of...

24 CFR 886.138 - Displacement, relocation, and acquisition.

Code of Federal Regulations, 2013 CFR

2013-04-01

... 24 Housing and Urban Development 4 2013-04-01 2013-04-01 false Displacement, relocation, and... Additional Assistance Program for Projects With HUD-Insured and HUD-Held Mortgages § 886.138 Displacement, relocation, and acquisition. (a) Minimizing displacement. Consistent with the other goals and objectives of...

24 CFR 574.630 - Displacement, relocation and real property acquisition.

Code of Federal Regulations, 2014 CFR

2014-04-01

... 24 Housing and Urban Development 3 2014-04-01 2013-04-01 true Displacement, relocation and real... Other Federal Requirements § 574.630 Displacement, relocation and real property acquisition. (a) Minimizing displacement. Consistent with the other goals and objectives of this part, grantees and project...

24 CFR 968.108 - Displacement, relocation, and real property acquisition.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 24 Housing and Urban Development 4 2011-04-01 2011-04-01 false Displacement, relocation, and real..., DEPARTMENT OF HOUSING AND URBAN DEVELOPMENT PUBLIC HOUSING MODERNIZATION General § 968.108 Displacement, relocation, and real property acquisition. (a) Minimizing displacement. Consistent with the other goals and...

75 FR 67227 - Relocation Cost Sharing in the Broadcast Auxiliary Service

Federal Register 2010, 2011, 2012, 2013, 2014

2010-11-02

... relocating parties. In the process, the Commission balances the responsibilities for and benefits of... well as to balance the responsibilities for and benefits of relocating incumbent BAS operations among... adhere closely to these time-tested principles to balance the interest of incumbent licensees, new...

44 CFR 63.6 - Reimbursable relocation costs.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 44 Emergency Management and Assistance 1 2010-10-01 2010-10-01 false Reimbursable relocation costs... OF HOMELAND SECURITY INSURANCE AND HAZARD MITIGATION National Flood Insurance Program IMPLEMENTATION... costs. In addition to the coverage described in § 63.5 of this part, relocation costs for which benefits...

Feature-Free Activity Classification of Inertial Sensor Data With Machine Vision Techniques: Method, Development, and Evaluation.

PubMed

Dominguez Veiga, Jose Juan; O'Reilly, Martin; Whelan, Darragh; Caulfield, Brian; Ward, Tomas E

2017-08-04

Inertial sensors are one of the most commonly used sources of data for human activity recognition (HAR) and exercise detection (ED) tasks. The time series produced by these sensors are generally analyzed through numerical methods. Machine learning techniques such as random forests or support vector machines are popular in this field for classification efforts, but they need to be supported through the isolation of a potentially large number of additionally crafted features derived from the raw data. This feature preprocessing step can involve nontrivial digital signal processing (DSP) techniques. However, in many cases, the researchers interested in this type of activity recognition problems do not possess the necessary technical background for this feature-set development. The study aimed to present a novel application of established machine vision methods to provide interested researchers with an easier entry path into the HAR and ED fields. This can be achieved by removing the need for deep DSP skills through the use of transfer learning. This can be done by using a pretrained convolutional neural network (CNN) developed for machine vision purposes for exercise classification effort. The new method should simply require researchers to generate plots of the signals that they would like to build classifiers with, store them as images, and then place them in folders according to their training label before retraining the network. We applied a CNN, an established machine vision technique, to the task of ED. Tensorflow, a high-level framework for machine learning, was used to facilitate infrastructure needs. Simple time series plots generated directly from accelerometer and gyroscope signals are used to retrain an openly available neural network (Inception), originally developed for machine vision tasks. Data from 82 healthy volunteers, performing 5 different exercises while wearing a lumbar-worn inertial measurement unit (IMU), was collected. The ability of the proposed method to automatically classify the exercise being completed was assessed using this dataset. For comparative purposes, classification using the same dataset was also performed using the more conventional approach of feature-extraction and classification using random forest classifiers. With the collected dataset and the proposed method, the different exercises could be recognized with a 95.89% (3827/3991) accuracy, which is competitive with current state-of-the-art techniques in ED. The high level of accuracy attained with the proposed approach indicates that the waveform morphologies in the time-series plots for each of the exercises is sufficiently distinct among the participants to allow the use of machine vision approaches. The use of high-level machine learning frameworks, coupled with the novel use of machine vision techniques instead of complex manually crafted features, may facilitate access to research in the HAR field for individuals without extensive digital signal processing or machine learning backgrounds. ©Jose Juan Dominguez Veiga, Martin O'Reilly, Darragh Whelan, Brian Caulfield, Tomas E Ward. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 04.08.2017.

Feature-Free Activity Classification of Inertial Sensor Data With Machine Vision Techniques: Method, Development, and Evaluation

PubMed Central

O'Reilly, Martin; Whelan, Darragh; Caulfield, Brian; Ward, Tomas E

2017-01-01

Background Inertial sensors are one of the most commonly used sources of data for human activity recognition (HAR) and exercise detection (ED) tasks. The time series produced by these sensors are generally analyzed through numerical methods. Machine learning techniques such as random forests or support vector machines are popular in this field for classification efforts, but they need to be supported through the isolation of a potentially large number of additionally crafted features derived from the raw data. This feature preprocessing step can involve nontrivial digital signal processing (DSP) techniques. However, in many cases, the researchers interested in this type of activity recognition problems do not possess the necessary technical background for this feature-set development. Objective The study aimed to present a novel application of established machine vision methods to provide interested researchers with an easier entry path into the HAR and ED fields. This can be achieved by removing the need for deep DSP skills through the use of transfer learning. This can be done by using a pretrained convolutional neural network (CNN) developed for machine vision purposes for exercise classification effort. The new method should simply require researchers to generate plots of the signals that they would like to build classifiers with, store them as images, and then place them in folders according to their training label before retraining the network. Methods We applied a CNN, an established machine vision technique, to the task of ED. Tensorflow, a high-level framework for machine learning, was used to facilitate infrastructure needs. Simple time series plots generated directly from accelerometer and gyroscope signals are used to retrain an openly available neural network (Inception), originally developed for machine vision tasks. Data from 82 healthy volunteers, performing 5 different exercises while wearing a lumbar-worn inertial measurement unit (IMU), was collected. The ability of the proposed method to automatically classify the exercise being completed was assessed using this dataset. For comparative purposes, classification using the same dataset was also performed using the more conventional approach of feature-extraction and classification using random forest classifiers. Results With the collected dataset and the proposed method, the different exercises could be recognized with a 95.89% (3827/3991) accuracy, which is competitive with current state-of-the-art techniques in ED. Conclusions The high level of accuracy attained with the proposed approach indicates that the waveform morphologies in the time-series plots for each of the exercises is sufficiently distinct among the participants to allow the use of machine vision approaches. The use of high-level machine learning frameworks, coupled with the novel use of machine vision techniques instead of complex manually crafted features, may facilitate access to research in the HAR field for individuals without extensive digital signal processing or machine learning backgrounds. PMID:28778851

Modelling rollover behaviour of exacavator-based forest machines

Treesearch

M.W. Veal; S.E. Taylor; Robert B. Rummer

2003-01-01

This poster presentation provides results from analytical and computer simulation models of rollover behaviour of hydraulic excavators. These results are being used as input to the operator protective structure standards development process. Results from rigid body mechanics and computer simulation methods agree well with field rollover test data. These results show...

Three rings-per-inch, dense southern pine-- Should it be developed?

Treesearch

P. Koch

1972-01-01

Three-rings-per-inch, dense southern pine is a debatable goal in forests managed for solid wood products, but possibilities are evident. The problems lie in product specifications, strength, attractiveness, paint retention, gluing characteristics and machinability. An interim goal for southern pine silviculturists is suggested.

Three rings-per-inch dense southern pine - should it be developed?

Treesearch

Peter Koch

1971-01-01

Three-rings-per-inch, dense southern pine is a debatable goal in forests managed for solid wood products, but possibilities are evident. The problems lie in product specifications, strength, attractiveness, paint retention, gluing characteristics and machinability. An interim goal for southern pine silviculturists is suggested.

41 CFR 302-2.3 - What determines my entitlements and allowances for relocation?

Code of Federal Regulations, 2010 CFR

2010-07-01

... 41 Public Contracts and Property Management 4 2010-07-01 2010-07-01 false What determines my entitlements and allowances for relocation? 302-2.3 Section 302-2.3 Public Contracts and Property Management Federal Travel Regulation System RELOCATION ALLOWANCES INTRODUCTION 2-EMPLOYEES ELIGIBILITY REQUIREMENTS...

41 CFR 302-3.209 - What is overseas tour renewal travel?

Code of Federal Regulations, 2010 CFR

2010-07-01

... 41 Public Contracts and Property Management 4 2010-07-01 2010-07-01 false What is overseas tour renewal travel? 302-3.209 Section 302-3.209 Public Contracts and Property Management Federal Travel Regulation System RELOCATION ALLOWANCES RELOCATION ALLOWANCES 3-RELOCATION ALLOWANCE BY SPECIFIC TYPE Types...

41 CFR 302-3.209 - What is overseas tour renewal travel?

Code of Federal Regulations, 2011 CFR

2011-07-01

... 41 Public Contracts and Property Management 4 2011-07-01 2011-07-01 false What is overseas tour renewal travel? 302-3.209 Section 302-3.209 Public Contracts and Property Management Federal Travel Regulation System RELOCATION ALLOWANCES RELOCATION ALLOWANCES 3-RELOCATION ALLOWANCE BY SPECIFIC TYPE Types...

41 CFR 302-3.100 - What is a transferred employee?

Code of Federal Regulations, 2013 CFR

2013-07-01

... 41 Public Contracts and Property Management 4 2013-07-01 2012-07-01 true What is a transferred employee? 302-3.100 Section 302-3.100 Public Contracts and Property Management Federal Travel Regulation System RELOCATION ALLOWANCES RELOCATION ALLOWANCES 3-RELOCATION ALLOWANCE BY SPECIFIC TYPE Transferred...

41 CFR 302-3.100 - What is a transferred employee?

Code of Federal Regulations, 2011 CFR

2011-07-01

... 41 Public Contracts and Property Management 4 2011-07-01 2011-07-01 false What is a transferred employee? 302-3.100 Section 302-3.100 Public Contracts and Property Management Federal Travel Regulation System RELOCATION ALLOWANCES RELOCATION ALLOWANCES 3-RELOCATION ALLOWANCE BY SPECIFIC TYPE Transferred...

41 CFR 302-3.209 - What is overseas tour renewal travel?

Code of Federal Regulations, 2012 CFR

2012-07-01

... 41 Public Contracts and Property Management 4 2012-07-01 2012-07-01 false What is overseas tour renewal travel? 302-3.209 Section 302-3.209 Public Contracts and Property Management Federal Travel Regulation System RELOCATION ALLOWANCES RELOCATION ALLOWANCES 3-RELOCATION ALLOWANCE BY SPECIFIC TYPE Types...

41 CFR 302-3.209 - What is overseas tour renewal travel?

Code of Federal Regulations, 2014 CFR

2014-07-01

... 41 Public Contracts and Property Management 4 2014-07-01 2014-07-01 false What is overseas tour renewal travel? 302-3.209 Section 302-3.209 Public Contracts and Property Management Federal Travel Regulation System RELOCATION ALLOWANCES RELOCATION ALLOWANCES 3-RELOCATION ALLOWANCE BY SPECIFIC TYPE Types...

41 CFR 302-3.100 - What is a transferred employee?

Code of Federal Regulations, 2012 CFR

2012-07-01

... 41 Public Contracts and Property Management 4 2012-07-01 2012-07-01 false What is a transferred employee? 302-3.100 Section 302-3.100 Public Contracts and Property Management Federal Travel Regulation System RELOCATION ALLOWANCES RELOCATION ALLOWANCES 3-RELOCATION ALLOWANCE BY SPECIFIC TYPE Transferred...

41 CFR 302-3.209 - What is overseas tour renewal travel?

Code of Federal Regulations, 2013 CFR

2013-07-01

... 41 Public Contracts and Property Management 4 2013-07-01 2012-07-01 true What is overseas tour renewal travel? 302-3.209 Section 302-3.209 Public Contracts and Property Management Federal Travel Regulation System RELOCATION ALLOWANCES RELOCATION ALLOWANCES 3-RELOCATION ALLOWANCE BY SPECIFIC TYPE Types...

41 CFR 302-3.100 - What is a transferred employee?

Code of Federal Regulations, 2010 CFR

2010-07-01

... 41 Public Contracts and Property Management 4 2010-07-01 2010-07-01 false What is a transferred employee? 302-3.100 Section 302-3.100 Public Contracts and Property Management Federal Travel Regulation System RELOCATION ALLOWANCES RELOCATION ALLOWANCES 3-RELOCATION ALLOWANCE BY SPECIFIC TYPE Transferred...

41 CFR 302-3.100 - What is a transferred employee?

Code of Federal Regulations, 2014 CFR

2014-07-01

... 41 Public Contracts and Property Management 4 2014-07-01 2014-07-01 false What is a transferred employee? 302-3.100 Section 302-3.100 Public Contracts and Property Management Federal Travel Regulation System RELOCATION ALLOWANCES RELOCATION ALLOWANCES 3-RELOCATION ALLOWANCE BY SPECIFIC TYPE Transferred...

39 CFR 777.22 - Relocation assistance advisory services.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 39 Postal Service 1 2010-07-01 2010-07-01 false Relocation assistance advisory services. 777.22... persons. (b) Relocation Information. The Postal Service must contact each displaced person to provide an... Provided. The advisory program shall include such services as may be necessary or appropriate to: (1...

75 FR 58329 - Federal Travel Regulation (FTR); Relocation Expenses Test Programs

Federal Register 2010, 2011, 2012, 2013, 2014

2010-09-24

...; Docket 2010-0016; Sequence 1] RIN 3090-ZA01 Federal Travel Regulation (FTR); Relocation Expenses Test... extended the authority for relocation expenses test programs for Federal employees, made by the passage of..., permits the Administrator of General Services to authorize Federal agencies to test new and innovative...

The Four-Factor Taxonomy of Relocation Outcomes

ERIC Educational Resources Information Center

Matthiesen, Jane Kirsten; Tissington, Patrick

2008-01-01

Relocation, an intraorganizational geographical transfer, can be used for human resource development (HRD) because of the positive developmental effects it can induce. It is, thus, important for HRD professionals to understand the implications of relocation to ensure it is used appropriately and effectively as an HRD technique. Research on…

Toward an Understanding of Geriatric Relocation.

ERIC Educational Resources Information Center

Coffman, Thomas L.

1983-01-01

Debates the effect of relocation on elderly patients in a critique of an earlier study and a rebuttal by the original author. Questions whether patient mortality is related to the stress of moving or a simple function of age, health status, or choice. Additional evidence on relocation effects is needed. (JAC)

24 CFR 891.510 - Displacement, relocation, and real property acquisition.

Code of Federal Regulations, 2014 CFR

2014-04-01

... 24 Housing and Urban Development 4 2014-04-01 2014-04-01 false Displacement, relocation, and real... DISABILITIES Loans for Housing for the Elderly and Persons with Disabilities § 891.510 Displacement, relocation, and real property acquisition. (a) Minimizing displacement. Consistent with the other goals and...

24 CFR 891.510 - Displacement, relocation, and real property acquisition.

Code of Federal Regulations, 2012 CFR

2012-04-01

... 24 Housing and Urban Development 4 2012-04-01 2012-04-01 false Displacement, relocation, and real... DISABILITIES Loans for Housing for the Elderly and Persons with Disabilities § 891.510 Displacement, relocation, and real property acquisition. (a) Minimizing displacement. Consistent with the other goals and...

24 CFR 891.510 - Displacement, relocation, and real property acquisition.

Code of Federal Regulations, 2013 CFR

2013-04-01

... 24 Housing and Urban Development 4 2013-04-01 2013-04-01 false Displacement, relocation, and real... DISABILITIES Loans for Housing for the Elderly and Persons with Disabilities § 891.510 Displacement, relocation, and real property acquisition. (a) Minimizing displacement. Consistent with the other goals and...

24 CFR 891.510 - Displacement, relocation, and real property acquisition.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 24 Housing and Urban Development 4 2011-04-01 2011-04-01 false Displacement, relocation, and real... DISABILITIES Loans for Housing for the Elderly and Persons with Disabilities § 891.510 Displacement, relocation, and real property acquisition. (a) Minimizing displacement. Consistent with the other goals and...

24 CFR 891.510 - Displacement, relocation, and real property acquisition.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 24 Housing and Urban Development 4 2010-04-01 2010-04-01 false Displacement, relocation, and real... DISABILITIES Loans for Housing for the Elderly and Persons with Disabilities § 891.510 Displacement, relocation, and real property acquisition. (a) Minimizing displacement. Consistent with the other goals and...

76 FR 32340 - Federal Travel Regulation; Temporary Duty (TDY) Travel Allowances (Taxes); Relocation Allowances...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-06-06

..., as taxable income. When you receive taxable benefits, you must pay income tax on the amount or value... Allowances (Taxes); Relocation Allowances (Taxes) AGENCY: Office of Governmentwide Policy (OGP), General...) concerning calculation of reimbursements for taxes on relocation expenses. In addition, this proposed rule...

40 CFR 4.1 - Uniform relocation assistance and real property acquisition.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 40 Protection of Environment 1 2010-07-01 2010-07-01 false Uniform relocation assistance and real property acquisition. 4.1 Section 4.1 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY GENERAL... § 4.1 Uniform relocation assistance and real property acquisition. Effective April 2, 1989...

Preschoolers' Preparation for Retrieval in Object Relocation Tasks.

ERIC Educational Resources Information Center

Beal, Carole R.; Fleisig, Wayne E.

The finding that young children do not prepare markers to help themselves relocate objects after a delay may have resulted from children's misunderstanding of the difficulty of unassisted retrieval. This study examined children's ability to recognize that they should prepare markers in two simplified object relocation tasks after they had been…

25 CFR 700.703 - Authority.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 25 Indians 2 2010-04-01 2010-04-01 false Authority. 700.703 Section 700.703 Indians THE OFFICE OF NAVAJO AND HOPI INDIAN RELOCATION COMMISSION OPERATIONS AND RELOCATION PROCEDURES New Lands Grazing § 700.703 Authority. It is within the authority of the Commissioner on Navajo and Hopi Indian Relocation to...

Law, Morality and the Relocation Camps.

ERIC Educational Resources Information Center

Stearns, Hal

1990-01-01

Presents a lesson examining the historical background of the U.S. government's relocation and internment of Japanese-Americans and Japanese aliens during World War II. Asks secondary students to consider both the legal and moral dimensions of the relocation effort and current compensation arguments. Includes handouts and suggestions for role…

34 CFR 75.613 - Relocation assistance by the grantee.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 34 Education 1 2014-07-01 2014-07-01 false Relocation assistance by the grantee. 75.613 Section 75... Must Be Met by a Grantee? Construction § 75.613 Relocation assistance by the grantee. A grantee is.... (Authority: 20 U.S.C. 1221e-3 and 3474) ...

Robustness of survival estimates from radio-telemetry studies with uncertain relocation of individuals

USGS Publications Warehouse

Bunck, C.M.; Chen, C.-L.; Pollock, K.H.

1995-01-01

Traditional methods of estimating survival from radio-telemetry studies use either the Trent-Rongstad approach (Trent and Rongstad 1974, Heisey and Fuller 1985) or the Kaplan-Meier approach (Kaplan and Meier 1958; Pollock et al. 1989a,b). Both methods appear to require the assumption that relocation probability for animals with a functioning radio is 1. In practice this may not always be reasonable and, in fact, is unnecessary. The number of animals at risk (i.e., risk set) can be modified to account for uncertain relocation of individuals. This involves including only relocated animals in the risk set instead of also including animals not relocated but that were seen later. Simulation results show that estimators and tests for comparing survival curves should be based on this modification.

Relocation of net-acid-generating waste to improve post-mining water chemistry.

PubMed

Morin, K A; Hutt, N M

2001-01-01

Acidic drainage and metal leaching are long-term environmental liabilities that can persist for many decades to millennia. One technique to improve the water chemistry and ecology of post-mining landscapes is to relocate and submerge net-acid-generating mine materials in a lake or water-retaining impoundment. One example of a carefully executed relocation of waste rock took place at the Eskay Creek Mine in Canada. Pre-relocation studies included an empirical relationship that related (1) the amount of acidity retained by the waste rock during past oxidation to (2) the amount of lime needed in each truckload for neutralization of the acidity and for suppression of metal release. During relocation, thousands of rinse pH measurements indicated net acidity varied significantly over short distances within the waste rock and that acidic rock could not be reliably segregated from near-netural rock. After relocation, water from the watershed continued to be acidic for a few years, then returned to near-neutral pH and near-background concentrations of metals. The chemistry of the lake where the waste rock was submerged remains near background conditions. Therefore, with careful planning and implementation, the relocation and submergence of net-acid-generating materials can greatly improve post-mining water chemistry.

Community-based research as a mechanism to reduce ...

EPA Pesticide Factsheets

Racial and ethnic minority communities, including American Indian and Alaska Natives, have been disproportionately impacted by environmental pollution and contamination. This includes siting and location of point sources of pollution, legacies of contamination of drinking and recreational water, and mining, military and agricultural impacts. As a result, both quantity and quality of culturally important subsistence resources are diminished, contributing to poor nutrition and obesity, and overall reductions in quality of life and life expectancy. Climate change is adding to these impacts on Native American communities (Wildcat 2013), variably causing drought, increased flooding and forced relocation (Maldonado et al. 2013), affecting Tribal water resources (Cozzetto et al. 2013), traditional foods (Lynn et al. 2013; Gautam et al. 2013), forests and forest resources (Voggesser et al. 2013) and Tribal health (Donatuto et al 2014; Doyle et al. 2013). This article will highlight several extramural research projects supported by the United States Environmental Protection Agency (USEPA) Science to Achieve Results (STAR) Tribal environmental research grants as a mechanism to address the environmental health inequities and disparities faced by Tribal communities (USEPA, 2014a, www.epa.gov/ncer/tribalresearch). The Tribal Research portfolio has focused on addressing tribal environmental health risks through community based participatory research. Specifically, the STA

Selection of summer roosting sites by Indiana bats (Myotis sodalis) in Missouri

USGS Publications Warehouse

Callahan, E.V.; Drobney, R.D.; Clawson, R.L.

1997-01-01

Summer roosting sites were studied at four maternity colonies of Indiana bats (Myotis sodalis) in northern Missouri. Colonies of Indiana bats used two types of roosts, primary and alternate, that differed in intensity of use, number, and probable function. Primary roosts were denned as roosts where use by >30 bats on more than one occasion was observed. The number of primary roosts per colony ranged from one to three. All primary roosts were in standing dead trees situated in trees exposed to direct sunlight. Alternate roosts were used by smaller numbers of bats. These roosts included both living and dead trees that typically were located within the shaded forest interior. Differences in patterns of use between types of roosts seemed to be influenced by weather conditions in that use of alternate roost trees increased during periods of elevated temperature and precipitation. Indiana bats have specific requirements for roost sites, but also must be able to relocate when loss of bark, tree fall, or other events render their current roost sites unusable. Practices of forest management within the summer range of Indiana bats should favor retention of large-diameter, mature, and senescent trees.

Forecasting Solar Flares Using Magnetogram-based Predictors and Machine Learning

NASA Astrophysics Data System (ADS)

Florios, Kostas; Kontogiannis, Ioannis; Park, Sung-Hong; Guerra, Jordan A.; Benvenuto, Federico; Bloomfield, D. Shaun; Georgoulis, Manolis K.

2018-02-01

We propose a forecasting approach for solar flares based on data from Solar Cycle 24, taken by the Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory (SDO) mission. In particular, we use the Space-weather HMI Active Region Patches (SHARP) product that facilitates cut-out magnetograms of solar active regions (AR) in the Sun in near-realtime (NRT), taken over a five-year interval (2012 - 2016). Our approach utilizes a set of thirteen predictors, which are not included in the SHARP metadata, extracted from line-of-sight and vector photospheric magnetograms. We exploit several machine learning (ML) and conventional statistics techniques to predict flares of peak magnitude {>} M1 and {>} C1 within a 24 h forecast window. The ML methods used are multi-layer perceptrons (MLP), support vector machines (SVM), and random forests (RF). We conclude that random forests could be the prediction technique of choice for our sample, with the second-best method being multi-layer perceptrons, subject to an entropy objective function. A Monte Carlo simulation showed that the best-performing method gives accuracy ACC=0.93(0.00), true skill statistic TSS=0.74(0.02), and Heidke skill score HSS=0.49(0.01) for {>} M1 flare prediction with probability threshold 15% and ACC=0.84(0.00), TSS=0.60(0.01), and HSS=0.59(0.01) for {>} C1 flare prediction with probability threshold 35%.

Pre-operative prediction of surgical morbidity in children: comparison of five statistical models.

PubMed

Cooper, Jennifer N; Wei, Lai; Fernandez, Soledad A; Minneci, Peter C; Deans, Katherine J

2015-02-01

The accurate prediction of surgical risk is important to patients and physicians. Logistic regression (LR) models are typically used to estimate these risks. However, in the fields of data mining and machine-learning, many alternative classification and prediction algorithms have been developed. This study aimed to compare the performance of LR to several data mining algorithms for predicting 30-day surgical morbidity in children. We used the 2012 National Surgical Quality Improvement Program-Pediatric dataset to compare the performance of (1) a LR model that assumed linearity and additivity (simple LR model) (2) a LR model incorporating restricted cubic splines and interactions (flexible LR model) (3) a support vector machine, (4) a random forest and (5) boosted classification trees for predicting surgical morbidity. The ensemble-based methods showed significantly higher accuracy, sensitivity, specificity, PPV, and NPV than the simple LR model. However, none of the models performed better than the flexible LR model in terms of the aforementioned measures or in model calibration or discrimination. Support vector machines, random forests, and boosted classification trees do not show better performance than LR for predicting pediatric surgical morbidity. After further validation, the flexible LR model derived in this study could be used to assist with clinical decision-making based on patient-specific surgical risks. Copyright © 2014 Elsevier Ltd. All rights reserved.

Non-invasive estimate of blood glucose and blood pressure from a photoplethysmograph by means of machine learning techniques.

PubMed

Monte-Moreno, Enric

2011-10-01

This work presents a system for a simultaneous non-invasive estimate of the blood glucose level (BGL) and the systolic (SBP) and diastolic (DBP) blood pressure, using a photoplethysmograph (PPG) and machine learning techniques. The method is independent of the person whose values are being measured and does not need calibration over time or subjects. The architecture of the system consists of a photoplethysmograph sensor, an activity detection module, a signal processing module that extracts features from the PPG waveform, and a machine learning algorithm that estimates the SBP, DBP and BGL values. The idea that underlies the system is that there is functional relationship between the shape of the PPG waveform and the blood pressure and glucose levels. As described in this paper we tested this method on 410 individuals without performing any personalized calibration. The results were computed after cross validation. The machine learning techniques tested were: ridge linear regression, a multilayer perceptron neural network, support vector machines and random forests. The best results were obtained with the random forest technique. In the case of blood pressure, the resulting coefficients of determination for reference vs. prediction were R(SBP)(2)=0.91, R(DBP)(2)=0.89, and R(BGL)(2)=0.90. For the glucose estimation, distribution of the points on a Clarke error grid placed 87.7% of points in zone A, 10.3% in zone B, and 1.9% in zone D. Blood pressure values complied with the grade B protocol of the British Hypertension society. An effective system for estimate of blood glucose and blood pressure from a photoplethysmograph is presented. The main advantage of the system is that for clinical use it complies with the grade B protocol of the British Hypertension society for the blood pressure and only in 1.9% of the cases did not detect hypoglycemia or hyperglycemia. Copyright © 2011 Elsevier B.V. All rights reserved.

Building a profile of subjective well-being for social media users.

PubMed

Chen, Lushi; Gong, Tao; Kosinski, Michal; Stillwell, David; Davidson, Robert L

2017-01-01

Subjective well-being includes 'affect' and 'satisfaction with life' (SWL). This study proposes a unified approach to construct a profile of subjective well-being based on social media language in Facebook status updates. We apply sentiment analysis to generate users' affect scores, and train a random forest model to predict SWL using affect scores and other language features of the status updates. Results show that: the computer-selected features resemble the key predictors of SWL as identified in early studies; the machine-predicted SWL is moderately correlated with the self-reported SWL (r = 0.36, p < 0.01), indicating that language-based assessment can constitute valid SWL measures; the machine-assessed affect scores resemble those reported in a previous experimental study; and the machine-predicted subjective well-being profile can also reflect other psychological traits like depression (r = 0.24, p < 0.01). This study provides important insights for psychological prediction using multiple, machine-assessed components and longitudinal or dense psychological assessment using social media language.

Building a profile of subjective well-being for social media users

PubMed Central

Kosinski, Michal; Stillwell, David; Davidson, Robert L.

2017-01-01

Subjective well-being includes ‘affect’ and ‘satisfaction with life’ (SWL). This study proposes a unified approach to construct a profile of subjective well-being based on social media language in Facebook status updates. We apply sentiment analysis to generate users’ affect scores, and train a random forest model to predict SWL using affect scores and other language features of the status updates. Results show that: the computer-selected features resemble the key predictors of SWL as identified in early studies; the machine-predicted SWL is moderately correlated with the self-reported SWL (r = 0.36, p < 0.01), indicating that language-based assessment can constitute valid SWL measures; the machine-assessed affect scores resemble those reported in a previous experimental study; and the machine-predicted subjective well-being profile can also reflect other psychological traits like depression (r = 0.24, p < 0.01). This study provides important insights for psychological prediction using multiple, machine-assessed components and longitudinal or dense psychological assessment using social media language. PMID:29135991

Can We Train Machine Learning Methods to Outperform the High-dimensional Propensity Score Algorithm?

PubMed

Karim, Mohammad Ehsanul; Pang, Menglan; Platt, Robert W

2018-03-01

The use of retrospective health care claims datasets is frequently criticized for the lack of complete information on potential confounders. Utilizing patient's health status-related information from claims datasets as surrogates or proxies for mismeasured and unobserved confounders, the high-dimensional propensity score algorithm enables us to reduce bias. Using a previously published cohort study of postmyocardial infarction statin use (1998-2012), we compare the performance of the algorithm with a number of popular machine learning approaches for confounder selection in high-dimensional covariate spaces: random forest, least absolute shrinkage and selection operator, and elastic net. Our results suggest that, when the data analysis is done with epidemiologic principles in mind, machine learning methods perform as well as the high-dimensional propensity score algorithm. Using a plasmode framework that mimicked the empirical data, we also showed that a hybrid of machine learning and high-dimensional propensity score algorithms generally perform slightly better than both in terms of mean squared error, when a bias-based analysis is used.

75 FR 22611 - Recovery Policy RP9523.3, Provision of Temporary Relocation Facilities

Federal Register 2010, 2011, 2012, 2013, 2014

2010-04-29

...] Recovery Policy RP9523.3, Provision of Temporary Relocation Facilities AGENCY: Federal Emergency Management... Management Agency (FEMA) is accepting comments on Recovery Policy RP9523.3, Provision of Temporary Relocation... major disaster. Specifically, Section 403(a)(3)(D) of the Stafford Act allows for the provision of...

41 CFR 302-3.210 - What is an overseas tour of duty?

Code of Federal Regulations, 2010 CFR

2010-07-01

... 41 Public Contracts and Property Management 4 2010-07-01 2010-07-01 false What is an overseas tour of duty? 302-3.210 Section 302-3.210 Public Contracts and Property Management Federal Travel Regulation System RELOCATION ALLOWANCES RELOCATION ALLOWANCES 3-RELOCATION ALLOWANCE BY SPECIFIC TYPE Types...

41 CFR 302-3.210 - What is an overseas tour of duty?

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2013-07-01

... 41 Public Contracts and Property Management 4 2013-07-01 2012-07-01 true What is an overseas tour of duty? 302-3.210 Section 302-3.210 Public Contracts and Property Management Federal Travel Regulation System RELOCATION ALLOWANCES RELOCATION ALLOWANCES 3-RELOCATION ALLOWANCE BY SPECIFIC TYPE Types...

41 CFR 302-3.210 - What is an overseas tour of duty?

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2012-07-01

... 41 Public Contracts and Property Management 4 2012-07-01 2012-07-01 false What is an overseas tour of duty? 302-3.210 Section 302-3.210 Public Contracts and Property Management Federal Travel Regulation System RELOCATION ALLOWANCES RELOCATION ALLOWANCES 3-RELOCATION ALLOWANCE BY SPECIFIC TYPE Types...

41 CFR 302-3.1 - Who is a new appointee?

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2014-07-01

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41 CFR 302-3.1 - Who is a new appointee?

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2011-07-01

... 41 Public Contracts and Property Management 4 2011-07-01 2011-07-01 false Who is a new appointee? 302-3.1 Section 302-3.1 Public Contracts and Property Management Federal Travel Regulation System RELOCATION ALLOWANCES RELOCATION ALLOWANCES 3-RELOCATION ALLOWANCE BY SPECIFIC TYPE New Appointee § 302-3.1...

41 CFR 302-3.210 - What is an overseas tour of duty?

Code of Federal Regulations, 2014 CFR

2014-07-01

... 41 Public Contracts and Property Management 4 2014-07-01 2014-07-01 false What is an overseas tour of duty? 302-3.210 Section 302-3.210 Public Contracts and Property Management Federal Travel Regulation System RELOCATION ALLOWANCES RELOCATION ALLOWANCES 3-RELOCATION ALLOWANCE BY SPECIFIC TYPE Types...

41 CFR 302-3.1 - Who is a new appointee?

Code of Federal Regulations, 2012 CFR

2012-07-01

... 41 Public Contracts and Property Management 4 2012-07-01 2012-07-01 false Who is a new appointee? 302-3.1 Section 302-3.1 Public Contracts and Property Management Federal Travel Regulation System RELOCATION ALLOWANCES RELOCATION ALLOWANCES 3-RELOCATION ALLOWANCE BY SPECIFIC TYPE New Appointee § 302-3.1...

41 CFR 302-3.210 - What is an overseas tour of duty?

Code of Federal Regulations, 2011 CFR

2011-07-01

... 41 Public Contracts and Property Management 4 2011-07-01 2011-07-01 false What is an overseas tour of duty? 302-3.210 Section 302-3.210 Public Contracts and Property Management Federal Travel Regulation System RELOCATION ALLOWANCES RELOCATION ALLOWANCES 3-RELOCATION ALLOWANCE BY SPECIFIC TYPE Types...