scholarly journals GIS-data related route optimization, hierarchical clustering, location optimization, and kernel density methods are useful for promoting distributed bioenergy plant planning in rural areas

2019 ◽  
Vol 32 ◽  
pp. 47-57 ◽  
Author(s):  
K. Laasasenaho ◽  
A. Lensu ◽  
R. Lauhanen ◽  
J. Rintala
Keyword(s):  
Hierarchical Clustering ◽  
Rural Areas ◽  
Kernel Density ◽  
Route Optimization ◽  
Location Optimization ◽  
Bioenergy Plant ◽  
Gis Data ◽  
Density Methods

Regression Techniques for the Prediction of Lower Limb Kinematics

2005 ◽  
Vol 127 (6) ◽  
pp. 1020-1024 ◽  
Author(s):  
J. Y. Goulermas ◽  
D. Howard ◽  
C. J. Nester ◽  
R. K. Jones ◽  
L. Ren
Keyword(s):  
Kernel Density ◽  
Cost Effective ◽  
Nearest Neighbors ◽  
Human Gait ◽  
Laboratory Equipment ◽  
Resampling Methods ◽  
Kinematic Data ◽  
Regression Techniques ◽  
Limb Kinematics ◽  
Density Methods

This work presents a novel and extensive investigation of mathematical regression techniques, for the prediction of laboratory-type kinematic measurements during human gait, from wearable measurement devices, such as gyroscopes and accelerometers. Specifically, we examine the hypothesis of predicting the segmental angles of the legs (left and right foot, shank and thighs), from rotational foot velocities and translational foot accelerations. This first investigation is based on kinematic data emulated from motion-capture laboratory equipment. We employ eight established regression algorithms with different properties, ranging from linear methods and neural networks with polynomial support and expanded nonlinearities, to radial basis functions, nearest neighbors and kernel density methods. Data from five gait cycles of eight subjects are used to perform both inter-subject and intra-subject assessments of the prediction capabilities of each algorithm, using cross-validation resampling methods. Regarding the algorithmic suitability to gait prediction, results strongly indicate that nonparametric methods, such as nearest neighbors and kernel density based, are particularly advantageous. Numerical results show high average prediction accuracy (ρ=0.98∕0.99,RMS=5.63°∕2.30°,MAD=4.43°∕1.52° for inter∕intra-subject testing). The presented work provides a promising and motivating investigation on the feasibility of cost-effective wearable devices used to acquire large volumes of data that are currently collected only from complex laboratory environments.

Robust inferential control using kernel density methods

2000 ◽  
Vol 24 (2-7) ◽  
pp. 835-840 ◽  
Author(s):  
P.R. Goulding ◽  
B. Lennox ◽  
Q. Chen ◽  
D.J. Sandoz
Keyword(s):  
Kernel Density ◽  
Inferential Control ◽  
Density Methods

National Survey of Volunteer Pharmacy Preceptors

2010 ◽  
Vol 23 (3) ◽  
pp. 265-272 ◽  
Author(s):  
Maryann Z. Skrabal ◽  
Rhonda M. Jones ◽  
Ryan W. Walters ◽  
Ruth E. Nemire ◽  
Denise A. Soltis ◽  
...  
Keyword(s):  
Population Density ◽  
Experiential Education ◽  
Rural Areas ◽  
Online Survey ◽  
Pharmacy Practice ◽  
The United States ◽  
Geographic Region ◽  
Education Environment ◽  
Rural Sites ◽  
Density Methods

Objectives: To survey volunteer pharmacy preceptors regarding experiential education and determine whether differences in responses relate to such factors as geographic region, practice setting, and population density. Methods: An online survey was sent to 4396 volunteer experiential preceptors. The survey consisted of 41 questions asking the preceptor to comment on the experiential education environment. Experiential education administrators from 9 schools of pharmacy administered the survey to their volunteer preceptors in all regions (Northeast, Midwest, South, and West) of the United States, in various pharmacy practice settings, and areas of differing population densities. Results: A total of 1163 (26.5%) preceptors responded. Regionally, preceptors in the West disagreed more than those in the Midwest and the South that they had enough time to spend with students to provide a quality experience and also required compensation less often than their counterparts in the Northeast and South. Concerning practice settings, hospital preceptors accepted students from more schools, had greater increases in requests, turned away more students, and spent less time with the students compared to preceptors in other settings. Population density differences reflected that preceptors at urban sites took and turned away more students than those at rural sites. Preceptors from rural areas spent more time with students and felt they were spending enough time with their students to provide quality experiences when compared to other preceptors. Conclusions: The results of this national volunteer preceptor survey may assist pharmacy school leaders in understanding how location, practice type, and population density affect experiential education, preceptor time-quality issues, and site compensation so they can take necessary actions to improve quality of student practice experiences.

scholarly journals Internet Access in Rural Areas: Brake or Stimulus as Post-Covid-19 Opportunity?

2020 ◽  
Vol 12 (22) ◽  
pp. 9619
Author(s):  
Irune Ruiz-Martínez ◽  
Javier Esparcia
Keyword(s):  
Hierarchical Clustering ◽  
Rural Areas ◽  
Internet Access ◽  
Local Economy ◽  
The Internet ◽  
Agglomerative Hierarchical Clustering ◽  
Territorial Development ◽  
Efficient Access ◽  
Contextual Elements ◽  
Local Actors

The lack of internet access in most rural areas has become a challenge worldwide. The Covid-19 pandemic has highlighted trends such as teleworking and e-commerce, meaning an opportunity for the local economy of these areas, but with serious difficulties in carrying it out. This paper aims to detect this lack of internet in inland areas of the region of Valencia through local actors, in order to identify clear priorities and real needs through an explorative and replicable approach based on agglomerative hierarchical clustering (AHC). The main findings suggest that there are different patterns in the rural internet access related to adequate infrastructure and planned actions by local councils. In this way, a multitude of contextual elements have emerged that influence the importance of efficient access to the internet in rural areas. It is essential to know the real needs and demands of the population before implementing plans and programs that may not be relevant for the actors involved in territorial development.

Service Location Optimization Model for Improving Rural Emergency Medical Services

2018 ◽  
Vol 2672 (32) ◽  
pp. 83-93 ◽  
Author(s):  
Zhaoxiang He ◽  
Xiao Qin ◽  
Yuanchang Xie ◽  
Jianhua Guo
Keyword(s):  
Emergency Medical Services ◽  
Rural Areas ◽  
Optimization Model ◽  
Resource Constraints ◽  
Prehospital Care ◽  
Cost Effective ◽  
Medical Services ◽  
Location Optimization ◽  
Emergency Medical ◽  
Multiple Challenges

Approximately 35,000 fatalities are attributed to accidents on U.S. highways each year and more than half of them occurred in rural areas. With such a high percentage of fatalities, rural areas are in critical need of timely and reliable Emergency Medical Services (EMS). EMS provide important prehospital care to victims before they are transferred to a hospital. After an accident occurs, the time it takes for victims to receive care from EMS is crucial to their survival. Compared with urban EMS, rural EMS face multiple challenges. One of them is how to properly site EMS stations to provide cost-effective services in rural areas. The goals of this paper include analyzing the spatial patterns of EMS station and incident locations, and optimizing rural EMS station locations. The data were collected from South Dakota, a rural state. This dataset was used to perform spatial analysis and to develop and evaluate an EMS location optimization model. The location optimization model aims to maximize the rural EMS coverage while taking service equity into consideration. The model was solved by a genetic algorithm toolbox in R. The proposed model provides an important and practical tool for rural EMS officials to select new EMS stations or relocate existing stations to improve service performance under budget and resource constraints.

Uncovering Fine Structure in Gene Expression Profile by Maximum Entropy Modeling of cDNA Microarray Images and Kernel Density Methods

2009 ◽  
pp. 221-238 ◽  
Author(s):  
George Sakellaropoulos ◽  
Antonis Daskalakis ◽  
George Nikiforidis ◽  
Christos Argyropoulos
Keyword(s):  
Gene Expression ◽  
Maximum Entropy ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Kernel Density ◽  
Modeling Tools ◽  
Maximum Entropy Modeling ◽  
Model Free ◽  
Biological Entities ◽  
Density Methods

The presentation and interpretation of microarray-based genome-wide gene expression profiles as complex biological entities are considered to be problematic due to their featureless, dense nature. Furthermore microarray images are characterized by significant background noise, but the effects of the latter on the holistic interpretation of gene expression profiles remains under-explored. We hypothesize that a framework combining (a) Bayesian methodology for background adjustment in microarray images with (b) model-free modeling tools, may serve the dual purpose of data and model reduction, exposing hitherto hidden features of gene expression profiles. Within the proposed framework, microarray image restoration and noise adjustment is facilitated by a class of prior Maximum Entropy distributions. The resulting gene expression profiles are non-parametrically modeled by kernel density methods, which not only normalize the data, but facilitate the generation of reduced mathematical descriptions of biological variability as mixture models.

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