Corrigendum to “Soil sample collection and analysis for the fugitive dust characterization study”

2003 ◽  
Vol 37 (29) ◽  
pp. 4177 ◽  
Author(s):  
Lowell L Ashbaugh ◽  
Omar F Carvacho ◽  
Michael S Brown ◽  
Judith C Chow ◽  
John G Watson ◽  
...  
Keyword(s):  
Soil Sample ◽  
Fugitive Dust ◽  
Sample Collection ◽  
Characterization Study

scholarly journals Soil sample collection and analysis for the Fugitive Dust Characterization Study

2003 ◽  
Vol 37 (9-10) ◽  
pp. 1163-1173 ◽  
Author(s):  
Lowell L Ashbaugh ◽  
Omar F Carvacho ◽  
Michael S Brown ◽  
Judith C Chow ◽  
John G Watson ◽  
...  
Keyword(s):  
Soil Sample ◽  
Fugitive Dust ◽  
Sample Collection ◽  
Characterization Study

scholarly journals A new soil core sampler for determination bulk density in soil profile

2011 ◽  
Vol 50 (No. 6) ◽  
pp. 250-256 ◽  
Author(s):  
P. Prikner ◽  
F. Lachnit ◽  
F. Dvořák
Keyword(s):  
Bulk Density ◽  
Soil Sample ◽  
Soil Profile ◽  
Soil Compaction ◽  
Sampling Methods ◽  
Sample Volume ◽  
Soil Core ◽  
Sample Collection ◽  
Core Sampler ◽  
Inner Diameter

The portable soil core sampler was engineered for gradual sampling of soil profile in the depth up to 0.5 m, which ensures extraction of the whole sample volume of soil profile in determinable depth. The portable soil core sampler was compared with the professional soil probe Eijkelkamp P1.31 (Eijkelkamp Agrisearch Equipment, Netherlands) in field conditions. The portable sampler was compared with the physical soil sample rings in laboratory conditions to eliminate all of possible restrictive aspects affecting the procedure of measurement. The portable soil core sampler with inner diameter 71 mm, depth 120 mmenables gradually take samples of soil profile by step of 50 mmand is able to detect possible local extremes. On the other hand a soil probe is not able to reach desired accuracy in taking of a&nbsp;soil sample. Values measured from a soil probe approximately taken by step of 150 mmare inaccurate. The values of bulk density of both sampling methods were variable at significant interval from 40 into 80 kg/m<sup>3</sup>. Different values could be caused by soil profile condition and by the use of different sampling methods. The design of a portable soil sampler should be of assistance in fast and precise soil profiling sample collection, which is required to determine bulk density of the soil, its variance depending on moisture content in soil compaction determining criteria.

scholarly journals Soil Organic Carbon Baselines for Land Degradation Neutrality: Map Accuracy and Cost Tradeoffs with Respect to Complexity in Otjozondjupa, Namibia

2018 ◽  
Vol 10 (5) ◽  
pp. 1610 ◽  
Author(s):  
Ravic Nijbroek ◽  
Kristin Piikki ◽  
Mats Söderström ◽  
Bas Kempen ◽  
Katrine Turner ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Soil Sample ◽  
Land Degradation ◽  
Absolute Error ◽  
Lessons Learned ◽  
Economic Losses ◽  
Complex Method ◽  
Sample Collection ◽  
Map Accuracy

Recent estimates show that one third of the world’s land and water resources are highly or moderately degraded. Global economic losses from land degradation (LD) are as high as USD $10.6 trillion annually. These trends catalyzed a call for avoiding future LD, reducing ongoing LD, and reversing past LD, which has culminated in the adoption of Sustainable Development Goal (SDG) Target 15.3 which aims to achieve global land degradation neutrality (LDN) by 2030. The political momentum and increased body of scientific literature have led to calls for a ‘new science of LDN’ and highlighted the practical challenges of implementing LDN. The aim of the present study was to derive LDN soil organic carbon (SOC) stock baseline maps by comparing different digital soil mapping (DSM) methods and sampling densities in a case study (Otjozondjupa, Namibia) and evaluate each approach with respect to complexity, cost, and map accuracy. The mean absolute error (MAE) leveled off after 100 samples were included in the DSM models resulting in a cost tradeoff for additional soil sample collection. If capacity is sufficient, the random forest DSM method out-performed other methods, but the improvement from using this more complex method compared to interpolating the soil sample data by ordinary kriging was minimal. The lessons learned while developing the Otjozondjupa LDN SOC baseline provide valuable insights for others who are responsible for developing LDN baselines elsewhere.

Evaluation of Acoustic Analyses of Voice in Nonoptimized Conditions

2020 ◽  
Vol 63 (12) ◽  
pp. 3991-3999
Author(s):  
Benjamin van der Woerd ◽  
Min Wu ◽  
Vijay Parsa ◽  
Philip C. Doyle ◽  
Kevin Fung
Keyword(s):  
Repeated Measures ◽  
Voice Quality ◽  
Data Sets ◽  
Acoustic Measurements ◽  
Sample Collection ◽  
Experimental Conditions ◽  
Environment Analysis ◽  
Acoustic Measures ◽  
Recording Conditions ◽  
Cepstral Peak Prominence

Objectives This study aimed to evaluate the fidelity and accuracy of a smartphone microphone and recording environment on acoustic measurements of voice. Method A prospective cohort proof-of-concept study. Two sets of prerecorded samples (a) sustained vowels (/a/) and (b) Rainbow Passage sentence were played for recording via the internal iPhone microphone and the Blue Yeti USB microphone in two recording environments: a sound-treated booth and quiet office setting. Recordings were presented using a calibrated mannequin speaker with a fixed signal intensity (69 dBA), at a fixed distance (15 in.). Each set of recordings (iPhone—audio booth, Blue Yeti—audio booth, iPhone—office, and Blue Yeti—office), was time-windowed to ensure the same signal was evaluated for each condition. Acoustic measures of voice including fundamental frequency ( f o ), jitter, shimmer, harmonic-to-noise ratio (HNR), and cepstral peak prominence (CPP), were generated using a widely used analysis program (Praat Version 6.0.50). The data gathered were compared using a repeated measures analysis of variance. Two separate data sets were used. The set of vowel samples included both pathologic ( n = 10) and normal ( n = 10), male ( n = 5) and female ( n = 15) speakers. The set of sentence stimuli ranged in perceived voice quality from normal to severely disordered with an equal number of male ( n = 12) and female ( n = 12) speakers evaluated. Results The vowel analyses indicated that the jitter, shimmer, HNR, and CPP were significantly different based on microphone choice and shimmer, HNR, and CPP were significantly different based on the recording environment. Analysis of sentences revealed a statistically significant impact of recording environment and microphone type on HNR and CPP. While statistically significant, the differences across the experimental conditions for a subset of the acoustic measures (viz., jitter and CPP) have shown differences that fell within their respective normative ranges. Conclusions Both microphone and recording setting resulted in significant differences across several acoustic measurements. However, a subset of the acoustic measures that were statistically significant across the recording conditions showed small overall differences that are unlikely to have clinical significance in interpretation. For these acoustic measures, the present data suggest that, although a sound-treated setting is ideal for voice sample collection, a smartphone microphone can capture acceptable recordings for acoustic signal analysis.

Issues in Language Sample Collection and Analysis With Children Using AAC

2014 ◽  
Vol 23 (2) ◽  
pp. 65-74 ◽  
Author(s):  
Gail Van Tatenhove
Keyword(s):  
Clinical Practice ◽  
Augmentative And Alternative Communication ◽  
Language Production ◽  
Expressive Language ◽  
Sample Analysis ◽  
Alternative Communication ◽  
Sample Collection ◽  
Speech Language Pathologists ◽  
Language Sample Analysis ◽  
Language Sample

Language sample analysis is considered one of the best methods of evaluating expressive language production in speaking children. However, the practice of language sample collection and analysis is complicated for speech-language pathologists working with children who use augmentative and alternative communication (AAC) devices. This article identifies six issues regarding use of language sample collection and analysis in clinical practice with children who use AAC devices. The purpose of this article is to encourage speech-language pathologists practicing in the area of AAC to utilize language sample collection and analysis as part of ongoing AAC assessment.

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