Same Time Detection of PM2.5 and Location Data by Android Portable Device

2016 ◽  
Vol 136 (5) ◽  
pp. 142-147
Author(s):  
Yusuke Harada ◽  
Yoshinori Matsumoto
Keyword(s):  
Portable Device ◽  
Location Data

Quantification of Vitamin A in Fortified Rapeseed, Groundnut and Soya Oils Using a Simple Portable Device: Comparison to High Performance Liquid Chromatography

2013 ◽  
Vol 83 (2) ◽  
pp. 122-128 ◽  
Author(s):  
Cécile Renaud ◽  
Jacques Berger ◽  
Arnaud Laillou ◽  
Sylvie Avallone
Keyword(s):  
High Performance Liquid Chromatography ◽  
Liquid Chromatography ◽  
Vitamin A ◽  
High Performance ◽  
Vitamin A Deficiency ◽  
Developed Countries ◽  
Portable Device ◽  
Least Developed Countries ◽  
Control Food ◽  
Assay Precision

Vitamin A deficiency is still one of the major public health problems in least developed countries. Fortification of vegetable oils is a strategy implemented worldwide to prevent this deficiency. For a fortification program to be effective, regular monitoring is necessary to control food quality in the producing units. The reference methods for vitamin A quantification are expensive and time-consuming. A rapid method should be useful for regular assessment of vitamin A in the oil industry. A portable device was compared to high-performance liquid chromatography (HPLC) for three plant oils (rapeseed, groundnut, and soya). The device presented a good linearity from 3 to 30 mg retinol equivalents per kg (mg RE.kg- 1). Its limits of detection and quantification were 3 mg RE.kg- 1 for groundnut and rapeseed oils and 4 mg RE.kg- 1 for soya oil. The intra-assay precision ranged from 1.48 % to 3.98 %, considered satisfactory. Accuracy estimated by the root mean squares error ranged from 3.99 to 5.49 and revealed a lower precision than HPLC (0.4 to 2.25). Although it offers less precision than HPLC, the device estimates quickly the vitamin A content of the tested oils from 3 or 4 to 15 mg RE.kg- 1.

Retail Mix Dan Kepuasan Konsumen Minimarket

Liquidity ◽  
2018 ◽  
Vol 7 (1) ◽  
pp. 53-62
Author(s):  
Siti Maryama ◽  
Yayat Sujatna
Keyword(s):  
Consumer Satisfaction ◽  
Quantitative Method ◽  
Descriptive Analysis ◽  
T Test ◽  
Primary Data ◽  
Retail Industry ◽  
Location Data ◽  
Store Design ◽  
Customer Services ◽  
The Difference

The purpose of this study is to (1) analyzing the level of retail mix consumer satisfaction; (2) analyze the dominant variable in retail mix consumer satisfaction; (3) analyze the difference of retail mix consumer satisfaction performed. The observed of the retail industry is Alfamidi and Indomaret. The study was designed into a descriptive-quantitative method. The source of primary data obtained from the questionnaire of 100 respondents. The formulating variable of retail mix includes: merchandise assortments, pricing, customer services Store design and display, communication mix, and location. Data analyze by using descriptive, analysis of factors, and t-test. The result confirmed that the level of retail mix consumer satisfaction in both industry is relatively similar. However, it can be stated that the respondents were more satisfied to Indomaret compared with Alfamart.

Marine geologic studies in the Beaufort Sea, Alaska, 1980; location, data type, and records obtained

1982 ◽  
Author(s):  
Edward W. Kempema ◽  
Erk Reimnitz ◽  
Peter W. Barnes
Keyword(s):  
Beaufort Sea ◽  
Data Type ◽  
Location Data

scholarly journals Identifying resting locations of a small elusive forest carnivore using a two-stage model accounting for GPS measurement error and hidden behavioral states

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Dalton J. Hance ◽  
Katie M. Moriarty ◽  
Bruce A. Hollen ◽  
Russell W. Perry
Keyword(s):  
Time Series ◽  
Measurement Error ◽  
Gaussian State ◽  
Parameter Estimates ◽  
Accelerometer Data ◽  
Two Stage ◽  
Location Data ◽  
Gps Measurement ◽  
Activity Measurements ◽  
Behavioral States

Abstract Background Studies of animal movement using location data are often faced with two challenges. First, time series of animal locations are likely to arise from multiple behavioral states (e.g., directed movement, resting) that cannot be observed directly. Second, location data can be affected by measurement error, including failed location fixes. Simultaneously addressing both problems in a single statistical model is analytically and computationally challenging. To both separate behavioral states and account for measurement error, we used a two-stage modeling approach to identify resting locations of fishers (Pekania pennanti) based on GPS and accelerometer data. Methods We developed a two-stage modelling approach to estimate when and where GPS-collared fishers were resting for 21 separate collar deployments on 9 individuals in southern Oregon. For each deployment, we first fit independent hidden Markov models (HMMs) to the time series of accelerometer-derived activity measurements and apparent step lengths to identify periods of movement and resting. Treating the state assignments as given, we next fit a set of linear Gaussian state space models (SSMs) to estimate the location of each resting event. Results Parameter estimates were similar across collar deployments. The HMMs successfully identified periods of resting and movement with posterior state assignment probabilities greater than 0.95 for 97% of all observations. On average, fishers were in the resting state 63% of the time. Rest events averaged 5 h (4.3 SD) and occurred most often at night. The SSMs allowed us to estimate the 95% credible ellipses with a median area of 0.12 ha for 3772 unique rest events. We identified 1176 geographically distinct rest locations; 13% of locations were used on > 1 occasion and 5% were used by > 1 fisher. Females and males traveled an average of 6.7 (3.5 SD) and 7.7 (6.8 SD) km/day, respectively. Conclusions We demonstrated that if auxiliary data are available (e.g., accelerometer data), a two-stage approach can successfully resolve both problems of latent behavioral states and GPS measurement error. Our relatively simple two-stage method is repeatable, computationally efficient, and yields directly interpretable estimates of resting site locations that can be used to guide conservation decisions.

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