scholarly journals A chironomid-based mean July temperature inference model from the south-east margin of the Tibetan Plateau, China

2016 ◽  
Author(s):  
Enlou Zhang ◽  
Jie Chang ◽  
Yanmin Cao ◽  
Hongqu Tang ◽  
Pete Langdon ◽  
...  
Keyword(s):  
Transfer Functions ◽  
Explanatory Power ◽  
Weighted Average ◽  
Partial Least Square ◽  
Environmental Data ◽  
The Tibetan Plateau ◽  
Calibration Data ◽  
Training Set ◽  
Maximum Bias ◽  
July Temperature

Abstract. Chironomid based calibration training sets comprised of 100 lakes from southwestern China and a subset of 47 lakes from Yunnan Province were established. Multivariate ordination analyses were used to investigate the relationship between the distribution of chironomid species and 15 environmental variables from these lakes. Canonical correspondence analyses (CCAs) and partial CCAs showed that mean July temperature is the sole independent and significant (p < 0.05) variable that explains 16 % of the variance in the chironomid data from the 47 Yunnan lakes. Mean July temperature remains one of the independent and significant variables explaining the second largest amount of variance after potassium ions (K+) in the 100 south-western Chinese lakes. Quantitative transfer functions were created using the chironomid assemblages for both calibration data sets. The first component of the weighted average partial least square (WA-PLS) model based on the 47 lakes training set produced a coefficient of determination (r2jack) of 0.83, maximum bias (jackknifed) of 3.15 and root mean squared error of prediction (RMSEP) of 1.72 °C. The two-component WA-PLS model for the 100 lakes training set produced an r2bootstrap of 0.63, maximum bias (bootstrapped) of 5.16 and RMSEP of 2.31 °C. We applied both transfer functions to a 150-year chironomid record from Tiancai Lake (26°38′3.8 N, 99°43’E, 3898 m a.s.l), Yunnan, China to obtain mean July temperature inferences. The reconstructed results based on both models showed remarkable similarity to each other in terms of pattern. We validated these results by applying several reconstruction diagnostics and comparing them to a 50-year instrumental record from the nearest weather station (26°51'29.22"N, 100°14'2.34"E, 2390 m a.s.l). Both transfer functions perform well in this comparison. We argue that the large training set is also suitable for reconstruction work despite the low explanatory power of MJT because it contains a more complete range of modern temperature and environmental data for the chironomid taxa observed and is therefore more robust.

scholarly journals A chironomid-based mean July temperature inference model from the south-east margin of the Tibetan Plateau, China

2017 ◽  
Vol 13 (3) ◽  
pp. 185-199 ◽  
Author(s):  
Enlou Zhang ◽  
Jie Chang ◽  
Yanmin Cao ◽  
Hongqu Tang ◽  
Pete Langdon ◽  
...  
Keyword(s):  
Transfer Functions ◽  
Explanatory Power ◽  
Weighted Average ◽  
Environmental Data ◽  
Western China ◽  
The Tibetan Plateau ◽  
Calibration Data ◽  
Training Set ◽  
Data Set ◽  
July Temperature

Abstract. A chironomid-based calibration training set comprised of 100 lakes from south-western China was established. Multivariate ordination analyses were used to investigate the relationship between the distribution and abundance of chironomid species and 18 environmental variables from these lakes. Canonical correspondence analyses (CCAs) and partial CCAs showed that mean July temperature is one of the independent and significant variables explaining the second-largest amount of variance after potassium ions (K+) in 100 south-western Chinese lakes. Quantitative transfer functions were created using the chironomid assemblages for this calibration data set. The second component of the weighted-average partial least squares (WA-PLS) model produced a coefficient of determination (r2bootstrap) of 0.63, maximum bias (bootstrap) of 5.16 and root-mean-square error of prediction (RMSEP) of 2.31 °C. We applied the transfer functions to a 150-year chironomid record from Tiancai Lake (26°38′3.8 N, 99°43′ E; 3898 m a.s.l.), Yunnan, China, to obtain mean July temperature inferences. We validated these results by applying several reconstruction diagnostics and comparing them to a 50-year instrumental record from the nearest weather station (26°51′29.22′′ N, 100°14′2.34′′ E; 2390 m a.s.l.). The transfer function performs well in this comparison. We argue that this 100-lake large training set is suitable for reconstruction work despite the low explanatory power of mean July temperature because it contains a complete range of modern temperature and environmental data for the chironomid taxa observed and is therefore robust.

Phytolith-inferred transfer function for paleohydrological reconstruction of Dajiuhu peatland, central China

The Holocene ◽  
2018 ◽  
Vol 28 (10) ◽  
pp. 1623-1630 ◽  
Author(s):  
Hongye Liu ◽  
Yansheng Gu ◽  
Zijian Lun ◽  
Yangmin Qin ◽  
Shenggao Cheng
Keyword(s):  
Environmental Variables ◽  
Transfer Functions ◽  
Model Performance ◽  
Detrended Correspondence Analysis ◽  
Environmental Data ◽  
Central China ◽  
Weighted Averaging ◽  
Calibration Data ◽  
Modern Analogue ◽  
Dajiuhu Peatland

Depth to water table (DWT, the depth from the water surface to the top of the peat surface) is one of the most important environmental variables related to the habitat types and distribution of vegetation within a subalpine peatland. The distribution of phytolith assemblages and basic environmental data from 43 surface soil samples with significant ecological and hydrological gradients were investigated to generate transfer functions for quantitative reconstruction of paleoenvironmental changes in Dajiuhu peatland, central China. Detrended correspondence analysis (DCA) and redundancy analysis (RDA) were employed to explore the relationship between main environmental variables and phytolith morphotypes and distributions. Our results indicate that the spatial distribution of phytoliths was significantly correlated with the DWT (25% variance), total organic carbon (TOC, 10% variance) and magnetic susceptibility (χ, 7% variance). We established the transfer functions for the significant variables based on modern analogue technique (MAT), weighted averaging techniques (WA) and weighted averaging partial least squares (WA-PLS), and model performance was assessed using bootstrap cross-validation. The high correspondence of the predicted DWT results based on phytolith-environment calibration data with observed data reflects that the phytolith-based WA-PLS is a reliable effective calibration method for the quantitative DWT reconstruction of ombrotrophic (rain-fed) subalpine peatland.

scholarly journals Modelling Soluble Solids Content Accumulation in ‘Braeburn’ Apples

Plants ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 302
Author(s):  
Konni Biegert ◽  
Daniel Stöckeler ◽  
Roy J. McCormick ◽  
Peter Braun
Keyword(s):  
Explanatory Power ◽  
Partial Least Square ◽  
Optical Measurements ◽  
Soluble Solids ◽  
Sensor Data ◽  
Soluble Solids Content ◽  
Mixed Effect ◽  
Laboratory Errors ◽  
Solids Content ◽  
Non Destructive

Optical sensor data can be used to determine changes in anthocyanins, chlorophyll and soluble solids content (SSC) in apple production. In this study, visible and near-infrared spectra (729 to 975 nm) were transformed to SSC values by advanced multivariate calibration models i.e., partial least square regression (PLSR) in order to test the substitution of destructive chemical analyses through non-destructive optical measurements. Spectral field scans were carried out from 2016 to 2018 on marked ‘Braeburn’ apples in Southwest Germany. The study combines an in-depth statistical analyses of longitudinal SSC values with horticultural knowledge to set guidelines for further applied use of SSC predictions in the orchard to gain insights into apple carbohydrate physiology. The PLSR models were investigated with respect to sample size, seasonal variation, laboratory errors and the explanatory power of PLSR models when applied to independent samples. As a result of Monte Carlo simulations, PLSR modelled SSC only depended to a minor extent on the absolute number and accuracy of the wet chemistry laboratory calibration measurements. The comparison between non-destructive SSC determinations in the orchard with standard destructive lab testing at harvest on an independent sample showed mean differences of 0.5% SSC over all study years. SSC modelling with longitudinal linear mixed-effect models linked high crop loads to lower SSC values at harvest and higher SSC values for fruit from the top part of a tree.

Is the introduction of benthic species necessary for open-water chemical reconstruction in diatom-based transfer functions?

2002 ◽  
Vol 59 (6) ◽  
pp. 938-951 ◽  
Author(s):  
Aline Philibert ◽  
Yves T Prairie
Keyword(s):  
Transfer Functions ◽  
Model Performance ◽  
Open Water ◽  
Partial Least Square ◽  
Least Square ◽  
Weighted Averaging ◽  
Benthic Species ◽  
Inference Models ◽  
Number Of Species ◽  
Chemical Conditions

Despite the overwhelming tendency in paleolimnology to use both planktonic and benthic diatoms when inferring open-water chemical conditions, it remains questionable whether all taxa are appropriate and necessary to construct useful inference models. We examined this question using a 75-lake training set from Quebec (Canada) to assess whether model performance is affected by the deletion of benthic species. Because benthic species are known to experience very different chemical conditions than their planktonic counterparts, we hypothesized that they would introduce undesirable noise in the calibration. Surprisingly, such important variables as pH, total phosphorus (TP), total nitrogen (TN), and dissolved organic carbon (DOC) were well predicted from weighted-averaging partial least square (WA-PLS) models based solely on benthic species. Similar results were obtained regardless of the depth of the lakes. Although the effective number of occurrence (N2) and the tolerance of species influenced the stability of the model residual error (jackknife), the number of species was the major factor responsible for the weaker inference models when based on planktonic diatoms alone. Indeed, when controlled for the number of species in WA-PLS models, individual planktonic diatom species showed superior predictive power over individual benthic species in inferring open-water chemical conditions.

Learnings From Strain Measurements on an In-Field Conductor and Wellhead System

2018 ◽  
Author(s):  
Rohit Shankaran ◽  
Alexander Rimmer ◽  
Alan Haig
Keyword(s):  
Fatigue Damage ◽  
Strain Gauge ◽  
Transfer Functions ◽  
Fatigue Loading ◽  
Accurate Method ◽  
Environmental Data ◽  
Analytical Methodology ◽  
Data Set ◽  
Soil Model ◽  
Drilling Operations

In recent years due to use of drilling risers with larger and heavier BOP/LMRP stacks, fatigue loading on subsea wellheads has increased, which poses potential restrictions on the duration of drilling operations. In order to track wellhead and conductor fatigue capacity consumption to support safe drilling operations a range of methods have been applied: • Analytical riser model and measured environmental data; • BOP motion measurement and transfer functions; • Strain gauge data. Strain gauge monitoring is considered the most accurate method for measuring fatigue capacity consumption. To compare the three approaches and establish recommendations for an optimal approach and method to establish fatigue accumulation of the wellhead, a monitoring data set is obtained on a well offshore West of Shetland. This paper presents an analysis of measured strain, motions and analytical predictions with the objective of better understanding the accuracy, limitations, or conservatism in each of the three methods defined above. Of the various parameters that affect the accuracy of the fatigue damage estimates, the paper identifies that the selection of analytical conductor-soil model is critical to narrowing the gap between fatigue life predictions from the different approaches. The work presented here presents the influence of alternative approaches to model conductor-soil interaction than the traditionally used API soil model. Overall, the paper presents the monitoring equipment and analytical methodology to advance the accuracy of wellhead fatigue damage measurements.

scholarly journals PROJECTION OF INCIDENT SURFACE SOLAR RADIATION IN CHINA UNDER A CLIMATE CHANGE SCENARIO

2019 ◽  
Vol XLII-3/W9 ◽  
pp. 187-194
Author(s):  
Y. K. Xiao ◽  
Z. M. Ji ◽  
C. S. Fu ◽  
W. T. Du ◽  
J. H. Yang ◽  
...  
Keyword(s):  
Climate Change ◽  
Solar Radiation ◽  
21St Century ◽  
Weighted Average ◽  
Global Climate Models ◽  
Future Climate Change ◽  
The Tibetan Plateau ◽  
Climate Change Scenarios ◽  
Model Ensemble ◽  
Surface Solar Radiation

Abstract. We projected incident surface solar radiation (SSR) over China in the middle (2040–2059) and end (2080–2099) of the 21st century in the Representative Concentration Pathway (RCP) 8.5 scenario using a multi-model ensemble derived from the weighted average of seven global climate models (GCMs). The multi-model ensemble captured the contemporary (1979–2005) spatial and temporal characteristics of SSR and reproduced the long-term temporal evolution of the mean annual SSR in China. However, it tended to overestimate values compared to observations due to the absence of aerosol effects in the simulations. The future changes in SSR showed increases over eastern and southern China, and decreases over the Tibetan Plateau (TP) and northwest China relative to the present day. At the end of the 21st century, there were SSR increases of 9–21 W m−2 over northwest, central, and south China, and decreases of 18–30 W m−2 over the TP in June–July–August (JJA). In northeast China, SSR showed seasonal variation with increases in JJA and decreases in December–January–February. The time series of annual SSR had a decreased linear trend for the TP, and a slightly increased trend for China during 2006–2099. The results of our study suggest that solar energy resources will likely decrease in the TP under future climate change scenarios.

Consumer adoption of smartphone fitness apps: an extended UTAUT2 perspective

2019 ◽  
Vol 12 (3) ◽  
pp. 363-388 ◽  
Author(s):  
Neeraj Dhiman ◽  
Neelika Arora ◽  
Nikita Dogra ◽  
Anil Gupta
Keyword(s):  
Self Efficacy ◽  
Role Models ◽  
Explanatory Power ◽  
Emerging Economy ◽  
Partial Least Square ◽  
Least Square ◽  
Behavioural Intention ◽  
Content Type ◽  
Personal Innovativeness ◽  
Effort Expectancy

Purpose The purpose of this paper is to examine the determinants of user adoption of smartphone fitness apps in context of an emerging economy. Design/methodology/approach The present study uses the extended unified theory of acceptance and use of technology (UTAUT2) as the base model along with two additional constructs, i.e. self-efficacy and personal innovativeness. The data collection was done through an online survey, wherein a total of 324 valid responses were obtained for the statistical analysis. All the hypothesized relationships were tested through partial least square structural equation modelling (PLS-SEM) using an open source programming language and software environment, i.e. R Software along with plspm-package. Findings Significant predictors of smartphone fitness app adoption intention include effort expectancy, social influence, perceived value, habit and personal innovativeness. Further, this study confirms significant relationship between personal innovativeness and habit, self-efficacy and effort expectancy and effort expectancy and performance expectation. This study reveals that personal innovativeness is the strongest predictor of behavioural intention. Contrary to the expectations, factors like performance expectancy, facilitating conditions and hedonic motivation did not influence behavioural intention. Practical implications This study gives significant clues to app developers that can drastically influence the adoption of fitness apps. The findings suggest that marketers should focus on users with high personal innovativeness that can further act as role models and significantly influence their social circle. Interestingly, the findings suggest that fitness apps, as compared to other apps, should not emphasize much on the hedonic value of their offerings. Originality/value This study is one of the few studies to examine the adoption of smartphone fitness apps in an emerging economy context by using extended version of UTAUT2 model. Further, this study shows how new endogenous and exogenous variables (i.e. self-efficacy and personal innovativeness) contribute to better explanatory power of the UTAUT2 framework.

scholarly journals BUMPER v1.0: A Bayesian User-friendly Model for Palaeo-Environmental Reconstruction

2016 ◽  
Author(s):  
Philip B. Holden ◽  
H. John B. Birks ◽  
Stephen J. Brooks ◽  
Mark B. Bush ◽  
Grace M. Hwang ◽  
...  
Keyword(s):  
Weighted Average ◽  
Real Data ◽  
Environmental Reconstruction ◽  
Probabilistic Framework ◽  
Training Set ◽  
General Applicability ◽  
User Friendly ◽  
Site Training ◽  
Artificial Datasets ◽  
Training Sets

Abstract. We describe the Bayesian User-friendly Model for Palaeo-Environmental Reconstruction (BUMPER), a Bayesian transfer function for inferring past climate from microfossil assemblages. BUMPER is fully self-calibrating, straightforward to apply, and computationally fast, requiring ~ 2 seconds to build a 100-species model from a 100-site training-set on a standard personal computer. We apply the model's probabilistic framework to generate thousands of artificial training-sets under ideal assumptions. We then use these data to demonstrate the sensitivity of reconstructions to the characteristics of the training-set, considering assemblage richness, species tolerances, and the number of training sites. We find that a useful guideline for the size of a training-set is to provide, on average, at least ten samples of each species. We demonstrate general applicability to real data, considering three different organism types (chironomids, diatoms, pollen) and different reconstructed variables. An identically configured model is used in each application, the only change being the input files that provide the training-set environment and species-count data. The performance of BUMPER is shown to be comparable with Weighted Average Partial Least Squares (WAPLS) in each case. Additional artificial datasets are constructed with similar characteristics to the real data, and these are used to explore the reasons for the differing performances of the different training-sets.

scholarly journals Advancement of Individualized Head-Related Transfer Functions (HRTFs) in Perceiving the Spatialization Cues: Case Study for an Integrated HRTF Individualization Method

2019 ◽  
Vol 9 (9) ◽  
pp. 1867
Author(s):  
Lei Wang ◽  
Xiangyang Zeng ◽  
Xiyue Ma
Keyword(s):  
Transfer Functions ◽  
Objective Evaluation ◽  
Differential Method ◽  
Partial Least Square ◽  
Least Square ◽  
Experimental Results ◽  
Partial Least Square Regression ◽  
Sound Sources ◽  
Integrated Method

Head-related transfer function (HRTF), which varies across individuals at the same direction, has grabbed widespread attention in the field of acoustics and been used in many scenarios. In order to in-depth investigate the performance of individualized HRTFs on perceiving the spatialization cues, this study presents an integrated algorithm to obtain individualized HRTFs, and explores the advancement of such individualized HRTFs in perceiving the spatialization cues through two different binaural experiments. An integrated method for HRTF individualization on the use of Principle Component Analysis (PCA), Multiple Linear Regression (MLR) and Partial Least Square Regression (PLSR) was presented first. The objective evaluation was then made to verify the algorithmic effectiveness of that method. Next, two subjective experiments were conducted to explore the advancement of individualized HRTFs in perceiving the spatialization cues. One was auditory directional discrimination degree based on semantic differential method, in which the azimuth information of sound sources was told to the listeners before listening. The other was auditory localization, in which the azimuth information was not told to the listeners before listening. The corresponding statistical analyses for the subjective experimental results were made. All the experimental results support that individualized HRTFs obtained from the presented method achieve a preferable performance in perceiving the spatialization cues.

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