Potential of LiDAR for species richness prediction at Mount Kilimanjaro

2020 ◽  
Author(s):  
Alice Ziegler ◽  
Keyword(s):  
Species Richness ◽  
Structural Information ◽  
Elevation Gradient ◽  
Partial Least Square ◽  
Airborne Lidar ◽  
Least Square ◽  
Trophic Levels ◽  
Partial Least Square Regression ◽  
Mount Kilimanjaro ◽  
The Impact

<p>To mitigate the negative effects of biodiversity loss, monitoring of species and functional diversity is an important prerequisite for focused management plans. However, sampling of biodiversity during field campaigns is labor- and cost-intensive. Therefore, researchers often use proxies extracted from three-dimensional and high-resolution airborne LiDAR (Light Detection and Ranging) data of the vegetation for predicting biodiversity measures (e.g. species richness or diversity).</p><p>This study aims at (i) assessing the suitability of LiDAR observations to map species richness across 17 taxonomic groups and four trophic levels at Mount Kilimanjaro and (ii) differentiating the predictive power of LiDAR-derived structural information from what is already explained by elevation, thereby comparing the prediction potential across taxa and trophic levels.</p><p>The field data for this study were collected across 59 plots along an elevation gradient of about 4000 meters at the southern slopes of Mount Kilimanjaro using established methods to sample the selected groups of organisms. The prediction is accomplished with three consecutive steps: (1) Species richness of each taxon is estimated using Partial Least Square Regression (PLSR) with only elevation and its square as independent variables. (2) The residuals of this model are then predicted using the LiDAR-derived variables and PLSR. (3) This third model is subsequently compared to a model that uses the same LiDAR-derived variables and PLSR to predict species richness directly rather than its residuals. This procedure allows to analyze the impact of elevation versus structure on each taxon. Furthermore, the standardized study design allows to compare the predictability of species richness across the selected groups of organisms.</p><p>Results of this study show that most taxa can be best predicted by elevation, even though in most cases the structural models perform almost equally. As expected, results of the model performances of trophic levels indicate, that herbivores are influenced more by structure than decomposers and generalists.</p>

Field Nitrogen Dioxide and Ozone Monitoring Using Electrochemical Sensors with Partial Least Squares Regression

2021 ◽  
Vol 5 (1) ◽  
pp. 61
Author(s):  
Rachid Laref ◽  
Etienne Losson ◽  
Alexandre Sava ◽  
Maryam Siadat
Keyword(s):  
Nitrogen Dioxide ◽  
Electrochemical Sensors ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Calibration Model ◽  
Least Squares Regression ◽  
Monitoring Networks ◽  
Concentration Estimation ◽  
The Impact

Low-cost gas sensors detect pollutants gas at the parts-per-billion level and may be installed in small devices to densify air quality monitoring networks for the spread analysis of pollutants around an emissive source. However, these sensors suffer from several issues such as the impact of environmental factors and cross-interfering gases. For instance, the ozone (O3) electrochemical sensor senses nitrogen dioxide (NO2) and O3 simultaneously without discrimination. Alphasense proposes the use of a pair of sensors; the first one, NO2-B43F, is equipped with a filter dedicated to measure NO2. The second one, OX-B431, is sensitive to both NO2 and O3. Thus, O3 concentration can be obtained by subtracting the concentration of NO2 from the sum of the two concentrations. This technique is not practical and requires calibrating each sensor individually, leading to biased concentration estimation. In this paper, we propose Partial Least Square regression (PLS) to build a calibration model including both sensors’ responses and also temperature and humidity variations. The results obtained from data collected in the field for two months show that PLS regression provides better gas concentration estimation in terms of accuracy than calibrating each sensor individually.

scholarly journals Detection of malaria in insectary-reared Anopheles gambiae using near-infrared spectroscopy

2019 ◽  
Author(s):  
Marta F. Maia ◽  
Melissa Kapulu ◽  
Michelle Muthui ◽  
Martin G. Wagah ◽  
Heather M. Ferguson ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Anopheles Gambiae ◽  
Near Infrared Spectroscopy ◽  
Large Scale ◽  
Near Infrared ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Human Malaria ◽  
The Impact

AbstractLarge-scale surveillance of mosquito populations is crucial to assess the intensity of vector-borne disease transmission and the impact of control interventions. However, there is a lack of accurate, cost-effective and high-throughput tools for mass-screening of vectors. This study demonstrates proof-of-concept that near-infrared spectroscopy (NIRS) is capable of rapidly identifying laboratory strains of human malaria infection in African mosquito vectors. By using partial least square regression models based on malaria-infected and uninfected Anopheles gambiae mosquitoes, we showed that NIRS can detect oocyst- and sporozoite-stage Plasmodium falciparum infections with 88% and 95% accuracy, respectively. Accurate, low-cost, reagent-free screening of mosquito populations enabled by NIRS could revolutionize surveillance and elimination strategies for the most important human malaria parasite in its primary African vector species. Further research is needed to evaluate how the method performs in the field following adjustments in the training datasets to include data from wild-caught infected and uninfected mosquitoes.

A Local Model and Calibration Set Ensemble Strategy for Open-Path FTIR Gas Measurement with Varying Temperature

2013 ◽  
Vol 20 (3) ◽  
pp. 513-524 ◽  
Author(s):  
Sławomir Cięszczyk
Keyword(s):  
Ensemble Methods ◽  
Partial Least Square ◽  
Least Square ◽  
Atmospheric Pollutants ◽  
Partial Least Square Regression ◽  
Local Model ◽  
Gas Concentration ◽  
Open Path ◽  
Ensemble Strategy ◽  
The Impact

Abstract Open-Path Fourier Transform Infrared OP-FTIR spectrometers are commonly used for the measurement of atmospheric pollutants and of gases in industrial processes. Spectral interpretation for the determination of gas concentrations is based on the HITRAN database line-by-line modeling method. This article describes algorithms used to model gas spectra and to determine gas concentration under variable temperatures. Integration of individual rotational lines has been used to reduce the impact of spectrometer functions on the comparison of both measured and synthetic modeled spectra. Carbon monoxide was used as an example. A new algorithm for gas concentration retrieval consisting of two ensemble methods is proposed. The first method uses an ensemble of local models based on linear and non-linear PLS (partial least square) regression algorithms, while the second is an ensemble of a calibration set built for different temperatures. It is possible to combine these methods to decrease the number of regression models in the first ensemble. These individual models are appropriate for specific measurement conditions specified by the ensemble of the calibration set. Model selection is based on comparison of gas spectra with values determined from each local model

scholarly journals Managing brand identity: effects on the employees

2017 ◽  
Vol 35 (1) ◽  
pp. 2-23 ◽  
Author(s):  
Rafael Bravo ◽  
Isabel Buil ◽  
Leslie de Chernatony ◽  
Eva Martínez
Keyword(s):  
Financial Services ◽  
Identity Management ◽  
Employee Attitudes ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Brand Identity ◽  
Content Type ◽  
The Uk ◽  
The Impact

Purpose The purpose of this paper is to better understand the brand identity management process from the employees’ perspective. Specifically, it explores how the different dimensions of brand identity management influence employees’ attitudinal and behavioural responses. Design/methodology/approach An empirical study was carried out to test the proposed model. The sample consisted of 297 employees in the UK financial services sector. Hypothesis testing was conducted using partial least square regression. Findings Results indicate that effective brand identity management can increase employees’ identification with their organisations. Specifically, the most influential dimension is the employee-client focus. Results also show that organisational identification is a key variable to explain job satisfaction, word-of-mouth and brand citizenship behaviour. Research limitations/implications This study focusses on the UK financial sector. To explore the generalisability of results, replication studies among other sectors and countries would be useful. The cross-sectional nature of the study also limits its causal inference. Practical implications This study shows the importance of brand identity management to foster positive employee attitudes and actions that go beyond their job responsibilities. The model developed may help organisations analyse the impact of managerial actions, monitoring the potential effects of changes in brand identity management amongst employees. Originality/value Although numerous conceptual frameworks highlight the importance of brand identity management, empirical studies in this area are scarce. The current work extends previous research by empirically analysing the effects of the dimensions of brand identity management from the employees’ perspective.

scholarly journals Mass-immigration determines the assembly of activated sludge microbial communities

2021 ◽  
Vol 118 (27) ◽  
pp. e2021589118
Author(s):  
Giulia Dottorini ◽  
Thomas Yssing Michaelsen ◽  
Sergey Kucheryavskiy ◽  
Kasper Skytte Andersen ◽  
Jannie Munk Kristensen ◽  
...  
Keyword(s):  
Activated Sludge ◽  
Microbial Communities ◽  
Abundant Species ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Similar Process ◽  
Microbial Composition ◽  
Species Abundances ◽  
The Impact

The assembly of bacterial communities in wastewater treatment plants (WWTPs) is affected by immigration via wastewater streams, but the impact and extent of bacterial immigrants are still unknown. Here, we quantify the effect of immigration at the species level in 11 Danish full-scale activated sludge (AS) plants. All plants have different source communities but have very similar process design, defining the same overall environmental growth conditions. The AS community composition in each plant was strongly reflected by the corresponding influent wastewater (IWW) microbial composition. Most species in AS across the plants were detected and quantified in the corresponding IWW, allowing us to identify their fate in the AS: growing, disappearing, or surviving. Most of the abundant species in IWW disappeared in AS, so their presence in the AS biomass was only due to continuous mass-immigration. In AS, most of the abundant growing species were present in the IWW at very low abundances. We predicted the AS species abundances from their abundance in IWW by using a partial least square regression model. Some species in AS were predicted by their own abundance in IWW, while others by multiple species abundances. Detailed analyses of functional guilds revealed different prediction patterns for different species. We show, in contrast to the present understanding, that the AS microbial communities were strongly controlled by the IWW source community and could be quantitatively predicted by taking into account immigration. This highlights a need to revise the way we understand, design, and manage the microbial communities in WWTPs.

scholarly journals Conservation priorities and distribution patterns of vascular plant species along environmental gradients in Aberdare ranges forest

PhytoKeys ◽  
2019 ◽  
Vol 131 ◽  
pp. 91-113 ◽  
Author(s):  
Solomon Kipkoech ◽  
David Kimutai Melly ◽  
Benjamin Watuma Mwema ◽  
Geoffrey Mwachala ◽  
Paul Mutuku Musili ◽  
...  
Keyword(s):  
Species Richness ◽  
Plant Species ◽  
Endemic Species ◽  
Vascular Plant ◽  
Distribution Patterns ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Conservation Priorities ◽  
Conservation And Management

Distribution patterns of biodiversity and the factors influencing them are important in conservation and management strategies of natural resources. With impending threats from increased human population and global climatic changes, there is an urgent need for a comprehensive understanding of these patterns, more so in species-rich tropical montane ecosystems where little is known about plant diversity and distribution. Vascular species richness along elevation and climatic gradients of Aberdare ranges forest were explored. A total of 1337 species in 137 families, 606 genera, 82 subspecies and 80 varieties were recorded. Correlations, simple linear regression and Partial least square regression analysis were used to assess richness and diversity patterns of total plants, herbs, shrubs, climbers, arboreal and endemic species from 2000–4000 m above sea level. Total plant species richness showed a monotonic declining relationship with elevation with richness maxima at 2000–2100 m a.s.l., while endemic species richness had a positive unimodal increase along elevation with peaks at 3600–3700 m a.s.l. Herbs, shrubs, climbers and arboreal had significant negative relationships with altitude, excluding endemism which showed positive relations. In contrast, both air and soil temperatures had positive relationships with taxa richness groups and negative relations with endemic species. Elevation was found to have higher relative influence on plant richness and distribution in Aberdare ranges forest. For effective conservation and management of biodiversity in Aberdare, localized dynamic conservation interventions are recommended in contrast to broad and static strategies. Establishment of conservation zones and migration corridors are necessary to safeguard biodiversity in line with envisaged global climatic vicissitudes.

scholarly journals INDICATORS FOR COMMON CROSSING STRUCTURAL HEALTH MONITORING WITH TRACK-SIDE INERTIAL MEASUREMENTS

2019 ◽  
Vol 59 (2) ◽  
pp. 170-181 ◽  
Author(s):  
Mykola Sysyn ◽  
Ulf Gerber ◽  
Olga Nabochenko ◽  
Yangyang Li ◽  
Vitalii Kovalchuk
Keyword(s):  
Principal Component ◽  
Remaining Useful Life ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Surface Degradation ◽  
Structural Health ◽  
Rolling Surface ◽  
Railway Crossing ◽  
The Impact

This paper focuses on the experimental study of an alteration in the railway crossing dynamic response due to the rolling surface degradation during a crossing’s lifecycle. The maximal acceleration measured with the track-side measurement system as well as the impact position monitoring show no significant statistical relation to the rolling surface degradation. The additional spectral features are extracted from the acceleration measurements with a wavelet transform to improve the information usage. The reliable prediction of the railway crossing remaining useful life (RUL) demands the trustworthy indicators of structural health that systematically change during the lifecycle. The popular simple machine learning methods like principal component analysis and partial least square regression are used to retrieve two indicators from the experimental information. The feature ranking and selection are used to remove the redundant information and increase the relation of indicators to the lifetime.

ANALYSIS THE IMPACT OF THE IMPLEMENTATION OF QUALITY MANAGEMENT SYSTEM (QMS) AND SAFETY AND HEALTHY AT WORK MANAGEMENT SYSTEM (SHWMS / SMK3) ON THE JOB PERFORMANCE OF EMPLOYEES

2018 ◽  
Vol 3 (01) ◽  
pp. 45
Author(s):  
Nur Hidayat ◽  
Indah Kusuma Hayati
Keyword(s):  
Quality Management ◽  
Job Performance ◽  
Latent Variables ◽  
Management System ◽  
Quality Management System ◽  
Partial Least Square ◽  
Least Square ◽  
Global Challenges ◽  
The Impact ◽  
Work Management

Recently, the evolvement of globalization era has been the global challenges that cannot be avoided either by private or government sectors, and they are requested to be survived encountering such the condition. The implementation of Quality Management System (QMS) in the operational company is the way how to guarantee the quality of products or services offered to the people. One of the purposes of QMS implementation is to provide a prime satisfaction to the customers. The impact of QMS implementation is expected to increase job performance of the employees. Besides the implementation of Quality Management System (QMS), the impact of global challenges has been increasing the competitive efforts to execute more effective production process. However, it has required manpower protection accordingly. This research aims to find out whether the implementation of quality management system and safety and healthy at work management system have impacted on the job performance of employees. Objects of this research are the employees in the production department at PT Guna Senaputra Sejahtera Plant 1 Bogor. Data analysis technique of this research has applied software Smart PLS (Partial Least Square). PLS has estimated a model of correlation among the latent variables and correlation between latent variables and its indicators. Result of data processing has indicated that the implementation of Quality Management System (QMS) and system of safety and healthy at work have positively and significantly impacted job performance of employees.Keywords : Quality Management System (QMS), Safety and Healthy at Work System ( SHWS / SMK3), and Job Performance of Employees

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