scholarly journals Mitigating the Impact of Field and Image Registration Errors through Spatial Aggregation

2019 ◽  
Vol 11 (3) ◽  
pp. 222 ◽  
Author(s):  
John Hogland ◽  
David L.R. Affleck
Keyword(s):  
Linear Models ◽  
Predictor Variable ◽  
Spatial Aggregation ◽  
Predictor Variables ◽  
Remotely Sensed Data ◽  
Substantial Impact ◽  
Landscape Characteristics ◽  
Registration Errors ◽  
Spatial Registration ◽  
The Impact

Remotely sensed data are commonly used as predictor variables in spatially explicit models depicting landscape characteristics of interest (response) across broad extents, at relatively fine resolution. To create these models, variables are spatially registered to a known coordinate system and used to link responses with predictor variable values. Inherently, this linking process introduces measurement error into the response and predictors, which in the latter case causes attenuation bias. Through simulations, our findings indicate that the spatial correlation of response and predictor variables and their corresponding spatial registration (co-registration) errors can have a substantial impact on the bias and accuracy of linear models. Additionally, in this study we evaluate spatial aggregation as a mechanism to minimize the impact of co-registration errors, assess the impact of subsampling within the extent of sample units, and provide a technique that can be used to both determine the extent of an observational unit needed to minimize the impact of co-registration and quantify the amount of error potentially introduced into predictive models.

scholarly journals The Impact of the Debt Ratio, Total Assets, and Earning Growth Rate on WACC: Evidence from Nepalese Commercial Banks

2020 ◽  
pp. 16-23
Author(s):  
Prem Bahadur Budhathoki ◽  
Chandra Kumar Rai
Keyword(s):  
Growth Rate ◽  
Commercial Banks ◽  
Predictor Variable ◽  
Ordinary Least Squares ◽  
Predictor Variables ◽  
Debt Ratio ◽  
Earning Growth ◽  
Earnings Growth ◽  
The Impact ◽  
Made In

This study examined the impact of the debt ratio, total assets, and earnings growth rate on banks’ WACC. This study employed bank scope data of twenty-eight commercial banks during the single period of 2018. Altogether, there were 28 observations were made in the study. The ordinary least squares model was used to analyze the data. The results indicated that two predictor variables debt ratio and total assets significantly affected the bank’s WACC. But the predictor variable earnings growth rate did not significantly affect banks’ WACC. The results of this study could help bankers and policymakers to take effective action to reduce banks’ WACC.

scholarly journals Using Tree Detection Based on Airborne Laser Scanning to Improve Forest Inventory Considering Edge Effects and the Co-Registration Factor

2019 ◽  
Vol 11 (22) ◽  
pp. 2675 ◽  
Author(s):  
Pascual
Keyword(s):  
Forest Inventory ◽  
Laser Scanning ◽  
Airborne Laser Scanning ◽  
Predictor Variables ◽  
Individual Tree ◽  
Tree Detection ◽  
Registration Errors ◽  
Airborne Laser ◽  
The Impact ◽  
Biophysical Attributes

The estimation of forest biophysical attributes improves when airborne laser scanning (ALS) is integrated. Individual tree detection methods (ITD) and traditional area-based approaches (ABA) are the two main alternatives in ALS-based forest inventory. This study evaluated the performance of the enhanced area-based approach (EABA), an edge-correction method based on ALS data that combines ITD and ABA, at improving the estimation of forest biophysical attributes, while testing its efficiency when considering co-registration errors that bias remotely sensed predictor variables. The study was developed based on a stone pine forest (Pinus pinea L.) in Central Spain, in which tree spacing and scanning conditions were optimal for the ITD approach. Regression modeling was used to select the optimal predictor variables to estimate forest biophysical attributes. The accuracy of the models improved when using EABA, despite the low-density of the ALS data. The relative mean improvement of EABA in terms of root mean squared error was 15.2%, 17.3%, and 7.2% for growing stock volume, stand basal area, and dominant height, respectively. The impact of co-registration errors in the models was clear in the ABA, while the effect was minor and mitigated under EABA. The implementation of EABA can highly contribute to improve modern forest inventory applications.

scholarly journals Homelessness and Unemployment During the Economic Recession: The Case of the City of Girona

2018 ◽  
Vol 14 (13) ◽  
pp. 59
Author(s):  
Fran Calvo ◽  
Xavier Carbonell ◽  
Marc Badia
Keyword(s):  
Linear Regression ◽  
Predictor Variable ◽  
Economic Recession ◽  
Predictor Variables ◽  
Structural Factors ◽  
Simple Linear Regression ◽  
Total Rate ◽  
Correlation Tests ◽  
The Impact ◽  
The City

Although the research suggests that the main causes of homelessness are classified in individual and structural factors, there are few scientific articles which evaluate the impact of structural factors such as unemployment during periods of economic recession. The objective of this study is to compare the evolution of the total rate of homelessness with the total rate of unemployment in the city of Girona (Catalonia) during the economical recession (2006-2016) and to determine if unemployment is a predictive factor of homelessness. This is the first study with a Catalan sample comparing unemployment and homelessness. The design was longitudinal, retrospective and observational. The correlation tests between unemployment and homelessness indicated strong connections in the combination of the sample (r = .914, p <.001), men (r = .924, p <.001), and women (r = .716, p = 0.013). The results of the different models of simple linear regression used to determine the predictor variables of homelessness indicate that the rise of global unemployment is a predictor variable of the rise of global homelessness (ß = 2.17, p = .002) and male homelessness (ß = .82, p <.001). However, it does not predict specific female homelessness (ß = .88, p =.68).

The Digital Divide and K-12 Student Computer Use

10.28945/2926 ◽  
2005 ◽  
Author(s):  
James N. Morgan ◽  
Craig A. VanLengen
Keyword(s):  
Digital Divide ◽  
Computer Use ◽  
Census Data ◽  
Internet Access ◽  
Demographic Characteristics ◽  
The Internet ◽  
Substantial Impact ◽  
Limited Basis ◽  
K 12 ◽  
The Impact

The divide between those who have computer and Internet access and those who do not appears to be narrowing, however overall statistics may be misleading. Measures of computer availability in schools often include cases where computers are only available for administration or are available only on a very limited basis (Gootman, 2004). Access to a computer and the Internet outside of school helps to reinforce student learning and emphasize the importance of using technology. Recent U.S. statistics indicate that ethnic background and other demographic characteristics still have substantial impact on the availability and use of computers by students outside of the classroom. This paper examines recent census data to determine the impact of the household on student computer use outside of the classroom. Encouragingly, the findings of this study suggest that use of a computer at school substantially increases the chance that a student will use a computer outside of class. Additionally, this study suggests that computer use outside of the classroom is positively and significantly impacted by being in a household with adults who either use a computer at work or work in an industry where computers are extensively used.

scholarly journals Predicting distribution of malaria vector larval habitats in Ethiopia by integrating distributed hydrologic modeling with remotely sensed data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ai-Ling Jiang ◽  
Ming-Chieh Lee ◽  
Guofa Zhou ◽  
Daibin Zhong ◽  
Dawit Hawaria ◽  
...  
Keyword(s):  
Malaria Vector ◽  
Malaria Control ◽  
Remotely Sensed ◽  
Larval Habitat ◽  
Validation Dataset ◽  
Significant Shift ◽  
Remotely Sensed Data ◽  
Larval Habitats ◽  
Common Land Model ◽  
The Impact

AbstractLarval source management has gained renewed interest as a malaria control strategy in Africa but the widespread and transient nature of larval breeding sites poses a challenge to its implementation. To address this problem, we propose combining an integrated high resolution (50 m) distributed hydrological model and remotely sensed data to simulate potential malaria vector aquatic habitats. The novelty of our approach lies in its consideration of irrigation practices and its ability to resolve complex ponding processes that contribute to potential larval habitats. The simulation was performed for the year of 2018 using ParFlow-Common Land Model (CLM) in a sugarcane plantation in the Oromia region, Ethiopia to examine the effects of rainfall and irrigation. The model was calibrated using field observations of larval habitats to successfully predict ponding at all surveyed locations from the validation dataset. Results show that without irrigation, at least half of the area inside the farms had a 40% probability of potential larval habitat occurrence. With irrigation, the probability increased to 56%. Irrigation dampened the seasonality of the potential larval habitats such that the peak larval habitat occurrence window during the rainy season was extended into the dry season. Furthermore, the stability of the habitats was prolonged, with a significant shift from semi-permanent to permanent habitats. Our study provides a hydrological perspective on the impact of environmental modification on malaria vector ecology, which can potentially inform malaria control strategies through better water management.

scholarly journals Review on Urban Heat Island in China: Methods, Its Impact on Buildings Energy Demand and Mitigation Strategies

2021 ◽  
Vol 13 (2) ◽  
pp. 762
Author(s):  
Liu Tian ◽  
Yongcai Li ◽  
Jun Lu ◽  
Jue Wang
Keyword(s):  
Energy Consumption ◽  
Urban Heat Island ◽  
Building Energy ◽  
Design Stage ◽  
Heat Island ◽  
Building Energy Consumption ◽  
Rapid Urbanization ◽  
Substantial Impact ◽  
Urban Heat ◽  
The Impact

High population density, dense high-rise buildings, and impervious pavements increase the vulnerability of cities, which aggravate the urban climate environment characterized by the urban heat island (UHI) effect. Cities in China provide unique information on the UHI phenomenon because they have experienced rapid urbanization and dramatic economic development, which have had a great influence on the climate in recent decades. This paper provides a review of recent research on the methods and impacts of UHI on building energy consumption, and the practical techniques that can be used to mitigate the adverse effects of UHI in China. The impact of UHI on building energy consumption depends largely on the local microclimate, the urban area features where the building is located, and the type and characteristics of the building. In the urban areas dominated by air conditioning, UHI could result in an approximately 10–16% increase in cooling energy consumption. Besides, the potential negative effects of UHI can be prevented from China in many ways, such as urban greening, cool material, water bodies, urban ventilation, etc. These strategies could have a substantial impact on the overall urban thermal environment if they can be used in the project design stage of urban planning and implemented on a large scale. Therefore, this study is useful to deepen the understanding of the physical mechanisms of UHI and provide practical approaches to fight the UHI for the urban planners, public health officials, and city decision-makers in China.

scholarly journals Socio-economic consequences of mental distress: quantifying the impact of self-reported mental distress on the days of incapacity to work and medical costs in a two-year period: a longitudinal study in Germany

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Gerhard Müller ◽  
Manuela Bombana ◽  
Monika Heinzel-Gutenbrenner ◽  
Nikolaus Kleindienst ◽  
Martin Bohus ◽  
...  
Keyword(s):  
Mental Disorders ◽  
Mental Distress ◽  
Linear Models ◽  
Longitudinal Research ◽  
Depression Scale ◽  
Routine Data ◽  
Medical Costs ◽  
First Year ◽  
Second Year ◽  
The Impact

Abstract Background Mental disorders are related to high individual suffering and significant socio-economic burdens. However, it remains unclear to what extent self-reported mental distress is related to individuals’ days of incapacity to work and their medical costs. This study aims to investigate the impact of self-reported mental distress for specific and non-specific days of incapacity to work and specific and non-specific medical costs over a two-year span. Method Within a longitudinal research design, 2287 study participants’ mental distress was assessed using the Hospital Anxiety and Depression Scale (HADS). HADS scores were included as predictors in generalized linear models with a Tweedie distribution with log link function to predict participants’ days of incapacity to work and medical costs retrieved from their health insurance routine data during the following two-year period. Results Current mental distress was found to be significantly related to the number of specific days absent from work and medical costs. Compared to participants classified as no cases by the HADS (2.6 days), severe case participants showed 27.3-times as many specific days of incapacity to work in the first year (72 days) and 10.3-times as many days in the second year (44 days), and resulted in 11.4-times more medical costs in the first year (2272 EUR) and 6.2-times more in the second year (1319 EUR). The relationship of mental distress to non-specific days of incapacity to work and non-specific medical costs was also significant, but mainly driven from specific absent days and specific medical costs. Our results also indicate that the prevalence of presenteeism is considerably high: 42% of individuals continued to go to work despite severe mental distress. Conclusions Our results show that self-reported mental distress, assessed by the HADS, is highly related to the days of incapacity to work and medical costs in the two-year period. Reducing mental distress by improving preventive structures for at-risk populations and increasing access to evidence-based treatments for individuals with mental disorders might, therefore, pay for itself and could help to reduce public costs.

scholarly journals The Impact of the Coronavirus Disease (COVID-19) on the Health and Social Needs of Sex Workers in Singapore

2021 ◽  
Author(s):  
Rayner Kay Jin Tan ◽  
Vanessa Ho ◽  
Sherry Sherqueshaa ◽  
Wany Dee ◽  
Jane Mingjie Lim ◽  
...  
Keyword(s):  
Food Insecurity ◽  
Sex Work ◽  
Sex Workers ◽  
Social Needs ◽  
Medical Services ◽  
Substantial Impact ◽  
Sex Worker ◽  
Permanent Resident ◽  
The Impact ◽  
Depth Interviews

AbstractWe evaluated the impact of the coronavirus disease (COVID-19) on the sex work industry and assessed how it has impacted the health and social conditions of sex workers in Singapore. We conducted a sequential exploratory mixed methods study amidst the COVID-19 pandemic from April to October 2020, including in-depth interviews with 24 stakeholders from the sex work industry and surveyor-administered structured surveys with 171 sex workers. COVID-19 had a substantial impact on sex workers' income. The illegality of sex work, stigma, and the lack of work documentation were cited as exclusionary factors for access to alternative jobs or government relief. Sex workers had experienced an increase in food insecurity (57.3%), housing insecurity (32.8%), and sexual compromise (8.2%), as well as a decrease in access to medical services (16.4%). Being transgender female was positively associated with increased food insecurity (aPR = 1.23, 95% CI [1.08, 1.41]), housing insecurity (aPR = 1.28, 95% CI [1.03, 1.60]), and decreased access to medical services (aPR = 1.74, 95% CI [1.23, 2.46]); being a venue-based sex worker was positively associated with increased food insecurity (aPR = 1.46, 95% CI [1.00, 2.13]), and being a non-Singaporean citizen or permanent resident was positively associated with increased housing insecurity (aPR = 2.59, 95% CI [1.73, 3.85]). Our findings suggest that COVID-19 has led to a loss of income for sex workers, greater food and housing insecurity, increased sexual compromise, and reduced access to medical services for sex workers. A lack of access to government relief among sex workers exacerbated such conditions. Efforts to address such population health inequities should be implemented.

scholarly journals Use of Artificial Intelligence to understand adults’ thoughts and behaviours relating to COVID-19

2021 ◽  
pp. 175791392097933
Author(s):  
SW Flint ◽  
A Piotrkowicz ◽  
K Watts
Keyword(s):  
Artificial Intelligence ◽  
Online Survey ◽  
Severe Illness ◽  
Free Text ◽  
Community Recovery ◽  
Substantial Impact ◽  
Lifestyle Behaviours ◽  
Artificial Intelligence Methods ◽  
High Concern ◽  
The Impact

Aims: The outbreak of severe acute respiratory syndrome coronavirus 2 (COVID-19) is a global pandemic that has had substantial impact across societies. An attempt to reduce infection and spread of the disease, for most nations, has led to a lockdown period, where people’s movement has been restricted resulting in a consequential impact on employment, lifestyle behaviours and wellbeing. As such, this study aimed to explore adults’ thoughts and behaviours in response to the outbreak and resulting lockdown measures. Methods: Using an online survey, 1126 adults responded to invitations to participate in the study. Participants, all aged 18 years or older, were recruited using social media, email distribution lists, website advertisement and word of mouth. Sentiment and personality features extracted from free-text responses using Artificial Intelligence methods were used to cluster participants. Results: Findings demonstrated that there was varied knowledge of the symptoms of COVID-19 and high concern about infection, severe illness and death, spread to others, the impact on the health service and on the economy. Higher concerns about infection, illness and death were reported by people identified at high risk of severe illness from COVID-19. Behavioural clusters, identified using Artificial Intelligence methods, differed significantly in sentiment and personality traits, as well as concerns about COVID-19, actions, lifestyle behaviours and wellbeing during the COVID-19 lockdown. Conclusions: This time-sensitive study provides important insights into adults’ perceptions and behaviours in response to the COVID-19 pandemic and associated lockdown. The use of Artificial Intelligence has identified that there are two behavioural clusters that can predict people’s responses during the COVID-19 pandemic, which goes beyond simple demographic groupings. Considering these insights may improve the effectiveness of communication, actions to reduce the direct and indirect impact of the COVID-19 pandemic and to support community recovery.

scholarly journals The Effect of Dual Language Activation on L2-Induced Changes in L1 Speech within a Code-Switched Paradigm

Languages ◽  
2021 ◽  
Vol 6 (3) ◽  
pp. 114
Author(s):  
Ulrich Reubold ◽  
Sanne Ditewig ◽  
Robert Mayr ◽  
Ineke Mennen
Keyword(s):  
Dual Language ◽  
Native Speakers ◽  
Predictor Variable ◽  
Predictor Variables ◽  
Acoustic Measures ◽  
Before And After ◽  
Language Activation ◽  
Dual Activation ◽  
Induced Changes

The present study sought to examine the effect of dual language activation on L1 speech in late English–Austrian German sequential bilinguals, and to identify relevant predictor variables. To this end, we compared the English speech patterns of adult migrants to Austria in a code-switched and monolingual condition alongside those of monolingual native speakers in England in a monolingual condition. In the code-switched materials, German words containing target segments known to trigger cross-linguistic interaction in the two languages (i.e., [v–w], [ʃt(ʁ)-st(ɹ)] and [l-ɫ]) were inserted into an English frame; monolingual materials comprised English words with the same segments. To examine whether the position of the German item affects L1 speech, the segments occurred either before the switch (“He wants a Wienerschnitzel”) or after (“I like Würstel with mustard”). Critical acoustic measures of these segments revealed no differences between the groups in the monolingual condition, but significant L2-induced shifts in the bilinguals’ L1 speech production in the code-switched condition for some sounds. These were found to occur both before and after a code-switch, and exhibited a fair amount of individual variation. Only the amount of L2 use was found to be a significant predictor variable for shift size in code-switched compared with monolingual utterances, and only for [w]. These results have important implications for the role of dual activation in the speech of late sequential bilinguals.

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