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1 – 10 of 528
Article
Publication date: 7 November 2023

Chunli Li, Liang Li, Yungming Cheng, Liang Xu and Guangming Yu

This paper aims to develop an efficient algorithm combining straightforward response surface functions with Monte Carlo simulation to conduct seismic reliability analysis in a…

Abstract

Purpose

This paper aims to develop an efficient algorithm combining straightforward response surface functions with Monte Carlo simulation to conduct seismic reliability analysis in a systematical way.

Design/methodology/approach

The representative slip surfaces are identified and based on to calibrate multiple response surface functions with acceptable accuracy. The calibrated response surfaces are used to determine the yield acceleration in Newmark sliding displacement analysis. Then, the displacement-based limit state function is adopted to conduct seismic reliability analysis.

Findings

The calibrated response surface functions have fairly good accuracy in predicting the yield acceleration in Newmark sliding displacement analysis. The seismic reliability is influenced by such factors as PGA, spatial variability and threshold value. The proposed methodology serves as an effective tool for geotechnical practitioners.

Originality/value

The multiple sources of a seismic slope response can be effectively determined using the multiple response surface functions, which are easily implemented within geotechnical engineering.

Open Access
Article
Publication date: 22 May 2023

Edmund Baffoe-Twum, Eric Asa and Bright Awuku

Background: Geostatistics focuses on spatial or spatiotemporal datasets. Geostatistics was initially developed to generate probability distribution predictions of ore grade in the…

Abstract

Background: Geostatistics focuses on spatial or spatiotemporal datasets. Geostatistics was initially developed to generate probability distribution predictions of ore grade in the mining industry; however, it has been successfully applied in diverse scientific disciplines. This technique includes univariate, multivariate, and simulations. Kriging geostatistical methods, simple, ordinary, and universal Kriging, are not multivariate models in the usual statistical function. Notwithstanding, simple, ordinary, and universal kriging techniques utilize random function models that include unlimited random variables while modeling one attribute. The coKriging technique is a multivariate estimation method that simultaneously models two or more attributes defined with the same domains as coregionalization.

Objective: This study investigates the impact of populations on traffic volumes as a variable. The additional variable determines the strength or accuracy obtained when data integration is adopted. In addition, this is to help improve the estimation of annual average daily traffic (AADT).

Methods procedures, process: The investigation adopts the coKriging technique with AADT data from 2009 to 2016 from Montana, Minnesota, and Washington as primary attributes and population as a controlling factor (second variable). CK is implemented for this study after reviewing the literature and work completed by comparing it with other geostatistical methods.

Results, observations, and conclusions: The Investigation employed two variables. The data integration methods employed in CK yield more reliable models because their strength is drawn from multiple variables. The cross-validation results of the model types explored with the CK technique successfully evaluate the interpolation technique's performance and help select optimal models for each state. The results from Montana and Minnesota models accurately represent the states' traffic and population density. The Washington model had a few exceptions. However, the secondary attribute helped yield an accurate interpretation. Consequently, the impact of tourism, shopping, recreation centers, and possible transiting patterns throughout the state is worth exploring.

Details

Emerald Open Research, vol. 1 no. 5
Type: Research Article
ISSN: 2631-3952

Keywords

Book part
Publication date: 29 January 2024

Rebecca Dickason

While the main emotional labor strategies are well-documented, the manner in which professionals navigate emotional rules within the workplace and effectively perform emotional…

Abstract

Purpose

While the main emotional labor strategies are well-documented, the manner in which professionals navigate emotional rules within the workplace and effectively perform emotional labor is less understood. With this contribution, I aim to unveil “the good, the bad and the ugly” of emotional labor as a dynamic theatrical performance.

Methodology/Approach

Focusing on three geriatric long-term care units within a French public hospital, this qualitative study relies on two sets of data (observation and interviews). Deeply rooted within the field of study, the chosen methodological approach substantializes the subtle hues of the emotional experience at work and targets resonance rather than generalization.

Findings

Using the theatrical metaphor, this research underlines the role of space in the practice of emotional labor in a unique way. It identifies the main emotionalized zones or emotional regions (front, back, transitional, mixed) and details their characteristics, before unearthing the nonlinearity and polyphonic quality of emotional labor performance and the versatility needed to that effect. Indeed, this research shows how health-care professionals juggle with the specificities of each region, as well as how space generates both constraints and resources. By combining static and dynamic prisms, diverse instantiations of hybridity and spatial in-betweens, anchored in liminality and trajectories, are revealed.

Originality/Value

This research adds to the current body of literature on the concept of emotional labor by shedding light on its highly dynamic and interactional nature, revealing different levels of porosity between emotional regions and how the characteristics of each type of area can taint others and increase/decrease the occupational health costs of emotional labor. The study also raises questions about the interplay of emotional labor performance with the level of humanization/dehumanization of elderly people. Given the global demographics about an aging population, this gives food for thought at a social level.

Details

Emotion in Organizations
Type: Book
ISBN: 978-1-83797-251-7

Keywords

Article
Publication date: 4 April 2023

Alireza Sharifi and Shilan Felegari

The purpose of this study is rangeland biomass estimation and its spatial–temporal dynamics. Remote sensing has been a significant method for estimating biomass in recent years…

Abstract

Purpose

The purpose of this study is rangeland biomass estimation and its spatial–temporal dynamics. Remote sensing has been a significant method for estimating biomass in recent years. The connection between vegetation index and field biomass will be used to assign probabilities, but in some cases, it does not provide acceptable results because of soil background and geographical and temporal variability.

Design/methodology/approach

In this study, the normalized difference red-edge (NDRE) index was used to calculate the rangeland biomass in comparison to five vegetation indices. Field measurements of biomass of natural rangeland in the West of Iran were taken in 2015, 2018 and 2021, and SENTINEL-2 data were used for analysis.

Findings

The results indicated that the overall advantage of NDRE stems from the fact that it adjusts for changes in leaf water content while overcoming the detrimental effects of soil substrate heterogeneity, both of these factors have a significant impact on pasture biomass. These results suggest that an NDRE-based biomass estimation model might be useful for estimating and monitoring biomass in large rangelands with significant geographical and temporal variability.

Originality/value

Identifying the best vegetation index to establish a vegetation-based biomass regression model for rangelands in large areas with different climatic conditions, plant compositions and soil types is the overall aim of this study.

Details

Aircraft Engineering and Aerospace Technology, vol. 95 no. 7
Type: Research Article
ISSN: 1748-8842

Keywords

Open Access
Article
Publication date: 11 March 2022

Edmund Baffoe-Twum, Eric Asa and Bright Awuku

Background: The annual average daily traffic (AADT) data from road segments are critical for roadway projects, especially with the decision-making processes about operations…

Abstract

Background: The annual average daily traffic (AADT) data from road segments are critical for roadway projects, especially with the decision-making processes about operations, travel demand, safety-performance evaluation, and maintenance. Regular updates help to determine traffic patterns for decision-making. Unfortunately, the luxury of having permanent recorders on all road segments, especially low-volume roads, is virtually impossible. Consequently, insufficient AADT information is acquired for planning and new developments. A growing number of statistical, mathematical, and machine-learning algorithms have helped estimate AADT data values accurately, to some extent, at both sampled and unsampled locations on low-volume roadways. In some cases, roads with no representative AADT data are resolved with information from roadways with similar traffic patterns.

Methods: This study adopted an integrative approach with a combined systematic literature review (SLR) and meta-analysis (MA) to identify and to evaluate the performance, the sources of error, and possible advantages and disadvantages of the techniques utilized most for estimating AADT data. As a result, an SLR of various peer-reviewed articles and reports was completed to answer four research questions.

Results: The study showed that the most frequent techniques utilized to estimate AADT data on low-volume roadways were regression, artificial neural-network techniques, travel-demand models, the traditional factor approach, and spatial interpolation techniques. These AADT data-estimating methods' performance was subjected to meta-analysis. Three studies were completed: R squared, root means square error, and mean absolute percentage error. The meta-analysis results indicated a mixed summary effect: 1. all studies were equal; 2. all studies were not comparable. However, the integrated qualitative and quantitative approach indicated that spatial-interpolation (Kriging) methods outperformed the others.

Conclusions: Spatial-interpolation methods may be selected over others to generate accurate AADT data by practitioners at all levels for decision making. Besides, the resulting cross-validation statistics give statistics like the other methods' performance measures.

Details

Emerald Open Research, vol. 1 no. 5
Type: Research Article
ISSN: 2631-3952

Keywords

Article
Publication date: 31 August 2022

Reta Lemessa, Gudeta Aga, Ararso Tafese and Temesgen Senbeto

This study aims to examine the differences within individuals and clusters in nutritional status and identify socioeconomic factors in the nutritional status of under-five…

Abstract

Purpose

This study aims to examine the differences within individuals and clusters in nutritional status and identify socioeconomic factors in the nutritional status of under-five children in Ethiopia.

Design/methodology/approach

A weighted sub-sample of 5,270 under-five children was drawn from the under-five children data set of the Ethiopian 2019 Mini Demographic and Health Survey. Multilevel modeling was used to look at the association between the nutritional status of children with predictors.

Findings

The proportion of stunting, underweight and wasting among under-five children were 39.3%, 28.6% and 16.3%, respectively. The observed Global Moran Index’s value for child malnutrition (stunting, wasting and underweight) prevalence in Ethiopia were I = 0.204 for stunting, I = 0.152 for wasting and I = 0.195 for underweight at p = 0.000 was statistically significant indicating that spatial variability of malnutrition of under-five children across survey clusters and regions was observed. Moreover, the result of heterogeneity between clusters obtained for stunting, underweight and wasting was significant providing evidence of variation among regional clusters concerning the status of nutrition of under-five children. Child’s age in months, breastfeeding, family educational level, wealth index, place of residence, media access and region were highly significantly associated with childhood malnutrition. The inclusion of the explanatory variables has shown a significant impact on the variation in malnutrition among regions.

Practical implications

Enhance education, expanding the activities regarding nutritional and health services using media, health extension workers, and health institutions.

Originality/value

The study provides the malnutrition situation status of Ethiopian country when the survey carried out.

Details

Nutrition & Food Science , vol. 53 no. 3
Type: Research Article
ISSN: 0034-6659

Keywords

Article
Publication date: 24 November 2022

Sean MacIntyre, Michael McCord, Peadar T. Davis, Aggelos Zacharopoulos and John A. McCord

The purpose of this study is to examine whether PV uptake is associated with key housing market determinants and linked to socio-economic profiles. An abundance of extant…

Abstract

Purpose

The purpose of this study is to examine whether PV uptake is associated with key housing market determinants and linked to socio-economic profiles. An abundance of extant literature has examined the role of solar photovoltaic (PV) adoption and user costs, with an emerging corpus of literature investigating the role of the determinants of PV uptake, particularly in relation to the built environment and the spatial variation of PV dependency and dissimilarity. Despite this burgeoning literature, there remains limited insights from the UK perspective on housing market characteristics driving PV adoption and in relation spatial differences and heterogeneity that may exist.

Design/methodology/approach

Applying micro-based data at the Super Output Area-level geography, this study develops a series of ordinary least squares, spatial econometric models and a logistic regression analysis to examine built environment, housing tenure and deprivation attributes on PV adoption at the regional level in Northern Ireland, UK.

Findings

The findings emerging from the research reveal the presence of some spatial clustering and PV diffusion, in line with several existing studies. The findings demonstrate that an urban-rural dichotomy exists seemingly driven by social interaction and peer effects which has a profound impact on the likelihood of PV adoption. Further, the results exhibit tenure composition and “economic status” to be significant and important determinants of PV diffusion and uptake.

Originality/value

Housing market characteristics such as tenure composition across local market structures remain under-researched in relation to renewable energy uptake and adoption. This study examines the role of housing market attributes relative to socio-economic standing for adopting renewable energy.

Details

Journal of Financial Management of Property and Construction , vol. 28 no. 3
Type: Research Article
ISSN: 1366-4387

Keywords

Article
Publication date: 7 September 2021

Alolote Amadi

The study is carried out to analytically reconnoiter geotechnical index properties of subgrade soils as key variables that shape the cost profile of road infrastructure projects…

Abstract

Purpose

The study is carried out to analytically reconnoiter geotechnical index properties of subgrade soils as key variables that shape the cost profile of road infrastructure projects in a tropical geographic setting with starkly heterogenous ground conditions.

Design/methodology/approach

Using the Niger Delta region, as a point of reference, data on geotechnical index properties of subgrade soils at spatially dispersed locations for 61 completed highway projects are collated. Exploratory statistical tests were carried out to infer significant associations with final project costs before regression analysis. Regression analysis is principally deployed as an explanatory analytical tool, relevant to quantify the sensitivity of highway project costs to the individual and collective impact of geotechnical variables.

Findings

Several parameters of expansivity and compressibility exhibited significantly strong associations with the final costs recorded on the highway projects. The statistical analysis further established a cause-effect relationship, whereby small changes in the geotechnical properties of sub-grade soils at project locations, would result in disproportionately large changes in the cost of road construction.

Practical implications

The study findings provide insight into the sensitivity of road construction costs to geotechnical variables, which can serve as a useful input in financial risk analysis for development appraisal and the generation of location adjustment factors.

Originality/value

The study statistically demonstrates location-induced construction cost profiles, triggered in response to the spatial geotechnical variability and occurrence of problem subgrade soils in the humid tropics, which may be different from those traditionally established in studies of cold and temperate climate soils.

Details

International Journal of Building Pathology and Adaptation, vol. 41 no. 4
Type: Research Article
ISSN: 2398-4708

Keywords

Article
Publication date: 14 February 2022

Huifa Chen, Yuan George Shan, Qingliang Tang and Junru Zhang

This study aims to investigate why companies use the internal price of carbon (IPC) for carbon management.

Abstract

Purpose

This study aims to investigate why companies use the internal price of carbon (IPC) for carbon management.

Design/methodology/approach

The authors adopt sustainable transition management theory to design the research and explain the findings of empirical models. The sample includes companies that participated in the Carbon Disclosure Project (CDP) questionnaire survey, derived from 37 countries and regions for the period 2015–2018.

Findings

The results first reveal that transition management facilitates an upward adoption trend annually during the study period. Second, the authors find that the proxies for transition management are all correlated with the adoption of the IPC in the predicted direction. Third, the authors identify spatial patterns and driving factors for adoption of the IPC.

Originality/value

This study provides additional insight beyond the limited prior literature in this area. In particular, the findings regarding the influence of physical environment on climate-related decisions have not been documented in extant literature. IPC is expected to interact with and complement external price of carbon for climate change governance. Thus, the exploring results of the paper fill an important gap and pave the way for future study to examine emerging issues in the burgeoning field of carbon accounting for climate change.

Article
Publication date: 30 January 2024

Rebecca Restle, Marcelo Cajias and Anna Knoppik

The purpose of this paper is to explore the significance impact of air quality as a contributing factor on residential property rents by applying geo-informatics to economic…

12

Abstract

Purpose

The purpose of this paper is to explore the significance impact of air quality as a contributing factor on residential property rents by applying geo-informatics to economic issues. Since air pollution poses a severe health threat, city residents should have a right to know about the (invisible) hazards they are exposed to.

Design/methodology/approach

Within spatial-temporal modeling of air pollutants in Berlin, Germany, three interpolation techniques are tested. The most suitable one is selected to create seasonal maps for 2018 and 2021 with pollution concentrations for particulate matter values and nitrogen dioxide for each 1,000 m2 cell within the administrative boundaries. Based on the evaluated pollution particulate matter values, which are used as additional variables for semi-parametric regressions the impact of the air quality on rents is estimated.

Findings

The findings reveal a compelling association between air quality and the economic aspect of the residential real estate market, with noteworthy implications for both tenants and property investors. The relationship between air pollution variables and rents is statistically significant. However, there is only a “willingness-to- pay” for low particulate matter values, but not for nitrogen dioxide concentrations. With good air quality, residents in Berlin are willing to pay a higher rent (3%).

Practical implications

These results suggest that a “marginal willingness-to-pay” occurs in a German city. The research underscores the multifaceted impact of air quality on the residential rental market in Berlin. The evidence supports the notion that a cleaner environment not only benefits human health and the planet but also contributes significantly to the economic bottom line of property investors.

Originality/value

The paper has a unique data engineering approach. It collects spatiotemporal data from network of state-certified measuring sites to create an index of air pollution. This spatial information is merged with residential listings. Afterward non-linear regression models are estimated.

Details

Journal of Property Investment & Finance, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1463-578X

Keywords

1 – 10 of 528