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The elimination of non-conformance is one of the goals of quality management, which can be accomplished by effectively managing and supervising the project. The excellent quality results in increased production and reduced costs, contributing to a rise in the competitive edge. The present body of research has examined the effect of quality management on the performance of building projects in the Indian industry. Different researchers have strived to identify the factors that affect the performance of construction projects. A structured questionnaire was floated to different professionals in the industry, that is, architects, engineers, consultants, developers and researchers, and >152 valid responses were received. The questionnaire asked respondents to respond on a Likert scale of 1–5. The questionnaire asked about the impact of quality on different aspects of the construction project’s performance. Relative importance index (RII) are obtained to rank the elements in order of importance. According to the factor analysis results, three primary components account for 62% of the variance. The results show that the significant aspects of the project affected by quality are rate of rework, project performance, cost, safety, labour productivity and profitability with RII scores of 0.85, 0.82, 0.78, 0.76, 0.75 and 0.74, respectively.

The success of the Ghanaian public road construction phase and other preconstruction phases has been studied. However, the success of the Ghanaian public road project consultant selection phase has not received any attention, even though it is prone to corruption. The goal of this study is to identify the critical success factors (CSFs) that, from the standpoint of a developing country, affect the success of the Ghanaian public road consultant selection phase. Data on the degree to which key success criteria identified in literature have an impact on the success of the Ghanaian public road consultant selection phase from the viewpoints of 156 sector practitioners in Ghana were acquired using a selfadministered questionnaire. After that, the relative importance index was used to analyse the data. According to the study, external environmental, project management, and procurement-related factors influence the success of the Ghanaian public road consultant selection phase. The government and organizations that administer public construction projects will now have a better grasp of the CSFs that affect the performance of the Ghanaian public road construction project consultant selection phase and be able to use them as a guide to improve the effective and efficient delivery of public road construction projects. In managing public road projects, the study’s findings will be useful to both industry professionals and the Ghanaian government. The study is limited to the consultant selection phase of Ghanaian public road construction projects.

Decision making is a significant responsibility for business managers, their decisions impacting business performance. Managers are therefore interested in acquiring and implementing reliable methods for making decisions both now and in the future. Currently, in the countries in the Albanian-speaking regions of the Western Balkans, intuitive decision-making methods predominate. In order to find appropriate methods for assessing and prioritizing goals, new approaches to decision making should be adopted. Various methods have been developed for multi-criteria decision making. One of these is the Analytic Hierarchy Process (AHP) method—a method which should receive more attention than it has up to now. We would like to show that the AHP method could be of great use in decision making. Through a case study, this paper explores the AHP, a method with three levels in which the identification of decision-making criteria is based on the perceptions of managers and consumers. The paper’s findings offer an important guide for managers to improve decision making and enhance performance in competitive markets.

This study utilizes stochastic boosted regression trees (BRT) to investigate the effects of the COVID-19 Movement Control Order (MCO) on air quality in Ipoh City, Malaysia. The model aims to explore the Strength of Interaction Effects (SIE) and Relative Variable Importance (RVI) of key pollutants and meteorological variables impacting PM2.5 concentrations. Hourly data on gaseous pollutants (SO₂, NO₂, CO, O₃) and meteorological conditions (wind direction, wind speed, relative humidity, and temperature) were obtained from the Department of Environment for the periods of January to June in both 2019 and 2020, resulting in 2,231 data points. The BRT model was constructed using R software, with the optimal number of trees (nt = 4,372) determined through Out-of-Bag (OOB) iterations. Model performance was evaluated using various statistical metrics, including a Factor of Two (FAC2) of 0.91, R² values exceeding 0.56 (R = 0.74), and an Index of Agreement (IOA) of 0.67, indicating the model’s robustness. The analysis revealed significant differences in the RVI during the MCO and non-MCO periods. In non-MCO data, PM2.5 concentrations were primarily influenced by CO (18.9%), SO₂ (14.6%), O₃ (12.9%), and wind direction (10.66%). During the MCO, the most important variables were CO (22.6%), RH (13.4%), SO₂ (14.7%), and O₃ (12.1%). Additionally, the SIE analysis highlighted interactions such as CO-wind direction (0.24), O₃-wind speed (0.19), and NO₂-CO (0.15). These findings demonstrate that the BRT model effectively captures the key factors influencing air pollution and their interactions. The results provide valuable insights for urban planners and local authorities, helping them design strategies to mitigate pollutant levels by addressing the most impactful variables. The model could guide policy decisions and optimize air quality management, particularly during periods of reduced human activity or emergency conditions like the MCO.

Three flawed practices associated with model averaging coefficients for predictor variables in regression models commonly occur when making multimodel inferences in analyses of ecological data. Model-averaged regression coefficients based on Akaike information criterion (AIC) weights have been recommended for addressing model uncertainty but they are not valid, interpretable estimates of partial effects for individual predictors when there is multicollinearity among the predictor variables. Multicollinearity implies that the scaling of units in the denominators of the regression coefficients may change across models such that neither the parameters nor their estimates have common scales, therefore averaging them makes no sense. The associated sums of AIC model weights recommended to assess relative importance of individual predictors are really a measure of relative importance of models, with little information about contributions by individual predictors compared to other measures of relative importance based on effects size or variance reduction. Sometimes the model-averaged regression coefficients for predictor variables are incorrectly used to make model-averaged predictions of the response variable when the models are not linear in the parameters. I demonstrate the issues with the first two practices using the college grade point average example extensively analyzed by Burnham and Anderson. I show how partial standard deviations of the predictor variables can be used to detect changing scales of their estimates with multicollinearity. Standardizing estimates based on partial standard deviations for their variables can be used to make the scaling of the estimates commensurate across models, a necessary but not sufficient condition for model averaging of the estimates to be sensible. A unimodal distribution of estimates and valid interpretation of individual parameters are additional requisite conditions. The standardized estimates or equivalently the t statistics on unstandardized estimates also can be used to provide more informative measures of relative importance than sums of AIC weights. Finally, I illustrate how seriously compromised statistical interpretations and predictions can be for all three of these flawed practices by critiquing their use in a recent species distribution modeling technique developed for predicting Greater Sage-Grouse ( Centrocercus urophasianus ) distribution in Colorado, USA. These model averaging issues are common in other ecological literature and ought to be discontinued if we are to make effective scientific contributions to ecological knowledge and conservation of natural resources.

This article advocates for the wider use of relative importance indices as a supplement to multiple regression analyses. The goal of such analyses is to partition explained variance among multiple predictors to better understand the role played by each predictor in a regression equation. Unfortunately, when predictors are correlated, typically relied upon metrics are flawed indicators of variable importance. To that end, we highlight the key benefits of two relative importance analyses, dominance analysis and relative weight analysis, over estimates produced by multiple regression analysis. We also describe numerous situations where relative importance weights should be used, while simultaneously cautioning readers about the limitations and misconceptions regarding the use of these weights. Finally, we present step-by-step recommendations for researchers interested in incorporating these analyses in their own work and point them to available web resources to assist them in producing these weights.

Off-policy learning exhibits greater instability when compared to on-policy learning in reinforcement learning (RL). The difference in probability distribution between the target policy ( ) and the behavior policy (b) is a major cause of instability. High variance also originates from distributional mismatch. The variation between the target policy’s distribution and the behavior policy’s distribution can be reduced using importance sampling (IS). However, importance sampling has high variance, which is exacerbated in sequential scenarios. We propose a smooth form of importance sampling, specifically relative importance sampling (RIS), which mitigates variance and stabilizes learning. To control variance, we alter the value of the smoothness parameter in RIS. We develop the first model-free relative importance sampling off-policy actor-critic (RIS-off-PAC) algorithms in RL using this strategy. Our method uses a network to generate the target policy (actor) and evaluate the current policy ( ) using a value function (critic) based on behavior policy samples. Our algorithms are trained using behavior policy action values in the reward function, not target policy ones. Both the actor and critic are trained using deep neural networks. Our methods performed better than or equal to several state-of-the-art RL benchmarks on OpenAI Gym challenges and synthetic datasets.

Forest fire patterns are likely to be altered by climate change. We used boosted regression trees modelling and the MODIS Global Fire Atlas dataset (2003–15) to characterise relative influences of nine natural and human variables on fire patterns across five forest zones in China. The same modelling approach was used to project fire patterns for 2041–60 and 2061–80 based on two general circulation models for two representative concentration pathways scenarios. The results showed that, for the baseline period (2003–15) and across the five forest zones, climate variables explained 37.4–43.5% of the variability in fire occurrence and human activities were responsible for explaining an additional 27.0–36.5% of variability. The fire frequency was highest in the subtropical evergreen broadleaf forests zone in southern China, and lowest in the warm temperate deciduous broadleaved mixed-forests zone in northern China. Projection results showed an increasing trend in fire occurrence probability ranging from 43.3 to 99.9% and 41.4 to 99.3% across forest zones under the two climate models and two representative concentration pathways scenarios relative to the current climate (2003–15). Increased fire occurrence is projected to shift from southern to central-northern China for both 2041–60 and 2061–80.

Phosphoric acid-hydrogen peroxide (PHP) pretreatment is an effective method to obtain a cellulose-enriched fraction from biomass. In this study, artificial neural network (ANN) was used to predict PHP pretreatment efficiency of cellulose content (C-C), cellulose recovery (C-Ry), hemicellulose removal (H-Rl), and lignin removal (L-Rl) under various conditions of pretreatment time (t), temperature (T), H3PO4 concentration (Cp), and H2O2 concentration (Ch). The final optimized topology structure of the ANN models had 1 hidden layers with 9 neurons for C-C and 10 neurons for C-Ry, 10 neurons for H-Rl, and 12 neurons for L-Rl. The actual testing data fit the predicted data with R2 values ranging from 0.8070 to 0.9989. The relative importance (RI) revealed that Cp and Ch were significant factors influencing the efficiency of PHP pretreatment with total RI values ranging from 12% to 62.6%. However, their weights for the three components of biomass were different. The value of T dominated hemicellulose removal effectiveness with an RI value of 78.6%, while t did not seem to be a main factor dominating PHP pretreatment efficiency. The results of this study provide insights into the convenient development and optimization of biomass pretreatment from ANN modeling perspectives.