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Challenges associated with ensuring the accuracy and reliability of cost estimation of highway construction bid items are of significant interest to state highway transportation agencies. Even with the existing research undertaken on the subject, the problem of inaccurate estimation of highway bid items still exists. This paper aims to assess the accuracy of the cost estimation methods employed in the selected studies to provide insights into how well they perform empirically. Additionally, this research seeks to identify, synthesize and assess the impact of the factors affecting highway unit prices because they affect the total cost of highway construction costs.

This paper systematically searched, selected and reviewed 105 papers from Scopus, Google Scholar, American Society of Civil Engineers (ASCE), Transportation Research Board (TRB) and Science Direct (SD) on conceptual cost estimation of highway bid items. This study used content and nonparametric statistical analyses to determine research trends, identify, categorize the factors influencing highway unit prices and assess the combined performance of conceptual cost prediction models.

Findings from the trend analysis showed that between 1983 and 2019 North America, Asia, Europe and the Middle East contributed the most to improving highway cost estimation research. Aggregating the quantitative results and weighting the findings using each study's sample size revealed that the average error between the actual and the estimated project costs of Monte-Carlo simulation models (5.49%) performed better compared to the Bayesian model (5.95%), support vector machines (6.03%), case-based reasoning (11.69%), artificial neural networks (12.62%) and regression models (13.96%). This paper identified 41 factors and was grouped into three categories, namely: (1) factors relating to project characteristics; (2) organizational factors and (3) estimate factors based on the common classification used in the selected papers. The mean ranking analysis showed that most of the selected papers used project-specific factors more when estimating highway construction bid items than the other factors.

This paper contributes to the body of knowledge by analyzing and comparing the performance of highway cost estimation models, identifying and categorizing a comprehensive list of cost drivers to stimulate future studies in improving highway construction cost estimates.

Role congruity theory and gender stereotypes research suggests men are expected to engage in agentic behavior and women in communal behavior as leaders, and that role violation results in backlash. However, extant gender and leadership research does not directly measure expectations–behavior incongruence. Further, researchers have only considered one condition of role incongruence – display of counter-role behavior – and have not considered the outcomes of failing to exhibit role-congruent behavior. Additionally, few studies have examined outcomes for male leaders who violate gender role prescriptions. The present study aims to address these shortcomings by conducting a novel empirical test of role congruity theory.

This experimental study used polynomial regression to assess how followers evaluated leaders under conditions of incongruence between follower expectations for men and women leaders’ behavior and leaders’ actual behavior (i.e. exceeded and unmet expectations). Respondents read a fictional scenario describing a new male or female supervisor, rated their expectations for the leader’s agentic and communal behavior, read manipulated vignettes describing the leader’s subsequent behavior, rated their perceptions of these behaviors, and evaluated the leader.

Followers expected higher levels of communal behavior from the female than the male supervisor, but no differences were found in expectations for agentic behavior. Regardless of whether expectations were exceeded or unmet, supervisor gender did not moderate the effects of agentic or communal behavior expectations–perceptions incongruence on leader evaluations in polynomial regression analyses (i.e. male and female supervisors were not evaluated differently when displaying counter-role behavior or failing to display role-congruent behavior).

In addition to providing a novel, direct test of role congruity theory, the study highlighted a double standard in gender role-congruent behavior expectations of men and women leaders. Results failed to support role congruity theory, which has implications for the future of theory in this domain.

Country and cultural heritage are inextricably linked for First Nations peoples. This chapter explores those relationships in the context of repatriating cultural heritage materials back to Country and conceptualising a place for its ‘awakening’ for the Ngadjuri community of Mid-North South Australia. These materials in the context of this book ‘interpreted’ as a form of data curation, requiring potentially unique information systems designs to achieve accessibility, recoverability, and durability in remote communities with limited internet and mobile phone coverage. On the other hand, it is critically important to note, that the processes, challenges and repatriation of culturally sensitive materials and remains, are dependant here on the limitations of language. The reference to the notion of ‘data’ as a descriptor, and an inadequate term on some level, does not, and is not intended to, diminish any of their cultural significance and gravity. These are challenges that are worth the intellectual and technological investment to realise a return to Country for generationally displaced peoples and their cultural property that also needs to make it home.

With the increasing diversity, including international students, in US American colleges, it is inescapable for faculty to make long-term adjustments to maximize learning for every participant in their courses. Creating an inclusive environment means that faculty are attuned to the diverse needs of college students regarding each task written or oral. In this chapter, the author describes an applicable academic class discussion model, an equitable process that faculty can adapt in their classes and facilitate frequently, especially if that is an inevitable component of their courses. The author explains how comprehensive notes on texts, adequate information literacy skills, and transparent class norms will lead to learning-centered academic class discussions and meaningful engagement of international college students.

Budget hotels are becoming an emerging industry for convenience and affordability, where consumer sentiments are of paramount importance. Tourism has become increasingly dependent on social media and online platforms to gather travel-related information, purchase travel products, food, lodging, etc., and share views and experiences. The user-generated data helps companies make informed decisions through predictive and behavioural analytics.

Design/Methodology/Approach: This study uses text mining, deep learning, and machine learning techniques for data collection and sentiment analysis based on 117,151 online reviews of the customers posted on the TripAdvisor website from May 2004 to May 2019 from 197 hotels of five prominent budget hotel groups spread across India using Feedforward Neural Network along with Keras package and Softmax activation function.

Findings: The word-of-mouth turns into electronic word-of-mouth through social networking sites, with easy access to information that enables customers to pick a budget hotel. We identified 20 widely used words that most customers use in their reviews, which can help managers optimise operational efficiency by boosting consumer acceptability, satisfaction, positive experiences, and overcoming negative consumer perceptions.

Practical Implications: The analysis of the review patterns is based on real-time data, which is helpful to understand the customer’s requirements, particularly for budget hotels.

Originality/Value: We analysed TripAdvisor reviews posted over the last 16 years, excluding the Corona period due to industry crises. The findings reverberate in consonance with the performance improvement theory, which states feed-forward a neural network enhances organisational, process, and individual-level performance in the hospitality industry based on customer reviews.

This paper aims to examine how emotions affect consumers' food choices and food preparation activities during stressful periods, using the context of the coronavirus disease 2019 (COVID-19) outbreak in China.

This study used an online survey, with a sample of 1,050 individuals from 32 regions in China. Multi-regression and mediation models were used to test the relationships among perceived knowledge, emotions and food behaviors.

The results show that positive emotions positively affect healthy food consumption and engagement in food preparations. In contrast, negative emotions contribute to an increase in indulgent food consumption and quick-and-easy meal preparations. Increased knowledge of the current situation can enhance positive emotions and thus promote healthy food behaviors. Lacking knowledge may result in unhealthy food behaviors through negative emotions.

This study contributes to the understanding of emotions and food behaviors by examining the effects of both negative and positive emotions in the general population, exploring a wider constellation of food behaviors and identifying perceived knowledge as an important antecedent to emotions' effects on food behaviors. Implications for consumers and public policy are offered.

This paper presents an experimental investigation in establishing the relationship between FDM process parameters and tensile strength of polycarbonate (PC) samples using the I-Optimal design.

I-optimal design methodology is used to plan the experiments by means of Minitab-17.1 software. Samples are manufactured using Stratsys FDM 400mc and tested as per ISO standards. Additionally, an artificial neural network model was developed and compared to the regression model in order to select an appropriate model for optimisation. Finally, the genetic algorithm (GA) solver is executed for improvement of tensile strength of FDM built PC components.

This study demonstrates that the selected process parameters (raster angle, raster to raster air gap, build orientation about Y axis and the number of contours) had significant effect on tensile strength with raster angle being the most influential factor. Increasing the build orientation about Y axis produced specimens with compact structures that resulted in improved fracture resistance.

The fitted regression model has a p-value less than 0.05 which suggests that the model terms significantly represent the tensile strength of PC samples. Further, from the normal probability plot it was found that the residuals follow a straight line, thus the developed model provides adequate predictions. Furthermore, from the validation runs, a close agreement between the predicted and actual values was seen along the reference line which further supports satisfactory model predictions.

This study successfully investigated the effects of the selected process parameters - raster angle, raster to raster air gap, build orientation about Y axis and the number of contours - on tensile strength of PC samples utilising the I-optimal design and ANOVA. In addition, for prediction of the part strength, regression and ANN models were developed. The selected ANN model was optimised using the GA-solver for determination of optimal parameter settings.

The proposed ANN-GA approach is more appropriate to establish the non-linear relationship between the selected process parameters and tensile strength. Further, the proposed ANN-GA methodology can assist in manufacture of various industrial products with Nylon, polyethylene terephthalate glycol (PETG) and PET as new 3DP materials.

This study aims to automate the process of converting grading patterns into parametric patterns using artificial intelligence and to objectively evaluate the fitness of the converted patterns.

The developed system consists of a user interface that defines input data by importing multi-size grading patterns, an artificial neural network that learns the relationship between human body size and pattern geometry, and a module that converts training results into parametric patterns. In order to evaluate the fitness of the generated pattern, an objective fitting evaluation method using drape simulation was developed.

The body sizes of the wearer were input to the converted parametric pattern to generate a customized pattern. Resulting pattern showed a better fit than the grading pattern on the off-average body model.

In this study, a method has been developed that enables the users with minimal pattern drafting knowledge to convert grading patterns into parametric patterns using artificial intelligence and drape simulation. The human body's symmetry and the physical properties of fabric were not considered.

The system developed in this study requires less data compared to existing methods that attempt to design clothing patterns with machine learning. In addition, it was possible to evaluate pattern fitness on various body models through drape simulation based fit evaluation process for the first time.

No study has investigated the effects of different parameters on publication bias in meta-analyses using a machine learning approach. Therefore, this study aims to evaluate the impact of various factors on publication bias in meta-analyses.

An electronic questionnaire was created according to some factors extracted from the Cochrane Handbook and AMSTAR-2 tool to identify factors affecting publication bias. Twelve experts were consulted to determine their opinion on the importance of each factor. Each component was evaluated based on its content validity ratio (CVR). In total, 616 meta-analyses comprising 1893 outcomes from PubMed that assessed the presence of publication bias in their reported outcomes were randomly selected to extract their data. The multilayer perceptron (MLP) technique was used in IBM SPSS Modeler 18.0 to construct a prediction model. 70, 15 and 15% of the data were used for the model's training, testing and validation partitions.

There was a publication bias in 968 (51.14%) outcomes. The established model had an accuracy rate of 86.1%, and all pre-selected nine variables were included in the model. The results showed that the number of databases searched was the most important predictive variable (0.26), followed by the number of searches in the grey literature (0.24), search in Medline (0.17) and advanced search with numerous operators (0.13).

The results of this study can help clinical researchers minimize publication bias in their studies, leading to improved evidence-based medicine.

To the best of the author’s knowledge, this is the first study to model publication bias using machine learning.

Completing Right-of-Way (ROW) acquisition process on schedule is critical to avoid delays and cost overruns on transportation projects. However, transportation agencies face challenges in accurately forecasting ROW acquisition timelines in the early stage of projects due to complex nature of acquisition process and limited design information. There is a need of improving accuracy of estimating ROW acquisition duration during the early phase of project development and quantitatively identifying risk factors affecting the duration.

The quantitative research methodology used to develop the forecasting model includes an ensemble algorithm based on decision tree and adaptive boosting techniques. A dataset of Georgia Department of Transportation projects held from 2010 to 2019 is utilized to demonstrate building the forecasting model. Furthermore, sensitivity analysis is performed to identify critical drivers of ROW acquisition durations.

The forecasting model developed in this research achieves a high accuracy to predict ROW durations by explaining 74% of the variance in ROW acquisition durations using project features, which is outperforming single regression tree, multiple linear regression and support vector machine. Moreover, number of parcels, average cost estimation per parcel, length of projects, number of condemnations, number of relocations and type of work are found to be influential factors as drivers of ROW acquisition duration.

This research contributes to the state of knowledge in estimating ROW acquisition timeline through (1) developing a novel machine learning model to accurately estimate ROW acquisition timelines, and (2) identifying drivers (i.e. risk factors) of ROW acquisition durations. The findings of this research will provide transportation agencies with insights on how to improve practices in scheduling ROW acquisition process.