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The health of an economy is heavily dependent on the productivity of the economy's major industries including construction. While most macro-measures of productivity in the USA construction industry indicate a decline, corresponding studies at the individual task level indicate an increase in productivity. Therefore, this paper aims to identify areas where productivity challenges exist and thus provide recommendations for improvement in the construction industry.

A model that relates the way construction projects are executed with the sources of data that inform productivity analyses is developed and presented. This effort/value-flow model informs the data analysis that is performed to determine productivity trends for management and field labor. Further analysis for field labor productivity using field data and management productivity was separately conducted. Management productivity was particularly difficult to gauge, resulting in the use of surrogate measures.

It was observed that while both field labor and management productivities at the industry level have been decreasing, the decrease in management productivity was five times that of field labor productivity. A similar trend was observed for management productivity at the project level.

The primary contribution of this paper to the body of knowledge and industry is the introduction of a holistic analysis of USA construction productivity. Recommendations to improve management productivity include the use of technology, especially project management software.

Free trade reform promotes and consolidates businesses’ orientation to international markets. Using a sample of twenty Latin American countries, this study finds support for the hypothesis that higher revealed trade openness implies faster economic growth. However, at low output growth levels, increased revealed trade openness does not translate to faster output growth. Why more trade does not necessarily imply faster growth at all levels of revealed trade openness growth remains a conundrum. Failure to derive faster economic growth may compromise the prospects for sustainable trade reforms and thus the consolidation of new business ventures as engines for further growth.

This paper aims to investigate the effect of L-citrulline supplementation on blood pressure (BP) in middle-aged and elderly populations.

Three electronic databases, namely, Google Scholar, PubMed and Scopus, were searched from 1990 to November 2020. Random effects model analysis was applied for quantitative data synthesis, and 6 trials with 150 participants were identified and included in the analysis.

Results showed an overall non-significant effect of L-citrulline supplementation on both systolic BP (SBP) and diastolic BP (DBP) in middle-aged and elderly participants. However, stratified analysis indicated a significant reduction in SBP (SMD: −0.41, 95% CI: −0.84, 0.02; p = 0.06, heterogeneity p = 0.41; I2 = 10%) but not in DBP (SMD:−0.15, 95% CI: −0.57, 0.28; p = 0.51, heterogeneity p = 0.60; I2 = 0%), following longer-term (= 8 weeks) supplementation of L-citrulline. Additionally, higher doses of L-citrulline (= 6 grams) showed a marginally significant reduction in DBP (SMD: −0.38, 95% CI: −0.78, 0.02; p = 0.06, heterogeneity p = 0.50; I2 = 0%).

In conclusion, a higher dosage and longer duration of supplementation with L-citrulline may have potential BP lowering effects in populations at an increased risk of hypertension

At the end of the millennium Mexico faced the double challenge of adjusting to an economic policy based on open markets and the protection of a reinvigorated democratic political system through an increased awareness of civil rights and responsibilities among citizens. Nevertheless, tertiary education reforms shifted the onus on education from the formation of social capital to that of human capital. I consider the background of the introduction of the neo-liberal model in the Mexican economy, and the economists’ critique of the adequacy of that model. I contrast the latter to the educationalists’ debate in response to where it becomes apparent that the neoliberal model had come to dominate the conceptual framework in which the impact of the introduction of the reform model could be analyzed. Finally, I consider a recent text in which the neo-liberal tendencies in tertiary education are more clearly outlined, although an alternative option is not forthcoming. By situating my consideration of the challenges of a knowledge society firmly within the historical, social and economic context of Mexico, I indicate factors which such an alternative would need to take into account.

This paper aims to test the idea generation capabilities of online text-based focus groups as compared to the traditional in-person focus groups using sustainability in the hospitality industry as the idea generation topic. Idea generation quantity and quality are analyzed and the theoretical and practical implications for the hospitality industry are discussed.

An experimental study tested the quality of ideas generated by online versus in-person focus groups. Participants were purposively sampled from the hospitality program at a large southeastern United States university and randomly assigned into one of two treatment groups: online text-based or traditional in-person focus groups. During both treatment groups participants generated ideas focused on sustainability in the hospitality industry.

The online focus group generated a comparable quantity of ideas, in addition to a similar average quality of ideas and number of good ideas.

The generation of ideas and the selection of opportunities drive the innovation process through which firms can strengthen their competitive advantage and maintain and grow market share and profitability. The results of this study may assist hospitality firms in determining which form of qualitative research delivers the highest return on investment by generating the best ideas at the lowest cost.

This paper breaks new ground by assessing the effectiveness of idea generation in online versus traditional focus groups, comparing both the quantity and quality of ideas generated from an experimental study that uses random assignment.

Drawing from two years of multi-sited fieldwork about international backpacking in Central America, I make important connections between the backpacking escape motive, the backpacker hostel, and tourism. I explain how backpackers experience the hostel as their “home base” and “home away from home” to escape into local cultures and natural environments that exist outside of it and an international community of travelers that convenes inside of it. I refer to theories on modern tourism, the backpacking escape motive, and the concept of community. I also theorize how the global spread of modern amenities and tourism shapes backpackers' escape experiences.

The purpose of this paper is to identify and discuss various factors that will influence the process of installing and maintaining subsea equipment in the oil and gas (O&G) industry.

Influencing factors and their attributes are identified using a case study on subsea installations conducted in the Norwegian O&G industry. A number of experts were interviewed. A survey was conducted to rank the importance of the influence factors.

The paper identifies, analyzes and validates the factors and attributes that may impact the installation and maintenance strategy of subsea components. The factors are ranked according to importance and as practiced in the industry.

The research is based on a case study in the Norwegian O&G industry, but the approach and results could be adapted to other industries as well.

The identified factors can be used by decision makers in the development of offshore projects.

Many applications in intelligent transportation demand accurate categorization of vehicles. The purpose of this paper is to propose a working image-based vehicle classification system. The first component vehicle detection is implemented by applying Dalal and Triggs's histograms of oriented gradients features and linear support vector machine (SVM) classifier. The second component vehicle classification, which is the emphasis of this paper, is accomplished by an improved stacked generalization. As an effective ensemble learning strategy, stacked generalization has been proposed to combine multiple models using the concept of a meta-learner. However, it was found that the well-known meta-learning scheme multi-response linear regression (MLR) for stacked generalization performs poorly on the vehicle classification.

A new meta-learner is then proposed based on kernel principal component regression (KPCR). The stacked generalization scheme consists of a heterogeneous classifier ensemble with seven base classifiers, i.e. linear discriminant classifier, fuzzy k-nearest neighbor, logistic regression, Parzen classifier, Gaussian mixture model, multiple layer perceptron and SVM.

Experimental results using more than 2,500 images from four types of vehicles (bus, light truck, car and van) demonstrated the effectiveness of the proposed approach. The improved stacked generalization produced consistently better results when compared to any of the single base classifier used and four other beta learning algorithms, including MLR, majority voting, logistic regression and decision template.

With the seven base classifiers, the KPCR-based stacking offers a performance of 96 percent accuracy and 95 percent κ coefficient, thus exhibiting promising potentials for real-world applications.