In the era of sustainability as the development concept, prefabricated buildings have gradually become an important way to achieve sustainable development of the construction process due to the advantages of high construction speed, energy-saving, and environmental protection. In order to make the prefabricated building develop in a sustainable direction, it is necessary to understand the importance and performance of the critical sustainability aspects of the prefabricated building. However, the existing research has not fully explored this point, and classification research on all aspects of sustainability according to the management priorities of sustainable development is lacking. The present study determines the critical sustainability characterization items (criteria) of prefabricated buildings and uses the importance-performance analysis (IPA) method to explore the sustainability importance and performance level of prefabricated buildings in Guangzhou on the basis of the three dimensions of economic, social, and ecological sustainability. In particular, this study revises the traditional IPA method and uses the comprehensive weight obtained by the analytical network process- (ANP-) entropy weight method to obtain the importance of items. Results show that items “environmental protection” and “construction civilization” are of high importance and perform well. “Construction cost” and “product quality” are considered high-importance items with relatively poor performance; that is, these areas require urgent improvement actions. The “policy support” item at the intersection of IPA coordinates is also an aspect worthy of attention and discussion. This study provides a useful reference for decision-makers and relevant personnel on determining the priority of project management and achieving the optimal allocation of resources to promote the sustainable development of prefabricated buildings.

Introduction: Recent studies suggested that sarcopenia may be a significant comorbidity of diabetes mellitus (DM). Nonetheless, studies with nationally representative data are scarce, and the changing trend of sarcopenia prevalence over time is largely unknown. Therefore, we aimed to estimate and compare the prevalence of sarcopenia in diabetic and non-diabetic United States (US) older population, and to explore the potential predictors of sarcopenia as well as the trend of sarcopenia prevalent in the past decades. Methods: Data was retrieved from the National Health and Nutrition Examination Survey (NHANES). Sarcopenia and DM were defined according to corresponding diagnosis criteria. Weighted prevalence was calculated and compared between diabetic and non-diabetic participants. The differences among age and ethnicity groups were explored. Results: A total of 6381 US adults (>50 years) were involved. The overall prevalence of sarcopenia was 17.8% for US elders, and the prevalence was higher (27.9% vs. 15.7%) in those with diabetes ones than those without. Stepwise regression revealed that sarcopenia was significantly associated with DM (Adjusted odds ratio=1.37, 95%CI: 1.08-1.22; P<0.05) after controlling for potential confounders including gender, age, ethnicity, educational level, BMI and muscle strengthening activity. A slightly fluctuate but overall increasing trend of sarcopenia prevalence was observed among diabetic elders while no obvious changing trend was observed in their counterparts in recent decades. Conclusion: Diabetic US older adults face significantly higher risk of sarcopenia when compared with their non-diabetic counterparts. Gender, age, ethnicity, educational level and obesity were important influencing factors of sarcopenia development.

Prefabricated buildings are the direction of the future development of the construction industry and have received widespread attention. The effective execution of prefabricated construction project scheduling should consider resource constraints and the supply arrangement of prefabricated components. However, the traditional construction resource-constrained project scheduling implementation method cannot simultaneously consider the characteristics of the linkage between component production and on-site assembly construction. It cannot also fully adapt to the scheduling implementation method of the prefabricated construction projects. It is difficult to work out a reasonable project schedule and resource allocation table. In order to determine the relevant schedule parameters that can reflect the actual construction situation of the prefabricated building and meet the scheduling requirements of the prefabricated project, this study proposes a prefabricated construction project scheduling model that considers project resource constraints and prefabricated component supply constraints. Additionally, it improves the design of traditional genetic algorithms (GAs). Research results of the experimental calculation and engineering application show that the proposed project scheduling optimization model and GA are effective and practical, which can help project managers in effectively formulating prefabricated construction project scheduling plans, reasonably allocating resources, reducing completion time, and improving project performance.

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Background and objective The health impacts of combined aerobic and resistance training on older populations are largely unknown. Therefore, we carried out the current study to systematically investigate the effects of combined exercise on body composition and physical functions of elders. Methodology Literature was searched from PubMed, Embase, Cochrane, Web of Science and Google Scholar. Inclusion criteria were: 1) healthy participants aged 55 years and above; 2) effects of combined exercise (aerobic combined with resistance training) examined; 3) effects on fat mass and lean mass reported. Research quality of the included studies was assessed by PEDro scale. Results Among the involved 11 studies, 9 out of 11 found that combined exercise increased the amount of lean mass of the elders (0.3–7.4%), while the other 2 reported a decline (3.0% and 3.4%). As for fat mass, all the included studies found that combined exercise decreased the total fat mass (2.19–16.5%) or local fat mass (0.7–40.7%). Furthermore, 5 out of the 11 studies examined the impact of combined training on muscle strength and aerobic power, and exercise was found to increase the lower limb strength (knee flexion: 15.1–15.9%; knee extension: 11.6–16.9%; and leg press 1 RM: 17.6–54.3%). Moreover, 5 studies assessed and reported that combined exercise was associated with an increased VO2peak (1.0–145.6%). Conclusions This systematic review revealed that a 8–52 weeks’ combined exercise, such as cycling combined with weight-lifting machines training, was beneficial in a decrease in whole-body and localized fat mass, and increase in the amount of body lean mass among older populations. Combined exercise was also shown to be more effective in increasing lower extremity strength and VO2peak compared with aerobic or resistance ones solely.

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PurposeProject scheduling plays an essential role in the implementation of a project due to the limitation of resources in practical projects. However, the existing research tend to focus on finding suitable algorithms to solve various scheduling problems and fail to find the potential scheduling rules in these optimal or near-optimal solutions, that is, the possible intrinsic relationships between attributes related to the scheduling of activity sequences. Data mining (DM) is used to analyze and interpret data to obtain valuable information stored in large-scale data. The goal of this paper is to use DM to discover scheduling concepts and obtain a set of rules that approximate effective solutions to resource-constrained project scheduling problems. These rules do not require any search and simulation, which have extremely low time complexity and support real-time decision-making to improve planning/scheduling.Design/methodology/approachThe resource-constrained project scheduling problem can be described as scheduling a group of interrelated activities to optimize the project completion time and other objectives while satisfying the activity priority relationship and resource constraints. This paper proposes a new approach to solve the resource-constrained project scheduling problem by combining DM technology and the genetic algorithm (GA). More specifically, the GA is used to generate various optimal project scheduling schemes, after that C4.5 decision tree (DT) is adopted to obtain valuable knowledge from these schemes for further predicting and solving new scheduling problems.FindingsIn this study, the authors use GA and DM technology to analyze and extract knowledge from a large number of scheduling schemes, and determine the scheduling rule set to minimize the completion time. In order to verify the application effect of the proposed DT classification model, the J30, J60 and J120 datasets in PSPLIB are used to test the validity of the scheduling rules. The results show that DT can readily duplicate the excellent performance of GA for scheduling problems of different scales. In addition, the DT prediction model developed in this study is applied to a high-rise residential project consisting of 117 activities. The results show that compared with the completion time obtained by GA, the DT model can realize rapid adjustment of project scheduling problem to deal with the dynamic environment interference. In a word, the data-based approach is feasible, practical and effective. It not only captures the knowledge contained in the known optimal scheduling schemes, but also helps to provide a flexible scheduling decision-making approach for project implementation.Originality/valueThis paper proposes a novel knowledge-based project scheduling approach. In previous studies, intelligent optimization algorithm is often used to solve the project scheduling problem. However, although these intelligent optimization algorithms can generate a set of effective solutions for problem instances, they are unable to explain the process of decision-making, nor can they identify the characteristics of good scheduling decisions generated by the optimization process. Moreover, their calculation is slow and complex, which is not suitable for planning and scheduling complex projects. In this study, the set of effective solutions of problem instances is taken as the training dataset of DM algorithm, and the extracted scheduling rules can provide the prediction and solution of new scheduling problems. The proposed method focuses on identifying the key parameters of a specific dynamic scheduling environment, which can not only reproduces the scheduling performance of the original algorithm well, but also has the ability to make decisions quickly under the dynamic interference construction scenario. It is helpful for project managers to implement quick decisions in response to construction emergencies, which is of great practical significance for improving the flexibility and efficiency of construction projects.