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Direction-of-arrival (DOA) estimation for wideband sources has attracted a growing interest in the recent decade because wideband sources are incorporated in many real-world applications such as communication systems, radar, sonar and acoustics. One way to estimate the DOAs of wideband signals is to decompose it into narrowband signals using discrete Fourier transform (DFT) and then apply well-established narrowband algorithms to each signal. Afterwards, results are averaged to yield the final DOAs. These techniques require scanning the full band of wideband sources, ultimately degrading the resolution and increasing complexity. This paper aims to propose a new DOA estimation methodology to solve these problems.

The new DOA estimation methodology is based on incoherent signal subspace method (ISSM). The proposed approach presents a criterion to select a single sub-band of the selected narrowband signals instead of scanning the whole signal spectrum. Then, the DOAs of wideband signals are estimated using the selected sub-band. Therefore, it is named as single sub-band (SSB)-ISSM.

The computational complexity of the proposed method is much lower than that of traditional DFT-based methods. The effectiveness and advantages of the proposed methodology are theoretically investigated, and computational complexity is also addressed.

To verify the theoretical analysis, computer simulations are implemented, and comparisons with other algorithms are made. The simulation results show that the proposed method achieves better performance and accurately estimates the DOAs of wideband sources.

Prefabricated buildings (PBs) have proven to effectively mitigate carbon emissions in the construction industry. Existing studies have analyzed the environmental performance of PBs considering the shift in construction methods, ignoring the emissions abatement effects of the low-carbon practices adopted by participants in the prefabricated building supply chain (PBSC). Thus, it is challenging to exploit the environmental advantages of PBs. To further reveal the carbon reduction potential of PBs and assist participants in making low-carbon practice strategy decisions, this paper constructs a system dynamics (SD) model to explore the performance of PBSC in low-carbon practices.

This study adopts the SD approach to integrate the complex dynamic relationship between variables and explicitly considers the environmental and economic impacts of PBSC to explore the carbon emission reduction effects of low-carbon practices by enterprises under environmental policies from the supply chain perspective.

Results show that with the advance of prefabrication level, the carbon emissions from production and transportation processes increase, and the total carbon emissions of PBSC show an upward trend. Low-carbon practices of rational transportation route planning and carbon-reduction energy investment can effectively reduce carbon emissions with negative economic impacts on transportation enterprises. The application of sustainable materials in low-carbon practices is both economically and environmentally friendly. In addition, carbon tax does not always promote the implementation of low-carbon practices, and the improvement of enterprises' environmental awareness can further strengthen the effect of low-carbon practices.

This study dynamically assesses the carbon reduction effects of low-carbon practices in PBSC, informing the low-carbon decision-making of participants in building construction projects and guiding the government to formulate environmental policies.

The purpose of this paper is to develop a bi-objective optimization framework to select prefabricated construction service composition. An improved algorithm-genetic simulated annealing algorithm (GSA) is employed to demonstrate the application of the framework.

The weighted aggregate multi-dimensional collaborative relationship is used to quantitatively evaluate the synergistic effect. The quality of service is measured using the same method. The research proposed a service combination selection framework of prefabricated construction that comprehensively considers the quality of service and synergistic effect. The framework is demonstrated by using a GSA that can accept poor solutions with a certain probability. Furthermore, GSA is compared with the genetic algorithm (GA), simulated annealing algorithm (SA) and particle swarm optimization algorithm (PSO) to validate the performance.

The results indicated that GSA has the largest optimal fitness value and synergistic effect compared with other algorithms, and the convergence time and convergence iteration of the improved algorithm are generally at a low level.

The contribution of this study is that the proposed framework enables project managers to clarify the interactions of the prefabricated construction process and provides guidance for project collaborative management. In addition, GSA helps to improve the probability of successful collaboration between potential partners, therefore enhancing client satisfaction.

The purpose of this paper is to develop a model considering synergy effect for prefabricated construction service combination selection.

This research defines prefabricated construction service as a service-led construction method that meets the specific requirements of clients. Based on network theory, the multi-dimensional collaborative relationships of the prefabricated construction inter-services are formulated. The synergy effect is quantitatively calculated through the linear weighting of the strengths of collaborative relationships. Further, a weighted synergy network (WSN) is developed, from which a service composition selection model considering the synergy effect is established. Then, a genetic algorithm is employed to implement the model.

The results showed that (1) when the number of prefabricated construction services is increased, the synergy effect of combination options is enhanced; (2) The finer-grained prefabricated construction services, the stronger the synergy effect of service combination; (3) Clients have heterogeneous preferences for collaborative relationships, and there are differences in the synergy effect of service combination.

The contribution of this research includes proposed a method to quantify the synergy effect from the perspective of collaborative relationships, explored the specific procedure for the prefabricated construction service combination selection under the service-led construction, and provided a reference for promoting the development in construction. Besides, the model proposed could be applied to prefabricated construction service composition selection with diverse research boundaries or client preferences by executing the same procedure.

In recent decades, infrastructure has continued to develop as an important basis for social development and people's lives. Resource management of these large-scale projects has been immensely concerned because dozens of construction enterprises (CEs) often work together. In this situation, resource collaboration among enterprises has become a key measure to ensure project implementation. Thus, this study aims to propose a systematic multi-agent resource collaborative decision-making optimization model for large projects from a matching perspective.

The main contribution of this work was an advancement of the current research by: (1) generalizing the resource matching decision-making problem and quantifying the relationship between CEs. (2) Based on the matching domain, the resource input costs and benefits of each enterprise in the associated group were comprehensively analyzed to build the mathematical model, which also incorporated prospect theory to map more realistic decisions. (3) According to the influencing factors of resource decision-making, such as cost, benefit and attitude of decision-makers, determined the optimal resource input in different situations.

Numerical experiments were used to verify the effectiveness of the multi-agent resource matching decision (MARMD) method in this study. The results indicated that this model could provide guidance for optimal decision-making for each participating enterprise in the resource association group under different situations. And the results showed the psychological preference of decision-makers has an important influence on decision performance.

While the MARMD method has been proposed in this research, MARMD still has many limitations. A more detailed matching relationship between different resource types in CEs is still not fully analyzed, and relevant studies about more accurate parameters of decision-makers’ psychological preferences should be conducted in this area in the future.

Compared with traditional projects, large-scale engineering construction has the characteristics of huge resource consumption and more participants. While decision-makers can determine the matching relationship between related enterprises, this is ambiguous and the wider range will vary with more participants or complex environment. The MARMD method provided in this paper is an effective methodological tool with clearer decision-making positioning and stronger actual operability, which could provide references for large-scale project resource management.

Large-scale engineering is complex infrastructure projects that ensure national security, increase economic development, improve people's lives and promote social progress. During the implementation of large-scale projects, CEs realize value-added through resource exchange and integration. Studying the optimal collaborative decision of multi-agent resources from a matching perspective can realize the improvement of resource transformation efficiency and promote the development of large-scale engineering projects.

The current research on engineering resources decision-making lacks a matching relationship, which leads to unclear decision objectives, ambiguous decision processes and poor operability decision methods. To solve these issues, a novel approach was proposed to reveal the decision mechanism of multi-agent resource optimization in large-scale projects. This paper could bring inspiration to the research of large-scale project resource management.

This study aims to analyze the interaction and key factors within the network of factors influencing the success of green highway projects.

Through literature review and interviews with experienced project managers, this study identified 33 influencing factors from the perspectives of stakeholders and life cycle. The interaction between these influencing factors was determined by surveying different experts, most of whom have participated in green highway projects in China. Then, social network analysis (SNA) was used to explore the impact and control ability of different factors.

According to the prioritization of these factors, the results showed that some key factors were identified, especially incremental cost, lack of standards and regulations, development of innovative technologies and materials, public awareness and environmental protection behavior. Finally, some meaningful suggestions were put forward for different influencing factors of green highway projects.

While the key influencing factors of the green highway projects have been identified by considering the interrelationships between different factors, the specific influencing paths and levels of different factors are not analyzed, more studies and methods should be conducted on this area in the future.

This finding of factors influencing the success of green highway projects which is useful for managers to overcome various obstacles encountered in similar projects. Moreover, considering the interaction among the influencing factors enables managers to make systematic and efficient decisions.

Developing green highways have been perceived as a major innovation to help achieve the synergy of environmental protection, economic development and social responsibility. Studying the key factors influencing the success of green highway projects and putting forward targeted suggestions are crucial for promoting the environmental protection transformation of highway construction.

Compared with most studies on the identification of key influencing factors of construction projects, this research emphasized the interaction between different factors within the system in the analysis process. The findings could provide useful references to promote the successful implementation of green highway projects.

The purpose of this paper is to develop nitrogen-enriched carbon (NC) with high conductivity and specific capacitance as electrode materials for supercapacitors.

Graphene oxide (GO) was synthesized by the modified Hummers–Offeman method. NC was synthesized by carbonization of melamine formaldehyde resin/graphene oxide (MF/GO) composites. Supercapacitors based on Ni(OH)2/Co(OH)2 composites as the positive electrode and NC as the negative electrode were assembled. The electrochemical performances of NC and supercapacitors are studied.

The results show that obtained NC has high nitrogen content. Compared to NC-GO0 without GO, high conductivity and specific capacitance were obtained for NC with GO due to the introduction of layered GO. The presence of pseudocapacitive interactions between potassium cations and the nitrogen atoms of NC was also proposed. When the weight ratio of GO to MF is 0.013:1, the obtained NC-GO3 has the highest specific capacitance of 154.07 F/g due to GO and its highest content of N-6. When the P of the asymmetric supercapacitor with NC-GO3 as the negative electrode is 1,326.70 W/kg, its Cps and Ep are still 23.84 F/g and 8.48 Wh/Kg, respectively. There is only 4.4 per cent decay in Cps of the supercapacitor over 1,000 cycles.

NC is a suitable electrode material for supercapacitors. The supercapacitors can be used in the field of automobiles and can solve the problems of energy shortage and environmental pollutions.

NC based on MF/GO composites with high nitrogen content and conductivity was novel and its electrochemical properties were excellent.

The matter of predicting disasters has always been one of the hottest and most challenging tasks in geology. Earthquakes are among the most destructive ones among all the natural hazards. Occurring often without any warning, they are the most feared and unpredictable natural phenomena. In recent years with the emergence of new remote sensing instruments and techniques, geologists interested themselves to define accurate and reliable procedures to foresee disasters using this new technology. This paper aims to examine some of the data that have been used so far in earthquake prediction as well as cheap, relevant remotely sensing and geographic information systems methods to acquire and manipulate data.

Earthquakes are not the same in terms of origins, places (depth) and effects. So after having a brief look at the earthquakes, this paper examines the data that can be used for predicting earthquakes and reviews some of the remote sensing methods used to predict tectonic earthquakes. According to the types of measurements, remote sensing methods can be categorized in three main types; crust displacement, thermal and electromagnetic detecting techniques. Regarding the area's geological characteristics, satellites with optical and/or synthetic aperture radar sensors applications in prediction of large‐scale natural disasters will be discussed.

Presentation of the definitions and characteristics of earthquakes, categorized representation of the types of data used in this field as well as the types and names of the ground, aerial‐ and space‐borne data providers are the most important products of this review paper.

This method, if fully and systematically conducted, can be the cornerstone of an earth‐predicting system.

The purpose of this paper is to investigate the ways to diminish or eliminate numerical diffusion and dispersion. Numerical dispersion and diffusion are present in the predicted macrosegregation profiles reported in the literature and they hinder the interpretation of the simulation results. With the motivation to eliminate these numerical problems by employing appropriate meshes, simulations of macrosegregation in a billet direct‐chill cast from a multi‐component aluminium alloy has been performed.

First the idea that numerical dispersion could be alleviated by refining the structured mesh size is tested and the extent of this mesh refining to overcome these numerical problems is discussed. Second the link of numerical dispersion and diffusion to the type of mesh used is investigated.

Unstructured mesh eliminates the numerical dispersion present in the structured mesh while it introduces the numerical diffusion. It is concluded by performing calculations with the same settings but different meshes that, although refining the structured mesh could alleviate the numerical oscillation, it increases the computation time dramatically. Therefore the best solution to overcome these numerical problems is the employment of a hybrid mesh consisting of both structured and unstructured mesh.

This work reveals the reasons behind the numerical dispersion and diffusion in macrosegregation modelling and gives a practical solution.