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Reliable construction waste generation data is a prerequisite for any evidence-based waste management effort, but such data remains scarce in many developing economies owing to their rudimentary recording systems. By referring to several models proposed for estimating waste generation, this study aims to develop a reliable and accessible method for estimating construction waste generation based on limited publicly available data. The study has two objectives. Firstly, it aims to estimate construction waste generation by focusing on the Greater Bay Area (GBA) in China, one of the world's most thriving regions in terms of construction activities. Secondly, it aims to compare the strengths and weaknesses of various waste quantification models. 43 sets of annual socio-economic, construction-related and C&D waste generation data ranging from 2005 to 2019 were collected from the local government authorities. By analyzing the data using four types of machine learning models, namely multiple linear regression, decision tree, grey models, and artificial neural network, it is found that all calibrated models, with their respective strengths and weaknesses, can produce acceptable results with the testing R2 ranging from 0.756 to 0.977. This study also reveals that the 11 cities in the GBA produced a total of about 364 million m3 of construction waste in 2018. The result can be used for monitoring the urban metabolism, quantifying carbon emission, developing a circular economy, valorizing recycled materials, and strategic planning of waste management facilities in the GBA. The research findings also contribute to the methodologies for estimating waste generation using limited data.

In this paper, an ultra-high sensitive plasmonic sensor is theoretically proposed for refractive index based on D-shaped photonic crystal fiber (PCF) with graphene-coated Ag-grating in mid-infrared region. Surface plasmon polariton at the metal/dielectric interface can be effectively excited by the fundamental guiding mode, leading to the surrounding medium-dependent loss spectrum. This metallic-grating PCF sensor exhibits a maximum sensitivity of 18612 nm/RIU with a detection resolution of 4.16 × 10(−6) RIU in the index range from 1.33 to 1.395. Dependences of loss spectrum on the PCF parameters (air hole diameter and lattice constant) and the grating structure (grating thickness, period and width) are systematically analyzed. Moreover, the influence of the material parameters on the sensor performance is also investigated in term of graphene-layer number and the thickness of Ag layer. The compact design not only has great potentials in the applications of liquid detection, but also offers a guidance in the engineering of the metallic-grating fiber sensor.

Recent advancement of remote sensing technologies has brought in accurate, dense, and inexpensive city-scale Light Detection And Ranging (LiDAR) point clouds, which can be utilized to model city objects (e.g., buildings, roads, and automobiles) for creating Digital Twin Cities (DTCs). However, processing such unstructured point clouds is very challenging, epitomized by high cost, movable objects, limited object classes, and high information inadequacy/redundancy. We noticed that many city objects are not in random shapes; rather, they have invariant cross-sections following the Gestalt design principles, including proximity, connectivity, symmetry, and similarity. In this paper, we present a novel unsupervised method, called Clustering Of Symmetric Cross-sections of Objects (COSCO), to process urban LiDAR point clouds to a hierarchy of objects based on their characteristic cross-sections. First, city objects are segmented as connected patches of proximate 3D points. Then, symmetric cross-sections are detected for symmetric city objects. Finally, the taxonomy and groups of city objects are recognized from a hierarchical clustering analysis of the dissimilarity matrix. Experimental results showed that COSCO detected the correct taxonomy and types of 12 cars from 24,126 LiDAR points in 8.28 s. Based on the cross-sections and taxonomy, a digital twin was created by registering online free 3D car models in 29.58 s. The contribution of this paper is twofold. First, it presents an effective unsupervised method for understanding and developing DTC objects in LiDAR point clouds by harnessing innate Gestalt design principles. Secondly, COSCO can be an efficient LiDAR pre-processing tool for recognizing symmetric city objects’ cross-sections, positions, heading directions, dimensions, and possible types for smart city applications in GIScience, Architecture, Engineering, Construction and Operation (AECO), and autonomous vehicles.

Construction projects require the effective collaboration among the various types of stakeholders involved to succeed, thus leading to stakeholder perspective studies in construction projects. The study proposes an integrated bibliometric approach to detect the knowledge evolution, domain and frontier with a broader searching scope compared with manual review. A total of 752 peer-reviewed academic papers published until the end of 2017 are used. The knowledge evolution indicates seven milestones in history, namely, stakeholder concept, method, identification, assessment, management, influence and complexity. The identified knowledge domain consists of four major research areas which are society, sustainability, analytical tool and project management. The knowledge frontier is also revealed, including a dearth of detailed discussions on stakeholder engagement in sustainable urban projects, lack of generalisation of stakeholder studies in complex construction projects, limited application of dynamic and simulation stakeholder analysis in uncertain project environment and few instant and accurate approaches to integrate stakeholder information. The study provides a holistic knowledge map for the past, current and future of stakeholder perspective studies in construction projects.

Understanding waste generation flow is vital to any evidence-based effort by policy-makers and practitioners to successfully manage construction project waste. Previous research has found that accumulative waste generation in construction projects follows an S-curve, but improving our understanding of waste generation requires its investigation at a higher level of granularity. Such efforts, however, are often constrained by lack of quality “bigger” data, i.e. data that is bigger than normal small data. This research aims to provide an anatomy of waste generation flow in building projects by making use of a large set of data on waste generation in 19 demolition, 59 foundation, and 54 new building projects undertaken in Hong Kong between 2011 and 2019. We know that waste is generated in far from a steady stream as it is always impacted by contingent factors. However, we do find that peaks of waste generation in foundation projects appear when project duration is at 50–85%, and in new building projects at 40–70% of total project time. Our research provides useful information for waste managers in developing their waste management plans, arranging waste hauling logistics, and benchmarking waste management performance.

7,8-dihydroxyflavone (7,8-DHF), a tyrosine kinase B (TrkB) receptor agonist, can mimick physiological actions of brain-derived neurotrophic factor (BDNF) to attenuate neurogenic disease. However, its use as a functional food, is limited by its low-water solubility, chemical instability, and poor bioavailability. The purpose of this work is to fabricate stable 7,8-DHF loaded zein/lactoferrin (LF) composite nanoparticles (zein/LF-DHF) to overcome these challenges. Results showed that mean particle size of zein/LF nanoparticles was about 74 nm with low polydispersity index (0.200) and turbidity (0.300) values. Zein/LF nanoparticles had good stability against pH (3.0-9.0), ionic strengths (0-500 mM NaCl at neutral pH) and long-term storage. Zein/LF nanoparticles showed spherical structures formed by hydrogen bonding and hydrophobic interactions, however, LF changed surface morphology of zein nanoparticles as observed by scanning electron microscope. X-ray diffraction indicated 7,8-DHF was presented in an amorphous state inside zein/LF nanoparticles. Most importantly, zein/LF-DHF had good redispersibility, and increased the encapsulation efficiency, chemical stability, water solubility and bioaccessibility of 7,8-DHF. Collectively, zein/LF nanoparticles are promising delivery systems for 7,8-DHF in functional foods.