The importance of catalyst design is shown by the contrasting reactivity of tethered NHC and Tp ligands with rare-earth metal centres highlighting that successful catalyst design is not as simple as linearly combining moieties with known catalytic activity. The heteroleptic fluorenyl-tethered-NHC rare-earth complexes LnLX2 [Ln = Y, La, Ce, Nd; L = (η5-C13H8)C2H4N(κ-C)C2H2NMe; X = N'' or Br] were synthesised from [Ln{N(SiMe3)2}3] (LnN''3), with X selected by varying the precursor. Direct addition of LnN''3 to the fluorene-tethered imidazolium salt LH2Br proceeded with loss of two equivalents of HN(SiMe3)2 forming [LnL(N'')Br] complexes; alternatively, initial deprotonation of LH2Br with nBuLi or KCH2Ph prior to addition of LnN''3 yielded [LnL(N'')2] complexes. [LnL(N'')Br] [Ln = Ce (3), Nd (4)] are dimeric through bridging Br ligands whereas [LnL(N'')2] [Ln = La (5), Ce (6), Nd (7)] are monomeric. These complexes were investigated for their activity and selectivity in the ring opening polymerization (ROP) of rac-lactide using benzyl alcohol as a co-initiator, showing high activity with full conversion typically being obtained in under 15 minutes at room temperature. Comparisons with the yttrium amide complex [Y(Tp)2(N'')] (8, Tp = hydrotris(1-pyrazolyl)borate) demonstrated the important role of the fluorenyl-tethered NHC ligand in generating an active catalyst, illustrating that simply having a rare-earth metal centre with a bis(trimethylsilyl)amide group is not enough to generate an effective (pre)catalyst. Finally, we hope to raise awareness that a trace impurity of lactic acid, sometimes present in commercial rac-lactide, greatly inhibited ROP and was not easily removed by sublimation or re-crystallisation.

European native oyster (Ostrea edulis) reef ecosystems are collapsed but were once a dominant feature of European seascapes. Recent restoration efforts, while increasing, remain diminutive compared to the ecosystem's former extent and what is required to restore functioning oyster reef ecosystems. A functioning European native oyster reef ecosystem requires larval connectivity at the metapopulation scale to ensure sustained recovery of resilient oyster reef ecosystems and biodiversity. To meet international nature recovery targets, restoration goals must therefore move beyond consideration of native oysters as features of habitats and toward their consideration as biogenic ecosystems, several hectares in size and functionally connected. As member states of the European Union seek to develop their Nature Restoration Plans in response to the Nature Restoration Regulation (NRR), it is critical in the case of European native oyster reef ecosystems, that the recently established historical baseline is referred to, such that native oyster reefs are appropriately defined for future implementation of the NRR and the EU Habitats Directive.

Analytical and creative thinking are essential skills for employers today. Higher education should therefore be about learning how to think, not just what to think. Despite general agreement on the purpose of university, not all students master thinking skills, in part due to widespread difficulties in comprehending what critical thinking is. Two studies sought to make more widely accessible critical thinking descriptions from a common taxonomy by Facione and to explore perceptions of their relative importance. In Study 1, 19 students and educators co-produced readable descriptions of critical thinking skills that were more understandable than the original set of descriptions, as measured by the Flesch–Kincaid metric. In Study 2, 406 students rated all core skills in the taxonomy as important, with no meaningful differences in opinion across discipline or study year. Students’ insights from the interviews in Study 1 supported this finding. Additionally, students expressed lower perceived self-efficacy in self-regulation skills, which they recognized as underdeveloped within their university curriculum. These findings have broad implications for Asian higher education by breaking down barriers to understanding critical thinking concepts, so that educators can design more engaging and effective learning experiences in the classroom.

Purpose This conceptual paper examines what underlies decision-making in generative change processes to explore how generative artificial intelligence (GAI) might shape the future of organisational change. Design/methodology/approach This investigation draws on Snowden and Stanbridge's (2004) social complexity concept, Archer's (1995) morphogenetic/morphostatic explanatory methodology and Mackenzie and Bititci's (2023) social systems-based model for organisational change to explain how structure, culture and agency influence generative change processes in complex adaptive social systems. Findings Both human-based decision-making and machine-based decision-making have roles to play in generative change. This paper proposes that human reflexivity mediates ideas, whereas the material aspects of artificial intelligence (AI) and GAI mediate tasks. The former shapes the change interventions that take place and the latter contributes to their more effective execution. Practical implications In generative change processes, AI and GAI technologies should be focused on tasks that support human-based decision-making. Originality/value This paper explores decision-making in organisational change from a social complexity perspective and identifies the complementary roles of human reflexivity and AI and GAI materiality in delivering emergent outcomes.

Purpose Reducing construction and demolition waste (C&DW) to further lower carbon reduction (CR) in the architecture, engineering and construction (AEC) industry is a critical challenge for achieving sustainable development. Enhancing the synergistic management efficiency (SME) for C&DW and CR aligns with the principles of the circular economy and the United Nations' Sustainable Development Goals (SDGs). By identifying the spatial and temporal patterns and driving factors of the SME for C&DW and CR, this study aims to improve the SME of implementing C&DW and CR policies in the AEC industry, promote regional coordinated development and provide beneficial references, as well as theoretical support for carbon neutrality in developing countries and areas. Design/methodology/approach The study employed the Theil index method, the standard deviation ellipse model method and the geographic detector model method to analyze spatiotemporal disparities, evolutionary trajectory and driving factors influencing SME in the AEC industry from 2010 to 2020 in China. Findings The average SME values increased from 0.54 in 2010 to 0.60 in 2020, with a consistent increasing trend. The SME had significant spatial heterogeneity across regions. The overall difference, regional and inter-regional disparities in SME declined over time. However, regional disparities remained the dominant contributor. The center of SME was located in Henan Province, exhibiting a “Northeast-Southwest” distribution pattern with a slight clockwise rotation. The population size and total revenue of construction enterprises were the main factors influencing SME. The interaction effect among different influencing factors manifested in two forms: dual-factor enhancement and nonlinear enhancement. Research limitations/implications In the investigation, we only selected China as a case study to analyze and present a pathway for improving the SME in the AEC construction industry. Future research should consider more countries and areas including developed countries and test comparative analysis of different countries, as well as provide more comprehensive empirical reference for the formulation of SDGs and policies in the AEC industry, which can help the AEC achieve SDGs and a circular economy to balance economic growth paths with environmental responsibility. Practical implications This study revealed the regional differences and driving factors of SME in the AEC industry, which was helpful for the decision-making of relevant stakeholders and proposed targeted development pathways to improve SME in the AEC industry toward fostering circular economy practices and sustainable development. The findings also provided valuable empirical insights for countries and regions facing similar sustainability challenges in the AEC industry. Social implications The carbon emission (CE) carbon emission and C&DW generation were integrated to measure the SME, provided effective evaluation indicator for the sustainable development in the AEC industry. It provided insight for optimizing resource allocation on cross regional coordinated development of the AEC industry and supporting on promoting social equity. Originality/value CE and C&DW generation were integrated into a unified framework for efficiency measurement, enriching the sustainable development theory for the AEC industry. The spatiotemporal evolution characteristics and driving effects of SME not only identified the precise control areas and main factors but also provided differentiated collaborative reduction strategies for the synergistic effect on the C&DW and CR policies in the AEC industry.

Simultaneous peak water flow (SPWF) is a fundamental parameter calculated during the design phase to ensure the proper sizing of water systems in buildings. Over the years, overestimation has been a persistent problem in SPWF studies, leading to inefficiencies in energy and water use. This research aims to address this problem by developing a flexible data-driven approach to estimate water fixture use probability (FUP), a key variable in SPWF calculations. The proposed method uses artificial neural networks to model the water demand based on the type and FUP of water fixtures in the building, drawing on the Wistort model and Water Demand Estimation Model (WDEM). Differential evolution was used to optimise the FUP values based on empirical water flow data. The model was applied to two non-residential buildings of different sizes and uses, where results showed that the models closely estimate the empirical SPWF compared to building design codes. Due to its use of adaptable parameters, flexible underlying models, and real-world data, the developed approach can be applied to buildings of any type and size. By reducing the risk of SPWF overestimation, the method contributes to more efficient water and energy use, ultimately supporting sustainability goals in the built environment. Practical application This study presents a new methodology for estimating SPWF for buildings. The methodology is flexible and can be applied to different types and sizes of buildings. The use of real-world data aims to provide a more practical model that will closely estimate the SPWF of the buildings, as compared to the design flow rates from the design codes, for more cost-efficient, energy-efficient, and water-efficient building water supply systems.

Coating plays an important role in advancing sensing technology by significantly enhancing sensitivity, stability, and response time. The unique properties of nanostructures, including high surface-to-volume ratio and tunable porosity, make them suitable candidates for improving sensor performance. By optimizing nanostructure coatings, advancements in high-precision humidity sensing devices are achievable, enabling a wide range of industrial applications, especially in humidity-controlled industries. In this study, the effects of annealing time, annealing temperature, and the number of coating layers on the properties of additive-enhanced SnO2 nanostructure were investigated. The experiment was carried out by subjecting the additive-enhanced SnO2 nanostructure to different annealing times and annealing temperatures to analyze its impact on crystallinity, porosity, and moisture adsorption properties. Upon optimizing the annealing parameters, multilayer coatings were carried out to assess the effect of the total number of coating layers on hygroscopic behavior. A hygroscopicity test was carried out on each sample to evaluate its moisture adsorption and desorption capabilities. The results demonstrated that controlled annealing conditions significantly improve the nanostructure’s hygroscopic properties, and the optimized coating layers further enhanced the moisture retention, making the developed SnO2 nanostructure a promising candidate for advanced sensing applications.