Wei Zhou

The ordered crystal structures and phase evolutions of potassium decahydro-closo-decaborate (K2B10H10) and monocarba-closo-decaborate (K-1-CB9H10) were investigated by using synchrotron X-ray diffraction in combination with first-principles calculations and neutron vibrational spectroscopy. Upon cooling from room temperature to low temperature (100 K), both compounds exhibit order-to-order structure transitions with two ordered K2B10H10 and three ordered K-1-CB9H10 structures being identified and solved. For K2B10H10, a gradual monoclinic to monoclinic phase transition is observed. In contrast, KCB9H10 undergoes a more abrupt first-order tetragonal-to-orthorhombic phase transition. Our detailed structural analyses indicate that these transitions are driven by the reorientation of the quasi-ellipsoidal anions, optimizing the anion packing efficiency and cation–anion interactions. Notably, the reduced cation stoichiometry and weaker cation–anion interactions in KCB9H10 lead to significantly lower anion reorientational barriers compared with K2B10H10. Uncovering the orientational flexibility of these quasi-ellipsoidal anions at low temperatures is essential for understanding their dramatic transformation into superionic conductors at elevated temperatures. This work provides the structural foundation necessary for designing and developing next-generation polyborate-based solid-state electrolytes.

A combination of in situ small-angle X-ray scattering (SAXS) microstructure characterization over scales ranging from 1 nm to 10 µm and powder X-ray diffraction (XRD) structure characterization under various gas/pressure/temperature conditions with density functional theory (DFT) calculations provides new insights for the CO ₂ sorption behavior of a one-dimensional porous coordination polymer: catena -bis(dibenzoylmethanato)(4,4′-bipyridyl)nickel(II), denoted NiDBM-Bpy. The NiDBM-Bpy chains are held together by van der Waals forces, but the structure of guest-free NiDBM-Bpy is unsolved due to a lack of suitable crystals and high-quality powder XRD patterns. Nevertheless, SAXS and powder XRD can follow microstructural and structural changes as a function of gas pressure, composition and temperature. Both mixed-gas flow and static supercritical CO ₂ regimes are explored experimentally. DFT calculations are used to model the structural variation associated with XRD changes and hysteresis in the sorption isotherms. XRD and DFT calculations suggest that an orthorhombic Fddd structure with two CO ₂ per Ni emerges following a transition from the structure with lower molar volume and symmetry that exists without CO ₂ present.

Universal machine learning interatomic potentials accelerate ab initio crystal structure prediction to resolve the complex coordination environments of technologically significant organic lithium and sodium salts.

Purpose: Higher education institutions (HEIs) are increasingly recognized as pivotal actors in the global transition toward sustainability. However, food waste management within campus canteens remains a persistent challenge often addressed through fragmented, behavior-focused interventions rather than systemic governance. This study aims to address this gap by constructing a closed-loop, data-driven framework that bridges the divide between waste diagnosis and actionable management. Design/methodology/approach: The study employs a deep learning hybrid model that integrates unstructured student feedback with multidimensional dish features to accurately diagnose waste risks at the micro-level. Building upon these diagnostic insights, a mixed-integer linear programming model is developed to optimize intervention strategies. Findings: The empirical results demonstrate that sustainable canteen management is not a zero-sum game; the model successfully achieves a scientific equilibrium among three competing objectives: minimizing the ecological footprint, controlling operational intervention costs and safeguarding student satisfaction. Originality/value: By transitioning environmental management from experience-based operations to algorithmic precision, this research provides a scalable model for precision environmental governance in HEIs. These findings directly support UN Sustainable Development Goals 12 (Responsible Consumption and Production) and 11 (Sustainable Cities), reinforcing the role of universities as living laboratories for verifying innovative sustainability solutions.

Recently, the uncrewed aerial vehicle (UAV) autonomous navigation has been widely used in logistics transportation and environmental monitoring. However, the interaction of subjective evaluation information has been the critical challenge for UAVs and its operations. Also, the correlation among different attributes of the evaluation information is unclear, and the interaction between early and late attributes is often overlooked. Then, how to describe this interactive evaluation information and realize classification decisions has become a key issue. To do it, this paper introduces the hesitant fuzzy set (HFS) and develop the temporal and interactive HFS. Further, we propose a self-attention interactive hesitant fuzzy-long short-term memory network to processes and recognizes the defined information. Then, a multi-probability classification algorithm is given to show the calculation process. At last, its feasibility and applicability are verified by an example of UAV autonomous navigation, which provides a new direction for the development of related UAV technologies.

Disputes become an accepted reality of construction projects, often resulting in serious consequences, including time and cost overruns, and broader macroeconomic impacts on the national economy. Disputes need to be managed effectively to reduce their negative impacts. Recently, an increasing trend has emerged toward integrating dispute management practices with innovative technologies of the digital era. Therefore, this research aims to investigate the applications of emerging digital technologies and Artificial Intelligence (AI) to manage disputes proactively. This research begins with a scientometric analysis, followed by a systematic review of dispute management using digital technologies with a special focus on AI. Following Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines, the systematic review identified 66 previous studies that combine dispute management and digital technologies. The analysis revealed that technologies such as Artificial Intelligence (AI), Building Information Modelling (BIM), blockchain, smart contracts, Document Management System (DMS), big data, cloud computing, and Unmanned Aerial Vehicle (UAV) are utilized, while AI and its technologies significantly contribute to managing disputes. AI technologies, especially natural language processing, show a growing trend in applications for predicting disputes using project documents. In addition, the study develops a conceptual framework to predict disputes using AI technologies. The study identified potential research areas involving the integration of digital technologies for dispute management in the construction industry, offering valuable direction for future research. The research suggests that the use of AI and emerging digital technologies potentially predicts and mitigates disputes, thereby paving the way for proactive dispute management.

In this work, we explore the possibility of applying automated crystal structure prediction to reproduce the experimentally identified metastable porous polymorphs.

Digital construction implementation has not yet realized its promised potential after three decades. Across the entire project lifecycle, adoption has encountered difficulties from high-level standard guidance, a lack of strategies, fragmented delivery approaches, and insufficient digital delivery competency. Establishing digital workflows tailored to organizations’ contexts is an essential linkage of the information layer to synthesize the business and technology layers to address these challenges within the ISO 19650 framework. The uneven implementation of building information modelling (BIM) in the Architecture, Engineering, Construction, and Operation (AECO) industry provides a holistic perspective to consider the digitalization workflow dynamics. This report performs a case study through a parallel approach to examining multiple projects’ digital construction implementation of an organization in the Forest City development. Applying an observation research method and real-world data of project records, it analyses its workflows’ digitalization and process digitization, combining with its organization’s structure and overall project strategy. Moreover, it highlights bespoke digital construction ecosystems and relevant stakeholders to streamline workflows. The digital construction implementation results and project benefits as project context indicators verify that fundamental digital workflows of design quality checking, project optimization, asset data collection, and defect management have significant applicability compared with the advanced workflows of integrated 5D cost management and precast design and production. Their adoptability keeps consistency with those of applicability using the extra cost, application complexity, and disruption level indicators from the technology–organization–environment (TOE) framework to measure. These multiple project studies reveal the feasibility for organizations to achieve lifecycle digital construction implementation competency. The feasibility is underpinned by introducing an in-house digital engineering team to organization structure, cultivating applicable digital delivery capabilities through workflows digitalization and process digitization, and synthesizing ISO 19650 with workflows to enable more contextualized digital construction implementation.

Maintenance of building fire protection facilities is crucial for preventing fires and safeguarding lives and property; the standardization and timeliness of these activities directly determine operational reliability. However, as fire-safety requirements escalate, manually drafting maintenance work orders remains inefficient and prone to omissions. Furthermore, regulatory documents in this domain are inherently complex, and annotated resources are scarce, hampering the digitalization of fire-safety management. To address these challenges, this paper presents an LLM-based method for automatically generating maintenance work orders for building fire protection facilities. The proposed approach integrates a domain-specific knowledge base and incorporates the FS-RAG (Fire Services–Retrieval-Augmented Generation) framework to enhance both the accuracy and practical usability of generated work orders. First, we construct a lightweight domain knowledge base, FSKB (Fire Services Knowledge Base), derived from extensive maintenance regulations, capturing key elements such as equipment types, components, maintenance actions, and frequencies. Second, we design an FS-RAG framework that leverages retrieval-augmented generation to extract critical information from regulations and fuse it with the knowledge base, ensuring high accuracy and operational feasibility. Multi-round evaluations across stages B0–B4 validate the effectiveness of our method. Results indicate significant improvements over traditional approaches: the line-level compliance rate reaches 97.3% (an increase of 5.7% over B1 and 30.4% over B0), and the F1 score achieves 90.42% (an increase of 12.62% over B1 and 29.87% over B0).