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The escalating demand for free-form buildings presents a formidable technological hurdle in the realm of prefabrication manufacturing and assembly. This research draws inspiration from recent advancements in large language models (LLMs) and seeks to harness their transformative potential in tandem with Building Information Modeling (BIM) to advance the Design for Manufacture and Assembly (DfMA) method. The ultimate aim is to catapult the prefabrication industry into an era characterized by unparalleled design freedom and heightened efficiency. The research includes contributions from the following four areas: (1) A review of the literature on DfMA theories, specifically fabrication and construction. (2) The development of a BIM-based DfMA method framework, including the methodological basis, Design for Manufacturing (DfM), and Design for Assembly (DfA). (3) A pipeline for LLMs driven BIM-based DfMA method framework and a validation using the Decathlon Solar Team design-build practice at the University of Washington’s Lotus House. (4) An evaluation of 692 conversation scenarios to verify the feasibility of our proposal. We get a 91.1% user agreement on average based on GPT4. The research results could guide the workflow in building a lifecycle for DfMA practices in the future smart manufacturing era.

Increasing efforts have been devoted to promoting sustainable demolition waste management (DWM) from a life cycle thinking perspective. To this end, facilitating sustainability-oriented decision-making for DWM planning requires a sustainability assessment framework for assessing the trade-offs among multifaceted criteria. This study develops a BIM-based DWM sustainability assessment approach to facilitate the life cycle sustainability assessment (LCSA) and decision-making by integrating LCSA-related properties and hybrid Multi-Criteria Decision-Aiding (MCDA) methods into a BIM environment using Dynamo visual scripting. A dynamic linkage is developed in the streamlined BIM-based LCSA process, where the enriched Industry Foundation Class (IFC) models are coupled with custom LCSA data templates to achieve seamless data exchange between the BIM platform and external LCA tools. Subsequently, hybrid MCDA methods convert the assessment results into DWM scenario ranking. A pilot study verifies the applicability of the BIM-based framework. The results unveil that the sustainability score ascended with the recycling rate. The optimal DWM alternative with the highest recycling rate yields the highest sustainability score at 91.63. Conversely, a DWM alternative reflecting the ‘status quo’ in China’s recycling industry has the lowest score at 8.37, significantly lower than the baseline scenario with a 50% recycling rate. It is worth noting that the ‘growth curve’ of the sustainability score continuously flattens as the target recycling rate escalates. The increment in recycling rate from the “Australian standard” scenario to the optimal scenario is 18.4%, whereas the sustainability score merely increases by 2.3%, implying that the former scenario arrived at an optimum point for maximising the cost-efficiency of DWM under the predefined settings.

Extracting entities and relations from the unstructured text has attracted increasing attention in recent years. The existing work has achieved considerable results, yet it is difficult to solve entity overlap and exposure bias. To address cascading errors, exposure bias, and entity overlap in existing entity relation extraction approaches, we propose a joint entity relation extraction model (SMHS) based on a span-level multi-head selection mechanism, transforming entity relation extraction into a span-level multi-head selection problem. Our model uses span-tagger and span-embedding to construct span semantic vectors, utilizes LSTM and multi-head self-attention mechanism for span feature extraction, multi-head selection mechanism for span-level relation decoding, and introduces span classification task for multi-task learning to decode out the relation triad in a single-stage. Experiments on the classic English dataset NYT and the publicly available Chinese relationship extraction dataset DuIE 2.0 show that this method achieves better results than the baseline method, which verifies the effectiveness of this method. Source code and data are published here(https://github.com/Beno-waxgourd/NLP.git).

The local electronic configuration of active sites in molybdenum disulfide (MoS2) could significantly alter the intrinsic activity towards hydrogen evolution reaction (HER). Herein, we report that the HER performance of MoS2 electrocatalysts was boosted via bismuth and palladium co-doping. The introduction of heteroatom Bi successfully altered the local electronic configuration of S atoms in the MoS2 inert basal plane to form an electron-enriched environment, conducive to boost active site density and proton adsorption, as experimentally verified by X-ray photoelectron spectroscopy (XPS) and ultraviolet photoelectron spectroscopy (UPS). Furthermore, after activating the MoS2 via Pd doping, a phase transition to stable 1 T phase occurred which was verified by Raman. In acidic media, the as-prepared Bi/Pd-MoS2 catalyst exhibited excellent electrochemical HER performance in terms of a low overpotential of -114 mV at a current density of 10 mA cm−2, a small Tafel slope of 65 mV dec−1, and outstanding long-term stability.

Ultraperformance convergence chromatography is an environmentally friendly analytical technique for dramatically reducing the use of organic solvents compared to conventional chromatographic methods. In this study, a rapid and sensitive ultraperformance convergence chromatography method was firstly established for quantification of thymol and carvacrol, two positional isomers of a major bioactive in the volatile oil of Thymi herba, the dried leaves and flowers of Thymus mongolicus or Thymus przewalskii, known in China as “Dijiao.” Using a TrefoilTM CEL1 column, thymol and carvacrol were separated in less than 2.5 min and resolution was enhanced. The method was validated with respect to precision, accuracy, and linearity according to the National Medical Products Administration guidelines. The optimized method exhibited good linear correlation (r = 0.9998−0.9999), excellent precision (relative standard deviations (RSDs) < 1.50%), and acceptable recoveries (87.29−102.89%). The limits of detection for thymol and carvacrol were 1.31 and 1.57 ng/L, respectively, while their corresponding limits of quantification were 2.63 and 3.14 ng/L. Finally, the quantities of the two compounds present in 16 T. mongolicus and four T. przewalskii samples were successfully evaluated by employing the developed method. It is hoped that the results of this study will serve as a guideline for the quality control of Thymi herba.