Prefabricated construction (PC) is an increasingly popular construction approach that offers numerous benefits, including greater efficiency, improved safety, and reduced material waste. However, this approach also introduces a new set of quality risks due to the complex process of off-site prefabrication, transportation, and on-site assembly. Currently, quality management (QM) in PC is mainly conducted through manual efforts that rely on human experience and established standards. This approach can be both inefficient and susceptible to human bias, which can lead to quality issues. To address these concerns, this study proposes a knowledge engineering-based framework that utilizes knowledge modeling and reasoning to enable intelligent QM. The framework consists of four layers: data acquisition, knowledge generation, knowledge storage, and value-added application. The proposed framework was applied to a case scenario, and the results showed that the open information stored in the knowledge base can guide the quality control process, and vice versa, enabling the quality control process to promote the updating of the knowledge base.

Accurate monitoring of physiological temperature is important for many biomedical applications, including monitoring of core body temperature, detecting tissue pathologies, and evaluating surgical procedures involving thermal treatment such as hyperthermia therapy and tissue ablation. Many of these applications can benefit from replacing external temperature probes with injectable wireless devices. Here we present such a device for real-time in vivo temperature monitoring that relies on 'chip-as-system' integration. With an on-chip piezoelectric transducer and measuring only 380 μm × 300 μm × 570 μm, the 0.065-mm ³ monolithic device, in the form of a mote, harvests ultrasound energy for power and transmits temperature data through acoustic backscattering. Containing a low-power temperature sensor implemented with a subthreshold oscillator and consuming 0.813 nW at 37 °C, the mote achieves line sensitivity of 0.088 °C/V, temperature error of +0.22/-0.28 °C, and a resolution of 0.0078 °C rms. A long-term measurement with the mote reveals an Allan deviation floor of <138.6 ppm, indicating the feasibility of using the mote for continuous physiological temperature monitoring.