Research Articles

Environmental concerns have been highlighted by the overuse of synthetic fibres and polymers in foam products because of their limited sustainability and low biodegradability. This study investigated the potential application of bamboo fibre (BF), a natural, renewable, and environmentally friendly material, as a reinforcement for polyurethane foam (PUF) to address this issue. This research aimed to evaluate the effects of different bamboo fibre loadings, ranging from 5 to 20 wt%, on the thermal, mechanical, and physical properties of BF/PUF composites. Composite samples were fabricated with different fibre loadings and analysed for thermal stability using thermogravimetric analysis (TGA), flexural and compression strength, and physical evaluations. The results showed that BF reinforcement improved the thermal stability of PUF, with BF5 and BF10 composites exhibiting the highest flexural strength (0.199 kPa at 10 wt%) and compression strength (0.223 kPa at 5 wt%), although higher fibre content reduced mechanical properties. In addition, the BF5 composites demonstrated the lowest percentage of moisture absorption (4.78%), thickness swelling (4.25%), and water absorption (49.7%), indicating better dimensional stability. With all factors, adding the bamboo fibre into PUF enhanced the eco-performance of polyurethane foams, making them promising candidates for environmentally friendly applications such as insulation, cushioning, and packaging.

Augmented reality (AR) interfaces for custom wood-veneered cabinets require accurate requirement elicitation that accounts for the anisotropic optical properties of natural veneer. However, existing approaches lack a systematic pipeline linking large-scale user feedback to interface specifications. This paper presents a data-driven requirement mining framework that couples BERTopic semantic topic modeling with user mental model construction and cross-validates the two through a Jaccard-based semantic mapping coefficient (SMC). Applying the framework to a corpus of 3163 multi-source semantic units, the pipeline consolidated 18 initial clusters into four core requirement themes through hierarchical merging (CV coherence improved from 0.421 to 0.573) and translated them into a three-tier priority specification (P0/P1/P2) via a Comprehensive Priority Index. A high-fidelity AR prototype incorporating physically based anisotropic veneer rendering was evaluated with 60 participants across four user groups. The prototype achieved a System Usability Scale score of 82.5 (SD = 4.4) with no significant inter-group differences (F(3, 56) = 1.24, p = 0.303), confirming that the requirement-driven design pipeline can yield robust cross-group usability.

The most notable characteristic of the yueqin is its high frets. A PicoScope oscilloscope and related data recorder were used to record the sound signals in real time. Fast Fourier Transform (FFT) analysis, and voltage-based triggering were all made possible by PicoScope software. The PicoScope recorded the fundamental frequency for open strings 1, 2, 3, and 4 as 222 Hz (A3=220), 146 Hz (D3=146), 222 Hz (A3=220), and 292 Hz (D4=294), respectively. The strings 1, 2, 3, and 4 were perceived as A3, D3, A2, and D2, respectively. The measured frequencies were not in accordance with perceived notes due to the phenomenon in which the listener claimed to recognize the note D2 when they actually heard D4. This is the well-known phenomenon of the ‘missing fundamental’. The frequency between the frets follows a musical system that might change depending on the tuning and regional tradition. The running note for open string 1 is A3 and fret 1, 2, 3, 6, 7, 9, 11,13, 14, 16, 18, 20, 22 and 23 are B3, C4, D4, E4, F4, G4, A4, B4, C5, D5, E5, F5, G5, A5 whereas fret 4, 5, 8, 10, 12, 15, 17, 19, and 21 have the notes D4↑, D4#↓, F4#↓, G4#↑, A4#↓, C5#↑, D5↑, F5↓, and F5#↑ respectively.