Decay and Termite Resistance of Wood Modified by High-Temperature Vapour-Phase Acetylation (HTVPA), a Simultaneous Acetylation and Heat Treatment Modification Process.

High-temperature vapour-phase acetylation (HTVPA) is a simultaneous acetylation and heat treatment process for wood modification. This study was the first investigation into the impact of HTVPA treatment on the resistance of wood to biological degradation. In the termite resistance test, untreated wood exhibited a mass loss (MLt) of 20.3%, while HTVPA-modified wood showed a reduced MLt of 6.6-3.2%, which decreased with an increase in weight percent gain (WPG), and the termite mortality reached 95-100%. Furthermore, after a 12-week decay resistance test against brown-rot fungi (Laetiporus sulfureus and Fomitopsis pinicola), untreated wood exhibited mass loss (MLd) values of 39.6% and 54.5%, respectively, while HTVPA-modified wood exhibited MLd values of 0.2-0.9% and -0.2-0.3%, respectively, with no significant influence from WPG. Similar results were observed in decay resistance tests against white-rot fungi (Lenzites betulina and Trametes versicolor). The results of this study demonstrated that HTVPA treatment not only effectively enhanced the decay resistance of wood but also offered superior enhancement relative to separate heat treatment or acetylation processes. In addition, all the HTVPA-modified wood specimens prepared in this study met the requirements of the CNS 6717 wood preservative standard, with an MLd of less than 3% for decay-resistant materials.

Assessing the Long-Term Creep Behaviour of Hydrothermally Treated Japanese Cedar Wood Using the Short-Term Accelerated Stepped Isostress Method.

In this study, short-term accelerated creep tests were conducted using the stepped isostress method (SSM) to investigate the impact of hydrothermal treatment on the long-term creep behaviour of Japanese cedar wood and to determine optimal hydrothermal treatment conditions. The results showed that SSM can effectively predict the creep behaviour of hydrothermally treated wood. Among the treatment conditions tested, Japanese cedar wood treated hydrothermally at 180 °C for 4 h exhibited higher flexural strength retention (91%) and moisture excluding efficiency (MEE) (44%) and demonstrated superior creep resistance compared to untreated wood. When subjected to a 30% average breaking load (ABL) over 20 years, the specimen's creep compliance, instantaneous creep compliance, b value, activation volume, and improvement in creep resistance (ICR) were 0.17 GPa-1, 0.139 GPa-1, 0.15, 1.619 nm3, and 4%, respectively. The results indicate that subjecting Japanese cedar wood to hydrothermal treatment at 180 °C for 4 h has a negligible effect on its flexural properties but results in significant improvements in both dimensional stability and creep resistance.

Characterization and Prediction of Physical Properties of Luanta Fir Wood with Vacuum Hydrothermal Treatment.

This study used the luanta fir (Cunninghamia konishii Hayata) wood, one of the most used wood construction and building materials in Taiwan, as specimens to examine the impact of different conditions of vacuum hydrothermal (VH) treatment on the physical properties of this wood. A prediction model for these properties was created using a nondestructive spectroscopy technique. The test results revealed that the mass loss, moisture exclusion efficiency, anti-swelling efficiency, color difference, and surface contact angle of the VH-treated wood all increased under increasing heat treatment temperature and time. Moreover, the use of near-infrared (NIR) spectroscopy in creating the prediction model for the physical properties of the VH-treated luanta fir wood revealed that the ratios of performance to deviation (RPD) for mass loss, equilibrium moisture content, and color difference were all above 2.5, indicating a high prediction accuracy. These results suggested that an NIR spectrometer can serve as a useful instrument for the accurate prediction of the physical properties or for controlling the quality of VH-treated wood.

Characterization and Prediction of Mechanical and Chemical Properties of Luanta Fir Wood with Vacuum Hydrothermal Treatment.

Since the chemical composition of wood is closely related to its mechanical properties, chemical analysis techniques such as near-infrared (NIR) spectroscopy provide a reasonable non-destructive method for predicting wood strength. In this study, we used NIR spectra with principal component analysis (PCA) to reveal that vacuum hydrothermal (VH) treatment causes degradation of hemicellulose as well as the amorphous region of cellulose, resulting in lower hydroxyl and acetyl group content. These processes increase the crystallinity of the luanta fir wood (Cunninghamia konishii Hayata), which, in turn, effectively increases its compressive strength (σc,max), hardness, and modulus of elasticity (MOE). The PCA results also revealed that the primary factors affecting these properties are the hemicellulose content, hydroxyl groups in the cellulose amorphous region, the wood moisture content, and the relative lignin content. Moreover, the ratios of performance deviation (RPDs) for the σc,max, shear strength (σs,max), hardness, and modulus of rupture (MOR) models were 1.49, 1.24, 1.13, and 2.39, indicating that these models can be used for wood grading (1.0 < RPD < 2.5). Accordingly, NIR can serve as a useful tool for predicting the mechanical properties of VH-treated wood.

Feasibility of Using Floor Vibration to Detect Human Falls.

With the increasing aging population in modern society, falls as well as fall-induced injuries in elderly people become one of the major public health problems. This study proposes a classification framework that uses floor vibrations to detect fall events as well as distinguish different fall postures. A scaled 3D-printed model with twelve fully adjustable joints that can simulate human body movement was built to generate human fall data. The mass proportion of a human body takes was carefully studied and was reflected in the model. Object drops, human falling tests were carried out and the vibration signature generated in the floor was recorded for analyses. Machine learning algorithms including K-means algorithm and K nearest neighbor algorithm were introduced in the classification process. Three classifiers (human walking versus human fall, human fall versus object drop, human falls from different postures) were developed in this study. Results showed that the three proposed classifiers can achieve the accuracy of 100, 85, and 91%. This paper developed a framework of using floor vibration to build the pattern recognition system in detecting human falls based on a machine learning approach.