Flexural behavior of wood in the transverse direction investigated using novel computer vision and machine learning approach

A deep-learning-based semantic segmentation approach (U-Net) was used to partition the anatomical features in the cross-section of hinoki (Chamaecyparis obtusa) wood during a micro three-point bending test. Using the Crocker–Grier linking algorithm, thousands of cells were successfully extracted, and several parameters (area, eccentricity, fitted ellipse aspect ratio, bounding box aspect ratio) were used to evaluate the intensity of the cells’ deformation. Thus, the 2D map of the deformation intensity distribution was constructed. By analyzing flat-sawn, quarter-sawn, and rift-sawn specimens, it was confirmed that the annual ring orientation affects the flexural behavior of wood in the transverse direction. The quarter-sawn specimens exhibited the largest modulus of elasticity (MOE) and modulus of rupture (MOR). The ray tissue aligned against the load may have contributed to the restriction of cell deformation. The rift-sawn specimens exhibited the smallest MOE and MOR, possibly owing to the loading of the specimen in the in-plane off-axial direction, which induced the shear deformation of the cell wall. For all three specimen types, the fracture had high occurrence probability in the tension part of the specimen, which exhibited large cell deformation. Therefore, the proposed method can be adapted to the prediction of wood specimen fractures. With different test wood species, this approach can be of great help in elucidating the relationship between the anatomical features and the mechanical behavior of wood to improve the effective utilization of wood resources

Intra-annual fluctuation in morphology and microfibril angle of tracheids revealed by novel microscopy-based imaging

Woody cells, such as tracheids, fibers, vessels, rays etc., have unique structural characteristics such as nano-scale ultrastructure represented by multilayers, microfibril angle (MFA), micro-scale anatomical properties and spatial arrangement. Simultaneous evaluation of the above indices is very important for their adequate quantification and extracting the effects of external stimuli from them. However, it is difficult in general to achieve the above only by traditional methodologies. To overcome the above point, a new methodological framework combining polarization optical microscopy, fluorescence microscopy, and image segmentation is proposed. The framework was tested to a model softwood species, Chamaecyparis obtusa for characterizing intra-annual transition of MFA and tracheid morphology in a radial file unit. According our result, this framework successfully traced the both characteristics tracheid by tracheid and revealed the high correlation (|r| > 0.5) between S2 microfibril angles and tracheidal morphology (lumen radial diameter, tangential wall thickness and cell wall occupancy). In addition, radial file based evaluation firstly revealed their complex transitional behavior in transition and latewood. The proposed framework has great potential as one of the unique tools to provide detailed insights into heterogeneity of intra and inter-cells in the wide field of view through the simultaneous evaluation of cells’ ultrastructure and morphological properties

Potential of machine learning approaches for predicting mechanical properties of spruce wood in the transverse direction

To predict the mechanical properties of wood in the transverse direction, this study used machine learning to extract the anatomical features of wood from cross-sectional stereograms. Specimens with different orientations of the ray parenchyma cell were prepared, and their modulus of elasticity (MOE) and modulus of rupture (MOR) were measured by a three-point bending test. The orientation of the ray parenchyma cell and wood density (ρ) were used as parameters for the MOE and MOR prediction. Conventional machine learning algorithms and artificial neural network were used, and satisfactory results were obtained in both cases. A regular convolutional neural network (CNN) and a density-informed CNN were used to automatically extract anatomical features from the specimens’ cross-sectional stereograms to predict the mechanical properties. The regular CNN achieved acceptable but relatively low accuracy in both the MOE and MOR prediction. The reason for this may be that ρ information could not be satisfactorily extracted from the images, because the images represented a limited region of the specimen. For the density-informed CNN, the average prediction coefficient for both the MOE and MOR drastically increased when ρ information was provided. A regression activation map was constructed to understand the representative anatomical features that are strongly related to the prediction of mechanical properties. For the regular CNN, the latewood region was highly activated in both the MOE and MOR prediction. It is believed that the ratio and orientation of latewood were successfully extracted for the prediction of the considered mechanical properties. For the density-informed CNN, the activated region is different. The earlywood region was activated in the MOE prediction, while the transition region between the earlywood and latewood was activated in the MOR prediction. These results may provide new insights into the relationship between the anatomical features and mechanical properties of wood

Nursing Activity Sensing Using Mobile Sensors and Proximity Sensors

In recent years, big data are utilized in many industries.In this study, in order to analyze duties of thenurses, we performed experiments to collect the dutiesactivity data of the nurses for a long term. Weset 38 nurses as subjects and asked them to carry outduties while attaching a wearable small sensor device,and collected the acceleration data, meeting informationbetween nurses and the nurse duties information.In addition, we collected the location information of the nurses by using infrared information and communication equipment at the same time. From various data collected, we analyzed intensity and positional information of duties activity of the nurse, meeting information and the duties information between nurses and considered the influence that each factor affected to the nurse. As the result, we found that intensity of the activity increases in such nurses as who has many times of meeting with other nurses, visits the patient room many times, or who works on jobs concerning with the assistance of the patients such as rehabilitation assistance duties or the activity assistance dutiesThe 47th ISCIE International Symposium on Stochastic Systems Theory and Its Applications (SSS\u2715), December 5-8, 2015, Waikiki Beach Marriott Resort & Spa, Hawaii, US

Relationship Between Balance Recovery From a Forward Fall and Lower-Limb Rate of Torque Development

The authors examined the relationship between the maximum recoverable lean angle via the tether-release method with early- or late-phase rate of torque development (RTD) and maximum torque of lower-limb muscle groups in 56 young healthy adults. Maximal isometric torque and RTD at the hip, knee, and ankle were recorded. The RTD at 50-ms intervals up to 250 ms from force onset was calculated. The results of a stepwise multiple regression analysis, early RTD for hip flexion, and knee flexion were chosen as predictive variables for the maximum recoverable lean angle. The present study suggests that some of the early RTD in the lower limb muscles, but not the maximum isometric torque, can predict the maximum recoverable lean angle

Suppression Effects of Betaine-Enriched Spinach on Hyperhomocysteinemia Induced by Guanidinoacetic Acid and Choline Deficiency in Rats

Betaine is an important natural component of rich food sources, especially spinach. Rats were fed diets with betaine or spinach powder at the same level of betaine for 10 days to investigate the dose-dependent effects of spinach powder supplementation on hyperhomocysteinemia induced by guanidinoacetic acid (GAA) addition and choline deprivation. The GAA-induced hyperhomocysteinemia in rats fed 25% casein diet (25C) was significantly suppressed by supplementation with betaine or spinach, and it was completely suppressed by taking 11.0% spinach supplementation. The choline deprivation-induced enhancement of plasma homocysteine concentration in rats fed 25% soybean protein diet (25S) was markedly suppressed by 3.82% spinach. Supplementation with betaine or spinach partially prevented the effects of GAA on hepatic concentrations of methionine metabolites. The decrease in activity of betaine-homocysteine S-methyltransferase (BHMT) and cystathionine β-synthase (CBS) in GAA-induced hyperhomocysteinemia was recovered by supplementation with betaine or spinach. Supplementation with betaine or spinach did not affect BHMT activity, whereas it partially restored CBS activity in choline-deprived 25S. The results indicated that betaine or spinach could completely suppress the hyperhomocysteinemia induced by choline deficiency resulting from stimulating the homocysteine removal by both remethylation and cystathionine formation

Hydrogen Isotope (H2 and D2) Sorption Study of CHA-Type Zeolites

Using either single H2 and D2 or H2-D2 mixed gases, the sorption abilities of CHA (chabazite)-type zeolites ion-exchanged with K, Na, or Ca were studied at 77, 201, and 250 K. The LTA (Linde Type A) (3A) and FAU (faujasite)-type zeolites were also examined for comparison. The pore diameters in these materials were found to decrease on the order of FAU > Ca-CHA > [K-CHA, Na-CHA, and LTA(3A)]. The quantities of D2 adsorbed on these zeolites were larger than the amounts of H2. At higher temperatures, the CHA-type zeolites having smaller pores exhibited superior D2/H2 selectivity compared with the LTA(3A) and FAU, suggesting that hydrogen isotope separation using zeolites is affected by pore size