Volume 19 Issue 4

Wood, due to its complex anatomy, requires meticulous characterization that imposes several tests to be carried out to evaluate its properties. Normative codes adopt different specimens to this aim. Geometric specificities proposed by NBR 7190-3:2022 and ASTM D143-22 to specimens used for estimating wood strength in tensile perpendicular to grain (f_t90) make them difficult to carry out. Thus it is advisable to consider relating f_t90 with another mechanical property, for example, the shear strength parallel to the grain (f_v0). This paper aims to establish a coefficient relating the characteristic values of f_t90 and f_v0, for strength classes D40 and D60 (frequently used for structural purposes) of Brazilian Code 7190:3-2022. A further aim, if possible, is to determine a single representative coefficient for both classes. Tests made it possible to obtain those properties for four species from each class, following NBR 7190-3:2022 guidelines. The optimal coefficient was determined using the least squares method (MMQ). Ratios f_t90,k/f_v0,k were 0.22 and 0.19, for classes D40 and D60, respectively. As these ratios don’t present a significant difference, it is viable to adopt a single relationship for both classes, thus simplifying characterization procedures.

Children’s physical and mental health development is highly related to furniture and integrating the emotional design concept into children’s furniture design helps cultivate children’s personalities and promote healthy growth. The model framework of children’s furniture emotional design was constructed from the instinctive level, behavioral level, and reflective level. The comprehensive weight of the design factors was calculated using the Analytic Hierarchy Process (AHP), and three schemes were designed according to the priority of the design factors. The evaluation matrix was constructed by combining the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and the distances of each scheme from the positive and negative ideal solutions were calculated, and the optimal scheme was selected according to the relative proximity ranking of the three schemes. The experimental results verified the importance of the reflective level in the three levels of the emotional design theory and emphasized the key role of emotional design in children’s furniture. Therefore, the evaluation system based on AHP and TOPSIS is feasible, which can effectively reduce the influence of subjective factors, make the design decision more scientific, and provide a new reference approach for furniture design.

Gigantochloa levis and G. scortechinii bamboo species were evaluated as material for plybamboo production. Plybamboo was composed of three layers with 12 mm thickness and used phenol formaldehyde (PF) as their binder. Representative samples were cut and tested for bonding, physical, mechanical, and finishing properties. Results indicated that G. levis plybamboo exhibited higher bending strength property compared to G. scortechinii plybamboo, as indicated by its higher modulus of rupture (121 N/mm²) and modulus of elasticity (16300 N/mm²). The G. levis plybamboo also displayed higher bond shear strength and was dimensionally stable compared to G. scortechinii plybamboo. The finishing properties revealed that all coatings performed well in the cross-cut tape and pull-off tests. Notably, plybamboo from both bamboo species showed excellent coating film adhesion. Based on minimum standard requirement, results revealed that both bamboo species were suitable to be used in plybamboo production for general use. The findings showed that those species are valuable renewable natural resources for plybamboo production and had potential to be utilized as a substitute for wood in board production.

The effects of mechanical defoliation and pinching (1 cm tip cutting) on Buxus plant growth, nutrient mobilization, and root architecture were determined. When 100% defoliation was applied, the highest increase rates of 80.3% in shoots and 88% in leaves were observed compared to the control group. In contrast, the overall effects of defoliation and pinching were negative, with 100% defoliation having the most negative effects. The chlorophyll content of the newly formed young leaves was also 50% lower with 100% defoliation. Leaves and root nutrient mobilization changed significantly, depending on the effects of defoliation and pinching. Apart from a very small increase in root length and number of forks, the effects of the treatments were negative, with 100% defoliation having the greatest negative effect on root development. Most affected was the number of crossings, which was 78% lower than in the control. In addition, machine learning (ML) algorithms were used in the study, including multilayer perceptron, J48, PART, and logistic regression. The input variables were evaluated to model and predict the root features. The performance values of the ML algorithms were noted in the following order: Logistic Regression> PART> J48> MultilayerPerceptron. As the severity of defoliation increased, the losses of the plant also increased. However, boxwood has mechanisms to compensate for these losses even when it suffers complete defoliation.

A laboratory incubation experiment was conducted for 36 days to study the effect of pigeon pea biochar (PPB) and wood biochar (WB) on carbon mineralization of native soil organic carbon (SOC) and municipal solid waste compost (MSWC) applied to soil. The MSWC addition enhanced soil respiration by 2-fold (231 mg C kg^-1 soil) over the control (118 mg C kg^-1 soil). The PPB addition significantly (P < 0.05) increased cumulative loss of carbon as CO_2, whereas WB significantly decreased the cumulative loss of C over control. Addition of PPB at 5% and 10% levels increased SOC mineralization (positive priming) +22.9% and +31.2%, respectively; whereas reduction in SOC mineralization (negative priming) was noticed in WB (5% and 10%) treated soils by -3.1% and -21.7%, respectively. Similarly, WB induced strong negative priming effects (-21.9% and -29.5%), while PPB caused a weak positive priming effect (+3.0% and +11.6%) at 5% and 10% levels on mineralization of added labile carbon substrate (MSWC), respectively. Results indicate the hardwood (Prosopis juliflora) biochar exhibits refractory properties that inhibit mineralization of both native SOC and applied organic compost (MSWC), and thereby it can be used as an amendment to stabilize native and applied organic matter in soil, which may significantly contribute to soil carbon sequestration.

Mechanical properties of wood-inspired cell wall geometries were considered through compression testing and Finite Element Analysis (FEA) with ANSYS simulation. Six models, including earlywood, latewood, and various array configurations, were fabricated via 3D printing using acrylonitrile butadiene styrene (ABS) filament. Compression tests highlighted the annual ring model’s robustness, exhibiting a maximum load of 12707 MPa, while the 4×3 matrix displayed the lowest strength at 4247 MPa. Shifting rows led to reduced strength, which was particularly evident in vertical prints. An analysis of variance revealed significant differences in mechanical properties. Discrepancies between experimental tests and FEA results ranged from -45.9% to 35.2%. Earlywood exhibited a maximum deformation of 2.6 mm, whereas latewood showed lower deformation, indicating geometry’s influence on material behavior. Mesh quality remained consistent, ensuring dependable simulation outcomes. These findings underscore the pivotal role of geometry in compression resistance, laying the groundwork for future studies on wood densification mechanisms and the development of customized wood composites.

The pyrolysis behavior of biomass is critical for industrial process design, yet the complexity of pyrolysis models makes this task challenging. This paper introduces an innovative hybrid model to quantify the pyrolysis potential of pine needles, predicting the entire process of their pyrolysis behavior. Through experimental analyses and kinetic parameter calculations of pine needle pyrolysis, the study employs a kinetic model with a chemical reaction mechanism. Additionally, it introduces an improved dung beetle optimization algorithm to accurately capture the primary trends in pine needle pyrolysis. The developed artificial neural network model incorporates meta-heuristic algorithms to address process error factors. Validation is based on experimental data from TG at three different heating rates. The results demonstrate that the hybrid model exhibits strong predictive performance compared to the standalone model, with coefficients of determination (R²) of 0.9999 and 0.999 for predicting the conversion degree and conversion rate of untrained data, respectively. Additionally, the standard errors of prediction (SEP) are 0.249% and 0.449% for predicting the conversion degree and conversion rate of untrained data, respectively.

In recent years, forest fires have become increasingly common, but also more damaging phenomena. These aspects are reflected in significant economic losses that affect the quality and quantity of wood volumes that can be used for industrial processing. For this reason, knowing the quality of the wood is important, especially in fire-affected trees. Because visual analyses cannot always reflect the quality of the wood inside the trunk, the present research aimed to evaluate the extent to which modern techniques based on the transfer of sounds can identify internal wood defects. In this sense, 42 tomograms made from beech trees affected by a litter fire were compared with the relative resistances of the wood to drilling and with the real condition of the wood inside the trunk, as made visible through the growth cores taken with a Pressler drill. From the cumulative interpretation of the results, it was found that the trees affected by the fire have serious defects, which lead to the downgrading of the wood and are not reproduced by the tomograph to their true extent. Conversely, sound transfer speeds through wood are influenced by the presence of beech red heartwood, which leads to an increase in sound transfer speeds through wood, and that can alter the accuracy of the tomogram.

To promote adsorption capacity of biochar as engineering material, pinewood biochar (PC) and bamboo biochar (BC) were prepared by slow pyrolysis (700 °C) and then directly irradiated with UV light at room temperature. The elemental analysis, SEM, FTIR, XPS, and Boehm titration measurements showed that UV irradiation significantly increased the BET surface area, porosity, and surface oxygen functional groups of the biochar. After UV treatment, the BET surface areas of PC and BC were increased by 63.0% and 217%, while the amount of total (acidic) groups increased by 62.0% (155%) and 24.9% (28.2%), respectively. FTIR and XPS measurements suggested that photochemical reactions, including photodegradation and photooxidation processes, may play primary roles in altering biochar the pore structure and surface functional groups of biochar. The Langmuir model capacities (q_max) of modified PC and BC were increased by 94.5% and 50.5% for Pb (II), 18.5% and 13.7% for Cd (II), respectively, compared to their unmodified counterparts. This study examined the effects of UV irradiation on the surface properties of biochar and demonstrate its potential as an effective, simple, and green method for functionalizing biomass material.

The increasing rates of illicit behaviors, particularly financial crimes, e.g., bank fraud and tax evasion, adversely affect national economies. In such cases, using nondestructive methods, scientists must evaluate relevant documents carefully to preserve their value as evidence. When forensic laboratories analyze paper as evidence, they typically investigate its origin and date of manufacture. If a document’s date is earlier than the earliest availability of the paper used in its creation, then this anachronism indicates that the document has been backdated. This study investigated weave marks and drainage marks for forensic purposes. Machine learning models for forensic document examination were developed and evaluated. The partial least squares discriminant analysis (PLS-DA), support vector machine (SVM), and artificial neural network (ANN) classification models achieved F1-scores of 0.903, 0.952, and 0.931, respectively. In addition, to enhance model effectiveness and construct a robust model, variables were selected using the VIP scores generated by the PLS-DA model. As a result, the SoftMax classifier in the ANN model maintained its performance with an F1-score of 0.951 even with a 50% reduction in the number of input variables.