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Online learning has become increasingly prevalent in the era of digitalization, impacted by both internet penetration and the COVID-19 pandemic. Classroom interaction, as a key factor in evaluating students' learning experience in online settings, has been identified to be associated with their academic achievement. While previous research has underscored the significance of classroom interaction in language learning settings, there is relatively limited research on its relationships with student satisfaction and learning. This research intends to explore classroom interaction and its mediating role in the correlation between student satisfaction and perceived online learning in online contexts. Given this, the paper reports a quantitative-method study that examined how Chinese EFL tertiary students perceived three types of classroom interaction (i.e., learner-learner, learner-instructor, and learner-content), their satisfaction, and its influences on their perceived online learning. Data was collected from 319 Chinese university students through convenience sampling and a self-designed questionnaire, and then analyzed with the help of SPSS. The results showed that: 1) the students had moderately positive perceptions of classroom interaction and online learning, but were neutral about their satisfaction; 2) positive correlations were observed between classroom interaction, student satisfaction, and online learning; and 3) student satisfaction predicted perceived online learning, with classroom interaction partially mediating this relationship. The findings of this research have implications for strengthening the effectiveness of online language teaching in the post-COVID-19 era.

Abstract This paper explores the ability of physics-informed neural networks (PINNs) to solve forward and inverse problems of contact mechanics for small deformation elasticity. We deploy PINNs in a mixed-variable formulation enhanced by output transformation to enforce Dirichlet and Neumann boundary conditions as hard constraints. Inequality constraints of contact problems, namely Karush–Kuhn–Tucker (KKT) type conditions, are enforced as soft constraints by incorporating them into the loss function during network training. To formulate the loss function contribution of KKT constraints, existing approaches applied to elastoplasticity problems are investigated and we explore a nonlinear complementarity problem (NCP) function, namely Fischer–Burmeister, which possesses advantageous characteristics in terms of optimization. Based on the Hertzian contact problem, we show that PINNs can serve as pure partial differential equation (PDE) solver, as data-enhanced forward model, as inverse solver for parameter identification, and as fast-to-evaluate surrogate model. Furthermore, we demonstrate the importance of choosing proper hyperparameters, e.g. loss weights, and a combination of Adam and L-BFGS-B optimizers aiming for better results in terms of accuracy and training time.

Precision irrigation systems have become indispensable tools in modern agriculture due to their ability to address water management challenges and optimize bamboo productivity. This paper presents a novel innovation called AquaBamboo, a next-generation precision irrigation system designed to revolutionize sustainable bamboo water management. The paper outlines the shortcomings of traditional irrigation methods, highlighting their inefficient water management for bamboo, lack of real-time data integration, and limited precision. This suggested approach overcomes these challenges by integrating IT sensors, advanced analytics, and predictive modelling to deliver water precisely tailored to each bamboo species' requirements. The system's dynamic adaptability to changing environmental factors ensures optimal water delivery, enhancing bamboo health and yield while minimizing water waste. AquaBamboo's holistic approach not only improves resource efficiency but also contributes to sustainable agriculture practices, aligning with the principles of responsible water management. The significance of this approach lies in its potential to transform conventional irrigation practices, optimize water utilization, promote bamboo species' health, and advance the sustainability of all bamboo agricultural operations. This paper offers a comprehensive understanding of AquaBamboo's features, advantages, and innovative contributions to precision bamboo agriculture management.

The World Health Organization has reported that over one million individuals drink contaminated water, resulting in over 30,000 deaths daily. Every year, over 7×107 tons of synthetic dyes are produced globally, with the textile industry utilizing over 104 tons of such materials. To protect freshwater from pollutants, wastewater treatment methods such as permeable reactive barrier disinfection use activated carbon. Bamboo charcoal is an emerging substance with great potential in this area. Bamboos represent a renewable biological resource for long-term development. This paper reviews the dye removal processes that can be utilized to avoid water pollution, using bamboo charcoal as a natural adsorbent. A literature search using used various keywords from several bibliographic databases yielded seven studies that supported the adsorptive properties of bamboo charcoal. These investigations used FTIR, EDS, SEM and XRD to demonstrate the porous nature of bamboo charcoal, activated bamboo charcoal and/or modified bamboo charcoal, as well as showing the effects of pH and temperature on dye removal. The best-fitting kinetic model and isotherm data were identified. Bamboo charcoal was an effective adsorbent, while modified or activated bamboo charcoal rapidly increased absorptive capacity, indicating the future potential of bamboo charcoal in the cleaning up of water pollutants and contributing to a clean environment.

Bamboo, a member of the grass family, is a fast growing and high yielding renewable resource. The popularity of bamboo has risen in recent times owing to its multifaceted and myriad of practical applications. There are unlimited uses of bamboo, therefore proper utilization of this beneficial resource will be useful to the industrial sector. This review provides a comprehensive summary regarding the potential of bamboo as a vital non-wood fibrous raw material for pulp, paper and other allied industries owing to its ability to conserve forests and foster sustainable economic development. Bamboo's abundance, renewability, mechanical strength, and other functional features make it an appealing and potential building biomaterial in the pursuit of sustainable raw materials needed for industrial development. This review provides an in-depth summary and unique perspective on the application of bamboo at a commercial scale in various sectors, thereby encouraging the utilization of this potential material towards environmental sustainability and economic growth.

Bamboo is gaining more and more attention in the field of building materials thanks to its unique characteristics, such as resistance, lightness, sustainability, and flexibility. However, the widespread adoption of bamboo as a building material presents some challenges, especially in relation to the durability of the material. In fact, being a natural material, it requires protection from atmospheric agents, such as rain and sun, as well as from attacks by insects and animals. Ensuring the longevity of bamboo involves implementing tailored treatments, precise precautions, and regular structural inspections to detect any potential degradation over time. In this context, the article focused on the monitoring of a structure made of bamboo and on the testing of an innovative system to identify the structural critical issues present, in the context of a FISR project which envisaged a cultural exchange between China and Italy. Starting from the survey of the structure carried out with a drone, a 3D model was then built using a commercial software. For the monitoring phase of the structure, an innovative system was used that exploits the YOLO v5s6 algorithm to identify structural critical issues, supported by a Virtual/Augmented/Mixed reality app developed by the authors. This app is useful not only during the monitoring phase, but also in the subsequent phase of disseminating information relating to the potential of the use of bamboo in the field of structural engineering. The tested and proposed methodologies have proven to be particularly useful and high-performance, especially regarding the monitoring of bamboo structures, in relation to the resolution of problems linked to their possible fragility and limitations in certain application areas. The contribution provided in this paper by Geomatics methodologies is evident to better highlight the potential of this eco-friendly material for its desirable ever-increasing use in the construction sector by becoming aware of its potential.

Computerized Numeric Control (CNC) plays an essential role in highly autonomous manufacturing systems for interlinked process chains for machine tools. NC-programs are mostly written in standardized G-code. Evaluating CNC-controlled manufacturing processes before their real application is advantageous due to resource efficiency. One dimension is the estimation of the energy demand of a part manufactured by an NC-program, e.g. to discover optimization potentials. In this context, this paper presents a Machine Learning (ML) approach to assess G-code for CNC-milling processes from the perspective of the energy demand of basic G-commands. We propose Latin Hypercube Sampling as an efficient method of Design of Experiments to train the ML model with minimum experimental effort to avoid costly setup and implementation time of the model training and deployment.

Material extrusion (MEX) of metallic components is an indirect additive manufacturing (AM) process that is recently gaining a lot of attention in the industry. This multi-step process with debinding and sintering, provides an inexpensive safe alternative, that is effective, flexible and office-friendly for several corporations compared to other metal AM techniques. However, optimizing the manufacturing parameters of the MEX process is still challenging due to the lack of research on their impact on the mechanical and surface properties of the fabricated materials.For this purpose, this paper investigates how various processing parameters impact the mechanical properties and surface roughness of 17-4 PH stainless steel parts produced by MEX. The parameters analyzed include layer thickness, build orientation, number of contours, and aging thermal treatment for 1 h at 482°C (H900). A Taguchi design of experiments (DoE) was employed to conduct the parametric analysis and the results were post-evaluated via the analysis of variance (ANOVA). The experimental results show that H900 treatment increases the micro-hardness by ∼50 HV0.3 and contributes in augmenting the ultimate tensile strength (UTS) by ∼200 MPa. The build orientation and its interaction with the layer thickness have the highest impact on the surface roughness. Moreover, the amount of enclosed porosity is higher in the samples with lower layer thickness. The absorbed impact energy (Wabs) is relatively low due to the enclosed porosity content and is not linked to the analyzed processing parameters. The best mechanical properties were obtained for parts built with solid infills, 0° build orientation, 0.125 mm layer thickness, two contours, and H900 as a post-treatment.

The manufacturing of three-dimensional sheet metal components, such as car body parts, heavily relies on deep drawing. With the increasing demand for lightweight measures in the automotive industry due to CO2 limitation requirements, various methods are currently employed to reduce the weight of deep drawn components. However, these methods often overlook the potential for influencing the stress-strain dependent damage state within the component, even though it can greatly affect its performance in subsequent applications, and thus offers lightweight potentials. Friction between the deep drawing tools and the sheet metal is a key factor influencing the stress-strain state, and hence, represents a lever that can be utilized to manipulate the damage state in the component. This paper focuses on the numerical analysis of the dependence of damage on friction during deep drawing. Therefore, a rectangular geometry with an asymmetrical material flow is numerically investigated. A friction ratio is introduced with different constant friction coefficients for the corner of the tools and the straight sides. The established load paths at a chosen reference point are then considered in the form of selected stress and strain characteristics and the numerically predicted damage state is compared. The results are used to derive a recommended friction ratio that leads to less damage in the corner of the geometry. Afterwards strip drawing tool surfaces are modified to manipulate their friction properties using machine hammer peening. Subsequently, the influence of the structures of the strip drawing tool surfaces is quantified using strip drawing tests. The identified contact normal stress dependent friction coefficients are then implemented in the numerical simulation and the established numerical predicted damage state is examined to gain a more comprehensive understanding of how the friction ratio and the structure of the tool surfaces influence the damage state.

The paper proposes a method and technique that allow, using a minimum sample (3 units) from a batch (30 units) of real gears, to obtain stochastic information about the actual distribution of the gear grinding allowance both on the left and right flanks of the gear gaps for all gears in the batch. The proposed gear grinding allowance stochastic model is based on the representation of the distribution of the gear grinding allowance along the gear periphery as a superposition of a sinusoidal component with a random amplitude and a random component of the white noise type. The conditions for the equivalence of the stochastic characteristics of real (measured) and virtual (not actually measured) gears are formulated, namely the ratio between the amplitude of the sinusoidal component and the average amplitude of the high-frequency harmonic components of the gear grinding allowance distribution along the gear periphery. The gear grinding allowance stochastic model adjusted according to the proposed method makes it possible to predict the distribution of the gear grinding allowance on the left and right flanks of the gear gaps for a batch of gears and, taking into account the information obtained (replacing the experimental data), both evaluate the existing technology and optimize the gear grinding allowance value. As a result, it becomes possible to assess the quality of the technological process of manufacturing the gear, taking into account the effect random deformations to gear grinding allowance at the stage of their heat treatment (hardening), which is performed before the gear grinding operation. In turn, optimization of the grinding allowance (based on its actual distribution along the periphery of the gear) makes it possible to reduce both defects in burns (with increased allowance) and defects in unground teeth (with insufficient allowance).