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The accurate prediction of driving torque demand is essential for the development of motion controllers for mobile robots on complex terrains. This paper aims to propose a hybrid model of torque prediction, adaptive EC-GPR, for mobile robots to address the problem of estimating the required driving torque with unknown terrain disturbances.

An error compensation (EC) framework is used, and the preliminary prediction driving torque value is achieved using Gaussian process regression (GPR). The error is predicted using a continuous hidden Markov model to generate compensation for the prediction residual caused by terrain disturbances and uncertainties. As the final step, a gain coefficient is used to adaptively tune the significance of the compensation term through parameter resetting. The proposed model is verified on a sample set, including the driving torque of a mobile robot on three different sandy terrains with two driving modes.

The results show that the adaptive EC-GPR yields the highest prediction accuracy when compared with existing methods.

It is demonstrated that the proposed model can predict the driving torque accurately for mobile robots in an unconstructed environment without terrain identification.

This study aims to realize the multipurpose use of inorganic materials in adsorption treatment of pigment wastewater and preparation of core-modified Color Index Pigment Red 57:1 (C.I. Pigment Red 57:1, PR 57:1).

In this paper, the inorganic materials (sepiolite and SiO₂·nH₂O) were used in both PR 57:1 production wastewater treatment and its core-modification. The inorganic material firstly adsorbed 3-hydroxy-2-naphthoic acid (bon acid) in the pigment wastewater to reduce chemical oxygen demand. Then, the inorganic material adsorbed with bon acid was reused to prepare core-modified PR 57:1.

In the pigment wastewater adsorption experiment, it was found that under pH = 3, the adsorption percentage of bon acid by inorganic material can reached up to 46.00%. The pigment characterization results showed that the core-modified PR 57:1 had a core-shell structure. Under UV light irradiation for 1 h, the core-modified PR 57:1 prepared with sepiolite and SiO₂·nH₂O showed total color difference ΔE value of 1.43 and 2.05, respectively, which was lower than that of unmodified PR 57:1 (ΔE = 2.89). In addition, the transmittance of pigment water suspension test results showed that the core-modified PR 57:1 showed better water dispersibility.

This paper attempts to develop a synergistic strategy based on the multipurpose use of inorganic materials in adsorption treatment of pigment wastewater and preparation of core-modified PR 57:1.

With the development of intelligent technology, deep learning has made significant progress and has been widely used in various fields. Deep learning is data-driven, and its training process requires a large amount of data to improve model performance. However, labeled data is expensive and not readily available.

To address the above problem, researchers have integrated semi-supervised and deep learning, using a limited number of labeled data and many unlabeled data to train models. In this paper, Generative Adversarial Networks (GANs) are analyzed as an entry point. Firstly, we discuss the current research on GANs in image super-resolution applications, including supervised, unsupervised, and semi-supervised learning approaches. Secondly, based on semi-supervised learning, different optimization methods are introduced as an example of image classification. Eventually, experimental comparisons and analyses of existing semi-supervised optimization methods based on GANs will be performed.

Following the analysis of the selected studies, we summarize the problems that existed during the research process and propose future research directions.

This paper reviews and analyzes research on generative adversarial networks for image super-resolution and classification from various learning approaches. The comparative analysis of experimental results on current semi-supervised GAN optimizations is performed to provide a reference for further research.

The purpose of this paper is to investigate the effect of mullite on the mechanical properties and friction of carbon fiber (CF)-reinforced friction material.

CF-reinforced friction materials with varying content of mullite were fabricated by hot press molding, and then the tribological properties were tested on the MRH-3-type tribometer under ambient conditions with the ring-on-block configuration.

The experimental results indicated that the addition of mullite increased the density and compressive strength of friction material. However, the flexural strength of friction material decreased by 16% with the addition of 15 Wt.% mullite. The friction coefficient was proportional to the mullite content. Friction material with 12.5 Wt.% mullite showed the highest friction stability under different loads, whereas friction material with 10 Wt.% mullite exhibited the highest friction stability under different sliding speeds.

By boosting the resistance to deformation under load and increasing the specific heat capacity, mullite contributed significantly to the friction stability of the friction material.

The objective was to investigate the utility of cottonseed oil (CSO) as a raw material for the synthesis of CSO water-based alkyd resin. The synthesis involved the polymerization of CSO, trimethylolpropane, phthalic anhydride (PA) and trimellitic anhydride (TMA). The prepared resin coating material was subsequently applied to the surface of steel structure material.

This study aimed to synthesize water-based alkyd resins using CSO. Therefore, the alkyd resin was introduced with TMA containing carboxyl groups and neutralized with triethylamine (TEA) to form a water-soluble salt. Then, the esterification kinetics of CSO water-based alkyd resin were investigated, and finally, the basic properties of CSO water-based alkyd resin coating were evaluated.

It was demonstrated that CSO water-based alkyd resin exhibited excellent water solubility and that the esterification kinetic of the synthesis reaction could be described by a second-order reaction. The coating properties of the material were investigated and found to have good basic properties, with 40% resin addition having the best corrosion resistance. Consequently, it could be effectively applied to the surface of steel structural materials.

This study not only met the requirement of environmentally friendly development but also expanded the application of CSO through the synthesis of CSO water-based alkyd resin via alcoholysis. Compared to fatty acid process, the alcoholysis reduced the need for fatty acid pre-extraction, simplifying the alkyd resin synthesis process. Thus, economic costs are effectively reduced.

Digital technology's integration into education has transformed learning frameworks, necessitating the exploration of factors influencing students’ engagement in digital informal settings. This study, grounded in self-determination theory (SDT), proposes a model comprising artificial intelligence (AI) competence, chatbot usage, perceived autonomy (PA), digital informal learning (DIL) and students’ engagement.

The study collected survey data from 409 participants at Saudi Arabian universities, ultimately using 387 valid responses for analysis. This dataset was subjected to a thorough examination to confirm the validity of our proposed model. To decipher the complex interactions within our model, we utilized partial least squares structural equation modeling (PLS-SEM). The study adopted a disjoint two-stage method to formulate a reflective-formative higher-order construct (HOC).

The study's findings showed that cognitive learning (CL), metacognitive learning (MCL) and social and motivational learning (SML) are the essential components of DIL. Significantly, the study determined that AI competence, chatbot usage, PA and DIL markedly affect students’ engagement. Moreover, the R² value of 0.592 for student engagement indicates the model's robustness in explaining 59.2% of the variance, highlighting its effectiveness in identifying key drivers of student engagement in DIL contexts.

This research enhances understanding by detailing the intricate relationships among AI competence, chatbot usage, and students’ engagement in informal digital learning. It extends SDT to emphasize intrinsic motivations and AI capabilities, introducing reflective-formative HOCs for comprehending educational intricacies. It provides practical strategies for enhancing AI abilities and chatbot use in education, promoting personalized, engaging and autonomous digital learning spaces, thereby advancing educational theory and practice.

This study aims to fabricate a novel electromagnetic interference (EMI) shielding composite aerogel with both thermal insulation and high temperature warning functions.

An emerging bio-based polypyrrole (PPy) gel/Fe₃O₄/calcium alginate (PFC) EMI shielding composite aerogel was prepared by freeze-drying and in situ polymerization method. First, Fe₃O₄/calcium alginate (CA) aerogel was obtained by freeze-drying the Fe₃O₄/CA mixture. Then, PPy/Fe₃O₄/CA was obtained by synthesizing PPy on the surface of CA/Fe₃O₄ aerogel through in situ polymerization. Finally, PPy/Fe₃O₄/CA was immersed in porphyrin solution (cross-linking agent) to get the final PFC EMI shielding composite aerogel.

Due to the matched impedance between Fe₃O₄ and PPy, the EMI shielding performance of PFC composite aerogel can reach up to −8 dB. In addition, the PFC EMI shielding composite aerogel also shows excellent self-extinguishing and thermal insulation properties. After leaving the flame, the burning PFC aerogel is quickly extinguished. When the PFC aerogel is placed on the heating plate at 230 °C, the temperature on the side of the aerogel away from the heating plate is only 90.3 °C after 5 min of heating. The electrical resistance of the PFC composite aerogel can be reduced from 3.62 × 104 O to 5 × 102 O to trigger the warning light after 3 s of exposure to the alcohol lamp flame. This reversible thermal resistance response characteristic can be used to give an early warning signal when the PFC encounters high temperature or flame.

This work provides a novel strategy for designing a multifunctional EMI shielding composite aerogel with repeatable high temperature warning performance. This PFC composite aerogel shows potential applications in the prevention of material combustion in high temperature electromagnetic environments.

The integration of digital technologies into education has brought about a profound transformation, fundamentally reshaping the learning landscape. The purpose of this study is to underscore the importance of investigating the factors influencing students’ engagement (SE) in this evolving digital era, particularly within formal digital learning environments. To address this need, the study is grounded in self-determination theory (SDT) and presents a comprehensive model comprising interconnected elements: digital competence (DC), smartphone use (SPU), perceived autonomy (PA), digital formal learning (DFL) and SE.

The research conducted an investigation within Saudi Arabian universities, collecting a robust data set of 392 cases. This data set underwent rigorous analysis to validate the proposed model. To untangle the intricate relationships within the framework, the study used partial least squares structural equation modelling. Given the distinct dimensions of the two constructs under study, the researcher used a disjoint two-stage approach to establish reflective-formative higher-order constructs (HOC).

The findings revealed that digital literacy and digital skills (DS) constitute the foundational constituents of DC. Simultaneously, the study identified facilitation, distraction and connectedness as integral components of SPU. Importantly, the study established that DC, SPU, PA and DFL significantly influence SE. Furthermore, the research illuminated the mediating roles played by SPU, PA and DFL in the complex relationship between DC and SE.

This study advances the literature by delineating the dynamic interplay between DC, SPU and SE in digital learning. It extends SDT within educational contexts, emphasizing the role of internal motivations and DS. Methodologically, it innovates through reflective-formative HOCs, deepening the analysis of complex educational constructs. Managerially, it guides institutions in enhancing DC and integrating smartphones effectively into learning, advocating for tailored strategies to foster engaging and autonomous digital learning environments, thereby enriching both theoretical understanding and practical application in education.

By comparing and contrasting the two scenarios of power producers investing in renewable energy and electricity sellers investing in renewable energy, we explore the conditions under which profit growth and carbon emission reduction can be realized, and provide a theoretical basis for decision-making on renewable energy investment by electric power companies as well as for government policy formulation.

This paper constructs a game model of a grid supply chain consisting of a leader generator and a follower seller in the context of the C&T mechanism, considering two scenarios in which the generator and the seller invest in renewable energy. Conclusions are drawn by comparing and analyzing the equilibrium solutions in different scenarios.

The scenario where electricity sellers invest in renewable energy exhibits a higher investment volume compared to the scenario involving power generators. In scenarios where power producers invest in renewable energy, electricity sellers achieve lower profits than power generators, while scenarios with electricity seller' investments yield higher profits for them. Increasing the cost coefficient of renewable energy investment reduces investment volume, electricity prices and electricity demand, leading to decreased profits for electricity seller but increased profits for power generator. A rise in the preference coefficient for renewable energy results in increased profits for electricity seller but decreased profits for power generator.

Addressing a literature gap in the context of low carbon, this study examines the investment scenario of electricity sellers in low carbon technologies, complementing existing research focused on power generators and consumers. The findings enrich knowledge in low carbon investment. By analyzing the investment decisions of both power producers and electricity sellers, this study explores the practical implications of renewable energy investments on the decision-making and operational dynamics of power supply chain enterprises. It sheds light on their profitability and investment strategies.