Due to the rising prices and high demand for panels and the fact that formaldehyde—a known carcinogen—is used to manufacture conventional particleboard, this study investigated the suitability of particleboard formed from ground macadamia nutshells mixed with 50%, 40%, 30%, and 20% of gum Arabic and determined its physical and mechanical properties. The specific gravity, the bulk density, the x-ray fluorescence, and the scanning electron microscopy analysis of the two materials were analyzed. After production, the particleboards were cured for 56 days in an acclimatized room; then, the physical and mechanical properties were evaluated. Particleboards mixed with 50% gum Arabic and 50% macadamia nutshell showed good results in terms of the lowest average values of water absorption (9.42%) and thickness swelling (6.22%) after 24 h of immersion in distilled water as well as the highest density (1219.20 kg/m³), modulus of rupture (12.21 MPa), modulus of elasticity (1.81 GPa), internal bond strength (1.25 MPa), and compressive strength (22.54 MPa). According to ANSI/A208.1-1999, the particleboards produced met the standard for general-purpose boards except for water absorption (WA) and thickness swelling (TS) characteristics, which were above the maximum of 8% and 3%, respectively.

In modern times, the rapid expansion of urban populations has intensified the urgency to optimize transportation systems, which has become an alarming issue in the face of urbanization and traffic congestion. This paper reviews the latest applications of Artificial Intelligence (AI) in the transport sector. It explores various AI methodologies, including Artificial Neural Networks (ANN), Genetic Algorithms (GA), Simulated Annealing (SA), Ant Colony Optimizer (ACO), Bee Colony Optimization (BCO), disruptive urban mobility, Fuzzy Logic Models (FLM), automated incident detection systems, and drones, which improve dynamic traffic management and route optimization. The study reveals that integrating these AI techniques with real-time data analytics improves traffic flow, automated incident management, and overall transportation efficiency. The results demonstrate that AI-driven systems, such as drones equipped with advanced sensors and AI algorithms, are increasingly capable of autonomous navigation, real-time monitoring, and predictive traffic management. These advancements in technologies, such as electric Vertical Take-off and Landing (eVTOL) aircraft, Hyperloop Transportation Technologies (HTT), Mobility-as-a-Service (MaaS) and autonomous delivery robots, contribute to smarter urban mobility solutions. However, it is important to focus on refining AI models for better performance, addressing challenges such as computational complexity and privacy concerns, and continuing to innovate in AI to improve the economic efficiency and reliability of transportation systems. Furthermore, to promote sustainability development in this sector, ethical considerations such as the protection of user information and the integration of the concepts of informed consent and human autonomy with community engagement programs should also be considered.

A Review of Particleboard Development and Performance Using Non-Toxic and Biodegradable Adhesives

In order to increase the efficiency of predicting concrete compressive strength, ensemble machine learning (ML) algorithms are required. Considering that each ML algorithm continuously varies in methodology, one ML algorithm cannot generate exhaustive prediction results since limited parameters are available to tune. This research serves as a beginning step towards predicting the compressive strength of concrete containing waste tyre rubber (WTR) and clay brick powder (CBP) using artificial neural network (ANN), random forest (RF), decision tree (DT) and support vector machine (SVM) algorithms. Taylor diagram model analysis shows that when the four algorithms are compared, the SVM (train) model demonstrates the highest performance in predicting the compressive strength of concrete containing CBP and WTR. The R2 values ranging from 0.60 – 0.97 imply that all the models fairly predict the compressive strength of concrete containing CBP and WTR. The same predictive abilities are demonstrated by the clustering of the data points for train and test models around the y = x line. It is shown that the majority of the data points lie within the error lines range of −20 and + 20 %. The SHapley Additive exPlanations (SHAP) analysis reveals that WTR has the highest impact on model predictions with a mean SHAP value of 3.83, while cement shows a moderate influence with a mean SHAP value of 0.77. Moreover, these findings suggest that WTR content is the most critical factor in controlling the concrete's compressive strength, while cement content plays a supporting role in the mixture design. Since the prediction behaviour of concrete using ML models is governed by the replacement levels of CBP and WTR, the models used in this study can be extended to the concrete mixes containing other waste materials.