A strain from the Bacillus group isolated from rice that produced excessive biofilm on glass was identified as Bacillus pacificus. This strain has operons related to biofilm production in Bacillus cereus, such as sipW-tasA-calY and eps1, which may explain the biofilm formation.
Raceway bioreactors are widely employed for microalgal production owing to their low construction and operational costs, in addition to their scalability benefits. Nonetheless, limited hydrodynamic studies are corroborated by computer models that have been experimentally validated. This paper delineates the methodology and validation of a computational fluid dynamics (CFD) model for a 10 L laboratory-scale Raceway bioreactor operating under abiotic conditions. In ANSYS Fluent, a multiphase technique was used with the RNG k-ε turbulence model, which is good for simulating flows that are curved or rotating in open-channels. Experimental validation was performed using Particle Image Velocimetry (PIV) at paddlewheel velocities of 20, 25, and 30 rpm. The CFD predictions showed a strong match with the experimental data, with a mean relative error of less than 8%. The examination of the flow field revealed the formation and subsequent reduction of low-velocity zones, depending on the intensity of agitation. Based on study on velocity distribution and Reynolds number, it was suggested that the design be changed so that the paddlewheel be moved to improve flow homogeneity without increasing energy use. The validated CFD model provides a reliable basis for improving the hydrodynamics, design, and operation of Raceway bioreactors. Additionally, it serves as a foundation for future research on biomass cultivation and expansion, facilitating the development of more efficient and sustainable microalgal production technologies.
The rapid integration of artificial intelligence (AI) into educational systems is transforming how student performance is analysed and how educational policies are informed by large-scale data. Within this context, machine learning techniques are increasingly used to identify patterns associated with academic success and educational inequality. However, the use of predictive algorithms in education also raises important questions regarding transparency, fairness, and potential algorithmic bias. This study examines the predictive performance and fairness implications of machine learning models used to identify academically resilient students using data from the Programme for International Student Assessment (PISA) 2022. The analysis is based on a dataset containing more than 600,000 student observations across multiple national education systems. Academic resilience is operationalised following the OECD framework, identifying students who belong to the lowest quartile of the socioeconomic status index (ESCS) within their country while simultaneously achieving mathematics performance in the top quartile (PV1MATH). A predictive framework incorporating six supervised learning algorithms—Logistic Regression, Random Forest, Gradient Boosting, XGBoost, LightGBM, and CatBoost—was implemented. The modelling pipeline includes data preprocessing, missing value imputation, class imbalance correction using SMOTE, and model evaluation through multiple classification metrics, including accuracy, F1-score, and the area under the ROC curve (AUC). In addition, fairness diagnostics are conducted to examine potential disparities in prediction outcomes across gender groups, while feature importance analysis and SHAP-based explanations are used to interpret the contribution of key predictors. The results indicate that ensemble-based models achieve the highest predictive performance, particularly those based on gradient boosting techniques. At the same time, the analysis reveals that socioeconomic status, migration background, and school repetition constitute the most influential predictors of academic resilience. Although gender displays relatively low predictive importance, measurable differences in positive prediction rates across gender groups suggest the presence of potential algorithmic disparities. These findings highlight the importance of integrating fairness evaluation, transparency, and interpretability into educational data science workflows. The study contributes to ongoing discussions on the responsible use of artificial intelligence in education by emphasising the need for governance frameworks capable of ensuring that algorithmic systems support equity-oriented educational policies.
The security of critical infrastructures, such as energy grids and water treatment plants, depends on protecting Industrial Control Systems (ICS) and SCADA environments. The convergence of operational technology (OT) with information technology (IT) under Industry 4.0 introduces severe cyber risks, as demonstrated by incidents like Stuxnet and Colonial Pipeline. This paper examines the unique vulnerabilities of industrial protocols, analyzes major cyber-physical attacks, and reviews defense frameworks like ISA/IEC 62443 and NIST SP 800-82. It emphasizes strategies such as network segmentation, anomaly detection, and OT-specific incident response to enhance resilience. Future directions include zero-trust architectures and AI-driven threat detection to safeguard the foundations of modern society.
This chapter, examines the pedagogical foundations of IoT-enabled intelligent experimental platforms as transformative infrastructures for multidisciplinary STEAM engineering education. The discussion progresses from the limitations of traditional laboratory models towards the emergence of smart ecosystems in which e-learning environments, IoT-based architectures, and embedded systems are reinterpreted as accessible mediators between theoretical knowledge and experimental practice. Attention is devoted to multidisciplinary data integration, cloud computing, and responsible educational technology governance. Adaptive and multimodal learning models are addressed as mechanisms for personalising experimental learning trajectories, whilst safety-aware strategies for high-risk environments are equally considered. Technological and pedagogical dimensions are examined as mutually constitutive, positioning intelligent experimental platforms as dynamic ecosystems for interdisciplinary engineering competency development aligned with Sustainable Development Goal 4.
