The purpose of this study was to optimize the parameters involved in the production of water-soluble phytosterol microemulsions for use in the food industry. In this study, response surface methodology (RSM) was employed to model and optimize four of the processing parameters, namely, the number of cycles of high-pressure homogenization (1−9 cycles), the pressure used for high-pressure homogenization (100−500 bar), the evaporation temperature (30−70 °C), and the concentration ratio of microemulsions (1−5). All responses—particle size (PS), polydispersity index (PDI), and percent ethanol residual (%ER)—were well fit by a reduced cubic model obtained by multiple regression after manual elimination. The coefficient of determination (R²) and absolute average deviation (AAD) value for PS, PDI, and %ER were 0.9628 and 0.5398%, 0.9953 and 0.7077%, and 0.9989 and 1.0457%, respectively. The optimized processing parameters were 4.88 (approximately 5) homogenization cycles, homogenization pressure of 400 bar, evaporation temperature of 44.5 °C, and concentration ratio of microemulsions of 2.34 cycles (approximately 2 cycles) of high-pressure homogenization. The corresponding responses for the optimized preparation condition were a minimal particle size of 328 nm, minimal polydispersity index of 0.159, and <0.1% of ethanol residual. The chi-square test verified the model, whereby the experimental values of PS, PDI, and %ER agreed with the predicted values at a 0.05 level of significance.

The Internet of Things (IoT) offers transformative capabilities in enhancing public safety, disaster response, and emergency management by leveraging interconnected devices and real-time data. Through the IoT, smart sensors and networks are deployed across cities and environments to monitor critical parameters including air quality, structural integrity, and environmental changes. These systems provide early warnings for natural disasters such as earthquakes, floods, and wildfires, enabling authorities to respond proactively. In emergency management, IoT devices help coordinate resources and improve situational awareness during crises. Real-time data from wearable devices, smart infrastructure, and communication systems allow responders to track people, manage evacuations, and deploy resources more effectively. For example, IoT-enabled drones and autonomous vehicles are used to deliver supplies or assess damage in hazardous areas without risking human lives. IoT technologies improve post-disaster recovery by continuously monitoring areas for safety hazards and supporting infrastructure restoration. Smart traffic management systems assist in controlling traffic flow for emergency vehicles, while IoT-based communication networks ensure connectivity when traditional systems fail. The IoT significantly enhances the speed, accuracy, and effectiveness of disaster response and public safety operations, leading to the better protection of communities and faster recovery from emergencies.

Artificial intelligence plays a decisive role in the healthy and sustainable development of society. The rapid development of the economy and society has put forward higher standards and requirements for fire supervision. The development of science and technology has advanced the intelligence on fire protection. Through the application of intelligent fire control, the fire department can better supervise the fire control of various units and further improve the pertinence and effectiveness of fire control supervision. Artificial intelligence technology is widely used in all stages of construction projects, which brings many conveniences to the construction and use of projects. Based on the analysis of the current situation and existing problems of the basic maintenance of fire-fighting facilities, this project analyzes the effect of the application of artificial intelligence technology in the management of fire-fighting facilities. Therefore, the purpose of this project is to study the role and effectiveness of artificial intelligence technology in the management of fire-fighting facilities and further verify this conclusion with questionnaires and interviews.

This paper explores the integration of Sustainable Development Goals (SDGs) education into a Design Thinking module within educational contexts. By merging the principles of Design Thinking with the global agenda of the SDGs, educators can empower students to address complex societal challenges creatively and responsibly. Through a structured framework, this paper outlines strategies for aligning SDGs with Design Thinking methodologies, incorporating case studies, promoting project-based learning, fostering collaboration, emphasizing ethical and sustainable design, facilitating presentation and reflection, and encouraging continued engagement. By adopting this approach, educators can equip students with the skills, mindset, and motivation needed to become agents of positive change in their communities and beyond.

Intrusion prevention and classification are common in the research field of cyber security. Models built from training data may fail to prevent or classify intrusions accurately if the dataset is imbalanced. Most researchers employ SMOTE to balance the dataset. SMOTE in turn fails to address the constraints associated with the dataset, such as diverse data types, preserving the data distribution, capturing non-linear relationships, and preserving oversampling noise. The novelty of this work is in addressing the issues associated with data distribution and SMOTE by employing Conditional Tabular Generative Adversarial Networks (CTGANs) on NSL_KDD and UNSW_NB15 datasets. The balanced input corpus is fed into the CNN model to predict the intrusion. The CNN model involves two convolution layers, max-pooling, ReLU as the activation layer, and a dense layer. The proposed work employs measures such as accuracy, recall, precision, specificity and F1-score for measuring the model performance. The study shows that CTGAN improves the intrusion detection rate. This research highlights the high-quality synthetic samples generated by CTGAN that significantly enhance CNN-based intrusion detection performance on imbalance datasets. This demonstrates the potential for deploying GAN-based oversampling techniques in real-world cybersecurity systems to improve detection accuracy and reduce false negatives.

The exponential growth in artificial intelligence (AI) and automation technologies is changing industries, creating a niche for a digitally competent workforce. Technical and vocational education (TVET) and training institutions are at the center of this transformational wave, with their role of equipping individuals with the competencies required for the digital era. The integration of AI and automation into the TVET curriculum and practice was explored as a game-changer for vocational education and training. AI-powered tools are used for personalized learning, intelligent tutoring systems, and virtual simulation of hands-on skills acquisition. The challenges and opportunities in using the technologies were explored to mitigate the digital divide, update instructor capabilities, and ensure inclusive access to modern training resources. Based on the results, TVET institutions can educate students, aligning with the need for Industry 4.0/5.0. Strategic frameworks for policy, curriculum design, and industry partnerships must be established to ensure that TVET continues to play a pivotal role in sustainable and equitable digital transformation.