Air quality monitoring and forecasting have become increasingly critical in urban environments due to rising pollution levels and their impact on public health. Recent advances in Internet of Things (IoT) technology and machine learning offer promising alternatives to traditional monitoring stations, which are limited by high costs and sparse deployment. This paper presents the development of a real-time, low-cost air quality forecasting system that integrates IoT-based sensing units with predictive machine learning algorithms. The proposed system employs low-cost gas sensors and microcontroller-based hardware to monitor pollutants such as particulate matter, carbon monoxide, carbon dioxide and volatile organic compounds. A fully functional prototype device was designed and manufactured using Fused Deposition Modeling (FDM) with modular and scalable features. The data acquisition pipeline includes on-device adjustment, local smoothing, and cloud transfer for real-time storage and visualization. Advanced feature engineering and a multi-model training strategy were used to generate accurate short-term forecasts. Among the models tested, the GRU-based deep learning model yielded the highest performance, achieving $R^2$ values above 0.93 and maintaining latency below 130 ms, suitable for real-time use. The system also achieved over 91% accuracy in health-based AQI category predictions and demonstrated stable performance without sensor saturation under high-pollution conditions. This study demonstrates that combining embedded hardware, real-time analytics, and ML-driven forecasting enables robust and scalable air quality management solutions, contributing directly to sustainable development goals through enhanced environmental monitoring and public health responsiveness.

Fire is a destructive hazard impacting residential, industrial, and forested environments. Once ignited, fire becomes difficult to control, and recovery efforts are often extensive. Therefore, early detection is critical for effective firefighting. This study presents a mobile robotic system designed for early fire detection, integrating a Raspberry Pi, RGB (red, green and blue), and night vision-NIR (near infrared reflectance) cameras. A four-stage hybrid-cascade machine learning model was developed by combining state-of-the-art (SotA) models separately trained on RGB and NIR images. The system accounts for both daytime and nighttime conditions, achieving F1 scores of 96.7% and 95.9%, respectively, on labeled fire/non-fire datasets. Unlike previous single-stage or two-stage vision pipelines, our work delivers a lightweight four-stage hybrid cascade that jointly fuses RGB and NIR imagery, integrates temporal consistency via ConvLSTM, and projects a robot-centric “safe-approach distance” in real time, establishing a novel edge-level solution for mobile robotic fire detection. Based on real-life test results, the robotic system with this new hybrid-cascade model could detect the fire source from a safe distance of 500 mm and with notably higher accuracy compared to structures with other models.

The main aim of this study was to decrease the weight of mobile transportation robots and to check the optimized shape using topology optimization and structural analysis. The analyses were applied using CAE Software with the methods of topology optimization and structural analysis. In the topology optimization process, the preserved areas were defined for connection and fixing areas. The aim was to decrease the weight of the robot structure and lower the energy consumption of the robotic system. After topology optimization, the structural strength analysis was applied to the new optimized structure to check the strength. The weight of the structure was decreased with a ratio of 20%. In addition to this, the structural strength of the robot was observed similar to the original body with the factor of safety value as three.

In this study, structural and dynamic analyses were applied to the mobile transportation robot to find the structural strength and to calculate the power and torque requirements. The mobile transportation robot was designed using CAD software as two modules; mecanum wheeled locomotion module and robotic arm module, respectively. The analyses were carried on the CAE Software using the static structural and rigid body dynamics tools. In the structural analysis, the weight of the robot and the additional load (98.1 N) were applied to the body of the robot. The material of the robot body was selected as Aluminum 6061 O sheet metal. In the dynamic analysis, the operation environment and conditions were simulated in the CAE software. The structural analysis results showed that the factor of safety value was obtained as 10.282 under applied loads and this value indicates that the chassis of mobile transportation robot is supposed to carry loads reaching up to 30 kg without any functional and mechanical problem. Furthermore, from the dynamic analysis, the total torque requirement of the mobile robot was about 16.56 Nm.