This research aims to design and implement a complete safety and comfort device that integrates a Driving Monitoring System (DMS) and a car Seat Electronic Control Unit (SECU). The DMS employs a combination of infrared sensors and a pre-trained Artificial Intelligence (AI) algorithm with advanced facial recognition capabilities. It aims to monitor and control the driver's distraction and drowsiness level during daytime and nighttime driving conditions by accurately detecting head pose and eye status. If the system detects the driver is drowsy or distracted from the road, a warning system will be activated by sending an electrical signal to the aforementioned car SECU. The designed SECU initiates a car seat vibration and triggers an audible signal to alert the driver of a dangerous situation by managing the seat movement process and protection features. It provides the basic drive and control of the seat motor drivers and position sensors. The most important consideration of this SECU is meeting the following requirements: real-time control of various motors, fast processing speed, low power, low heat dissipation, low quiescent current, and low Electromagnetic Interference (EMI). The two systems were integrated into a single compact device that includes a night vision camera mounted on the steering column to record data without obstructing the driver's view. The system has high precision at a maximum detection rate of 45 Frames Per Second (FPS) in real-time. Furthermore, the system is robust under actual driving conditions without being affected by the time of day, facial expressions, or eye occlusions.

This research introduces an innovative automotive child back seat monitoring system aimed at preventing heatstroke incidents associated with leaving children unattended in vehicles. The device integrates sensors, wireless connectivity, and intelligent algorithms to detect and respond to potential risks. Internal sensors continuously monitor factors such as occupancy status, in-vehicle temperature, and the child's respiration rate in real-time. A hybrid approach is adopted for enhanced accuracy, utilizing Force Sensing Resistor (FSR) sensors for presence and breathing detection, along with motion sensors for movement tracking. Intelligent algorithms process the data to identify critical conditions and activate preventive measures. Data is transmitted through a mobile application for immediate alerts to caregivers. The system incorporates a Simplified Frequency Analysis (SFA) for rapid processing, surpassing conventional methods by up to 80 times faster on a low-cost Microcontroller (MC), which makes it ideal for real-time applications. Testing results confirm its high accuracy in real-time occupancy detection and breath monitoring, triggering alerts as needed. The proposed system meets rigorous automotive industry standards for temperature and power requirements, ensuring optimal functionality and energy efficiency in extreme environments. With a minimal standby current of 104 $\mu$ A and the ability to withstand temperatures from -40 $^\circ$ C to +85 $^\circ$ C, this technology has the potential to save lives and establish child heatstroke prevention as a standard vehicle feature.

FLO is a high-throughput bioreactor for testing biomaterials in more physiologically similar environments including pulsation amplitude, flow rate, and pressure waveforms which is done through the manipulation of fluid velocity.

This research takes the capabilities of implicating sustainable community empowerment in the process of designing and planning Iraqi cities through the literature review that defined it, to get a final conclusion about the sustainable community empowerment definition that the research takes in its problem which is that there isn’t a theoretical frame for the community empowerment to take decisions in planning and designing cities aims for urban development for Iraqi cities. The research aims also to recognize the world implications of sustainable community empowerments to enrich the data background of the empowerment criteria and process, also the process of designing and planning the city. The research hypothesis is that the sustainable community empowerment is an urban strategy enrich the Iraqi situation in designing and planning cities also it could be implicated in Iraqi community to follow the developed countries around the world. The research methodology contains theoretical frame of elements extracted from examples around the world, the second is a practical frame depends on the theoretical frame elements with the questionnaire data and the personal interview with the expertise involved directly with the designing and planning process with the elements of the criteria and the process of empowerment. The research concluded number of conclusions due to recommendations of supporting the community role and develops it to get involved more seriously and clear in the planning and designing process,for being the true indicator for the success of the process.

Effective traffic control using Artificial Intelligence (AI) is essential to ensure safe passage for all road users. AI-based collision detection systems offer advanced mechanisms to prevent accidents and improve highway safety. This research investigates two distinct collision scenarios: vehicle–pedestrian and vehicle–motorcyclist interactions. The proposed method in this research involves the bidirectional Long Short Term Memory (LSTM), Convolutional Neural Network with LSTM (CNN–LSTM), and transformer models. The model is furthermore tuned using random or grid search. For the pedestrian–vehicle scenario, the CNN–LSTM model achieved 99.76% accuracy, 99.77% precision, and 99.76% recall, highlighting its strong classification performance. In the vehicle–motorcyclist scenario, the bidirectional LSTM reached 99.73% accuracy with precision and recall of 99.15%, demonstrating its effectiveness in detecting imminent crashes. The optimized CNN-LSTM by random search has focused on decreasing the false-positive rate and increasing the positive rate. It has achieved superior results compared to previous research. These results suggest that the system could be effectively implemented as an early collision warning solution on edge devices.