The development of upper limb prostheses poses a significant challenge in providing amputees with sensory feedback. This paper presents a novel approach by proposing a biomimetic circuit specifically designed to replicate the behavior of slowly adapting (SA-I) afferents, which are responsible for encoding sustained indentation and offering crucial sensory feedback. The circuit has been meticulously designed and simulated using Cadence Virtuoso software, a powerful tool for circuit design and optimization. To validate the functionality and performance of the biomimetic circuit, a grid of mechanoreceptors is simulated and tested, providing realistic inputs for the circuit. The circuit successfully emulates the response of SA-I afferents to sustained indentation, exhibiting a slowly adapting discharge that linearly correlates with the depth of indentation. This ability to replicate the natural behavior of SA-I afferents represents a significant advancement in the field of providing sensory feedback for upper limb prostheses.
The biomimetic circuit holds great promise in addressing the crucial need for sensory feedback in upper limb prosthetics. By integrating this circuit into upper limb prostheses, amputees can experience more intuitive and realistic sensations during interactions with their environment. The replication of SA-I afferent behavior provides users with vital information about the magnitude and duration of applied forces, enhancing their overall perception and control of the prosthesis. 
The findings of this study contribute to the ongoing progress in the field of prosthetics, particularly in the development of more sophisticated and advanced upper limb prostheses. The successful implementation and simulation of the biomimetic circuit demonstrate its potential as a viable solution for providing amputees with enhanced sensory feedback, ultimately improving their quality of life and reintegrating them into daily activities more seamlessly. The new approach emphasizes the development of a biomimetic circuit tailored to replicate SA-I afferent behavior. The proposal addresses the challenge of providing sensory feedback in upper limb prostheses. The study utilizes Cadence Virtuoso software for precise design, layout, and simulation, offering a practical solution for realistic sensory feedback. By accurately emulating the response of SA-I afferents to sustained indentation, the circuit holds the potential to significantly enhance amputees' quality of life and integration into daily activities. The proposed circuit contributes to the advancement of upper limb prosthetics and represents a significant leap forward in achieving more intuitive and authentic sensory experiences for prosthesis users.

Information about the arrival of floods in rivers must be informed as soon as possible to the community so that it can save people along the river and its surroundings from the dangers of flooding which are very detrimental. Arduino Nano and Microcontroller ESP8266 provide good performance in providing information about the arrival of floods quickly. The working system of the tool is based on a water level sensor installed in the upstream area of the river which will be received and processed by Arduino nano, then the sensor data is communicated to the ESP8266 device (as a wifi node). Furthermore, ESP8266 will send information to the Android application. This system is very cost-effective and has low power consumption. Flood information will be sent to people along the river that flows through the city and residential areas. The test results show that the current system is functioning well, and is useful for flood monitoring systems in rivers.

ACO-based routing protocols like AntHocNet have emerged as a solution for adaptive routing in MANETS. Likewise, deep Q-learning based protocols are suitable for complex and dynamic environments like MANETs and utilizing real time data for better decision-making. However, there is lack of studies in enhancing ACO-based protocols using Q-learning in a new hybrid protocol, and comparing it with the established ACO-based protocol AntHocNet.
By combining ACO’s strengths (eg. Multi-agent pathfinding and historical data creatd by pheromones) and combine it with key components of Q-learning, then we have a promising protocol ready to be compared with AntHocNet. Previous studies have explored integrating ML with MANET routing, but few of them, if any, have explored enhancing ACO using ML techniques. Therefore, we propose two new protocols: ACO-QL and ACO-DQN.
One uses Q-learning and the latter uses deep Q-learning. After conducting many experiments by running implementations of ACO-DQN, ACO-QL, and AntHocNet on a MANET simulation, we found out that AntHocNet is superior to ACO-DQN in terms of execution time, end-to-end delay, and path cost in most cases, but on the other hand ACO-DQN achieved better packet delivery ratio and throughput results. Meanwhile, ACO-QL consistently achieved lower packet delivery ratios than AntHocNet, and mostly matching AntHocNet’s performance in terms and of other metrics, making it a valid lightweight and faster alternative.

Recently, accidents caused by drowsy driving have emerged as a significant concern for society, often resulting in severe consequences for victims, including fatalities. Lives are the most valuable asset in the world and deserve greater safety on the road. Given the urgency, it is essential to develop an effective drowsiness detection system that can identify drowsiness in drivers and take necessary steps to alert them before any unfortunate incident occurs. Dlib and MediaPipe Face Mesh have shown promising results. However, most previous studies have relied solely on blinking patterns to detect drowsiness, while some have combined blinking with yawning patterns. The proposed research focuses on creating a straightforward drowsy driver detection system using Python, incorporating OpenCV and MediaPipe Face Mesh. The shape detector provided by MediaPipe Face Mesh assists in finding critical facial coordinates, allowing for the calculation of the driver's eye aspect ratio, mouth aspect ratio, and head tilt angle from video input. The system's performance evaluation utilizes standardized public datasets and real-time video footage. Notably, in both scenarios, the system exhibited remarkable recognition accuracy. A performance comparison was undertaken, demonstrating the proposed method's effectiveness. The proposed system has the potential to enhance travel safety and efficiency when integrated with vehicles' supplementary safety features and automation technology.

This study looks at how well a Support Vector Machine (SVM) with a quadratic polynomial kernel works for detecting Monkeypox. The SVM method is compared to other machine learning models like Neural Networks, KNN, Logistic Regression, Random Forest, Decision Tree, and Naïve Bayes. By using features from medical images called Local Binary Patterns (LBP), the SVM model showed the best results, with 93.33% accuracy, 95.24% recall, 91.67% true negative rate, and 90.91% precision. The LBP features are used because they exhibit unique textural patterns that can distinguish Monkeypox and normal cases. The results show that the SVM with this kernel is good at telling the difference between Monkeypox and normal cases, making it a helpful tool for early detection in healthcare.