This paper presents a comprehensive study encompassing both liquefaction susceptibility evaluation and slope stability analysis of embankment soil adjacent to road. The paper focuses on the vulnerability of embankment soil to liquefaction-related failures. It examines the liquefaction vulnerability of embankments ML-CL soil, previously considered non-liquefiable but raising concerns post-1999 Kocaeli Earthquake. The paper evaluates liquefaction susceptibility using Chinese Criteria and Modified Chinese Criteria based on index test results of embankments soil samples. The soil at various depths was found to be not susceptible to liquefaction as per Chinese criteria, whereas the second evaluation as per Modified Chinese criteria gave different and more specific results taking into the % clay-sized particles. Based on Modified Chinese criteria, the soils ranging from 10-20 ft, 25-30 ft and 30-35 ft were found explicitly non-susceptible, whereas soil ranging from 0-10 ft and 20-25 ft requires further study on non-plastic clay-sized grains as per the criteria. The paper delves into slope stability analysis using PLAXIS2D and GEOSLOPE software to determine the optimal earthworks layout for a embankment excavation based on Eurocode 7. Upon numerical modelling, various trials were carried out considering various factors of safety across different earthworks layouts and the one with satisfying factor of safety is considered safest, ensuring safety and cost-effectiveness of embankment cut besides the road.

In this study, a new optimal observer design is presented for linear systems. First, the system states are predicted in their receding horizon, and then, based on the error of state prediction, a quadratic cost function is defined  such that its minimization results in an optimal gain for the proposed observer. Since the present analytic approach uses off-line optimization, it can overcome the related difficulties that may arise in non-convex optimization problems. In addition, the condition ensuring the asymptotic stability of the proposed optimal observer is developed, thus guaranteeing the asymptotic convergence of the state estimation error to zero. The estimation of the optimal state based on the prediction of system states, achieved through the asymptotic stability of the estimation error, is among the main features of the proposed method. Furthermore, the proposed observer design is independent of the type of system controller. Finally, three examples are provided to demonstrate the effectiveness of the proposed method in state estimation.

This work comprehensively analyses the device physics of a charged plasma-doped InGaAs5.86GaAs5.65 Tunnel field effect transistor (TFET) biosensor featuring a dual metal gate configuration. The device is simulated using Silvaco Atlas TCAD, with HfO2 employed as the gate dielectric alongside a nanocavity to enhance biosensing performance. The investigation focuses on crucial device parameters, including energy band profiles, potential distribution, electric field variations in both lateral and vertical directions, and electron concentration dynamics. Outcomes indicate that the biosensor keeps a superior response in the ambipolar region, with a drain current (ID) or an on current (ION) of ~10-4, due to the amalgam of the dual metal gate and InGaAs/GaAs heterostructure. This configuration also assists in supervising the flow of the carriers and, therefore, improves biosensing sensitivity and specificity. The results emphasize the advantage of this TFET configuration for next-generation biosensing technologies.

A vital component of patient care is the diagnosis of blood cancer, which necessitates prompt and correct classification for efficient treatment planning. The limitations of subjectivity and different levels of skill in manual classification methods highlight the need for automated systems. This study improves blood cancer cell identification and categorization by utilizing deep learning, a subset of artificial intelligence. Our technique uses bespoke U-Net, MobileNet V2, and VGG-16, powerful neural networks to address problems with manual classification. For the purposes biomedical image segmentation U-Net architecture is used, MobileNet V2 is used for lightweight neural network model design and VGG-16 is used for image classification. A hand-picked dataset from Taleqani Hospital in Iran is used for the rigorous training, validation, and testing of the suggested models. The dataset is refined using denoising, augmentation, and linear normalisation, which improves model adaptability. The results show that the MobileNet V2 model outperforms related studies in terms of accuracy (97.42%) when it comes to identifying and categorizing blast cells from acute lymphoblastic leukemia. This work offers a fresh approach that adds to artificial intelligence's potentially revolutionary potential in medical diagnosis.

This work presents the design and prototyping of an Industrial Monitoring and Protection System aimed at enhancing safety and operational efficiency in industrial environments. The system integrates multiple sensors with a GSM module to monitor and respond to critical environmental parameters, such as ambient light levels, temperature, and smoke detection. A Light Dependent Resistor (LDR) is configured to detect excessive lighting levels, interfacing with a microcontroller to activate the GSM module and send alert messages when thresholds are exceeded. The temperature sensor continuously monitors ambient temperature, and upon detecting overheating, the microcontroller triggers the GSM module to notify operators. Similarly, a smoke sensor detects the presence of harmful smoke and initiates an alert through the GSM module for early fire hazard detection. These sensors are connected to the microcontroller via analog and digital input pins, with their outputs processed to enable condition-based responses. A relay switch, controlled by the microcontroller, automatically disconnects connected loads when safety thresholds are breached, preventing equipment damage and ensuring personnel safety. Real-time sensor readings and system status are displayed on an OLED screen, providing operators with comprehensive, up-to-date information on the monitored environment. The system dynamically responds to environmental conditions by triggering alerts and actions based on customizable safety thresholds for light intensity, temperature, and smoke levels. This integrated architecture ensures seamless communication between sensors, the microcontroller, and the GSM module, delivering real-time monitoring, automated protective mechanisms, and early warning capabilities. The proposed system demonstrates the feasibility of affordable and scalable solutions for industrial safety, offering immediate responses to hazardous conditions while minimizing downtime. Furthermore, its adaptable design allows for customization across different industrial environments, making it suitable for a wide range of applications.