Dr. Ambedkar Institute of Technology

Particle shape irregularity governs grain-scale interactions that influence dune stability and surface mobility in aeolian environments, yet its effects remain poorly quantified in constitutive models. This study presents a micromechanical investigation into the constitutive behavior of aeolian dune sand (ADS) at the scale of representative volume element (RVE), with a specific focus on the fundamental role of particle irregularity. Through a series of discrete element method (DEM) simulations incorporating high-fidelity particle shapes, assemblies with varying overall regularity ( O_R ) indices are systematically analyzed under direct shear test conditions, with varying packing density and normal stresses. The results reveal that as O_R decreases, both the peak and critical state shear strength increase non-linearly, demonstrating a saturation effect at high particle shape irregularity. A novel asymptotic model, φ =B-A(O_R)^n , is proposed, which provides a superior fit than the traditional power-law models by capturing this physical limit and implying an upper-bound strength for the ADS. A corresponding enhancement in dilatancy is observed, governed by a stress-dilatancy relationship with a material constant of 0.61, specific to the morphology of ADS. Micro-structural analysis shows that the evolution of fabric anisotropy and the mechanical coordination number strongly correlate with the macroscopic stress-strain response, with more irregular particles developing a more stable and anisotropic load-bearing network. A key finding is the establishment of unified scaling laws where the particle regularity index directly governs the pressure-dependence of both the critical state void ratio and coordination number. This provides a cross-scale framework linking particle irregularity to the bulk constitutive behavior, offering a micromechanical basis for the development of enhanced constitutive models for granular materials in aeolian environments.

The accurate capacity and fast life cycle predictions are important concerns for the safe and reliable operation of power sources. Recently, machine learning (ML) and deep learning algorithms have been employed to predict the remaining useful life (RUL) of batteries. This research work proposes a fusion-based hybrid model, which comprises a gated recurrent unit (GRU) with a convolutional neural network (CNN), i.e., GRU-CNN, to enhance the accuracy of RUL prediction of Li-ion batteries. The key feature of a Li-ion battery, i.e., capacity, is identified using linear, ridge, lasso, gradient boosting, and random forest regression ML models. Therefore, the capacity fading feature is used to predict the remaining life of the Li-ion battery. The moving average, exponential moving average (EMA), and Savitzky–Golay (Savgol) methods are applied for smoothing the data using a sliding window of ten data points. The proposed algorithm is trained on 80

Since the advent of liberalization, privatization, and globalization, it has become essential for organizations to attract potential candidates for specific positions and retain key employees to maintain an effective and competitive workforce. Currently, in any industry, regardless of size, there is a significant transformation in the functions of human resource management. In addition to its conventional functions, Human Resource Management has broadened its scope to evaluate and manage employee performance through a clearly defined systematic approach that has developed in response to advancements in the HRM field. SimpleImputer was employed to address missing values, while StandardScaler was utilized to standardize numerical features. PCA is used for feature extraction. The purpose of developing a decision tree model was to classify performance outcomes and identify critical assessment elements using the CHAID method. A useful tool for HR practitioners, the resultant model provides an interpretable framework for factor importance analysis and prediction. The reliability of the CHAID-based decision tree in evaluating employee performance was demonstrated by its high accuracy rate of 98.36%. In order to improve assessment processes, decision-making, and organisational competitiveness, the study emphasises the importance of incorporating analytical models into HRM systems.

Industrialization has played a meaningful contribution to the economic development of Dabaspete by creating employment opportunities and supporting industrial growth. However, the expansion of become a matter of concern for environmental pollution and impact local communities. Influence of industrial pollution on the environment, public health, together with the everyday well-being of people live and works in Dabaspete area. Descriptive research approach and primary data was obtained from 100 respondent’s through a structure questionnaires. The study focused on different forms of environmental degradation, including air water, soil and noise pollutions, and assessed public perceptions regarding their environmental consequences and health-related issues findings reveal that air pollution is the most commonly observed environmental issue, while many respondents reported concerns about declining air quality, contamination of water sources, health-related problems, and damage to vegetation and agricultural land. The study also highlights need’s for effectives waste management, stricter pollution control measures, regular environmental monitoring, and greater public awareness. Overall, the research emphasizes the importance of maintaining a balance between industrial development and environmental protection to ensure sustainable growth and a healthier future for the community.

The project features a Humanoid robot with the ability to interact, perceive, and operate in human-like environments while having human-like knowledge and sophistication. The robot is powered by the Jetson Orin Nano which enables the robot to execute high-performance edge Artificial Intelligence by allowing it to perform real-time image processing, gesture understanding, and autonomous decision making without having to utilize a cloud environment. The 28 MG995 servo motors are powered by 28 ESP32 controllers that make up the distributed control network and maintain natural walking, stable posture control, expressive upper body movement, and dexterous manipulation through 5-finger robotic hands. The camera, IMU, Mic, and optional depth sensor combination make up a rich sensor suite that gives the robot the ability to detect obstacles, identify faces, analyze gestures, and communicate with voices in a meaningful way. The robot includes safety features for; fall detection, collision avoidance, and controlled gait planning which support a high level of reliable navigation through dynamic environments. The robots modular Hardware and scalable Software architecture allow for easy addition of Hardware upgrades, additional sensors, and expansion into specialized task domains. The combination of mobility, perception, and autonomous reasoning provides further demonstration of the system’s potential in many of the commercial and institutional partner domains; Healthcare, Education, Home Services, and Research. As such, this cost-effective humanoid robot is a significant forward progression towards practical, flexible, and interactive human- robot collaboration.