Dr. Ambedkar Institute of Technology

The more carbon footprint associated with cement manufacture has enhanced the search for sustainable alternatives capable of successful concrete performance while enabling carbon sequestration. This research work studies the combined effect of agricultural and mineral-based additives, namely rice husk ash (RHA), zeolite 13X (Z13X), and along with their activated forms, on the mechanical properties and carbon capture potential of concrete. Concrete mixtures with varying replacement levels of above materials were prepared and their mechanical properties were evaluated. In addition, the carbonation behaviour of hardened concrete was evaluated depth of carbonation and CO₂ uptake capacity were assessed using the mass gain method, while the compressive strength of carbonated concrete was determined and compared with non-carbonated specimens to evaluate the influence of carbonation on structural performance.The test results show that an optimum replacement level of nearly 10% gives the most favourable balance between strength and carbonation performance. Among the investigated materials, followed by RHA with 41.11 N/mm², primarily due to enhanced particle packing and pozzolanic reactions that promote the formation of additional C–S–H gel. Activated materials exhibited significantly greater CO₂ uptake and carbonation depth, with activated rice husk ash (AR-10) achieving the peak CO₂ absorption of 1.8%. However, excessive carbonation in highly reactive activated materials slightly reduced compressive strength.The test results indicate that an optimum replacement level of 10% provides the best overall performance. The AR-10 mix achieved the highest CO₂ uptake of 1.8%, while maintaining satisfactory mechanical properties. The RHA-10 mix exhibited the highest compressive strength of 41.11 MPa, attributed to improved particle packing and enhanced pozzolanic reactions that promoted the formation of additional calcium silicate hydrate (C–S–H) gel. Activated materials showed greater carbonation depth and CO₂ absorption than non-activated. Although increased carbonation in highly reactive activated mixes resulted in a slight reduction in compressive strength, the carbonated specimens retained measurable residual compressive strength, with the extent of strength retention varying according to the replacement material and dosage.Further, the results demonstrate that the integration of agricultural waste and mineral additives can simultaneously enhance mechanical properties and carbon sequestration capacity, offering a favourable strategy for the advancement of low-carbon and sustainable concrete systems.

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

Wireless Sensor Networks (WSNs) play a vital role in critical applications such as environmental monitoring, healthcare, and military surveillance. However, their limited resources and vulnerability to security threats, particularly Distributed Denial-of-Service (DDoS) attacks, significantly degrade performance and network lifetime. To address these challenges, this paper proposes a Lightweight Trusted Clustering Framework that integrates Bidirectional Long Short-Term Memory (Bi-LSTM) networks for malicious node detection with the Walrus Optimization Algorithm (WOA) for optimized Cluster Head (CH) selection. The Bi-LSTM model identifies malicious nodes based on behavioral metrics such as Packet Delivery Rate (PDR), energy consumption, and response time, ensuring that only trusted nodes are included in clustering. Subsequently, WOA selects CHs by optimizing residual energy, distance to the base station, node density, and trust value, thereby achieving energy efficiency while maintaining resilience against attacks among the 3-level energy heterogeneous nodes. The proposed framework is evaluated under DDoS scenarios using MATLAB simulations, with performance assessed through stability period, network lifetime, remaining energy, throughput, and detection rate. Results demonstrate that the proposed model extends the stability period by 35

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.