Malaviya National Institute of Technology Jaipur

The mechanical behavior of discontinuities is significantly affected by the characteristics of the infill present along them. However, fewer studies have been performed to assess the response to discontinuities influenced by unsaturated infill. This study performs a parametric numerical study in Fast Lagrangian Analysis of Continua (FLAC-2D) to understand the shearing behavior of discontinuities influenced by unsaturated infills. The study was conducted in two steps. The initial step involves a detailed parametric study to analyze the impact of infill and discontinuity characteristics on the observed shear behavior. The study was performed by varying the thickness-to-asperity ratio (t/a) from 0.5 to 3.0, the Joint Roughness Coefficient (JRC) from 2.4 to 18.7, the degree of infill saturation (Sr) from 0.7 to 1.0, and the normal stress (σ) from 0.1 to 1.5 MPa. In the second step, a shear strength criterion was postulated based on the numerical data generated in the parametric study. The functional form of the criterion was kept similar to the Mohr–Coulomb criterion for its easy implementation in numerical and analytical analyses. The criterion was comprehensive because it could consider the implications of the above-mentioned parameters for the shear response of discontinuities. It was seen that the shear strength of infilled rock joints is enhanced with increasing JRC and σ and decreasing t/a and Sr. The numerical model was capable of capturing the critical t/a ratio beyond which the shear strength becomes a constant value. The expected mechanisms are discussed for the parametric results. The developed strength criterion was concluded to be comprehensive and accurate for shearing discontinuities under different conditions. The practical application of the developed strength criterion is also demonstrated for a rock slope undergoing failure, and comparative analyses were made with the results estimated via existing popular criteria.

The present study introduces FPGA(Field-Programmable Gate Array)-based Real-Time Multi-Class Vehicle Classification using Millimeter wave Radar (mmWave radar), which overcomes the limitations of conventional sensors such as LiDAR and cameras, which are sensitive to adverse weather and lighting conditions. On a hardware-software platform, the implementation of multi-class vehicle classification demonstrated its effectiveness. Within the realm of multi-class vehicle classification applications, the FPGA-based PYNQ-ZU (Python Productivity for Zynq) serves as an efficient embedded architecture. The reliability and accuracy of this method are improved, rendering it a promising solution for autonomous vehicles and advanced driver assistance systems (ADAS) in a variety of driving scenarios. We employed 3D point cloud data produced by mmWave radar via a PC, then transformed it into 2D point cloud images by top-view filtration methods. This method demonstrated greater efficacy in feature extraction with VGG-16. Multiple machine learning models were employed for classification tasks on both hardware and software platforms, achieving 100% accuracy with the Random Forest (RF) algorithm.

This study presents a year-long, multi-sensor residential monitoring dataset from a composite-climate home in Mohali, India, integrating appliance-level electricity use, indoor temperature–humidity measurements, and outdoor weather observations into an hourly residential energy informatics workflow. Nine appliances were monitored at 5-minute resolution and processed through a reproducible pipeline involving timestamp harmonization, anomaly handling, multi-source alignment, and feature extraction, yielding an analytics-ready dataset spanning 8,736 hourly observations (July 2024–June 2025). The monitored appliances consumed 845.25 kWh (58% of total household electricity use), with the refrigerator (21.95%) and air conditioner (cooling + heating; 19.07%) as the dominant monitored end uses. Psychrometric mapping quantified mixed-mode operation, distinguishing operating regions for Air Conditioner (AC) cooling (n=460 hours), evaporative cooling (n=172 hours), and AC heating (n=45 hours). Weather sensitivity analysis showed strong temperature dependence in baseline and seasonal loads (effect of outside temperature on refrigerator r=0.793; effect of outside temperature on geyser r=-0.694; slope -0.048 kWh/$$^{\circ }$$C), demonstrating the value of fusing weather variables into appliance analytics. Indoor comfort evaluation using the India Model for Adaptive Comfort (IMAC) indicated that 80.8% of summer hours and 51.7% of monsoon hours with valid measurements fell within adaptive comfort limits, with winter interpretation constrained by modeling assumptions and sensor availability. The primary contribution is a transparent, transferable workflow that converts heterogeneous residential sensing streams into a unified analytics dataset suitable for season-responsive appliance modeling, comfort-aware interpretation, and future multi-home deployments.

This paper presents an effective approach for lower-order (LO) modeling of an electric vehicle–integrated off-grid microgrid (OMG) system. The seventh-order system (SOS) of the OMG is reduced to a second-order model (SOM) while preserving the original system’s dynamic characteristics and ensuring computational efficiency. The Taylor series (TS) and Laurent series (LS) expansions are employed to simplify the complex system that plays a significant role in the reduction process. The expansion parameters of the higher-order system (HOS) of OMG and its lower-order model (LOM) are exploited to construct the fitness function. The proposed approach constructs three sub-objective functions based on TS and LS. These sub-objective functions are then combined into a single fitness function to obtain an improved LOM by enhancing the transient and steady-state responses with respect to the HOS of OMG. To minimize the error, the resultant fitness function is optimized using the brown bear optimization (BBO) algorithm. The optimization is performed under two key constraints: (i) ensuring zero steady-state error, and (ii) satisfying the Hurwitz stability criterion. To demonstrate the efficacy of the proposed LOM, it is compared with other LOMs obtained from different approximation techniques. The proposed LOM and other LOMs are graphically validated through step, impulse, Bode, Nichols, and Nyquist response comparisons with the HOS. Additionally, the performance error criteria (PEC), time-domain specifications (TDSs) and frequency domain specifications (FDSs) of the proposed LOM are compared with other LOMs using the HOS to establish the validation and applicability of the proposed method.