Government Engineering College, Idukki

Tumors, especially brain tumors-such as glioma, Meningioma, and pituitary tumors-scanned using Magnetic Image Resonance (MRI) shows difficulty in their classification and explainability due to their complex structure and irregular shapes, and the standard CNN may focus on the wrong features. Also, the native XAI approach “Grad-Cam” may work well with CNN, but when it comes to vision transformers, “GradCam” often misses out on the important features from the provided sample. To overcome this limitation, we propose our system, which uses multi-modal Eva02 and DCNN (Deformable Convolutional Neural Network) hybrids fused together using Cross Attention Fusion, and its explainability is measured by an adaptive mechanism which have two methods: custommade Cross Attention Gradient-Driven Multi-Head Attention Rollout CAD-GMAR, which proved to provide better results when the transformer dominates the output, and Grad-Cam which provides result when DCNN dominates the output. This framework was proven to be reliable, robust, and accurate even against the noise present in the image sample on which the framework was tested.

Seismic pounding between adjacent buildings remains a critical concern, particularly when base isolation is employed and near-fault (NF) ground motions dominate the response. This study develops a Bayesian probabilistic framework to determine minimum separation gaps for adjacent reinforced concrete buildings with fixed-base (FB) and base-isolated (BI) supports. Two Reinforced Concrete (RC) (10-storey and 8-storey) were analyzed in four adjacency configurations under 44 far-field (FF) and NF ground motions, scaled to PGAs from 0.1 to 1.2 g. More than 12,000 nonlinear time-history analyses with gaps ranging from 25 to 500 mm detected pounding via nonlinear gap elements. Pounding probabilities were quantified using Bayesian updating with Jeffreys prior and Monte Carlo bootstrapping to derive posterior medians, 95

Accurate in-situ appraisal of concrete quality becomes difficult when supplementary cementitious materials (SCMs) and high-range water-reducing admixtures weaken standard non-destructive testing (NDT) correlations. This study builds a compact but information-rich multi-output dataset ( n=147 ) that pairs Ultrasonic Pulse Velocity (UPV) and Rebound Number (RN) with mix descriptors and physically motivated ratios. Six heterogeneous base learners—Ridge, RBF-SVR, Random Forest, Gradient Boosting, GPU-XGBoost, and a shallow MLP—undergo nested cross-validation and then combine through convex stacking with weights proportional to each model’s global mean absolute SHAP value. This SHAP-weighted ensemble aligns predictive accuracy with interpretability in a single, transparent scheme for simultaneous UPV and RN prediction. The approach attains a cross-validated RMSE of 1.58 (mean across targets) and R^2=0.81 , outperforming the best single learner by about 11

Electromagnetic pollution is a big challenge in today’s wireless technological environment. Microwave absorbers are designed to mitigate this problem. These are the materials that absorb electromagnetic radiation and mitigate its harmful effects on humans and electronic devices. Magnatoplumbite (M-type) hexaferrites with the molecular formula SrCoyZryFe12-2yO19 were developed utilizing the sol–gel synthesis technique for microwave absorber applications. X-ray diffraction (XRD) was performed to investigate the structural purity of these synthesized hexaferrites. For the investigation on morphology, scanning electron microscopy (SEM) was done. An investigation on the magnetic characteristics was performed using different parameters like saturation magnetization (Ms), coercivity (Hc), remanence (Mr), and anisotropy field (Ha). There is a reduction in coercivity from 5011 to 2688 Oe, with a decrease in M_s from 151.84 to 87.72 emu/g. From the absorption analysis, it was evident that the doping of Zr4+ and Co2+ has improved the absorption. A reflection loss (RL) of -41.70 dB for an 8.3 mm thickness and an input impedance (Zin) of 383.25 Ω was achieved for composition SrCoZr 5 with a -10 dB absorption bandwidth of 504 MHz. With high absorption and low thickness, the prepared hexaferrites may be a promising candidate for defence and industrial applications as a microwave absorber.

Nowadays, seismic hazards increase day by day; it is not possible to stop any hazard, but the buildings can be strengthened. This study developed a framework for evaluating seismic reliability by considering six RC building models through analysis. The models include regular as well as irregular buildings with shear walls, bracings, dampers, and base isolation. Initially, three seismic response parameters were evaluated, such as storey drift, storey displacement, and base shear for all models. Three multi criteria decision making (MCDM) consisting of the Best Worst Method (BWM), Analytic Hierarchy Process (AHP), and Fuzzy AHP, were employed to quantify the importance of these parameters. The probability of reliability of each model were evaluated with the help of MCDM tools and the Weighted Sum Method, the reliability and probability of reliability of each model were evaluated. Model 2, consisting of dampers, shear walls, and bracings, exhibited the highest seismic reliability and lateral stiffness. A regular building with base isolation was considered as Model 3, where an increase in flexibility and a decrease in reliability were obtained. Moderate seismic performance was obtained from the irregular C-shaped and I-shaped models. Base shear was considered the most important seismic response parameter, followed by storey drift and displacement for all three MCDM tools. For reinforced concrete (RC) structures, nonlinear seismic analysis combining MCDM tools facilitates reliable structural performance evaluation in the proposed framework.