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Autonomous navigation in developing regions is challenged by heterogeneous traffic, dynamic occlusions, and weak road structure. Existing segmentation models, largely trained on structured Western datasets, struggle to generalize under these conditions. To address this gap, we propose IndiVNet, a semantic segmentation architecture tailored for unstructured Indian driving environments. IndiVNet introduces a progressive dilation encoder (6 16) that captures fine-grained details and broad contextual cues without inducing oversparsity. Evaluated on the India Driving Dataset (IDD), it achieves 69.98% mIoU, outperforming CNN and Transformer baselines, and reaches 73.2% mIoU on CAMVID, demonstrating strong cross-domain generalization. By combining contextual adaptability with real-time efficiency, IndiVNet offers a scalable, region-aware solution for robust autonomous navigation in complex environments.

Stroke analysis using game theory and machine learning techniques. The study investigates the use of the Shapley value in predictive ischemic brain stroke analysis. Initially, preference algorithms identify the most important features in various machine learning models, including logistic regression, K-nearest neighbor, decision tree, support vector machine (linear kernel), support vector machine ( RBF kernel), neural networks, etc. For each sample, the top 3, 4, and 5 features are evaluated and selected to evaluate their performance. The Shapley value method was used to rank the models using their best four features based on their predictive capabilities. As a result, better-performing models were found. Afterward, ensemble machine learning methods were used to find the most accurate predictions using the top 5 models ranked by shapely value. The research demonstrates an impressive accuracy of 92.39%, surpassing other proposed models’ performance. This study highlights the utility of combining game theory and machine learning in Ischemic stroke prediction and the potential of ensemble learning methods to increase predictive accuracy in ischemic stroke analysis.

Stroke prediction remains a critical area of research in healthcare, aiming to enhance early intervention and patient care strategies. This study investigates the efficacy of machine learning techniques, particularly principal component analysis (PCA) and a stacking ensemble method, for predicting stroke occurrences based on demographic, clinical, and lifestyle factors. We systematically varied PCA components and implemented a stacking model comprising random forest, decision tree, and K-nearest neighbors (KNN).Our findings demonstrate that setting PCA components to 16 optimally enhanced predictive accuracy, achieving a remarkable 98.6% accuracy in stroke prediction. Evaluation metrics underscored the robustness of our approach in handling class imbalance and improving model performance, also comparative analyses against traditional machine learning algorithms such as SVM, logistic regression, and Naive Bayes highlighted the superiority of our proposed method.

The three-dimensional femtoscopic correlations of pion and kaons are presented for Pb–Pb collisions at s NN = 5.02 TeV within the framework of (3+1)D viscous hydrodynamics combined with THERMINATOR 2 code for statistical hadronization. The femtoscopic radii for pions and kaons are obtained as a function of pair transverse momentum and centrality in all three pair directions. The radii showed a decreasing trend with an increase of pair transverse momentum and transverse mass. for all centralities. These observations indicate the presence of strong collectivity. A simple effective scaling of radii with pair transverse mass in the model (without hadron cascade stage) was observed for both pion and kaons.

In this article, we demonstrate the solution methodology of start-up electrokinetic flow of non-Newtonian fluids in a microfluidic channel having square cross-section using Spreadsheet analysis tool. In order to incorporate the rheology of the non-Newtonian fluids, we take into consideration the Ostwald-de Waele power law model. By making a comprehensive discussion on the implementation details of the discretized form of the transport equations in Spreadsheet analysis tool, and establishing the analytical solution for a special case of the start-up flow, we compare the results both during initial transience as well as in case of steady-state scenario. Also, to substantiate the efficacy of the proposed spreadsheet analysis in addressing the detailed flow physics of rheological fluids, we verify the results for several cases with the corresponding numerical results. It is found that the solution obtained from the Spreadsheet analysis is in good agreement with the numerical results—a finding supporting spreadsheet analysis's suitability for capturing the fine details of microscale flows. We strongly believe that our analysis study will open up a new research scope in simulating microscale transport process of non-Newtonian fluids in the framework of cost-effective and non-time consuming manner.

The transverse momentum ( p T ) distribution of strange hadrons ( K S 0 and Λ ) and multi-strange hadrons( Ξ and Ω ) measured in p–p, p–Pb, and Pb–Pb collisions at LHC energies has been studied for different multiplicity classes using Tsallis–Weibull (or q-Weibull) formalism. The distribution describes the measured p T spectra for all multiplicity (or centrality) classes. The multiplicity dependence of the extracted parameters were studied for the mentioned collision systems. The λ parameter was observed to increase systematically with the collision multiplicity and follows a mass hierarchy for all collision systems. This characteristic feature indicates that λ can be associated to the strength of collectivity in heavy ion collisions. It can also be related to strength of dynamic effects such as multi-partonic interactions and color reconnections which mimic collectivity in smaller systems. The non-extensive q parameter is found to be greater than one for all the particles suggesting that the strange particles are emitted from a source which is not fully equilibrated.

This paper establishes the wavelet transformation induced multi-time scaling (WATMUS) method as an enabler for modeling fatigue crack nucleation at microstructural and structural scales of polycrystalline metals. The WATMUS method derives its efficiency from (i) transformation of time-scale integration into cycle-scale integration for marching forward in time, and (ii) adaptive cycle-stepping in the integration process. The integration of the WATMUS method with crystal plasticity finite element models for micromechanical modeling, and the parametrically homogenized constitutive models (PHCM)-based FE solvers for macroscopic modeling provides a unique spatiotemporal multiscale platform for simulating large number of cycles (~ 104–106) to fatigue nucleation. Time-scale acceleration is highly relevant when material microstructure plays a significant role, such as with dwell loading. The model is tested for cyclic and dwell loadings at multiple spatial scales of a Ti alloy Ti7AL, viz. the μm scale of the microstructure, the mm–cm scale of laboratory specimen, and structural scale of turbine blades. Numerical results demonstrate the ability of WATMUS-accelerated FE solvers in accurately solving fatigue problems across multiple scales of the material.

This technical note presents a discussion on the dynamic modeling and analysis of the adaptive controller of a Mechanical Prosthetic finger with constraints mass (m) of the mechanical finger and constant friction (K1) with variation in spring constraint (K2) parameter during grasping action. For the design of the adaptive controller and assessment of adaptation gain, an underdamped second-order control system has been considered and variation of adaptation gain (γ) within certain pre-defined limits of system parameters, variation in the adaptation mechanism has been analyzed using Gradient method MIT rule. To ensure global stability along with the convergence on nonconformity of plant parameters Lyapunov Rule has been utilized towards closed-loop asymptotic tracking.

In this paper, Diagnostic Electromyography (D-EMG) signal interpretation of human arm towards characterization of arm-muscle interaction during various arm movements has been discussed. EMG signals from four important arm muscle (i.e., Bicepsbracci, Tricepsbracci, brachioradialis, and lateral deltoids) are recorded clinically during five different arm movements (i.e., Extension of the forearm, flexion of elbow joint, pronation of forearm, shoulder abduction, and Wrist flexor stretch) under load condition (a load of 2 Kg & 4 Kg maintained during experimental arm movement), the recorded D-EMG signals are properly enveloped within a range of 5-100 Hz and quantized within a proper sampling frequency range to produce a knowledge-based database of muscle activity. In addition, correlation of muscle activity and Power spectral density (PSD) analysis has been carried out towards muscle process discriminating during various arm actions.