Vellore Institute of Technology (VIT) is a private university located in Vellore, Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, by G. Viswanathan, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.
The flexural behavior of reinforced standard strength concrete (RSSC) beams made with ceramic waste materials are investigated in this study. Experiment was conducted on different types of beams made from, Control Standard Strength Concrete (C-SSC), Ceramic Binder Standard Strength Concrete (CB-SSC), Ceramic Filler Standard Strength Concrete (CF-SSC), and Ceramic Binder-Filler Standard Strength Concrete (CBF-SSC). The moment, deflection, ductility, stress, strain and fracturing parameters were measured on the tested beams. From the compressive strength test results of companion SSC specimens, it was found that 50 MPa compressive strength can be achieved by the mix design. At ultimate stage, the capacity ratio of RSSC beams made from CBF, CB and CF were 16.27
The Switched Reluctance Motor (SRM) has become increasingly favoured in the automotive industry owing to its absence of magnetic components in its design. This article proposes the structural modification of the SRM by integrating windows into the rotor core to diminish radial force and thereby mitigate vibration. The dimensions and placement of holes significantly affect flux linkage and torque generation. Consequently, a general criterion is formulated for the positioning of the window to avert flux saturation in the core. The hole dimensions are optimized using FEA analysis, based on the formulated criteria. This work focused on the design and manufacture of a 1.25 kW, 4-phase, 8/6 geometry-modified radial flux SRM for electric rickshaw applications. The motor specifications are determined from the tractive force. Noise, Vibration, and Harshness (NVH) analysis is conducted to determine the natural frequencies for various modes, ensuring that these frequencies do not align with the excitation frequency. Fabricated the conventional and geometry-modified SRM for the comparison of performance parameters. A real-time SRM drive system is developed to assess vibration. The overall vibration of the proposed model is reduced by 3.12 dB in comparison to the conventional SRM.
Data driven modelling can support manufacturing analysis when experiments are time and resourceintensive, but its reliability under severe data constraints requires careful assessment. This study presents an integrated framework combining machine-learning prediction, interpretability, classical Taguchi–ANOVA analysis, and surrogate assisted optimization for friction welding of tube to plate assemblies using an external tool (FWTPET). Nine experimental observations from an L9 orthogonal array design were analyzed using five regression models and leave one out cross validation (LOOCV), with rotational speed, pin diameter, and interference as inputs and with-hole (WH) and without-hole (WoH) joint strengths as separate responses. Second order polynomial regression achieved the best WH results, with R2 = 0.8983, a root mean square error (RMSE) of 3.6550 MPa, and a mean absolute error (MAE) of 2.8734 MPa. Linear regression performed best for WoH, yielding R2 = 0.7337, RMSE of 6.0642 MPa, and MAE of 4.6814 MPa. Permutation importance, SHapley Additive exPlanations (SHAP), and partial dependence and individual conditional expectation (PDP–ICE) analyses identified rotational speed as the leading parameter, consistent with Taguchi–ANOVA; however, the secondary parameter rankings differed, with a Spearman rank correlation of 0.50 between the approaches for both responses. Genetic algorithm and particle swarm optimization identified a candidate corresponding to an experimentally observed parameter combination. The principal contribution is a systematic comparison of interpretable machine learning and classical experimental design analysis within a small sample FWTPET framework. Given the nine observation dataset and the absence of independent validation, the findings remain exploratory and do not establish generalizability to untested conditions.
Lipid droplets (LDs) are vital organelles responsible for neutral lipid storage and play critical roles in cellular physiology. Interestingly, LDs are markedly more abundant in cancer cells than in normal cells, supporting rapid proliferation. To develop efficient viscosity-responsive fluorescent probes for LD detection, we designed a series of viscosity-sensitive probes (RT1-RT6) through a Knoevenagel condensation approach that couples rofecoxib with thiophene aldehyde derivatives. The conjugated vinyl linkage extends the It-system, enhances intramolecular charge transfer (ICT), and enables viscosity-dependent fluorescence through a twisted intramolecular charge transfer (TICT) mechanism. Structural characterization was performed using NMR, HRMS, and SCXRD techniques. Photophysical studies indicated strong emission, significant Stokes shifts, and pH stability with minimal aggregation, highlighting RT6's exceptional sensitivity in detecting viscosities as low as 1.68 cP in methanol-glycerol mixtures. Cytotoxicity assays confirmed biocompatibility, facilitating successful applications in live-cell imaging and food spoilage monitoring. Furthermore, the solid-state emission properties of these probes enabled high-resolution latent fingerprint visualization across diverse substrates, revealing ridge details at all three levels of fingerprint analysis. Density functional theory (DFT) calculations corroborated the electronic properties. Collectively, RT6 emerges as a multifunctional probe with broad utility across biological, environmental, and forensic domains.
Reliable carbon price forecasts support market monitoring, policy assessment, and operational planning in smart energy systems. Yet carbon prices are volatile, non-stationary, and driven by interacting regulatory, energy, commodity, and financial conditions. This study proposes an integrated Decomposition-Representation-Integration (DRI) framework that combines complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), a lightweight decoder-only causal Transformer, and XGBoost regression. CEEMDAN separates multi-scale dynamics; causal masked self-attention jointly reconstructs cross-scale dependencies without access to future observations; and XGBoost integrates the learned representation with exogenous drivers. The framework is evaluated using a strictly causal five-fold walk-forward protocol in which preprocessing, decomposition, validation, tuning, and model fitting are confined to each training window. Its CEEMDAN-CausalTransformer-XGBoost implementation achieves a minimum MAE of 0.6583, RMSE of 0.8071, and out-of-sample R2 of 0.7470. It consistently outperforms decomposition-only and standalone benchmarks across five tuning strategies, while Diebold-Mariano tests provide statistical evidence of improved predictive accuracy over key baselines. Prediction-path analysis suggests that gains are more visible during high-volatility periods. These results indicate that reconstructing cross-scale information improves carbon-market monitoring and provides decision support for smart energy system operators, market participants, and policymakers.
