Vardhaman College of Engineering

Hastelloy C276 is renowned for its exceptional resistance to corrosion and elevated temperatures, rendering it a preferred material for aerospace and chemical processing applications. However, its high strength and work-hardening tendency pose significant challenges during machining. This study systematically investigates the machinability of Hastelloy C276 under four sustainable lubrication and cooling environments—dry machining, minimum quantity lubrication (MQL), nano-enhanced MQL (NMQL), and cryogenic CO₂ (CCO₂). Experiments were designed using a Taguchi L16 orthogonal array, and the influence of cutting speed and feed rate on surface roughness, cutting force, tool wear, and cutting temperature was analysed using ANOVA. Compared to dry machining, cryogenic CO₂ cooling resulted in a reduction of surface roughness and cutting force by approximately 30–40%, along with a substantial decrease in tool wear and cutting temperature, whereas NMQL demonstrated moderate improvements due to enhanced lubrication at the tool–chip interface. ANOVA results revealed that feed rate and cutting speed were the most significant parameters, contributing up to 38.35% and 48.56% to variations in surface roughness and cutting temperature, respectively. To identify optimal machining conditions, Particle Swarm Optimization (PSO) and Teaching–Learning-Based Optimization (TLBO) algorithms were employed. Over 100 iterations, PSO achieved a higher optimization success rate of 83.6% compared to 79.1% for TLBO, while TLBO exhibited faster convergence with a computation time of 6.5 s against 9 s for PSO. The findings demonstrate that cryogenic CO₂-assisted machining combined with evolutionary optimization provides an effective and sustainable strategy for enhancing the machinability of Hastelloy C276.

Abstract Accurate rainfall forecasting is necessary for agriculture, water management and, disaster and drought preparedness particularly in semi-arid regions like Ahmedabad, Gujarat, India. The forecasts are considered reliable not only if it just captures the overall trends and seasonal patterns but also sudden rainfall spikes and irregular variations. This study proposes a hybrid forecasting model that combines Seasonal AutoRegressive Integrated Moving Average (SARIMAX) with the rolling mean and Gradient Boosting Machine (GBM) with moving average to overcome the limitations of traditional models in learning nonlinear variations in the rainfall data. The data utilized in this research is the monthly resampled data of Ahmedabad, ranging from 1969 to 2021, obtained by the IMD. A hybrid approach is used, where the SARIMAX component models long-term trend and seasonality, and the GBM component learns from its residuals to model complex behaviour. This hybrid approach helps in improving accuracy of the forecast as well as learn the nonlinearity better, which poses as a limitation in the classical models. Along with the forecasting, a comparative analysis has also been carried out to highlight the better results obtained for the hybrid model in comparison to the other base models. The model's dual nature ensures that it learns the rainfall peaks well enough, while the classification metrices helps to know the accuracy of the model and visualizations helps support the overall forecast validity of the model. Overall, the proposed method helps provide an effective solution for forecasting rainfall in the current rapidly changing climate.

Rapid urbanization and the rise of vehicles have caused major problems such as congestion, air pollution, and high energy consumption. In order to build sustainable Intelligent Transportation Systems (ITS), traffic management has become extremely important. Due to the large amount of data transmitted to central servers, traditional centralized systems suffer from communication delays, data privacy risks, and high energy consumption. In order to address these issues, this paper introduces a framework for Energy-Aware Federated Learning (EAFL). Edge devices work together to train multiple models without sending raw data anywhere. This keeps the data private and keeps the traffic on the network low. EAFL relies on the Hybrid Grey Wolf Optimizer - Firefly Algorithm. The method blends the wide search of the Grey Wolf Optimizer with the fine search of the Firefly Algorithm - the training step becomes faster plus more precise. The algorithm tunes the model parameters - every device spends less time and less power on the task. Simulations show that the HGWO-FA version of EAFL outperforms standard federated learning but also any single alternative approach. It cuts model loss, power draw and delay, which raises prediction accuracy. The framework offers a practical as well as low-impact way to predict and manage traffic in real time inside a smart city.

Abstract This present work focuses on the synthesis of scrambled SmNiO 3 nanopins by the hydrothermal route, structural and morphological characterizations, and observing their potential electric field emission response with density functional theory (DFT) study. The Rietveld refinement of X-ray diffraction (XRD) and Raman characterization study reveals an orthorhombic phase (space group Pnma , No. 62) of SmNiO 3 , along with Raman-active NiO 6 octahedral vibrational modes and Sm-O lattice motion. The high-resolution scanning and tunneling electron microscopy images exhibit monodispersed hierarchically self-assembled pin-like surface morphology with a crystal lattice spacing of 3.16 Å for 110 planes, which offers exceptional field emission response. The chemical states of Sm, Ni, and O were interpreted by X-ray photoelectron (XPS) spectroscopy in + 3 oxidation states (4f 5 configuration), mixed Ni³⁺/Ni²⁺ valence states, and − 2 lattice and defect states, respectively. The current density (J) vs. electric field (E) plots demonstrate a maximum field emission current density ~ 30 µA/cm 2 @ 8.4 V/µm, and very enduring field emission current stability was observed over a long period of time, 3 h 20 min, with bright field emission fringes. Very low turn-on (E turn−on ) and threshold (E threshold ) were discerned ~ 5.1 V/µm @1 µA/cm 2 and ~ 7.64 V/µm @10 µA/cm 2 from J-E curves. The local work function (φ) was calculated to be ~ 5.13 eV by density functional theory (DFT) calculations, and the field enhancement factor (β) was measured to be ~ 3343. The noteworthy electric field emission response, low turn-on field, and long-term stable field emission enable SmNiO 3 nanopins to be a high-efficiency electron source for field-emitting display (FED), X-ray source, vacuum transistor device, etc.

This research presents an AI and NLP-powered system that translates oral and written text into Indian Sign Language (ISL) gestures in real time. The goal is to bridge the gap of communication between the hearing-impaired community and those who are not sign language users. Voice input is processed through speech-to-text conversion and is translated into ISL.Deep learning models (CNN, R-CNN) identify and classify gestures. To support more efficient real-time communication, IoT devices are interfaced, enhancing gesture detection rate and efficiency. Predictive gesture creation is also implemented in the system, and the responses are natural and accurate. By leveraging contextual knowledge via NLP, translations are accurate and conversation is effective. The user-friendliness of the system enables smooth conversion between speech and text and gestures in ISL. This study encourages diversity with more facile interactions in diverse everyday environments. Future developments in AI, NLP, and computer vision will continue to empower further languages, making communication barriers even easier and more accessible.

Sentiment Analysis represents a fundamental component of Natural Language Processing (NLP), which detects emotional content in digital text, offering positive, negative, and neutral categories. The system enables businesses to analyze customer opinions and detect brand reputation through market trend assessment, which supports improved decision-making processes. Such applications utilize sentiment analysis across ecommerce platforms, customer services, social media websites, and financial sectors to gain important customer feedback. Our paper presents a decision-level, precedence-rule hybrid that combines T5 with RoBERTa. Sentiment analysis from textual data using RoBERTa relies on transformer technologies that were specifically optimized for sequence classification. Through its text-to-text operation, T5 produces human-like sentiment classifications that appear as prompts within its responses. The models enable precise analysis of large datasets through their improved accuracy, scalability, and contextual understanding features. On the Twitter data, the hybrid consistently outperforms standalone models, improving neutrality resolution and achieving accuracy of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{9 6. 0 1 \%}$</tex>, precision of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{9 6. 1 3 \%}$</tex>, recall of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{9 6. 0 0 \%}$</tex> and F1-score of 96.05%.

A Deep learning model that turns satellite images into road maps with the help of a Conditional Generative Adversarial Network (cGAN). A U-Net generator is applied in the architecture to draw details from the road and map, and a multi-scale PatchGAN discriminator reviews the results at several resolutions to enhance both the fineness and accuracy. Adversarial loss works on realism, L1 loss helps with pixel by pixel, and SSIM loss protects the structural elements. The model is designed using the Massachusetts Roads Dataset and it has attained 95% pixel accuracy, along with impressive accuracy and structure similarity. The proposed model provides road masks that are clearer and more reliable, regardless of environmental complexity or lighting conditions. Using this strategy, it is possible to skip manual mapping and support applications in urban planning, navigation, and responding to disasters. In the future, the optimal solution might involve trimming the model's size to make it work more quickly.

Two significant worldwide health issues that call for the creation of new therapeutic medicines are oxidative stress and antibiotic resistance. Quinazolinone-1,2,4-triazole derivatives, which have a variety of pharmacological characteristics, may be able to help with these problems. As possible antibacterial and anticancer targets, this study sought to synthesized novel quinazoline-4(3H)-one derivatives containing an NH group at position 3 with 1,2,4-triazoles (7a–7k). The chemical structures of all compounds were characterized by NMR (1H/13C), IR and mass spectroscopy. Notably, derivatives 7d and 7e exhibited the greatest MIC values against S. epidermidis, while 7f was the best against S. aureus with MIC of 3.5 μg mL−1, two-fold efficacy more than that was recorded with moxifloxacin. All of the synthesized compounds showed promising anti-proliferative activity, with one compounds 7e having potent cytotoxic activity against MDA-MB-231 cell line (IC50 = 11.80 ± 1.2 μM) and active against MCF-7 (IC50 = 11.80 ± 1.2 μM respectively) in comparison to the reference DXN (IC50 = 9.48 ± 0.6 and 7.12 ± 2.0 μM, respectively). Molecular docking was performed using the protein structure of E. coli Topoisomerase IV, with the interacting effectively with key residues IleA:116, SerA:117, ArgA:93, GluA:46, ThrA:163, and GlyA:71, consistent with their antimicrobial activity. The chemical nature of these compounds was revealed by performing the density functional theory (DFT) calculation using hybrid B3LYP functional with 6-31 g(d) basis set. Additionally, these compounds exhibited promising physicochemical properties, paving the way for discovering new antimicrobial drugs.

With the increasing number of electrical equipment in households, power consumption is increasing. Low. efficiency use of power and ignorance of end-user appliance consumption pattern are the major reasons. To overcome this problem, here we conceive of a real time power consumption monitoring device that will improve the energy efficiency. Data Acquisition and Communication: The system sends acquired data to a web-based application using an ESP32 microcontroller via Wi-Fi, and. non-invasive voltage and current sensors. It computes instantaneous power and energy consumption (E) to offer real-time insight of energy usage so you can monitor and manage this usage more effectively. Initial tests show that power readings are accurate, proving the system’s ability. Future work will include integrating the device with smart home platforms and using machine learning