Pondicherry Engineering College

Recently, LaNbO4-based proton-conducting materials have emerged as promising alternatives to conventional electrolytes, particularly due to their lower sintering temperatures, making them suitable for hydrogen and humidity sensing applications at temperatures below 700 °C. However, LaNbO4 undergoes a structural phase transition from a monoclinic fergusonite to a tetragonal scheelite-type structure at elevated temperatures, which hinders its performance. Controlling this phase transition is, therefore, a critical to enhance proton conduction. The synthesis method plays a pivotal role in stabilizing the phases and optimizing the microstructure of ceramic materials, thereby improving their transport properties. This study demonstrates a novel synthesis of pristine and calcium-doped LaNbO4 nanocrystals using the microwave hydrothermal (MH) method. X-ray diffraction (XRD) analysis confirms the formation of single-phase monoclinic LaNbO4 at a significantly lower calcination temperature (800 °C for 3 h) than conventional methods ( 1000 °C). Calcium doping enhances phase stability and proton conductivity by introducing oxygen vacancies and reducing grain boundary resistance. Impedance analysis further reveals that La0.99Ca0.01NbO4 a proton conductivity of 5.23 × 10‒4 S·cm‒1 at 700 °C, markedly higher than pristine LaNbO4 (9.5 × 10‒5 S·cm‒1). These findings position La0.99Ca0.01NbO4 as a highly promising candidate for hydrogen energy applications.

This survey explores the advancements in agricultural precision through deep learning-driven the research employs satellite imaging analysis which depends on multiple time-based products along with spectral assessment techniques. The paper provides a thorough examination of the many deep learning models used in agricultural monitoring, including crop classification, yield prediction, and disease detection. It also discusses the integration of multi-temporal and multi-spectral satellite imagery help increase these models' accuracy. Important difficulties, like data scarcity, sensor limitations, and the need for high-quality annotated datasets, are addressed. The survey presents contemporary progress in methods for performing chemical fusion between two compounds multiple models and data sources to enhance predictive capabilities. The usefulness of these techniques in actual agricultural environments is also examined.

PurposeNowadays people are connected by social media like Facebook, Instagram, Twitter, YouTube and much more. Bullies take advantage of these social networks to share their comments. Cyberbullying is one typical kind of harassment by making aggressive comments, abuses to hurt the netizens. Social media is one of the areas where bullying happens extensively. Hence, it is necessary to develop an efficient and autonomous cyberbullying detection technique.Design/methodology/approachIn this paper, the authors proposed a transformer network-based word embeddings approach for cyberbullying detection. RoBERTa is used to generate word embeddings and Light Gradient Boosting Machine is used as a classifier.FindingsThe proposed approach outperforms machine learning algorithms such as logistic regression, support vector machine and deep learning models such as word-level convolutional neural networks (word CNN) and character convolutional neural networks with short cuts (char CNNS) in terms of precision, recall, F1-score.Originality/valueOne of the limitations of traditional word embeddings methods is context-independent. In this work, only text data are utilized to identify cyberbullying. This work can be extended to predict cyberbullying activities in multimedia environment like image, audio and video.

The present study advances object detection and tracking techniques by proposing a novel model combining Automated Image Annotation with Inception v2-based Faster RCNN (AIA-IFRCNN). The research methodology utilizes the DCF-CSRT model for image annotation, Faster RCNN for object detection, and the inception v2 model for feature extraction, followed by a softmax layer for image classification. The proposed AIA-IFRCNN model is evaluated on three benchmark datasets: Bird (Dataset 1), UCSDped2 (Dataset 2), and Under Water (Dataset 3), to determine prediction accuracy, annotation time, Center Location Error (CLE), and Overlap Rate (OR). The experimental results indicate that the AIA-IFRCNN model outperformed existing models regarding detection accuracy and tracking performance. Notably, it achieved a maximum detection accuracy of 95.62 % on Dataset 1, outperforming other models. Additionally, it achieved minimum average CLE values of 4.16, 5.78, and 3.54, and higher overlap rates of 0.92, 0.90, and 0.94 on the respective datasets (1, 2 and 3). Hence, this research work on object detection and tracking using the AIA-IFRCNN model is essential for improving system efficiency and fostering innovation in the field of computer vision and object tracking.

Natural fibre-reinforced polymer composites have been most widely used in automotive, aerospace, and structural products in recent years. The synthesis and analysis of mechanical properties of bamboo fibre with polyester matrix nanocomposites produced with titanium oxide nanoparticles are the focus of this research. The electroless plating process was used to prepare bamboo fibre samples that were both uncoated and coated with nickel-phosphorus. These properties were studied both without coating and with coated bamboo fibre content of 4.5, 9, 13.5, and 18 wt. % and nanoparticle weight content of 0.5, 1, 1.5, and 2%. The x-ray diffractometer, field emission scanning electron microscope, and transmission electron microscope were used to investigate the structural and morphological characteristics of polyester matrix composites. Coated bamboo fibre with 13.5 wt.% and 1.5 wt.% titanium oxide. As a result, it can be concluded that the mechanical properties of a polyester matrix-reinforced coated bamboo fibre with nanoparticles have excellent bonding when compared to those of a polyester matrix-reinforced nanocomposite without coated bamboo fibre.