ZnO has several potential applications into its credit. This review article focuses on the influence of processing parameters involved during the synthesis of ZnO nanoparticles by sol-gel method. During the sol-gel synthesis technique, the processing parameters/experimental conditions can affect the properties of the synthesized material. Processing parameters are the operating conditions that are to be kept under consideration during the synthesis process of nanoparticles so that various properties exhibited by the resulting nanoparticles can be tailored according to the desired applications. Effect of parameters like pH of the sol, additives used (like capping agent, surfactant), the effect of annealing temperature and calcination on the morphology and the optical properties of ZnO nanoparticles prepared via sol-gel technique is analyzed in this study. In this study, we tried to brief the experimental investigations done by various researchers to analyze the influence of processing parameters on ZnO nanoparticles. This study will provide a platform to understand and establish a correlation between the experimental conditions and properties of ZnO nanoparticles prepared through sol-gel route which will be helpful in meeting the desired needs in various application areas.

A decade ago, non-radiative wireless power transmission re-emerged as a promising alternative to deliver electrical power to devices where a physical wiring proved impracticable. However, conventional “coupling-based” approaches face performance issues when multiple devices are involved, as they are restricted by factors like coupling and external environments. Zenneck waves are excited at interfaces, like surface plasmons and have the potential to deliver electrical power to devices placed on a conducting surface. Here, we demonstrate, efficient and long range delivery of electrical power by exciting non-radiative waves over metal surfaces to multiple loads. Our modeling and simulation using Maxwell’s equation with proper boundary conditions shows Zenneck type behavior for the excited waves and are in excellent agreement with experimental results. In conclusion, we physically realize a radically different class of power transfer system, based on a wave, whose existence has been fiercely debated for over a century.

One of the key challenges in the practical realization of industrial internet of things (IoT) is overcoming Faraday shielding of free space electromagnetic waves emanating from the antennas of wireless systems used for power and data transfer. Metallic structures, machinery, pipeline, etc., cause interference resulting in the loss of signal connectivity among sensor network. Currently available techniques based on ultrasonic-electromagnetic transducers pose severe limitations on frequency, efficiency, and alignment. Here we demonstrate exciting Zenneck type interface waves propagating as localized charge oscillations (modes) along the metal profile, which overcomes the restrictions on frequency, metal obstacles, partial enclosures, and alignment. A finite element methods (FEM) model developed to predict the signal reception and power transfer efficiency across metal infrastructure shows excellent agreement with the experimental results. Electrical power transfer using Zenneck, under open conditions, show 4% drop for distance of 1 to 8 meters (68 to 64%), and 9% drop under shielded conditions for the same range (66 to 57%). For data transmission results, we demonstrate a feasibility, for an input power of 0dBm with several metallic pipelines as obstacles show received power of -11.8 dBm and -19.01 dBm at 6 and 25 meters, respectively.

In this paper we explored the underlying physics of sheet like waveguides to generate surface waves at metal-air interfaces to carry power and data transmission. The presented system is capable of transmitting 40W power along 1.2 m and 16 W along 4 m and across 80 mm metal wall. The system was able to achieve 2.13-3.46 Mbps data throughput using WIFI access-points across 2 metal floors separated by 25m,on an LNG carrier ship. Internet of things device charging application has also been presented.

Power grids today operate under unpredictable and rapidly changing conditions, making reliable stability prediction increasingly important. This study evaluates two hybrid learning frameworks that integrate deep feature transformation with ensemble classification. In the first framework, an autoencoder (AE) is used for feature encoding before classification with extreme gradient boosting (XGBoost), while the second applies a TabTransformer (TT) followed by the same classifier. For comparison, conventional ensemble models, including random forest and standalone LightGBM, are also assessed. The models are tested on a large public dataset using stratified cross-validation and standard performance metrics. Results show that the AE-XGBoost hybrid achieves the highest performance, with a test accuracy of 97.73% and an F1-score of 0.98 for both stable and unstable states. LightGBM also performs strongly, offering consistent accuracy (95.8%) and good interpretability. In contrast, TT-XGBoost, despite its architectural novelty, achieves lower accuracy (89.4%) and struggles with unstable states. These findings highlight that model effectiveness depends not only on architectural complexity but also on the synergy between feature transformation and classification. The results provide practical insights for building dependable, confidence-aware predictive systems to support smart grid decision-making.

We present a comparative analysis of traditional machine learning (ML) models, random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGB), and deep learning (DL) architectures, convolutional neural networks (CNN), and bidirectional long short-term memory (BiLSTM) for high-precision 3D positional forecasting. Conventional approaches often underperform when modeling complex spatiotemporal dependencies, limiting their use in dynamic systems such as robotics and autonomous vehicles. This study highlights BiLSTM's advantage in learning bidirectional temporal features, achieving superior R² scores and stable prediction intervals compared to both classical ML and spatially-focused CNN models. Uncertainty metrics, prediction interval coverage probability (PICP), and mean prediction interval width (MPIW) provide additional insight into model reliability. Experiments on a 22-hour GPS dataset confirm that BiLSTM achieves both high accuracy and predictive confidence, underscoring its suitability for real-world trajectory forecasting.