Unmanned aerial vehicles (UAVs) have gained significant attention in recent times due to their suitability for a wide variety of civil, military, and societal missions. Development of an unmanned amphibious vehicle integrating the features of a multi-rotor UAV and a hovercraft is the focus of the present study. Components and subsystems of the amphibious vehicle are developed with due consideration for aerodynamic, structural, and environmental aspects. Finite element analysis (FEA) on static thrust conditions and skirt pressure are performed to evaluate the strength of the structure. For diverse wind conditions and angles of attack (AOA), computational fluid dynamic (CFD) analysis is carried out to assess the effect of drag and suitable design modification is suggested. A prototype is built with a 7 kg payload capacity and successfully tested for stable operations in flight and water-borne modes. Internet of things (IoT) based water quality measurement is performed in a typical lake and water quality is measured using pH, dissolved oxygen (DO), turbidity, and electrical conductivity (EC) sensors. The developed vehicle is expected to meet functional requirements of disaster missions catering to the water quality monitoring of large water bodies.

In this work, we have employed an intramolecular charge transfer-based DMN colorimetric probe for the rapid naked-eye detection of cyanide ions in solution as well as real water samples. The intermolecular interaction between the DMN probe and cyanide ions in solution was investigated using a combination of spectroscopic and computational methods in this study. The DMN probe exhibited a selective colorimetric response for cyanide ions over the other anions exposed. The cyanide sensing mechanism of the probe has been investigated by ¹H NMR titration and density functional theory calculations. The results reveal that the colorimetric response of the DMN probe is due to the Michael adduct formation in the β-conjugated position of the dicyanovinyl group with cyanide, which blocks intramolecular charge transfer transition. Under optimized experimental conditions, the DMN probe showed a linear plot in the concentration range of 0.01–0.25 μM, with a detection limit of 23 nM. Further, a 3D printed portable accessory for the smartphone and an open-source android application is developed to suit the DMN probe for on-site work. In addition, we have developed the microfluidic paper-based analytical device that could selectively detect cyanide ions at very low concentration using a colorimetric DMN probe. In addition, the DMN probe was effectively used to determine the cyanide ion in a variety of water samples.

The multihop underwater acoustic sensor network (M-UASN) collects oceanographic data at different depths. Due to the harsh underwater environment, the route is a major research problem. In this article, the routing path from source to sink is adapted by the vector-based forwarding (VBF) protocol. In VBF, based on the vector size, the packets are transmitted within the pipe from hop to hop. The limitation is that every node inside the pipe vector receives the same packets. That results in a waste of battery energy and, in turn, reduces the lifetime of the acoustic node. To enhance, in this article, it is divided into two parts. The first part is that the first hop nodes from the source are optimally divided into subsets such that all the second hop nodes will receive packets from each subset. This optimal route cover subset is identified with an evolutionary memetic algorithm. The election of subset is done through a voltage reference model, and the battery voltage is modeled mathematically and the role of the nodes is given based on the voltage profile and Markov probability approach. This method enhances the lifetime of the underwater acoustic network when compared with the VBF algorithm. The proposed model also provides improved throughput and equal load sharing. The results are compared with VBF, quality-of-service aware evolutionary routing protocol (QERP), and multiobjective optimized opportunistic routing (BMOOR).

The global shrimp aquaculture industry is highly vulnerable to water quality fluctuations, disease outbreaks and the inefficiencies of manual monitoring. To address these challenges, this paper presents a unified Artificial Intelligence (AI) and Internet of Things (IoT) enabled framework that integrates real-time sensing, predictive analytics and automated pH sensor calibration for precision aquaculture management. A custom-designed aqua buoy system continuously measures the critical parameters including pH, Dissolved Oxygen (DO) and Oxidation Reduction Potential (ORP) using a distributed IoT sensor network with cloud connectivity. A responsive web dashboard and mobile application deliver real-time visualization, farmer advisories and automated alert notifications, facilitating timely interventions. Machine learning models are employed in the proposed model for disease classification and intelligent decision-making: Random Forest achieves 98.35% accuracy in disease prediction, while Random Forest regression provides highly reliable pH voltage prediction by minimizing manual calibration requirements. Furthermore, Long Short-Term Memory (LSTM) networks are utilized for time-series forecasting, achieving up to 94% accuracy in predicting pH, DO and ORP trends, thereby enabling early corrective actions. The proposed method enhances monitoring efficiency, reduces shrimp mortality risk and supports environmentally sustainable farming practices. This integrated approach demonstrates the potential of combining IoT, disease classification & prediction and automated calibration techniques to transform aquaculture into a data-driven, precision-managed and resource-efficient industry.