Synthesis and characterization of polypyrrole decorated graphene/β-cyclodextrin composite for low level electrochemical detection of mercury (II) in water

Mercury (Hg(II)) is considered as one of the most toxic element that directly affects the human health and the environment. Therefore, in this study, we propose a sensitive and disposable electrochemical sensor for the detection of Hg(II) in various water samples using polypyrrole (PPy) decorated graphene/-cyclodextrin (GR-CD) composite modified screen-printed carbon electrode (SPCE). The GRCD/PPy composite was synthesized by chemical oxidation of PPy monomer in GR-CD solution using FeCl3. Differential pulse voltammetry (DPV) is used for the detection of Hg(II) and the DPV results reveal that GR-CD/PPy composite modified SPCE has high sensitivity towards Hg(II) than bare, GR, GR-CD and PPy modified SPCEs. The optimization studies such as effect of pH, accumulating time and effect of scanning potential towards the detection of Hg(II) were investigated. The GR-CD/PPy composite modified SPCE could detect the Hg(II) up to 51.56 M L−1 with the limit of detection (LOD) of 0.47 nM L−1. The obtained LOD was well below the guideline level of Hg(II) set by the World’s Health Organization (WHO) and U.S. Environmental Protection Agency (EPA). In addition, the fabricated GR-CD/PPy composite modified SPCE selectively detected the Hg(II) in the presence of potentially interfering metal cations

Control of pests and diseases in plants using IOT Technology

The term ''smart agriculture'' describes how farming is carried out in the modern day as technology develops. Application of diverse insect protection and agricultural production tactics is crucial for crop monitoring. The structure as it is now has problems. A particular core Graphical Processing Unit (GPU) is used to manage the numerous sensors connected for crop surveillance and pest management. A Parallel and Distributed Simulation Framework (PDSF) with the Internet of Things (IoT) is proposed for pest management and agricultural monitoring tools. It lessens the pressure on a certain GPU, evenly distributes the workload over all available GPUs at simultaneously, and delivers data to the dashboards even when it's broken. The procedure will decrease system performance. In the PDSF multi-threading paradigm, each GPU unit distributes workloads to specific additional cores. To carry out the various tasks, the four levels of this system—crop management, pest identification and control, output activities, and input functional areas—are distributed among them. The information is processed simultaneously and handled in an efficient and controlled manner. The proposed system improves the performance measures of 98.65%

ADC–CF: Adaptive deep concatenation coder framework for visual question answering

Multimodal teaching activity faces significant problems in Visual Question Answering (VQA), which involves simultaneous comprehension with reduced performance fidelity. However, Conventional methods are employed for portrayal and queries in a defined manner, which fails to accomplish the required performance accuracy rate. For elucidating the excellent image and question representation, this paper suggests an Adaptive Deep Concatenated Coder Framework (ADC–CF) that enrolls both the image and question attributes simultaneously with the optimized residual layer. The Coder Framework comprises of cascaded layers of Encoder-Decoder architecture, which captures rich, meaningful query characteristics and image details through the use of keywords employing significant object areas in the picture. ADC–CF layer has an encoder segment that blueprints the self-recognition of queries in which questions are concatenated to limit the answers and decoder segment blueprints the commanded-recognition of images. The simulation results of ADC–CF are tested with both the VQA datasets 1.0 and 2.0 and manifests an improved performance accuracy ratio of 72.45% for 1.0 dataset and 73.57% for 2.0 datasets, thus proving the reliability of the proposed framework

Comparaison et evaluation des techniques de pseudo affinite sur support et sur membranes derivatisees

Available at INIST (FR), Document Supply Service, under shelf-number : AR 14833 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueSIGLEFRFranc