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Prem Chandra Pandey

Bio: Prem Chandra Pandey is an academic researcher from Shiv Nadar University. The author has contributed to research in topics: Cyclic voltammetry & Ormosil. The author has an hindex of 24, co-authored 81 publications receiving 1462 citations. Previous affiliations of Prem Chandra Pandey include Birla Institute of Technology, Mesra & Tel Aviv University.


Papers
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TL;DR: In this article , four machine learning and regression-based algorithms, namely, generalized linear model, maximum entropy, boosted regression tree, and random forest (RF) are used to model the geographical distribution of Rhododendron arboreum, which is economically and medicinally important species found in the fragile ecosystem of Himalayas.
Abstract: Abstract. Species distribution models (SDMs) have been used extensively in the field of landscape ecology and conservation biology since its origin in the late 1980s. But there is still a void for a universal modeling approach for SDMs. With recent advancements in satellite data and machine learning algorithms, the prediction of species occurrence is more accurate and realistic. Presently, four machine learning and regression-based algorithms, namely, generalized linear model, maximum entropy, boosted regression tree, and random forest (RF) are used to model the geographical distribution of Rhododendron arboreum, which is economically and medicinally important species found in the fragile ecosystem of Himalayas. To establish complex relation between the occurrence data and regional climatic and land use parameters, several satellite products, namely, MODIS, Sentinel-5p, GPM, ECOSTRESS, and shuttle radar topography mission (SRTM), are acquired and used as predictor variables to the different SDM algorithms. The performance evaluation has been conducted using the area under curve (AUC), which showed the best result for Maxent (AUC = 0.871) and poor result was observed for RF (AUC = 0.755) among all. The overall prediction confirmed the distribution of Rhododendron arboreum in the mid to high altitudes of central and southern parts of the Garhwal Division. We provide crucial evidence that combining multisatellite products using machine learning algorithms can provide a much better understanding of species distribution that can eventually help the researchers and policymakers to take the necessary step toward its conservation.

1 citations

DOI
TL;DR: In this paper , the authors utilized earth observation Sentinel5P TROPOMI (TROPOspheric Monitoring Instrument) data for monitoring submarine volcanic activities and found that SO2 column density increased after 20th December, and it was observed to be 0.002 mol/m2, the highest concentration during the period of study.
Abstract: ABSTRACT The present study utilized earth observation Sentinel5P TROPOMI (TROPOspheric Monitoring Instrument) data for monitoring submarine volcanic activities. The world has seen a sequence of abrupt volcanic eruptions within one-month duration i.e., on 20th December 2021, 13th January 2022 and 15th January 2022. This caused the emissions of natural gaseous pollutants such as Sulphur Dioxide (SO2), Hydrogen Sulphide (H2S) and ash particles. Atmospheric SO2, AAI (Absorbing Aerosol Index) and Cloud fraction data are retrieved from the TROPOMI onboard the Copernicus Sentinel5P European Space Agency satellite. Results confirmed the increased concentration of SO2 column density after 20th December, and it was observed to be 0.002 mol/m2, the highest concentration during the period of study. Generally, SO2 concentration stays within 0.001 mol/m2 or less. Concentration was seen increasing after the first eruption and reached maximum after 15th January 2022 eruption to a level up to 0.002 mol/m2. Similar outcomes were observed for AAI for the chosen site. AAI values increased to 25,000 fold (AAI values of 1.25) during the first eruption and increased to 29,000 fold on 15th January 2022 after eruptions (AAI values of 1.45) as compared AAI values of 0.005 or negative AAI values before eruptions. A cloud fraction of value 1 is observed during this time, along with the top most cloud height at 14,233.16 m (14.23 kms) above the crater of the volcano (maximum estimation limit by S5P). These values are higher as compared to isolated islands, as there are no other sources of emissions at surrounding environment. The outcomes support the role of Earth Observation datasets for disaster monitoring and management plans for mitigation. HIGHLIGHTS Demonstrating the capabilities of Sentinel5P TROPOMI for SO2 column density, AAI and Cloud fraction monitoring for submarine volcanic eruption. Unique landform variation- appearance and submergence for the submarine volcano due to eruptions. Formation and erosion of landform of the active submarine volcano during 2009 -2022.

Cited by
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Journal ArticleDOI
TL;DR: The advent of AuNP as a sensory element provided a broad spectrum of innovative approaches for the detection of metal ions, small molecules, proteins, nucleic acids, malignant cells, etc. in a rapid and efficient manner.
Abstract: Detection of chemical and biological agents plays a fundamental role in biomedical, forensic and environmental sciences1–4 as well as in anti bioterrorism applications.5–7 The development of highly sensitive, cost effective, miniature sensors is therefore in high demand which requires advanced technology coupled with fundamental knowledge in chemistry, biology and material sciences.8–13 In general, sensors feature two functional components: a recognition element to provide selective/specific binding with the target analytes and a transducer component for signaling the binding event. An efficient sensor relies heavily on these two essential components for the recognition process in terms of response time, signal to noise (S/N) ratio, selectivity and limits of detection (LOD).14,15 Therefore, designing sensors with higher efficacy depends on the development of novel materials to improve both the recognition and transduction processes. Nanomaterials feature unique physicochemical properties that can be of great utility in creating new recognition and transduction processes for chemical and biological sensors15–27 as well as improving the S/N ratio by miniaturization of the sensor elements.28 Gold nanoparticles (AuNPs) possess distinct physical and chemical attributes that make them excellent scaffolds for the fabrication of novel chemical and biological sensors (Figure 1).29–36 First, AuNPs can be synthesized in a straightforward manner and can be made highly stable. Second, they possess unique optoelectronic properties. Third, they provide high surface-to-volume ratio with excellent biocompatibility using appropriate ligands.30 Fourth, these properties of AuNPs can be readily tuned varying their size, shape and the surrounding chemical environment. For example, the binding event between recognition element and the analyte can alter physicochemical properties of transducer AuNPs, such as plasmon resonance absorption, conductivity, redox behavior, etc. that in turn can generate a detectable response signal. Finally, AuNPs offer a suitable platform for multi-functionalization with a wide range of organic or biological ligands for the selective binding and detection of small molecules and biological targets.30–32,36 Each of these attributes of AuNPs has allowed researchers to develop novel sensing strategies with improved sensitivity, stability and selectivity. In the last decade of research, the advent of AuNP as a sensory element provided us a broad spectrum of innovative approaches for the detection of metal ions, small molecules, proteins, nucleic acids, malignant cells, etc. in a rapid and efficient manner.37 Figure 1 Physical properties of AuNPs and schematic illustration of an AuNP-based detection system. In this current review, we have highlighted the several synthetic routes and properties of AuNPs that make them excellent probes for different sensing strategies. Furthermore, we will discuss various sensing strategies and major advances in the last two decades of research utilizing AuNPs in the detection of variety of target analytes including metal ions, organic molecules, proteins, nucleic acids, and microorganisms.

3,879 citations

01 Jan 2016
TL;DR: The remote sensing and image interpretation is universally compatible with any devices to read and is available in the digital library an online access to it is set as public so you can get it instantly.
Abstract: Thank you very much for downloading remote sensing and image interpretation. As you may know, people have look hundreds times for their favorite novels like this remote sensing and image interpretation, but end up in malicious downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they are facing with some malicious virus inside their computer. remote sensing and image interpretation is available in our digital library an online access to it is set as public so you can get it instantly. Our book servers spans in multiple countries, allowing you to get the most less latency time to download any of our books like this one. Merely said, the remote sensing and image interpretation is universally compatible with any devices to read.

1,802 citations

Book
02 Jan 1991

1,377 citations

Journal ArticleDOI
TL;DR: The role of polymers as gas sensors, pH sensors, ion-selective sensors, humidity sensors, biosensor devices, etc., are reviewed and discussed in this article, and current trends in sensor research and also challenges in future sensor research are discussed.

1,126 citations

Journal ArticleDOI
TL;DR: The review covers main applications of conducting polymers in chemical sensors and biosensors, such as pH sensitivity, sensitivity to inorganic ions and organic molecules as well as sensitivity to gases, and induced receptor properties.

819 citations