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With recent advances in polarimetry, Synthetic Aperture Radar (SAR) with Hybrid–polarity architecture, a demonstration of compact polarimetry enabled larger swath coverage, reduced PRF and SAR system complexity as compared to fully polarimetric systems. The first Hybrid Polarimetric Space-borne SAR in Earth Observation orbit, India’s Radar Imaging Satellite (RISAT-1) is a new-fangled gateway to remote sensing user community for land and oceanic applications. In response to a right-circular polarized transmitted signal, based on the derived stokes vectors, Stokes parameters are estimated to produce several useful quantitative measures for generating polarimetric decomposed image. m-delta, m-chi and m-alpha polarimetric decomposition methods along with suitable weighting functions in terms of three principal components are implemented which maps Stokes parameters to RGB image space for representing odd bounce, even bounce and volume scattering targets. Various RISAT-1 Hybrid Fine Resolution Stripmap Single-Look Complex SAR datasets acquired over deployed corner reflectors at calibration site, Shadnagar have been considered over which different hybrid polarimetric decomposition techniques are implemented using in-house developed software. Further analysis produced encouraging results with standard point targets like dihedral and trihedral corner reflectors against distributed targets in the same scene to demonstrate the scattering mechanisms as per their characteristics when interacted with a polarized signal were presented in this paper.

Oil spills in oceans have a significant long term effect on the marine ecosystem and are of prime concern for maritime economy. In order to locate and estimate the oil spread area and for quantitative damage assessment, it is required to continually monitor the affected area on the sea and its surroundings and space based remote sensing makes this technically viable. Synthetic Aperture Radar SAR with its high sensitivity to target dielectric constant, look angle and polarization-dependent target backscatter has become a potential tool for oil-spill observation and maritime monitoring. From conventional single-channel SAR (single-pol, HH or VV) to multi-channel SAR – (Dual/Quad-polarization) and more recently compact polarimetric (Hybrid/Slant Linear) SAR systems have been widely used for oil-spill detection in the seas. Various polarimetric features have been proposed to classify oil spills using full, dual and compact polarimetric SAR. RISAT-1 is a C-band SAR with Circular Transmit and Linear Receive (CTLR) hybrid polarimetric imaging capability.This study is aimed at the polarimetric processing of RISAT-1 hybrid pol single look complex (SLC) data for derivation of the decisive polarimetric parameters which can be used to identify oil spills in oceans and their discrimination from look-alike signatures. In order to understand ocean–oil spill signatures from full-quad pol SAR, pseudo-quad pol covariance matrix is constructed from RISAT-1 hybrid pol using polarimetric scattering models .Then polarimetric processing is carried out over pseudo-quad pol data for oil slick detection. In-house developed software is used for carrying out the above oil-spill study.

Corner Reflectors (CR) are standard passive radar targets which offer one of the best solutions for SAR calibration. Radar Cross Section (RCS) of corner reflectors plays a vital role for estimation of calibration parameters and hence back scatter coefficient for airborne and spaceborne SAR images. There is a stringent requirement to characterize RCS of corner reflectors by measuring its scattering properties in a controlled environment. RCS characterization of square trihedral corner reflectors, dihedrals including polarization selective dihedrals is addressed. These measurements were carried out at X, C and S band frequencies with wide scan angles at definite sampling interval. The design details of corner reflectors, specifications of Compact Antenna Test Range Facility, technical modalities involved for RCS measurements, variation of measured RCS from theoretical value for trihedral and dihedral reflectors at different frequency bands and polarizations are presented in this paper.

Traditional processing of Circular Polarimetric (Compact Polarimetric)—Synthetic Aperture Radar (CP-SAR) images from the Satellites has been done after receiving the raw data in the Ground Station. The availability of million gate FPGAs has given scope for doing many processing steps on-board itself which has given scope for Smart Small Satellites. The feasibility exists especially for small scenes of interest which have limited data in smaller volumes for ease of processing. National Remote Sensing Centre, Indian Space Research Organisation (NRSC, ISRO) have carried out ground processing with software tools hosted in high performance computers, using the m-delta (δ) decomposition method on the L1-SLC data of CP-SAR payload of RISAT-1 satellite for classification. In our research work, the same data set, and m-delta(ẟ) decomposition method have been chosen for implementation and processing in FPGA fabric, for comparison with earlier Ground based processed results. As part of prototyping, an Intel FPGA evaluation board kit was used. Preliminary results are encouraging and can lead to potential future Smart Small Satellite missions with CP-SAR and on-board processing for direct classification of the scene under imaging for its possible direct delivery to the end user. Major advantage of this approach is the reduction in bandwidth, as the volume of data to be downlinked is reduced, due to lower latency, computational and round-trip times, potential scope for delivery to user with near real-time processing.

Over past few years Synthetic Aperture Radar(SAR) has received a considerable attention for monitoring and detection of oil spill due to its unique capabilities to provide wide-area surveillance and day and night measurements, almost independently from atmospheric conditions. The critical part of the oil spill detection is to distinguish oil spills from other natural phenomena. Stokes vector analysis of the image data is studied to estimate the polarized circular and linear components of the backscatter signal which essentially utilize the degree of polarization(m) and relative phase (δ) of the target. In a controlled oil spill experiment conducted at Norwegian bay during 17th to 22nd June 2014, RISAT-1 hybrid polarimetry images were utilized to study the characteristics of oil spill in the sea. The preliminary results obtained by using polarimetric decomposition technique on hybrid polarimetric data to decipher the polarimetric characteristics of oil spills from natural waters are discussed in the paper.

( ) is a species of red algae belonging to the family . The objective of the present study was to evaluate antioxidant and antiproliferative activity of four fractions (petroleum ether, chloroform, ethyl acetate, and methanol) of . The red seaweed, was collected from Mandapam Coastal Region, Gulf of Mannar, Tamil Nadu. Epiphytes present in algal extracts were cleaned and washed with seawater and fresh water. antioxidant activity was determined by hydrogen peroxide scavenging, ferric reducing antioxidant power, superoxide radical, metal-chelating activity, and phosphomolybdenum reduction assay. Further, the cytotoxic activity was evaluated using brine shrimp lethality assay. This method is rapid, reliable, inexpensive, and convenient as compared to other cytotoxicity assays. Gallic acid, ethylenediaminetetraacetic acid, ascorbic acid, and quercetin were used as reference antioxidant compounds. Reducing power of chloroform extract increased with increasing concentration of the extract. The radical scavenging activity of extracts was in the following order: ascorbic acid > methanol > chloroform > petroleum ether > ethyl acetate. Highest metal-chelating activity was observed in petroleum ether fractions (63%). Reduction of Mo (VI) to Mo (V) increased in methanol extract (27%) at 100 μg/ml. Moreover, all fractions had an inhibitory effect on the formation of hydroxyl radicals. Results showed that ethyl acetate, methanol, and petroleum ether fractions exhibited potent cytotoxic activity with median lethal concentration values of 84.33, 104.4, and 104.4 μg/ml, respectively. Thus, the results showed that red algae possess strong antioxidant and cytotoxic activity that suggests their possible use in the development of pharmaceutical drugs. Various fractions of red algae was evaluated for antioxidant and antiproliferative studies. All results indicate potential use of red algae for drug development. : Mo: Molybdenum, AlCl H O: Aluminum chloride, NaNO : Sodium nitrite, NaOH: Sodium hydroxide, H O : Hydrogen peroxide, NADH: Nicotinamide adenine dinucleotide, NBT: Nitroblue tetrazolium chloride, PMS: Phenyl methanesulfonate, FeCl : Ferrous chloride.

The synthesized water-soluble ternary complexes [Co(met)(gly)(Cl) 2 ] (1), [Co(met)(hist)(Cl) 2 ] (2), and [Co(met)(pro)(Cl) 2 ] (3), (met = metformin, gly = glycine, hist = histidine, and pro = proline) were evaluated using spectro-analytical techniques, and the stereochemistry of the complexes was determined to be octahedral. UV–Vis absorption, competitive DNA-binding experiments using ethidium bromide (EB) by fluorescence, fluorescence emission studies, viscosity studies, and gel electrophoresis techniques were all employed to explore the binding characteristics of the cobalt (II) complexes with CT-DNA and groove-binding mechanism established. The salt-dependent association of the complexes to CT-DNA was investigated using UV–Vis spectrophotometric analysis. The association of the cobalt (II) complexes with BSA and HSA was explored by utilizing UV–Vis absorption and fluorescence spectroscopy approaches. The findings show that the complexes exhibit adequate capacity to quench BSA and HSA fluorescence and that the binding response is mostly a static quenching mechanism. The cytotoxicity of the complexes has also been appraised with the human breast adenocarcinoma cell lines (MCF-7) and (MDA-MB-231) by utilizing the MTT assay. For each cell line, the IC 50 values were computed. In both cell lines, all the complexes were active.

This paper explains the effectiveness of TBLT in enhancing the speaking skills of engineering undergraduates in select colleges in the East Godavari District of Andhra Pradesh. In the contemporary educational landscape, particularly in Engineering Institutions, strong communication skills are essential for students' professional success. However, Traditional language Teaching Methods often fall short of addressing the practical communication needs of students. TBLT, with its focus on real-world Tasks and communicative competence, offers a promising alternative. The findings reveal that students in the TBLT group improved significantly in their speaking abilities compared to those in the control group, who were taught using traditional techniques. The tasks for the TBLT (Task-Based Language Teaching) sessions were designed to closely align with students’ real-life communication needs, including group discussions, presentations, and interviews. This alignment made the learning process more relevant, practical, and engaging. Furthermore, the TBLT approach fostered a collaborative learning environment, which enhanced students' confidence and reduced anxiety about speaking skills.

Artificial Intelligence (AI) and AI-driven technologies are transforming industries across the board, with the pharmaceutical sector emerging as a frontrunner beneficiary. This article explores the growing impact of AI and Machine Learning (ML) within pharmaceutical Regulatory Affairs, particularly in dossier preparation, compilation, documentation, submission, review, and regulatory compliance. By automating time-intensive tasks, these technologies streamline workflows, accelerate result generation, and shorten the product approval timeline. However, despite their immense potential, AI and ML also introduce new challenges. Issues such as AI software validation, data management security and privacy, potential biases, ethical concerns, and change management requirements must be addressed. This review highlights current AI-based tools actively used by regulatory professionals such as DocShifter, Veeva Vault, RiskWatch, Freyr SubmitPro, Litera Microsystems, cortical.io etc., examines both the benefits and obstacles of integrating these advanced systems into regulatory practices. Given the rapid pace of technological innovation, the article underscores the need for proactive collaboration with regulatory bodies to manage these developments. It also stresses the importance of adapting to evolving regulatory frameworks and embracing new technologies. Although regulatory agencies like the United Sates Food and Drug Administration (USFDA), European Medicines Agency (EMA), and Medicines and Healthcare products Regulatory Agency (MHRA) are working on guidelines for AI and ML adoption, clear, standardized protocols are still in the works. While the journey ahead may be complex, the integration of AI promises to fundamentally reshape regulatory processes and accelerate the approval of safe, effective pharmaceutical products. Graphical Abstract

Recommender systems (RS) are utilized in various domains, including travel, movies, and music. The increase in social activity has led to an increase in the usage of RS in individual and group recommender systems (GRS). A GRS recommends perfect items to users according to their preferences. A bi-stage adaptive deep learning-based group recommendation system model is proposed to overcome these challenges. The aim of the proposed Bi-stage Adaptive Deep Learning-based GRS (BADLGRS) is to enhance the effectiveness of GRSs. At the GRL level, an undirected Tripartite Graph (TG) represents the interaction among groups, users, and items. Then, constructing a TG effectively represents the semantic features of both users and items within the group context. Then, a novel Deep Learning (DL) network, the Gated Recurrent Unit-based Attention Neural Network, is used to learn the semantic features of the group. Generate optimized semantic features t o produce refined and optimized semantic feature representations for both users and items, which are fed into the next stage. A two-layer graph convolutional network (TGCN) is employed for user preference learning at the GPL level, enabling the accurate learning and capture of individual user preferences. After learning the group’s preferences, we employ the Pairwise Learning Method (PLM) to effectively learn and model the aggregated preferences of the group. Additionally, the Network model optimizes the parameters of the two-layered network within the GPL stage using the PLM. Additionally, the proposed model is validated using four different datasets and outperforms existing models in terms of HR, NDCG, MAP, accuracy, recall, and f1-score for group recommendation. The proposed model acquired enhanced outcomes in terms of various assessment metrics like accuracy of 0.893, which is 28.33%, 28.89%, 29.79%, 26.54%, 25.20%, 23.96%, 22.73%, and 17.02% superior to DFM-AVG, DFM-LM, DFM-MS, COM, DPMF-CNN, AGR, AGREE, and MAGRM methods.