Indira Gandhi National Open University, known as IGNOU, is a Central Open Learning University located at Maidan Garhi, New Delhi, India. Named after former Prime Minister of India Indira Gandhi, the university was established in 1985 with a budget of ₹20 million, after the Parliament of India passed the Indira Gandhi National Open University Act, 1985 (IGNOU Act 1985). IGNOU is run by the central government of India, and with total active enrollment of over 3 million students, it is the largest university in the world.IGNOU was founded to serve the Indian population by means of distance and open education, providing quality higher education opportunities to all segments of society. It also aims to encourage, coordinate and set standards for distance and open education in India, and to strengthen the human resources of India through education. Apart from teaching and research, extension and training form the mainstay of its academic activities. It also acts as a national resource center, and serves to promote and maintain standards of distance education in India. IGNOU hosts the Secretariats of the SAARC Consortium on Open and Distance Learning (SACODiL) and the Global Mega Universities Network (GMUNET), initially supported by UNESCO.IGNOU has started a decentralisation process by setting up five zones: north, south, east, west and north-east. The first of the regional headquarters, catering to four southern states, Pondicherry, Andaman and Nicobar Islands and Lakshadweep, is being set up in the outskirts of Thiruvananthapuram in Kerala.[citation needed] The Ministry of Educationhas entrusted the responsibility of developing Draft Policy on Open and Distance Learning and Online Courses to IGNOU. IGNOU also partners up with other organizations to launch courses. IGNOU offers a BBA in Retail distance learning course in association with Retailers Association of India (RAI).Recently, the university has implemented the CBCS method to the various bachelor's degree courses including BA, BAVTM, BCOM, BSC, and others. As per the new CBCS system, the examination will be conducted through the semester system that was earlier conducted on an annual mode..
The Konark coastal belt, a morphodynamically active segment of the Mahanadi deltaic system, is undergoing rapid shoreline transformation driven by the interplay of sea-level rise, altered wave regimes, and intensifying anthropogenic pressures. This study aims to present 20-year spatio-temporal assessment (2005–2025) of shoreline dynamics across three morphodynamically distinct segments at Gop, Kakatpur, and Astarang using multi-temporal Landsat imageries, Digital Shoreline Analysis System (DSAS) metrics, and ecological indices. This study focuses on long-term shoreline dynamics; cyclone and extreme event influences were not explicitly considered. DSAS analyses showed peak erosion of − 3.87 m/year at Kakatpur and accretion up to + 3.12 m/year near Gop, with long-term retreat averaging − 2.14 m/year across erosion zones, and + 1.98 m/year in accreting areas. The results reveal pronounced accretion in Kakatpur (0.59 m/year), moderate gain with erosion hotspots in Gop (0.15 m/year), and highly variable dynamics in Astarang (2.8 m/year). Despite net progradation, spatial heterogeneity is evident with 20.55 to 33.71
This study explores the application of deep learning, specifically Convolutional Neural Networks (CNNs), for the detection and classification of brain tumors in medical images. The research addresses the challenges of tumor differentiation and network distribution complexity under various models of deeplearning and to find out which model is most effective. The state of the art as well as classical models —alongside two complementary models, were evaluated for their performance in classifying brain tumors. The study utilized a comprehensive dataset of brain MRI scans, representing various tumor types, sizes, and locations. Image preprocessing techniques, including normalization and scaling, were employed to enhance model performance. Each CNN model was meticulously designed, trained, and tested, with a focus on accuracy, sensitivity, specificity, and computational efficiency. The comparative analysis of the models revealed distinct strengths and weaknesses, with each architecture showing varying degrees of effectiveness in brain tumor classification tasks. Key findings include the models’ ability to accurately detect and differentiate between different tumor types, with specific models excelling in certain performance metrics. This study provides insights into the suitability of various CNN models for real-world clinical applications in automated brain tumor detection. The results contribute to the ongoing advancements in medical imaging, highlighting the potential of deep learning in improving diagnostic precision and reducing the need for manual intervention and MobileNet has turned out to be best model under our observation with respect to the given data set available. MobileNet achieved 96.6
The coexistence of heavy metals and synthetic dyes in water systems necessitates multifunctional platforms for the simultaneous detection and degradation of these pollutants. Zeolitic Imidazole Frameworks (ZIFs) postmodification with organic ligands has gained significant attention for environmental applications. In this regard, a terpyridine-functionalized ZIF framework (ZATpy) was synthesized via post-synthetic grafting to introduce the terpyridine ligand (Tpy) on the ZIF-8 framework. Unlike most MOF sensors using coordinated ligands or single-function designs, ZATpy features covalently attached uncoordinated terpyridine sites offering direct metal-binding access and enabling both ultra-sensitive Cu(II) sensing and efficient photocatalysis in a single robust platform. UV-Vis spectroscopy confirmed high selectivity of Cu (II) recognition with optimal response in the neutral pH (5-7) range, minimally affected by competing ions with a limit of detection (LOD) of 0.131 mu M. Notably, ZATpy retained its Cu(II) sensing ability in complex real water matrices, including Yamuna River water, Ganga River water, seawater, and tap water. Furthermore, Inductively Coupled Plasma Mass Spectrometry (ICPMS) validated Cu (II) capture efficiency under sensing conditions with 28.7% adsorption efficiency. Photocatalytic studies revealed excellent degradation of methyl red dye, with 94.4% degradation in 60 mins and good reusability. Computational studies also indicated favourable adsorption energetics for Cu (II) and pi-pi interactions with aromatic dye substrate. The integration of free ligand functionality onto the porous ZIF surface enables concurrent ion recognition and charge-guided photo-catalysis, offering a viable approach for water purification.
Heavy metal contamination of water resources significantly impacts water quality. This study assesses heavy metal concentrations along the Periyar’s industrial belt through 14 surface water samples and 14 riverbank soil samples analysed by inductively coupled plasma optical emission spectroscopy (ICP-OES). The results reveal high concentrations of lead (Pb) and zinc (Zn) in surface waters that exceed aquatic life standards. The study also reports high levels of zinc (Zn) and nickel (Ni) in riverbank soils. Ecological and biological risk assessments indicate extreme risks from cadmium (Cd) in surface water and nickel (Ni) in soil, highlighting considerable health risks through both carcinogenic and non-carcinogenic assessments. Notably, the hazard index for soil surpasses that of surface water and nickel (Ni) which poses a significant carcinogenic risk. The results suggest significant pollution in the Periyar River, exceeding established regulatory thresholds, with heavy metals playing a prominent role in its deterioration. Sustained efforts in pollution management and broader environmental considerations are critical in restoring this essential resource and thereby sustaining livelihoods.
Manufacturing firms face intensifying pressure to achieve sustainability while remaining competitive under environmental stress, rapid technological change, and institutional uncertainty—challenges that are particularly acute in developing economies. Although Industry 5.0 has emerged as a human-centric and sustainability-oriented industrial paradigm, limited research explains how it can be systematically operationalized to enhance sustainable business performance. This study addresses this gap by developing an integrative conceptual framework linking Industry 5.0, Smart Circular Economy (SCE), and Green Human Resource Management (GHRM) within manufacturing contexts. Drawing on resource-based, dynamic capability, and institutional perspectives, the framework conceptualizes Industry 5.0 as a strategic digital orientation that enables circular resource orchestration and sustainability-aligned human capital systems. SCE and GHRM are positioned as complementary operational mechanisms that translate Industry 5.0 principles into organizational capabilities. Innovation capability is introduced as a mediating dynamic capability explaining how technological and human resource investments generate environmental, social, and economic performance outcomes. Digital maturity and policy support are incorporated as contextual moderators shaping transformation pathways in developing economies. The proposed model advances sustainability-oriented industrial transformation theory by integrating previously fragmented research streams into a coherent socio-technical capability architecture. It also offers actionable insights for managers and policymakers seeking to align digital industrial development with long-term sustainability objectives under conditions of institutional heterogeneity.
