It is one of the largest and oldest institution of higher education of Chhattisgarh. Established under Central Universities Act 2009, No. 25 of 2009.Formerly known as Guru Ghasidas University (GGU), established by an Act of the State Legislative Assembly, was formally inaugurated on June 16, 1983. GGV is an active member of the Association of Indian Universities and Association of Commonwealth Universities. The National Assessment & Accreditation Council (NAAC) has accredited the University as B+. The university is named to honor the great Satnami Saint Guru Ghasidas (born in the 17th century), who championed the cause of the downtrodden and waged a relentless struggle against all forms of social evils and injustice prevailing in the society..
Brain tumors (BTs), characterized by abnormal cell proliferation in the brain, lead to severe neurological symptoms and can be fatal if untreated. This review explores primary BTs such as gliomas, meningiomas, pituitary tumors, medulloblastomas, schwannomas, craniopharyngiomas, and primary central nervous system lymphomas, as well as secondary tumors from metastasis. In the medical field, one of the greatest difficult and time-consuming processes is BT detection and identification. There has been a significant growth in the total number of brain illnesses reported recently. To help doctors with early diagnostic and treatment measures, this has indirectly raised the need for automatic detection and identification systems. With regard to brain magnetic resonance imaging (MRI) identification and detection techniques, this article aims to provide a critical analysis of current trends. This research mainly aims to support the review and systematic analysis of hybrid computational intelligence methods that use machine learning (ML) and deep learning (DL) for BT and cancer diagnosis. The most important part of this research is the introduction of a unified comparative framework, which not only depicts existing methods but also points out present difficulties and gives future research directions for the construction of clinically interpretable and scalable diagnostic models. This research examines a variety of identification and detection techniques, beginning with the simplest ones and progressing to the most sophisticated methods including ML, DL, and hybrid approaches. The article presents a comprehensive review of the triad of methods consisting of ML, DL, and hybrid computational intelligence for the purpose of diagnosing BTs and cancer. One of the important aims is to provide proof of the effectiveness of the hybrid models that already exist while, at the same time, showing their capability of elevating the diagnosis's precision and trustworthiness. The authors put forward a comparison framework that encompasses the three approaches-traditional, DL, and hybrid-in connection with their data preprocessing, model fusion, and performance aspects. The integration of various methodologies is aimed at providing a more transparent methodological perspective and at steering future research toward hybrid diagnostic systems that will be easier to understand, simple to scale up, and, ultimately, usable in hospitals. The pros and cons of BT detection and identification are discussed here. The review indicates that the hybrid techniques based on DL are more effective in the accurate classification of BTs. Research intends to make these technologies even better, thereby increasing the precision of diagnosis and the efficacy of treatment in neuro-oncology.
Ensuring a sustainable supply of mineral resources, while reducing industrial accidents and environmental harm, requires continuous expansion and integration of innovative technologies within the mining sector. These technologies offer significant potential to support the long-term stability of the mineral resource industry. It can enhance resilience to fluctuations in demand, improve operational profitability and reinforce adherence to environmental regulations. Mining 4.0 (M4.0) has emerged as the sector strategic response to rapid digital transformation shaping both mining and associated infrastructure industries. Though, it has been observed that the adoption of M4.0 in developing economies such as India remains challenging. Many mining organisations lack clarity regarding which technologies are most critical and how they should be prioritised for effective implementation. Addressing this gap requires a systematic identification and evaluation of key M4.0 technologies relevant to developing-country contexts. The present study contributes to this need by assessing and ranking the prominent M4.0 technologies applied within Indian coal mining companies through the Decision-Making Trial and Evaluation Laboratory method. The originality of this work lies in the limited number of prior studies focused specifically on prioritising M4.0 technologies in Indian mining sector. Through a literature analysis and expert consultation, 14 core technologies were shortlisted. The results reveal that 'Big Data and Analytics' is perceived as the most influential enabler of digital transformation, whereas 'Virtual Reality' is considered least significant at present. Overall, this study offers actionable insights that can guide Indian mining firms in strategically adopting M4.0 technologies based on their relative significance.
Hybrid polymer nanocomposites are increasingly explored as lightweight dielectric materials for flexible electronic systems, where simultaneous electrical performance and mechanical robustness are required. This study investigates interfacial polarization in hybrid polymethyl methacrylate (PMMA) nanocomposites reinforced with n-type silicon carbide (SiC) and p-type nickel oxide (NiO) nanoparticles. The nanocomposite films were synthesized via a solution-casting route to examine how p–n interfaces influence the electrical and mechanical properties simultaneously, which remains relatively unexplored in PMMA-based hybrid nanocomposites. Structural and morphological characterizations (XRD, FTIR, SEM) confirmed the amorphous nature of PMMA and the uniform dispersion of SiC and NiO, enabling the formation of stable polymer–ceramic interphases. The simultaneous incorporation of n-type and p-type fillers generated localized p–n interfacial polarization, which enhanced Maxwell–Wagner–Sillars interfacial polarization and space-charge accumulation. Consequently, the hybrid nanocomposites exhibited a substantial dielectric enhancement, achieving a dielectric constant of ε′ ≈ 392 at 5 wt
Soil erosion jeopardizes agricultural production and the sustainability of watersheds, especially in sensitive river basins, and thus it is important to accurately measure and monitor erosion. The spatial processes of soil loss in the Arpa River Basin, Chhattisgarh State, India, were mapped in this study using the Revised Universal Soil Loss Equation (RUSLE) in a Geographic Information System (GIS), in combination with satellite and Remote Sensing data. The variables required for RUSLE, which include rainfall erosivity (R), soil erodibility (K), slope length and steepness (LS), cover-management (C), and conservation-practice (P), were compiled from multiple data sources into ESRI ArcGIS, and then an erosion area map-vector layer was produced. The soil erosion map between the classes of erosion risk included: slight (<10 t/(ha yr)), moderate (10–20 t/(ha yr)), high (20–30 t/(ha yr)), very high (30–40 t/(ha yr)), severe (>40 t/(ha yr)), where it was noted that severe soil erosion primarily occurred within cultivated areas that were steep. The classification of the soil erosion level was verified by field points, for an accuracy of 86 percent, and a Kappa coefficient value of 0.75, indicating reasonable reliability. The results of this study showed spatiotemporal variability in soil loss, therefore further necessitating the need to develop and use site-specific soil conservation tactics and watershed sustainability management plans for the Arpa River Basin.
Chromium and Manganese ions are considered non-essential and highly toxic elements in drinking water. In this study, the adsorption of these ions from water was examined using pomegranate peel (PP) and activated carbon obtained from pomegranate peel (ACPP) as adsorbents. Pomegranate peel powder was chemically modified with phosphoric acid (H3PO4) to enhance the adsorption characteristics of activated carbon. A batch adsorption study was conducted to assess the effect of the solution's pH, temperature and contact time on adsorbent removal effectiveness. This adsorption isotherms study revealed that the adsorption of Mn(II) and Cr(VI) onto PP and ACPP follows the Langmuir isotherm with a correlation coefficient of more than 0.95. The maximum monolayer adsorption capacities of ACPP as well as PP were found to be 142.86 and 100.52 mg/g as well as 90.91 and 55.56 mg/g for Mn(II) and Cr(VI), respectively. The adsorption kinetics were investigated using the pseudo-first-order, pseudo-second-order and intraparticle diffusion models. The correlation coefficient indicated that the adsorption process adhered to pseudo-second-order kinetics. Moreover, the concurrence between the measured and computed values of qe indicated a closely aligned adsorption equilibrium. The findings suggest that PP could be a cost-effective and promising adsorbent for effectively removing Cr(VI) and Mn(II).
