Shri Rawatpura Sarkar University (SRU) is a private university located at the village Dhaneli in Raipur district, Chhattisgarh, India. It was established in 2018 by the Shri Rawatpura Sarkar Lok Kalyan Trust under the Chhattisgarh Private Universities (Establishmentand Operation) (Amendment) Act, 2018. The foundation stone for its campus in the village Dhaneli, in Raipur district was laid on 11 October 2018. The university offers diploma, undergraduate and postgraduate courses in the fields of engineering and technology, science, art, pharmacy, fashion and interior design, management and commerce, library science, education, yoga and naturopathy and journalism and mass communication.
Bergenia ciliata a himalayan medicinal herb, which has been traditionally used due to its extensive pharmacological properties. Nevertheless, the possible anticancer application on the molecular level has not been fully explored. This experiment was developed to determine the phytochemicals of B. ciliata as natural inhibitors of the epidermal growth factor receptor (EGFR), which is a major target in most epithelial cancers. Phytochemical data on the IMPPAT and PubChem databases were collected. The compounds were using SwissADME and ProTox-II on drug-likeness, absorption, and toxicity. Then six candidates with Lipinski rule and pharmacokinetics conditions were docked to EGFR (PDB ID: 4HJO) with AutoDock vina. The binding affinities of cianidanol and Leucocianidol were the highest, − 8.8 kcal/mol and − 8.7 kcal/mol respectively, as compared to the reference drug erlotinib which has a binding affinity of − 8.3 kcal/Mol. There were several hydrogen bonds and hydrophobic interactions with such critical residues as Lys_721, Thr_766, Asp_831 and Phe_832.Simulations of 100 ns of molecular dynamics showed constant RMSD (0.10–0.20 nm), low fluctuations of residues, and small radius of gyration of all the complexes. MM/PBSA required interactions revealed that the stabilization was dominated by van der Waals forces and electrostatic repulsions with the total binding energies of − 51 kJ/mol, − 46 kJ/mol, and − 34 kJ/mol with cianidanol, leucocianidol, and erlotinib respectively. The studies suggests that EGFR is strongly bound by B. ciliata phytochemicals, and their biocompatible profiles are safer and more inclined to biocompatibility compared to the conventional inhibitor. These findings have indicated that these compounds can be useful lead scaffolds in the development of anticancer drugs in future as they have been shown to possess promising properties that would be further validated by studies conducted in in vitro and in vivo.
Wireless networks are undergoing a paradigm shift with emergence of high performance internet of things (IoT). The demand for bandwidth has significantly risen due to multimedia applications and high speed data transfer. Due to the large data speeds and wireless nature of the channel, error detection and correction measures are indispensable for high trustworthiness in networks. This paper presents a comprehensive review on need of error detection and correction in wireless networks and the related work done in the domain. The emphasis has been laid on the error detection and correction employing the turbo encoding mechanism. The turbo encoding mechanism has been chosen as the baseline technique as it shows adherence to the Shannon’s limit while exhibiting a steep plummet in the error rate, in the error rate even at low SNR values. The important features of the turbo encoding-decoding process has been cited with explanations. A review of the various categories of turbo codes and similar approaches has been presented. Salient features of contemporary work have been cited. Keywords: Internet of Things (IoT), Error Detection and Correction, Trustworthiness, Turbo Codes, Shannon’s Limit.
Green banking means using banking services in a way that helps protect the environment. It includes services like online banking, mobile banking, internet banking, ATM services, digital payments, e-statements, and paperless transactions. Green banking helps reduce the use of paper, saves time, and lowers environmental pollution. It also makes banking services faster and easier for customers. Nowadays, many banks are promoting green banking services to encourage customers to use digital methods instead of traditional paper-based banking. This study focuses on customer satisfaction towards green banking services in Chhattisgarh. The study is based on secondary data collected from 80 bank customers. The main purpose of the study is to understand the level of satisfaction among customers using green banking services. The study also tries to identify the problems faced by customers while using these services. The findings of the study show that most customers are satisfied with green banking services because they are convenient, fast, and easy to use. Customers feel that green banking saves time and reduces the need to visit bank branches frequently. However, some customers still face problems such as lack of awareness, technical issues, internet problems, and security concerns. Some users also find digital banking difficult because of low digital knowledge. The study concludes that green banking has a positive impact on customers and the environment. It also suggests that banks should increase awareness programs, improve technical support, and provide proper guidance to customers. Better internet facilities and stronger security systems can also help in making green banking services more effective and user-friendly.
Oral cancer is a life-threatening disease where early diagnosis plays a critical role in improving patient survival rates. However, existing computer-aided diagnostic systems are often limited by single-modal data usage, lack of interpretability, and insufficient capability to detect early-stage lesions and affected regions. To address these challenges, this study proposes an intelligent multimodal deep learning framework for the early diagnosis and detection of oral cancer. A diversified multi-source dataset is constructed by integrating publicly available oral cancer, lesion, and anatomical image datasets, ensuring variability in lesion types, anatomical regions, and imaging conditions. The proposed system incorporates a hybrid CNN–RNN architecture with transfer learning and attention mechanisms to effectively extract spatial and contextual features from oral images. Additionally, patient metadata is integrated through a multimodal feature fusion strategy to enhance diagnostic performance. An automated preprocessing and segmentation pipeline is employed to perform region-aware classification, enabling not only binary classification (cancer vs non-cancer) but also localization of cancer-affected areas. Furthermore, Explainable Artificial Intelligence (XAI) techniques such as Grad-CAM are integrated to provide visual interpretability and improve clinical trust. Experimental results demonstrate that the proposed hybrid multimodal model outperforms conventional CNN-based approaches in terms of accuracy, robustness, and generalization. The system shows strong potential as a reliable decision-support tool for early-stage oral cancer detection and clinical diagnosis.
