Mittal hospital

This chapter explores gamification's potential to enhance service quality and maximize ecosystem engagement. By utilizing game mechanics like points, badges, and challenges, organizations can create immersive experiences that boost productivity, loyalty, and customer satisfaction. Through case studies, the chapter highlights successful gamification implementations and best practices. It also addresses psychological and behavioral aspects, emphasizing personalized experiences. Ethical considerations and potential pitfalls are discussed to provide a balanced view. Ultimately, the chapter offers a roadmap for leveraging gamification to drive meaningful engagement and improve service quality.

This research explores how digital solutions, including artificial intelligence (AI), drive sustainability in terrestrial (green) and aquatic (blue) economies. By examining case studies and technological implementations, it highlights the potential and challenges of digital innovations, particularly AI, in promoting environmental sustainability, economic growth, and social well-being. The study covers the current state of green and blue economies, digital interventions like smart agriculture, renewable energy, marine monitoring, and sustainable fisheries, and their impacts. The research uses both qualitative and quantitative methods, gathering data through interviews, surveys, and literature. Results will provide success stories, challenges, and best practices, offering recommendations for policymakers and stakeholders. The study aims to contribute to the theoretical and practical understanding of how digital technologies, especially AI, can advance sustainability in these economies, with future research directions identified.

Traditional Indian medicine is deeply rooted in a holistic, preventive approach, addressing mind, body, and spirit . The amalgamation of traditional Indian medicine, such as Ayurveda, Siddha, and Unani, with modern allopathic practices represents an evolving approach in healthcare aimed at harmonizing ancient holistic healing methods with modern scientific rigor . Ayurveda, for example, focuses on balancing the body's energies and employs natural remedies, which can complement the disease-centered, evidence-based approach of modern medicine . However, challenges in this integration include the need for scientific validation of traditional practices, regulatory standardization, and overcoming historical biases between practitioners of both systems . Literature reveals the potential for significant mutual benefits. Ayurveda’s therapies, such as Panchakarma , Shatkriyakala and Rasayana, have shown promise in managing chronic diseases , enhancing stress tolerance and integrates Ayurvedic principles with modern genetics for personalized medicine . The Herbal involvement in apothecary practices augments a steady treatment approach which can be slow paced than allopathic ways but are much more fruitful . On the modern side, evidence-based research methodologies, like randomized controlled trials, have begun evaluating the efficacy of traditional treatments, though more extensive studies are necessary to fully validate their use . Successful models of integration, such as the AYUSH ministry’s initiatives in India, provide pathways for incorporating traditional medicine into contemporary healthcare systems . This paper discusses the evolving scenario of unification of medicine, highlights existing collaborative efforts, and proposes future directions for research, clinical practice, and policy to further bridge the gap between traditional and modern approaches. It aims to emphasize the importance of ensuring both cultural preservation and scientific validation to enhance patient outcomes and healthcare accessibility in a diverse, global context. Keywords Traditional medicine, Ayurveda , Ayush , Allopathic medicine , Chronic diseases, Collaborative healthcare , Evidence-based research, Herbal treatment, Personalised medicine , Scientific approach

Regardless of their origin, natural bioactive compounds have always been important as cutting-edge medicinal treatments. They offer tremendous therapeutic capabilities in addition to having a significant direct and indirect impact on human health. Throughout the world, fruit species are widely regarded and heavily consumed. Epidemiologic data confirms the link between a high fruit intake and a low risk of developing chronic diseases. There are various biological reasons why eating fruits may help to minimize or avoid chronic diseases. Vitamins, minerals, fibre, and a variety of other kinds of biologically active substances are all present in fruits, making them important sources of nutrients and energy. An indigenous fruit to the tropics of North America, guava is used commercially for its flavour, scent, and taste. There have been many reported pharmacological uses for it, including the antibacterial properties of its leaves and the use of fresh fruit and tea made from its leaves to cure diabetes mellitus, diarrhoea, dysentery, and many other conditions. Guava, however, is also a promising source of natural antioxidants that might be exploited in the creation of new formulations for a variety of medicinal purposes due to its rich content. Quercetin, kaempferol, and schottenol, among many other phenolic components, are abundant in the guava extract. Each ingredient in guava extract has biological impacts, primarily antioxidant qualities.

Early detection of diabetic retinopathy (DR) is crucial as it allows for timely intervention, preventing vision loss and enabling effective management of diabetic complications. This research performs detection of DR and DME at an early stage through the proposed framework which includes three stages: preprocessing, segmentation, feature extraction, and classification. In the preprocessing stage, noise filtering is performed by fuzzy filtering, artefact removal is performed by non-linear diffusion filtering, and the contrast improvement is performed by a novel filter called Adaptive Variable Distance Speckle (AVDS) filter. The AVDS filter employs four distance calculation methods such as Euclidean, Bhattacharya, Manhattan, and Hamming. The filter adaptively chooses a distance method which produces the highest contrast value amongst all 3 methods. From the analysis, hamming distance method was found to achieve better results for contrast and Euclidean distance showing less error value with high PSNR. The segmentation stage is performed using Improved Mask-Regional Convolutional Neural Networks (Mask RCNN). In the final stage, feature extraction and classification using novel Self-Spatial Attention infused VGG-16 (SSA-VGG-16), which effectively captures both global contextual relationships and critical spatial regions within retinal images, thereby improving the accuracy and robustness of DR and DME detection and grading. The effectiveness of the proposed method is assessed using two distinct datasets: IDRiD and MESSIDOR.

This study aimed to compare efficacy, safety, and immunogenicity of the biosimilar ranibizumab in comparison with the Innovator Ranibizumab in treatment-naive patients with neovascular (wet) age-related macular degeneration (nAMD or wAMD). This comparative, double blind, multicentre, Phase III clinical study randomized eligible patients in a 3:1 ratio to receive either OPTIMAB® (Alkem Laboratories Ltd./ Enzene Biosciences Ltd.) or Innovator Ranibizumab. Intravitreal injections of Innovator Ranibizumab (0.5 mg in 0.05 mL) and OPTIMAB® (0.5 mg in 0.05 mL) were administered every four weeks for 12weeks (three doses). Primary efficacy endpoints included loss of <15 letters from baseline, gain of ≥15 letters from baseline in visual acuity, mean change in best corrected visual acuity (BCVA) from baseline, and change in central subfoveal thickness (CSFT) from baseline at week 12. Safety was assessed through monitoring of adverse events (AEs) and serious adverse events (SAEs) throughout the study. Overall, of the 152 patients randomized, 141 (92.8%) patients (mean age, 66.6 ± 9.37 years) completed the study. Percentage of patients who lost < 15 letters in BCVA at week 12 from baseline was comparable in both the groups (100.0%, each). On secondary end point analysis, the two groups had comparable mean changes in BCVA (OPTIMAB®, 11.8 ± 9.18; innovator ranibizumab, 12.9 ± 10.29; P =0.5509); proportion of patients who gained ≥ 15 letters in visual acuity (OPTIMAB®, 32.18%; innovator ranibizumab, 25.74%; P = 0.4785) and mean change in CSFT (OPTIMAB®, -76.6 ± 89.03; Innovator ranibizumab, -73.1 ± 92.23μm; P = 0.8422) at week 12 as compared to baseline. OPTIMAB® and innovator ranibizumab demonstrated comparable safety over the 12-week treatment period and no patient expressed anti-ranibizumab antibody in either group patient. Biosimilar ranibizumab (OPTIMAB®) was non-inferior to innovator ranibizumab in terms of efficacy, safety, and immunogenicity in the patients of nAMD.

The relationship between subclinical hypothyroidism (SCH) and type 2 diabetes mellitus (DM) is complex and controversial. Many epidemiological studies have reported a positive association; however, few have reported none. In animal models and humans, evidences suggest that hypothyroidism induces insulin resistance, beta cell dysfunction, and increased hepatic glucose production. Untreated SCH can increase the risk of diabetic complications. Beneficial effect of treatment of SCH in patients with diabetic nephropathy (DN) and retinopathy has been seen. Treatment of SCH in patients with DM should be individualized. Treatment of SCH is justified in the patients with thyroid-stimulating hormone (TSH) &lt;10 and DN, diabetic retinopathy and patients positive for antithyroid peroxidase antibody. An algorithm for the treatment of SCH in patients with DM is also provided.

Abstract This paper presents a comprehensive evaluation of a novel speckle reduction framework tailored for polarimetric synthetic aperture radar (PolSAR) data. The study investigates the framework's performance across various polarization data configurations, including Full Polarimetry (Full Pol) and Single Polarization (Single Pol), operating across different frequency bands such as L, and S band. With a specific focus on T3 matrix elements (T11, T12, T13, T22, T33, and T23) and full polarimetric (full pol) data, the primary objective is to conduct an in-depth comparative analysis of the proposed framework against established speckle reduction techniques. To achieve this, the proposed framework is rigorously compared with well-known methods such as Refined Lee, Lopez, Lee Sigma, Non-Local Means, and IDAN filters across the aforementioned polarization and frequency configurations. Performance evaluation is conducted using a diverse set of metrics, including Signal-to-Noise Ratio (SNR), Equivalent Number of Looks (ENL), Coefficient of Variation (CV), Speckle Suppression Index (SSI), Speckle Suppression and Mean Preservation Index (SMPI), and Structural Similarity Index Measure (SSIM). The results of this evaluation demonstrate the superior performance of the proposed framework, with significant improvements in speckle reduction and preservation of image detail compared to existing techniques. These findings highlight the potential of the proposed framework as a valuable tool for the processing and analysis of PolSAR data across different polarization configurations and frequency bands, providing a comprehensive solution to the challenges posed by speckle noise in SAR imagery.

Smart cities integrate multiple mobile or web solutions to build a comfortable human habitation.One of these solutions is to provide an environmentally friendly, efficient and effective garbage management system.The proposed system, where admin manage the garbage app for full online based monitoring and analyze the system.Here admin handle bins, driver, complaints from user and work report from driver.This paper proposes a cost-effective web based system for the government to utilize available resources to efficiently manage the over whelming amounts of garbage collected each day, while also providing a better solution for the inconvenience of garbage disposal for the citizens.This is done by a driver, app will provide predictive and guide routes generated through for garbage trucks.Then driver update the status of work done will be data collected.