Barkatullah University

Climate change is most important topic for discussion of the current century. The main obstacle to understand is not only scientific but also how it impacts humans to human culturally and linguistically. Due to the changes happening in the 21st century climate change is now a threat to the humankind. It can be rectified only if it is understood based on language that is easily understood, related to culture, and the overall mentality of the people. India, is a diverse land where people following different cultures and traditions live together. Thus the awareness about the environment cannot be only spread in terms of scientific information and facts, but it needs to be understood with the help of stories, metaphor, and memories. The paper will show that the need for understanding of the changes that is the climate changes not only through scientific knowledge, but from the realistic point through empathetic language used in literature and media. The term 'Eco linguistics' best describes it, in which it tells about how words and the way it is narrated can together draw the scientific understanding of climate change in an emotional and ethical way. The works of writers like Amitav Ghosh in 'The Great Derangement', the soothing nature writings of Ruskin Bond, and the ancient ecological wisdom emersed in the Indian Knowledge System becomes a clear picture reflecting the connected relationship with nature. The media also strongly affects and explains about the environmental crisis. Media platforms like 'The Hindu', 'Down to Earth', and NDTV's environmental campaigns help in understanding of the Care required by the nature in a language easily and make people connected. When we compare literature. and media texts, it will show her figures of speech like personification, metaphors are used to convey message of environmental crisis. Thus the description of the scientific data and how much important is to save environment should be conveyed in a very compassionate language and also through cultural traditional stories that keep us awakened of our duties towards the Earth. Literature and media belong to different ways of expression, but the one thing common in them is the use of emotion, symbolism, and storytelling to make people aware of the climate change and their moral duties towards it. Thus language is the key that will bridge the distance between the scientific knowledge and the traditional knowledge which has been passed on through storytelling through decades. The main aim of the study is to explain that both literature and media communication plays important role to create awareness regarding climate changes in a wise and moral manner. This research focused on the discussion global conversation on climate communication has given this conversation a shift from metaphor to message .its the need of the hour as people need to understand but its transformation to shape public consciousness and inspires them to think about the future of the earth which is a major call of today.

The mandate for high data rates in mobile communication is increasing and will continue to do so in the future. Although the latest network technologies can meet this demand, they result in more-dense networks. Networks like ultra-dense networks and massive multiple-input multiple-output provide very high data rates, but they cannot meet the future demand. The main issue with existing networks is inter-cell interference and variations in quality of service esp. at the cell edges, leading to research on new network architectures that offer intelligent coordination and collaboration capabilities are being researched, like user-centric cell-free (UC-CF) massive-multipleinput-multiple-output (mMIMO). This network combines the best of ultradense networks and mMIMO and eliminates cell edge problems. It is served by access points that cooperate and coordinate with each other. This paper reviews the challenges and opportunities in physical layer parameterfronthaul signaling for UC-CF mMIMO. We discuss the basics of the network, the importance of fronthaul signaling, and propose various approaches in the literature to address challenges and identify research gaps and provide future directions. Our aims to provide a comprehensive overview of the current state of fronthaul signaling and highlight the key issues that need to be addressed to realize its full potential.

Abstract This paper investigates the enhancement of communication performance in mixed radio frequency–free space optical (RF–FSO) wireless networks by employing unmanned aerial vehicles (UAVs) as mobile relays. A dual-hop transmission model is considered, where the UAV acts as an intermediary between a fixed ground source and multiple mobile users distributed along the boundary of a predefined geographical region. To improve the reliability and efficiency of the system, we propose a joint optimization framework that determines the UAV’s optimal altitude and horizontal location. Additionally, an opportunistic user scheduling mechanism is implemented, selecting the mobile user with the highest instantaneous signal-to-noise ratio (SNR) for communication. The RF link is modeled using Rayleigh fading statistics, and the FSO link is modeled using the Gamma–Gamma fading distribution, capturing the effects of atmospheric turbulence, pointing errors, and angle-of-arrival fluctuations. Closed-form expressions for outage probability and its asymptotic approximation at high SNR levels are derived to evaluate system performance analytically. The analytical results are further validated through Monte Carlo simulations. The outcomes demonstrate that the proposed UAV positioning strategy and user scheduling scheme effectively minimize outage probability and significantly enhance the robustness of the RF–FSO communication system. These results provide valuable insights for the deployment of UAV-assisted hybrid optical wireless networks in dynamic or infrastructure-constrained environments.

Education is a fundamental right essential for the development of children and the progress of nations. The UNCRC (1989) is the first comprehensive document that acknowledges children’s rights, including education. SAARC consists of eight member states: Afghanistan, Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan and Sri Lanka. The Association advances the welfare and standard of living of South Asian people. It is important to analyse the educational policies of SAARC nations as it will help to achieve SDG 4, which talks about inclusive and equitable quality education. This study examines the implementation of Article 28 of the United Nations Convention on the Rights of the Child (UNCRC) in SAARC nations, highlighting the challenges and recommendations provided by the CRC Committee. It analyses the constitutional, legislative, and policy measures undertaken by Afghanistan, Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan, and Sri Lanka to ensure the right to education. The study identifies common concerns raised by the CRC Committee, including limited access to education, poor quality of learning, high dropout rates, low budgetary allocations, gender disparities, corporal punishment, and attacks on schools. The findings reveal that while all SAARC nations have ratified the UNCRC and implemented various policies, significant gaps remain in enforcement and resource allocation. The CRC Committee’s recommendations focus on reviewing the legal framework, increasing budgetary provisions for education, taking away all forms of discrimination, ensuring safety in schools, and providing better and inclusive education to children. The recommendations of the study highlight the need for the SAARC countries to formulate a rights-based policy framework and strengthen inter-regional collaboration to meet the requirements set under Article 28 of the UNCRC.

Cyber-physical systems (CPS) in smart energy and smart healthcare must continue to be safe, fast, and reliable, even in the face of evolving cyber threats and fluctuating network/edge conditions. An AI-based, security-aware framework of CPS reconfiguration is presented in this paper. It is comprised of (i) privacy-preserving federated anomaly detection at distributed edge nodes, (ii) a reinforcement-learning decision module that selects risk-aware reconfiguration actions under the objectives of latency, energy, and safety, and (iii) auditability, backed by blockchain, to provide trustworthy governance and accountability in the aftermath of the event. The detection module learns behavioral baselines at the local level and shares only protected updates to support data minimization and privacy compliance while still achieving high accuracy in heterogeneous deployments. The RL controller dynamically modifies actions to reduce service disruption, quicken recovery, and avert unsafe control transitions. A lightweight operational ledger captures reconfiguration activities and trust updates to provide auditable governance in multi-stakeholder CPS settings. In energy and healthcare CPS scenarios, there is strong operational performance. The system achieves 97.8% detection accuracy and an F1-score of 97.4% with an end-to-end latency of 41ms, coupled with a reconfiguration time of 65ms and a mean time to recovery of 5.6 seconds. The framework provides 99.4% uptime, consumes approximately 10 W at the edge, and with low error rates (2.1% false positive and 1.0% false negative), achieves 19.5 Mbps secure throughput. These results demonstrate that self-reconfigurable CPS can maintain mission-critical operational continuity while enhancing privacy, scalability, and governance in large-scale deployments.

Surgical video analysis has become an essential component in computer-assisted interventions and clinical documentation. The rapid growth of minimally invasive surgery has produced large volumes of surgical recordings that require detailed frame-level annotations for training intelligent systems. Manual annotation of surgical videos remains a labor-intensive and time-consuming process that often requires expert knowledge. As a result, the development of automated annotation systems has become a critical research direction in medical image analysis. Existing segmentation and annotation approaches have faced limitations in handling complex surgical scenes, instrument occlusions, illumination variations, and tissue deformation. Conventional deep learning models often rely on large labelled datasets, whereas surgical datasets usually remain limited due to the difficulty of manual labeling. This challenge has reduced the reliability and scalability of automated surgical video segmentation systems. To address these issues, this study has proposed an Active Deep Ensemble Segmentation Network (ADES-Net) for automated surgical video segmentation annotation. The framework has integrated an ensemble of convolutional segmentation models with an active learning strategy that has selectively identified informative frames for annotation. The ensemble architecture has combined multiple deep segmentation networks that have captured diverse spatial representations from surgical frames. An uncertainty-driven active sampling mechanism has prioritized frames that required expert labeling, which has reduced redundant annotations. Feature representations that were extracted from each model have contributed to robust segmentation predictions, while iterative learning cycles have refined the annotation quality. The experimental evaluation demonstrates that the proposed ADES-Net framework achieves superior segmentation performance across multiple metrics. The model achieves a Dice similarity coefficient of 0.93, an IoU of 0.86, precision of 0.93, recall of 0.91, and an F1 score of 0.92 when trained with twenty-five annotated frames. These results indicate that the active ensemble mechanism effectively captures spatial and contextual features, reduces false positives, and improves boundary delineation. Compared with baseline methods such as U-Net, Attention U-Net, and DeepLabV3+, the proposed framework achieves improvements of 5–10% across all metrics, demonstrating enhanced segmentation reliability, efficiency, and robustness in automated surgical video annotation tasks.