Volume 13 | Issue 11 |

 Abstract

Paisha Adhiniyam : Chunautiya Aur Samadhan

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Paisha Adhiniyam : Chunautiya Aur Samadhan

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Creative Commons Attribution 4.0 and The Open Definition

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Global Climate Models (GCMs) form the backbone of modern climate science, providing essential simulations for long-term projections of temperature, precipitation, and hydrological cycles. However, despite their sophistication, GCMs continue to exhibit systematic and spatially persistent biases arising from coarse horizontal resolution, uncertainties in cloud and convection parameterization, simplified land-atmosphere interactions, and numerical approximation errors. These biases significantly degrade the reliability of climate projections, particularly at regional and local scales where accurate information is crucial for agriculture, water resource management, disaster preparedness, and policy planning. This study presents a hybrid deep-learning framework designed to correct GCM outputs using observationally constrained ERA5 reanalysis data. The proposed architecture integrates Convolutional Neural Networks (CNNs) for capturing complex spatial structures with Transformer-based attention mechanisms for modeling long-range temporal dependencies in atmospheric processes. Instead of predicting climate variables directly, the network learns bias fields that are added to raw GCM outputs, enabling stability, physical consistency, and improved generalization. Using variables from CMIP6 models (CESM2, MPI-ESM1-2, CanESM5), the framework achieves substantial reductions in prediction errors--34.7% for temperature, 41.2% for precipitation, and 32.9% for humidity--outperforming traditional statistical techniques such as linear scaling and quantile mapping, as well as contemporary deep-learning baselines. Moreover, the corrected outputs demonstrate improved spatial coherence, sharper gradients, and enhanced representation of critical phenomena such as monsoon rainfall and land- ocean contrasts. Overall, this research demonstrates that AI-driven post-processing can serve as a scalable, computationally efficient, and scientifically robust enhancement layer for next-generation climate simulations. The findings suggest significant potential for operational deployment in climate services, seasonal forecasting systems, and impact- model pipelines.

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Climate Modeling, Deep Learning, CMIP6, Bias Correction,Downscaling, Transformers, CNN, Reanalysis Data, GCM Errors

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The intersection of sports and orthodontics represents a vital area in ensuring athlete safety, oral health, and performance optimization. Athletes undergoing orthodontic treatment are at increased risk of dental trauma, appliance damage, and soft tissue injuries, necessitating preventive strategies and customized protection. Orthodontic mouthguards particularly custom-fabricated, dual-laminate, and digitally designed variants offer superior fit, comfort, and impact absorption compared to conventional types. Advances in 3D scanning, CAD/CAM, and AI-driven fabrication now allow precise, adaptive designs suited for evolving dentition. Orthodontic treatment can further enhance airway patency, occlusal balance, and self-confidence, supporting athletic endurance and performance. Smart mouthguards integrated with sensors enable real-time monitoring of impact forces and physiological data, representing the future of personalized sports safety. A multidisciplinary collaboration among orthodontists, sports dentists, and trainers is essential to ensure comprehensive protection and continuity of orthodontic care in athletic environments.

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Orthodontics, Sports Dentistry, Mouthguards, 3D Printing, Smart Devices

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Critical infrastructure--including energy grids, transportation networks, water systems, telecommunications, and healthcare--forms the backbone of national stability and economic growth. As digitalization increases, these sectors face sophisticated and persistent cyber threats from state-sponsored actors, hacktivists, cybercriminals, and insider risks. Effective cyber defence strategies have become essential to ensure the resilience, reliability, and continuity of critical infrastructure operations. This research paper explores major cyber threats targeting critical infrastructure systems, analyzes existing defence frameworks, and identifies strategic approaches such as Zero Trust Architecture, network segmentation, threat intelligence integration, robust incident response, and continuous monitoring. The study concludes with policy recommendations and best practices for enhancing the security posture of critical infrastructure organizations.

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Cyber defence, critical infrastructure protection, cyber-attack mitigation, resilience, SCADA security, incident response, threat intelligence, cybersecurity frameworks, network segmentation, Zero Trust, ICS/OT security.

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The rise of remote work, particularly accelerated by the COVID-19 pandemic, has fundamentally transformed the landscape of professional employment and created both opportunities and challenges for gender equality. This research paper examines the multifaceted impact of remote work on working women, analyzing how flexible work arrangements influence career advancement, wage equity, work-life balance, and the reinforcement or dismantling of traditional gender roles. Drawing upon recent empirical studies, statistical data, and theoretical frameworks, this paper reveals that remote work operates as a double-edged sword: while it offers unprecedented flexibility and access to opportunities, it simultaneously risks reinforcing gender stereotypes and exacerbating existing inequalities. The findings indicate that 66% of women report that hybrid working creates more career opportunities, yet women working remotely are 3.68 times more likely to experience psychological distress compared to non-remote workers[6]. The paper concludes that achieving genuine gender equality in remote work environments requires intentional organizational policies, cultural shifts that challenge traditional gender norms, and systemic support structures that address the disproportionate caregiving burden on women.

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remote work, gender equality, work-from-home, women's employment, work-life balance, gender pay gap, flexible work arrangements, caregiving responsibilities

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Crane hooks are widely used load-bearing components in lifting and material-handling applications. Their structural reliability and weight efficiency play an essential role in ensuring safe operation. Conventional crane hooks contain excess material, resulting in higher self-weight and reduced operational efficiency. In this study, a crane hook is analyzed and optimized using Finite Element Analysis (FEA) to improve structural performance while reducing weight. The existing geometry is evaluated under static loading conditions to identify stress concentration zones and maximum deformation. Topology optimization techniques are then applied to remove non-functional material from low-stress regions. The optimized design demonstrates a significant reduction in overall weight while maintaining equivalent stress performance within safe limits. The results prove that computational optimization can effectively enhance strength-to-weight ratio and improve the overall efficiency of lifting systems.

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Crane Hook, Finite Element Analysis (FEA), Topology Optimization, Stress Distribution, Structural Optimization, Weight Reduction

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The study aimed to assess the effectiveness of a Self-Instructional Module (SIM) on improving the knowledge of staff nurses regarding the nursing management of congestive cardiac failure (CCF). A quasi-experimental pre-test post-test control group design was used with 60 staff nurses. Findings showed significant improvement in knowledge scores in the experimental group compared to the control group. The study concludes that SIM is an effective educational tool

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Congestive Cardiac Failure, Self-Instructional Module, Nursing Management, Staff Nurses

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Abstract--Modern healthcare administration is currently facing two types of challenges at the same time: the continuous increase in operational costs and more people getting chronic disease. The study explores the use of computational intelligence AI and related techniques to better manage pressures. It does this by creating systems that help us to not only forecast potential problems or outcomes prognostic decision-making but also to implement preventative measures prophylactic decision- making before those problems become severe. By analyzing multi- dimensional health data, from clinical records and diagnostic imagery to genomic profiles, algorithmic systems identify subtle patterns in health trajectories with remarkable precision. The main benefit of this technology is that shifts healthcare from treating sickness to active and managing health problems. This means waiting for people to sick and then reacting with the treatment , the technology helps to manage the health actively to keep people healthy and happy. The main goal is to focus on health management rather than just treating problems. Key applications include the very early identification of patient populations at risk, prior to the manifestation of symptoms, and the implementation of timely, targeted interventions.On a system level, such predictive models will provide strategic guidance on the allocation of hospital resources, smoothing patient flow, and increasing administrative efficiency. For the individual, this means highly personalized treatment plans will be provided. These protocols are designed to be as effective as possible while causing the fewest negative side effects. The responsible use of such systems requires consideration of issues in implementation associated with data integrity, patient privacy, and the mitigation of algorithmic bias. The goal, rather, is not to supplant clinical judgment but to enhance it-a synergy created between human expertise and machine analysis. Such a partnership could form the basis for a more resilient, equitable, and effective standard of care in modern healthcare management.

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Maternal mortality remains a significant global public health concern, particularly in low- and middle-income countries where preventable infections contribute substantially to maternal deaths. Quality Improvement (QI) initiatives offer a structured approach to strengthen health systems, reduce clinical errors, and enhance maternal health outcomes. This article discusses the implementation of a QI project focused on infection prevention to reduce maternal mortality. The project incorporates evidence-based practices such as hand hygiene compliance, antimicrobial stewardship, aseptic techniques, safe childbirth checklists, and surveillance systems for early detection of maternal sepsis. It also highlights the importance of staff training, data-driven decision-making, leadership engagement, and multidisciplinary teamwork. The article concludes with recommendations for sustaining QI strategies in maternal health care settings

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Quality improvement, maternal mortality, infection prevention, maternal sepsis, obstetric care, patient safety, maternal health

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Managing complex obstetric patients requires seamless coordination among obstetricians/gynecologists (OB/GYN), critical care physicians, and nursing staff. Conditions such as hypertensive disorders, sepsis, cardiac disease, postpartum hemorrhage, and high-risk pregnancies demand rapid decision-making and specialized expertise from all disciplines. Interdisciplinary collaboration enhances patient safety, reduces maternal morbidity and mortality, and improves communication across units. This article explores evidence-based strategies to foster effective interdisciplinary teamwork in maternal critical care, examining communication frameworks, structured protocols, training programs, simulation-based learning, and leadership engagement. Challenges such as hierarchical barriers, role ambiguity, and fragmented information flow are discussed alongside solutions for building a collaborative culture. Recommendations for optimizing team performance and improving outcomes in complex obstetric care are presented

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Interdisciplinary collaboration, obstetric critical care, maternal health, OB/GYN, nursing, teamwork, patient safety

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