Volume 13 | Issue 7 |

 Abstract

The increasing complexity and scale of enterprise cloud deployments necessitate secure, scalable, and compliant governance models. As organizations adopt multi-account strategies on Amazon Web Services (AWS), the need for automated policy enforcement, continuous compliance, and real-time visibility becomes paramount. This review synthesizes current practices and academic research related to AWS Control Tower, Service Control Policies (SCPs), and guardrails, and introduces a novel theoretical model for adaptive governance across AWS Organizations. Drawing on recent case studies and technological advancements, the proposed model integrates policy-as-code frameworks, security data lakes, and AI-enhanced risk analytics to outperform traditional architectures in compliance accuracy, scalability, and response time. A comparative analysis demonstrates the superiority of this adaptive, data-integrated approach over baseline models. The paper concludes with recommendations for practitioners, policymakers, and researchers, offering a roadmap for the development of secure and reliable prediction systems for cloud-native infrastructures. This work contributes to both theoretical discourse and practical implementation, supporting the evolution of cloud security architecture in regulated and dynamic environments.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

AWS Control Tower, Guardrails, Multi-Account Architecture, Cloud Security, Service Control Policies (SCPs), Policy-as-Code, Compliance Automation, Zero Trust, Adaptive Governance, Cloud Risk Management, Continuous Compliance, Cloud Architecture Review

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The dynamic nature of modern sales environments demands automation solutions that are scalable, intelligent, and resilient. This review explores the integration of orchestrator patterns in distributed systems to automate sales planning processes, including forecasting, lead management, and quota allocation. Orchestrators such as Apache Airflow, Camunda, and Kubernetes-based tools facilitate seamless workflow execution, improving agility and performance. Through analysis of architectural models, experimental benchmarks, and AI-enabled orchestration, the review underscores the advantages of orchestrated automation over manual systems. Key challenges such as integration complexity, fault tolerance, and standardization are also discussed. This work contributes a comprehensive synthesis of the current landscape and offers actionable insights for future innovation in distributed sales automation.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Sales automation, distributed systems, orchestrator patterns, Apache Airflow, Camunda, Kubernetes, CRM integration, business process management, workflow orchestration, AI-driven forecasting.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Agile Project Management (APM) has evolved as a cornerstone methodology for navigating the intricacies of complex IT environments, particularly those shaped by rapid technological change, regulatory constraints, and distributed team structures. This review synthesizes over a decade of research, case studies, and emerging frameworks to understand how agile methodologies enhance project delivery, stakeholder satisfaction, and organizational adaptability when supported by appropriate scaling models and enabling technologies. The findings highlight key challenges such as legacy system integration, inter-team coordination, and cultural resistance while exploring innovative approaches like AI-assisted Scrum and DevOps synergies. The review concludes with a roadmap for future research to address current limitations and to leverage technological advancements for more intelligent and scalable agile ecosystems.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Agile Project Management, Complex IT Environments, Scaling Frameworks, DevOps, AI-Augmented Scrum, Organizational Agility, Continuous Delivery, Remote Collaboration, Hybrid Agile Models

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

As LTE continues to underpin global mobile communication infrastructure, the demand for robust, deterministic, and low-latency real-time software frameworks for LTE base stations (eNodeBs) is higher than ever. This review synthesizes over a decade of research on the architecture, implementation, and performance of embedded real-time software systems that enable LTE base station functionality. The analysis spans real-time operating systems (RTOS), hardware-software co-design, multi-core scheduling, and open-source frameworks. Key findings include the superiority of modular and layered architectures, the importance of deterministic task scheduling, and the growing role of FPGA-based acceleration. The paper concludes with a forward-looking perspective on how these frameworks will evolve in response to 5G, open RAN, and AI-driven radio access network (RAN) management. This review serves as a guide for researchers, engineers, and system designers aiming to optimize next-generation embedded communication systems.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

LTE Base Station; Real-Time Embedded Systems; RTOS; eNodeB; FPGA Acceleration; Multi-Core Scheduling; Hardware-Software Co-Design; MAC Scheduler; Turbo Decoding; 5G Evolution; Open RAN.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Pain is a localized or generalized unpleasant bodily sensation or complex of sensations that causes mild to severe physical discomfort and emotional distress and typically results from bodily disorder such as injury or disease. "The aim of the research is to assess the effectiveness of McKenzie back exercise among people with back pain at selected rural community at Gorakhpur." The research approach was quantitative and research design was quasi-experimental non randomized pre-test post-test design total 60 people were selected who met with inclusion criteria using purposive sampling technique and level of pain was assessed using Wong-Baker FACES Pain Rating Scale. The study revealed that for experimental group the calculated 't" value is 12.10 and for control group the calculated 't" value is 3.08. At 0.05 level of significance the table "t" value is 2.045. Since the calculated "t" value is more than table "t" value, hence the McKenzie back exercise was effective in reducing back pain. It was concluded that the significant association was found between post-test pain score with their selected demographic variables such as age, gender, education, occupation, religion, type of family, duration of pain, previous knowledge, past illness and consumption of analgesics medication. So, we concluded that the McKenzie back exercise is effective to reduce the back pain.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Assess, Effectiveness, McKenzie back exercise, People, Back Pain, Rural community

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Classification of fake news on social media has gained a lot of attention in the last decade due to the ease of adding fake content through social media sites. In addition, people prefer to get news on social media instead of on traditional televisions. These trends have led to an increased interest in fake news and its identification by researchers. This study focused on classifying fake news on social media with textual content (text classification). In this classification, four traditional methods were applied to extract features from texts (term frequency�inverse document frequency, count vector, character level vector, & n-gram vector employing different machine learning classifiers to categorize the fake news dataset.. This work provides a solution by introducing a fake news detection model using machine learning. This model requires prerequisite data extracted from various news websites. Web scraping technique is used for data extraction which is further used to create datasets. The data is classified into two major categories which are true dataset and false dataset. Classifiers used for the classification of data are Random Forest, Logistic Regression, Decision Tree & Gradient boosting.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

: fake news, text classification, TF-IDF, Machine Learning, Decision Tree Classifier, Logistic Regression, Gradient Boosting classifier, Random forest classifier, AdaBoosting , SVM.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

In villages, forests, riverbanks and scores of hidden places across the country, numerous women have risen quietly, but powerfully, to guard over the Earth. Living the values of Mahatma Gandhi through nonviolence, simplicity, and self-sufficiency, these women are leading with love, courage, and profound ecological insight. This essay focuses on such Gandhian women environmentalists--those who speak not just for their communities but for tegai, water and future generations. In a sense rooted in an ethics of care, their eco-activism is profoundly personal, derived from interactions with the natural world on a daily basis, and evolved from existential struggles for life.In movements such as Chipko, Narmada Bachao Andolan, and anti-mining protests in forested areas in their habitats, they are confronting structures of exploitation and propounding an alternative for social and economic growth that is premised on harmony rather than dominance. This paper investigates how their practices evoke Gandhi's influence in a very real way,while also making visible what their voices bring that is so pertinent to the ecologyof India, yet so frequently ignored. These women remind us that the making of new things, indeed, the making of a new, new world will hinge on care -- for the earth, each other, the generations to come and those long past to come

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Gandhian environmentalism, eco-feminism, women environmentalists, ethics of care, grassroots movements, nonviolence

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The sustainable utilization of waste rubber from end-of-life tyres (ELTs) represents a critical step towards achieving a circular economy. A significant proportion of discarded tyres globally are either landfilled or incinerated--processes that contribute to environmental degradation. This study explores an alternative path by incorporating crumb rubber (CR) as a partial substitute for river sand in cement mortar used for plastering applications. Experimental trials evaluated the performance of mortar containing varying mesh sizes (40, 60, 100) and volume fractions (10-15%) of CR, comparing results against conventional mortar. Key findings include a marginal reduction in compressive strength (up to 5-10%) and a consistent or improved flexural performance. Additionally, CR-modified mortars demonstrated reduced thermal conductivity (by 7-9%), contributing to energy savings in building interiors. Pull-out (adhesion) strength showed no adverse impact when CR was pre-treated with KMnO? and NaOH. The reduced density of CR mortars also contributes to lightweight construction. The results support the feasibility of CR as a sustainable material for plastering, precast panels, and energy-efficient construction.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Crumb Rubber, Cement Mortar, Energy Efficiency, Thermal Insulation, Circular Economy, Sustainable Construction, Building Materials

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

"Uttarakhand Ke Media Chhatron Mein Photo Patrakarita Ke Prati Rujhan: Ek Sarvekshanaatmak Adhyayan"

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

"Uttarakhand Ke Media Chhatron Mein Photo Patrakarita Ke Prati Rujhan: Ek Sarvekshanaatmak Adhyayan"

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Handwritten character recognition is an important component in many real-world applications, such as form processing automation and historical document scanning. Various methods have been advocated for mapping handwritten characters to machine-readable codes over the years. This paper introduces a hybrid deep learning method that integrates Convolutional Neural Networks (CNNs) for spatial feature discovery and Recurrent Neural Networks (RNNs), more specifically Long Short-Term Memory (LSTM) units, for sequence modelling. The model involves key preprocessing methods like normalization and missing value handling. The model was pre-trained on a large dataset of handwritten characters to make it stable. The hybrid CNN-RNN model is able to capture spatial and temporal patterns present in handwritten data well, resulting in high validation accuracy. For overcoming overfitting, dropout regularization was implemented. Nevertheless, noisy input and small dataset size restricted the model's ability to generalize. Additional improvements, such as sophisticated hyperparameter tuning and aggressive data augmentation, are needed to further boost performance. The study demonstrates the promise of hybrid deep learning models in handwritten character recognition and emphasizes the necessity for architectural enhancement and expansion of the dataset to facilitate more accurate and reliable performances.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Handwriting, Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Accuracy, LSTM, Visualization

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Creative Commons Attribution 4.0 and The Open Definition