International Journal of Recent Advances in Science and Technology
International Journal of Recent Advances in science and technology (ISSN: 2395-2318) is an official peer-reviewed journal published quarterly, with a total of 4 issues a year. The journal aims to publish high-quality scientific research in the field of science and technology. Original articles, review articles, short communications, and letters to the editor in science & technology are accepted. All manuscript is first double-blind reviewed by an Editorial Advisory Group with the power to recommend rejection or referral for review by one or more subject referees. Manuscripts from the review may be accepted, returned to the author for revision or rejected. All final decisions on a manuscript are at the Editor's discretion.
Agentic AI Framework for Multi-Cloud DevSecOps Automation and Software Supply Chain Security
The rapid adoption of multi-cloud environments has transformed software development and deployment practices, creating complex challenges in DevSecOps automation, security governance, and software supply chain protection. Traditional DevSecOps approaches often depend on rule-based automation, fragmented security tools, and human-driven decision-making, which limits scalability and responsiveness against emerging cyber threats. This research proposes an Agentic Artificial Intelligence (AI) framework designed to enhance multi-cloud DevSecOps automation and strengthen software supply chain security through autonomous, intelligent, and adaptive agents. The proposed framework integrates AI-driven agents capable of continuous monitoring, threat detection, policy enforcement, vulnerability management, compliance assessment, and automated remediation across heterogeneous cloud platforms. The framework combines machine learning, large language models, security orchestration, infrastructure-as-code validation, and supply chain intelligence to create a proactive security ecosystem. Agentic AI enables autonomous reasoning, collaboration among specialized agents, and dynamic decision-making throughout the software development lifecycle. The research methodology adopts a design science research approach involving framework development, architecture modeling, and evaluation through security automation scenarios. The proposed model aims to reduce operational complexity, improve threat response time, and enhance visibility across distributed cloud environments. The study contributes an intelligent DevSecOps paradigm that supports secure, resilient, and automated software delivery pipelines.
Copyright (c) 2025 International Journal of Recent Advances in Science and Technology
Agile Project Governance in Hybrid Workplaces: Balancing Engineering Efficiency and Team Autonomy
Agile methods were codified for co-located teams: the informative workspace, the stand-up huddle, and osmotic communication were architectural assumptions, not incidental details. Hybrid work — a persistent mix of remote and on-site participation — has removed those assumptions while leaving the ceremonies in place, and organisations have responded with a governance reflex: more tooling telemetry, more written process, more reporting. The result is a mounting tension between engineering efficiency, now pursued through standardisation and measurement, and team autonomy, the motivational core of agility. This paper analyses that tension and proposes a governance model for agile delivery in hybrid settings. We identify four governance dilemmas — visibility versus surveillance, standardisation versus self-organisation, synchronisation versus focus, and accountability versus psychological safety — and argue that each dilemma is resolvable through outcome-anchored, minimally invasive governance mechanisms rather than through activity control. The resulting model, organised around guardrails (non-negotiable constraints), instrumentation (flow-level rather than person-level metrics), cadence design (deliberate allocation of synchronous bandwidth), and team-owned working agreements, restores the division of labour that made agile governance coherent: leadership governs outcomes and boundaries; teams govern their own methods. We map dilemmas to mechanisms in a reference table and derive implications for engineering leadership, PMO redesign, and future empirical study of hybrid agile performance.
Ethical Procurement and Green IT Strategy: Integrating Sustainability into Enterprise Technology Management
Enterprise technology has become a material sustainability issue in its own right: data centres and networks draw significant and growing energy; device fleets embody carbon, conflict minerals, and labour risk across opaque supply chains; and accelerating refresh cycles feed one of the world’s fastest-growing waste streams. Yet in most organisations, the two functions that could act on this — IT management and procurement — pursue sustainability separately, with IT focused on operational energy efficiency and procurement on supplier codes of conduct, leaving the largest impacts (embodied emissions, lifecycle extension, end-of-life integrity) in the gap between them. This paper develops an integrated framework for ethical and green enterprise technology management structured around the technology lifecycle: demand governance (whether to buy at all), sourcing (what and from whom), operation (how efficiently), lifetime management (how long), and end-of-life (what happens after). For each stage we specify decision principles, management practices, and governance instruments, and we consolidate these into a reference table linking stages to practices and to the maturity conditions under which each becomes feasible. We argue that the pivotal, least-practised interventions are demand governance and lifetime extension — because avoided and deferred purchases dominate lifecycle impact — and that embedding sustainability criteria into procurement scoring, contract clauses, and asset-management policy converts espoused ESG commitments into enforceable operating decisions. Implications are drawn for CIO–CPO collaboration, ESG reporting integrity, and research on green IT maturity.
Leveraging Federated Artificial Intelligence for Hybrid Cloud Data Intelligence and Secure Enterprise Integration
The rapid expansion of hybrid cloud computing has enabled enterprises to integrate distributed data sources, optimize business operations, and accelerate digital transformation. However, managing sensitive organizational data across heterogeneous cloud environments presents significant challenges related to privacy, security, governance, interoperability, and regulatory compliance. Traditional centralized Artificial Intelligence (AI) models require data consolidation, increasing the risks of data exposure, privacy violations, and compliance issues. This research proposes a Federated Artificial Intelligence (Federated AI)-enabled hybrid cloud framework that supports secure enterprise data intelligence and intelligent integration without transferring sensitive data from local environments. The proposed framework enables decentralized machine learning by allowing AI models to be trained collaboratively across distributed cloud infrastructures while preserving data privacy. Hybrid cloud platforms provide scalable computational resources, cloud-native services, and intelligent orchestration for enterprise analytics, whereas secure aggregation, encryption, Zero Trust Security, and federated governance ensure confidentiality and regulatory compliance. Machine learning, deep learning, and predictive analytics continuously analyze distributed enterprise data, identify operational patterns, detect anomalies, and generate intelligent business insights. The framework further incorporates intelligent automation, identity and access management, API security, and continuous monitoring to strengthen enterprise resilience and cloud governance. By integrating Federated AI with hybrid cloud computing, organizations can improve data intelligence, strengthen cybersecurity, enhance collaboration, optimize operational efficiency, and accelerate secure enterprise digital transformation while maintaining privacy and regulatory compliance.
Cyber Resilient Cloud Computing Platform with Artificial Intelligence-Driven Predictive Analytics for Enterprise Transformation
The rapid evolution of digital technologies has transformed the way organizations manage business processes, analyze data, and make strategic decisions. Intelligent cloud computing platforms integrated with artificial intelligence (AI)-driven predictive analytics have emerged as powerful solutions for enterprise transformation. These platforms combine scalable cloud infrastructure, machine learning algorithms, big data analytics, and automation to enhance operational efficiency, improve decision-making, and create sustainable competitive advantages. AI-driven predictive analytics enables organizations to forecast future trends, identify potential risks, optimize resource utilization, and personalize customer experiences by analyzing large volumes of structured and unstructured data. Cloud computing provides flexible, cost-effective, and secure environments that support real-time data processing and seamless collaboration across geographically distributed enterprises. This study examines the role of intelligent cloud computing platforms in facilitating digital transformation through predictive analytics. It reviews the existing literature, identifies key technological components, and proposes a research methodology for evaluating the effectiveness of AI-enabled cloud solutions in modern enterprises. The findings are expected to demonstrate that integrating AI with cloud computing significantly improves organizational agility, innovation, and business performance while addressing challenges related to data security, privacy, governance, and implementation complexity. The study contributes to understanding how intelligent technologies reshape enterprise operations and support long-term organizational growth in the digital economy.
Copyright (c) 2026 International Journal of Recent Advances in Science and Technology
Architecting Federated Learning with Multi-Cloud Infrastructure for Privacy-Preserving Enterprise Cybersecurity Analytics
The increasing frequency and sophistication of cyber threats have compelled enterprises to adopt intelligent security analytics capable of detecting, predicting, and mitigating attacks in real time. However, conventional centralized machine learning approaches require organizations to aggregate sensitive security logs and user data into a single repository, creating significant privacy, regulatory, and operational challenges. Federated Learning (FL) has emerged as a promising paradigm that enables collaborative model training without exposing raw data, thereby preserving organizational confidentiality while leveraging distributed intelligence. When integrated with multi-cloud infrastructure, federated learning enhances scalability, fault tolerance, computational flexibility, and resilience by utilizing heterogeneous cloud platforms across multiple service providers. This study explores the architectural design of federated learning within a multi-cloud environment for enterprise cybersecurity analytics, emphasizing secure model aggregation, privacy preservation, and cross-organizational collaboration. The research examines existing literature, identifies architectural challenges, and proposes a methodology for implementing privacy-preserving cybersecurity analytics through distributed learning frameworks. Security mechanisms including differential privacy, secure aggregation, encryption, and blockchain-based auditing are considered to strengthen trust and protect model updates from adversarial attacks. The proposed architecture aims to improve threat detection accuracy while maintaining compliance with data protection regulations. The findings contribute to developing scalable, secure, and privacy-aware cybersecurity ecosystems capable of supporting modern enterprises operating across geographically distributed and cloud-enabled environments.
Developing Predictive Enterprise Intelligence Using Federated Learning with Autonomous Analytics in Cloud Computing
In the modern digital economy, enterprises generate vast amounts of highly sensitive, distributed operational data across multi-cloud environments, edge nodes, and regional databases. Traditional centralized predictive intelligence frameworks demand the ingestion of this raw data into a single cloud data warehouse, presenting severe challenges regarding data gravity, bandwidth saturation, and regulatory compliance. To overcome these limitations, this paper presents a novel framework for Developing Predictive Enterprise Intelligence Using Federated Learning with Autonomous Analytics in Cloud Computing. The proposed architecture decentralizes model training by deploying autonomous, self-tuning machine learning models directly to localized enterprise nodes. These localized models process data at the source, transmitting only secure parameter updates back to a central cloud orchestrator. The orchestrator dynamically aggregates these updates using a secure federated averaging protocol to synthesize a globally optimized predictive model. Furthermore, the framework integrates autonomous analytics to automate feature engineering, hyperparameter optimization, and model validation at the edge, eliminating manual operational bottlenecks. Experimental evaluations indicate that this collaborative, privacy-preserving paradigm reduces data transmission overhead by up to 85%, significantly mitigates data privacy risks, and maintains predictive performance comparable to traditional centralized machine learning models.
Empowering Connected Enterprises through Artificial Intelligence Secure Digital Infrastructure and Resilient Cloud Engineering
The rapid evolution of digital technologies has transformed the way enterprises operate, collaborate, and deliver services in highly connected business environments. Artificial Intelligence (AI), secure digital infrastructure, and resilient cloud engineering have emerged as foundational technologies that enable organizations to improve operational efficiency, strengthen cybersecurity, and support continuous innovation. AI-driven analytics and intelligent automation facilitate real-time decision-making, predictive maintenance, customer personalization, and business process optimization. Simultaneously, secure digital infrastructure ensures confidentiality, integrity, and availability of enterprise data through advanced identity management, encryption, zero-trust security, and continuous monitoring. Resilient cloud engineering provides scalable, fault-tolerant, and highly available computing environments capable of supporting mission-critical applications while minimizing downtime and enhancing disaster recovery capabilities. The integration of these technologies creates intelligent enterprise ecosystems capable of adapting to dynamic market conditions, cyber threats, and increasing customer expectations. This study explores the synergistic relationship among AI, secure digital infrastructure, and cloud resilience in empowering connected enterprises. It highlights architectural components, implementation strategies, technological benefits, and operational challenges associated with enterprise digital transformation. The paper demonstrates that organizations adopting integrated AI-enabled cloud platforms with robust security frameworks achieve improved business continuity, enhanced governance, optimized resource utilization, and sustainable competitive advantage. These findings provide valuable insights for researchers, industry practitioners, and policymakers designing next-generation intelligent enterprise systems.
Machine Learning Models for Early Detection of Chronic Stress Through Neuroendocrine and Immune Biomarkers
Chronic stress represents one of the most prevalent and undertreated contributors to global morbidity, increasing risk for cardiovascular disease, metabolic syndrome, psychiatric disorders, and immunosuppression, yet current clinical assessment relies almost exclusively on self-report instruments that are vulnerable to recall bias, stigma-related underreporting, and limited sensitivity for subclinical stress states. Objective biomarker-based early detection offers a transformative pathway toward timely, stigma-reduced intervention, but the multivariate, temporally dynamic nature of the neuroendocrine and immune response to chronic stress has historically resisted the threshold-based diagnostic paradigms of conventional clinical chemistry. Machine learning (ML) provides the analytical tools to integrate complex, high-dimensional biomarker profiles into clinically actionable stress detection systems. This article presents a comprehensive review and original meta-analysis of ML models applied to early detection of chronic stress using neuroendocrine biomarkers — including cortisol, dehydroepiandrosterone sulphate (DHEA-S), alpha-amylase, melatonin, and neuropeptide Y — and immune biomarkers — including interleukin-6 (IL-6), tumour necrosis factor-alpha (TNF-α), C-reactive protein (CRP), natural killer (NK) cell activity, and lymphocyte subpopulation ratios. Synthesising 97 peer-reviewed studies published between 2015 and 2024, we evaluate ML performance across five analytical tasks: binary stress classification, continuous stress severity estimation, longitudinal stress trajectory prediction, stress phenotype clustering, and multimodal biomarker-wearable integration. Our meta-analysis reveals that ensemble models — particularly Gradient Boosted Machines and Random Forests incorporating both neuroendocrine and immune biomarker panels — achieve area under the receiver operating characteristic curve (AUROC) values of 0.87–0.94 for chronic stress classification, substantially outperforming single-biomarker thresholding (AUROC 0.61–0.74). Transformer-based deep learning architectures applied to longitudinal diurnal cortisol profiles achieve predictive accuracy for 6-month stress trajectory estimation of R² = 0.79–0.86, enabling prospective risk stratification for stress-related disease. We introduce the Biomarker-Integrated Stress Phenotyping (BISP) framework — a clinically deployable ML pipeline encompassing specimen collection standardisation, multi-biomarker feature engineering, model training and validation, and output communication for clinical decision support. Critical barriers — including biomarker pre-analytical variability, lack of longitudinal population biobanks, model generalisability across ethnically diverse cohorts, and regulatory pathways for AI-based diagnostic classification — are examined in depth. The article concludes with a research agenda and implications for clinical implementation of ML-based chronic stress biomarker diagnostics.
Nature-Based and AI-Enabled Stormwater Management for Sustainable Urban Development
The convergence of nature-based solutions (NbS) and artificial intelligence (AI) presents an unprecedented opportunity to transform urban stormwater management from a single-purpose drainage challenge into an integrated, adaptive, and ecologically intelligent system supporting sustainable urban development. This paper presents the first comprehensive review of hybrid NbS-AI integration strategies for urban stormwater management, synthesizing evidence from 115 peer-reviewed studies, engineering case studies, and pilot programs published between 2018 and 2025 across six continents. We examine how AI techniques — including deep learning, reinforcement learning, physics-informed neural networks, and digital twin platforms — are being coupled with nature-based infrastructure elements such as bioretention systems, constructed wetlands, urban forests, green roofs, and blue-green corridors to create stormwater systems that are simultaneously high-performing, self-optimizing, and ecologically embedded. Quantitative evidence demonstrates that NbS-AI integrated systems achieve 38–52% improvements in hydrological performance metrics over standalone NbS designs, reduce operational maintenance costs by up to 35%, and extend effective flood forecast lead times by 6–8 hours compared to conventional models. We propose the Nature-AI Urban Water Framework (NAUWF) — a five-pillar architecture for implementing NbS-AI integrated stormwater systems across diverse urban development contexts — and identify the governance, data, and capacity-building prerequisites for its global deployment. Cross-cultural dimensions of NbS-AI integration, including knowledge equity, community co-design, and adaptive governance, are examined as foundational rather than peripheral concerns for sustainable implementation.
Predictive Cloud Resource Optimization and Intelligent Workload Scaling for Enterprise Business Intelligence Systems
Enterprise Business Intelligence (BI) systems operating in modern cloud environments face increasing complexity due to rapidly changing workloads, massive data ingestion, and diverse analytical processing demands. Traditional static or reactive resource provisioning approaches are insufficient for handling dynamic enterprise workloads, often leading to inefficiencies such as resource underutilization, high operational costs, and performance degradation during peak demand. Predictive cloud resource optimization introduces a proactive paradigm where historical data, machine learning models, and statistical forecasting techniques are leveraged to anticipate workload fluctuations and allocate resources in advance. Intelligent workload scaling complements this approach by dynamically adjusting compute, storage, and network resources in response to predicted demand patterns. This integrated strategy enhances system responsiveness, reduces latency, improves service availability, and ensures compliance with enterprise-level Service Level Agreements (SLAs). The convergence of artificial intelligence, cloud computing, and distributed systems enables adaptive BI infrastructures capable of self-optimization. This study explores predictive scaling frameworks, evaluates machine learning-based forecasting models, and examines orchestration mechanisms used in enterprise cloud ecosystems. It highlights how predictive analytics transforms traditional BI infrastructures into intelligent, autonomous systems capable of continuous optimization. The findings suggest that predictive resource management significantly improves operational efficiency, cost-effectiveness, and scalability while reducing energy consumption and infrastructure waste in enterprise environments.
Towards Smart Cities: AI and SAP for Integrated Urban Water and Waste Management
Rapid urbanisation is placing unprecedented stress on municipal water distribution and solid waste management systems, while simultaneously generating vast quantities of sensor, operational, and citizen data that intelligent systems can exploit. This article examines how the convergence of artificial intelligence (AI) and SAP enterprise software—particularly SAP S/4HANA, SAP Business Technology Platform (BTP), SAP AI Core, SAP IoT, and the Joule generative AI copilot—can enable city administrations and utility operators to transition from reactive infrastructure management to proactive, data-driven intelligent urban services. We map AI techniques including time-series deep learning, computer vision, anomaly detection, and reinforcement learning against specific water and waste management use cases: demand forecasting, non-revenue water reduction, real-time quality monitoring, predictive asset maintenance, dynamic waste collection routing, automated material recovery, and biogas yield optimisation
The Fourth Industrial Revolution and Its Impact on Social Structures and Development
Background: The Fourth Industrial Revolution (4IR) represents a convergence of digital, biological, and physical technologies that is fundamentally reshaping global social structures. Unlike preceding industrial transitions, 4IR operates at an exponential pace and a systems-level scale, simultaneously disrupting labour markets, governance frameworks, healthcare delivery, education systems, and cultural identities. Objective: This review examines how 4IR technologies—including artificial intelligence (AI), blockchain, the Internet of Things (IoT), big data analytics, and advanced robotics—are transforming social structures across developed and developing economies, with particular focus on equity, governance, and sustainable human development. Methods: A systematic narrative review was conducted drawing on peer-reviewed literature, World Economic Forum reports, United Nations Development Programme frameworks, and empirical studies published between 2015 and 2024. Conclusion: While 4IR presents transformative opportunities for accelerating development, it simultaneously risks entrenching structural inequalities. Policy responses must be proactive, inclusive, and grounded in ethical frameworks that centre human dignity and social cohesion.
Scalable Web Services Architecture for Healthcare Data Processing
Healthcare organizations are facing a challenge concerning the rapid growth of healthcare data that they have to manage, process, and efficiently deliver.The increasing volume of healthcare data from electronic health records, medical imaging systems, wearables, remote patient monitoring platforms, and clinical decision support applications is causing difficulties for healthcare organizations. Many traditional healthcare information systems face challenges with scalability, interoperability, reliability, and the need for real-time access to critical patient information as these needs continue to grow. To meet these challenges, scalable web services architectures have become a viable option, by providing distributed, service-oriented, and cloud-based healthcare environments. This paper discusses the design and deployment of a scalable web services based system for healthcare data processing. The study focuses on the integration of service-oriented architecture (SOA), cloud computing, big data technologies, web services and the Internet of Things (IoT) infrastructure for enabling efficient management of healthcare information. The proposed architectural structure involves several layers, such as presentation, service, processing, storage, and security layers that provide seamless data exchange, high system availability, flexibility in resource usage, and secure healthcare operations. In addition, the study explores the impact of cloud-based platforms, data lakes, healthcare analytics systems, and remote patient monitoring services on enhancing healthcare service provision and operational efficiency. The results show that scalable web services architectures can considerably improve access to healthcare data, as well as interoperability and processing capabilities, while enabling large-scale clinical and administrative applications. The study also identifies important issues for security, data privacy protection, data integration and system reliability. The findings overall show that scalable web services architectures offer a solid platform for the design of secure, resilient, and efficient healthcare information systems that can meet the needs of today's healthcare markets.
Digital Transformation in Academic Libraries: A Comparative Analysis of ICT Impact in Government and Private Medical Universities of Rajasthan
This study examines the impact of Information and Communication Technology (ICT) on academic library services in selected medical universities of Rajasthan, with a comparative focus on government and private institutions . A quantitative research design was adopted, and data were collected from 400 respondents using a structured questionnaire. Statistical analyses, including t-test, ANOVA, and correlation, were applied to evaluate differences and relationships among key variables. The findings reveal that private universities demonstrate significantly higher ICT impact than government institutions (p < 0.001), indicating more effective adoption and utilization of digital technologies. ICT-based resources such as e-books and online databases were found to significantly enhance accessibility and research efficiency. A strong positive correlation between ICT usage and ICT impact (r = 0.902) highlights that effective utilization is critical for maximizing benefits. The study concludes that while ICT has substantially improved library services, disparities in infrastructure and implementation persist, emphasizing the need for strategic development and user-focused approaches to achieve sustainable digital transformation.
Next-Generation Enterprise Transformation through Quantum Security Federated AI and Autonomous Cloud Operations
The accelerating pace of digital transformation is reshaping enterprise operations, requiring organizations to adopt innovative technologies capable of addressing emerging security, privacy, scalability, and operational challenges. Future-ready enterprises are increasingly exploring the convergence of quantum security, federated artificial intelligence, and autonomous cloud operations to establish resilient, intelligent, and adaptive digital ecosystems. Quantum security introduces advanced cryptographic mechanisms designed to withstand the computational capabilities of quantum computers, ensuring long-term protection of sensitive information and critical business assets. Federated artificial intelligence enables collaborative machine learning across distributed environments while preserving data privacy and regulatory compliance, making it particularly valuable in data-sensitive industries. Autonomous cloud operations leverage artificial intelligence, machine learning, automation, and predictive analytics to optimize infrastructure management, improve resource utilization, and reduce human intervention in operational processes. The integration of these technologies creates a comprehensive framework for secure, intelligent, and self-managing enterprise environments. This study examines the theoretical foundations, technological advancements, and organizational implications associated with the convergence of quantum security, federated AI, and autonomous cloud operations. Through an extensive review of existing literature and a qualitative analytical research methodology, the study explores how these technologies collectively enhance enterprise resilience, cybersecurity, operational efficiency, and innovation capabilities. The findings suggest that future-ready enterprises will increasingly depend on intelligent and autonomous digital infrastructures capable of adapting to evolving technological and business requirements while maintaining security, privacy, and sustainability.
Cloud-Driven SAP Enterprise Systems for Artificial Intelligence Reliability and Digital Transformation Frameworks
Cloud-driven SAP enterprise systems have emerged as a cornerstone of modern digital transformation strategies, enabling organizations to integrate artificial intelligence (AI), advanced analytics, and scalable cloud computing architectures into core business processes. This study explores the role of cloud-enabled SAP ecosystems in enhancing AI reliability, operational efficiency, and enterprise-wide digital transformation. The research examines how SAP S/4HANA and related cloud-based enterprise solutions support intelligent automation, predictive analytics, real-time decision-making, and integrated business process management. It also investigates the impact of AI-driven modules within SAP systems for improving data accuracy, system reliability, and workflow optimization across distributed enterprise environments. The study highlights the integration of machine learning models, cloud infrastructure services, and intelligent ERP frameworks to enhance enterprise agility and resilience. A qualitative research methodology is adopted using secondary data from academic literature, industry reports, and enterprise case studies. Findings suggest that cloud-driven SAP systems significantly improve scalability, system reliability, and digital transformation outcomes while enabling organizations to transition from traditional ERP models to intelligent enterprise architectures. However, challenges such as integration complexity, data migration issues, and security risks remain critical barriers. The study concludes by proposing a conceptual framework for AI-powered cloud SAP ecosystems supporting sustainable enterprise innovation.
Scalable AI-Enabled Enterprise Platforms for Cybersecurity Predictive Intelligence and Autonomous Governance
AI-powered distributed computing combined with secure cloud transformation frameworks is redefining the architecture of intelligent enterprise applications. Modern enterprises require scalable, resilient, and adaptive computingenvironments capable of processing massive datasets while maintaining high levels of security and performance. Thispaper explores the integration of artificial intelligence techniques with distributed computing systems deployed overhybrid and multi-cloud infrastructures. The proposed framework enhances workload optimization, resource allocation,and predictive scaling through machine learning-based orchestration. Additionally, secure cloud transformationmechanisms such as zero-trust security models, encryption protocols, and AI-driven threat detection ensure robustprotection against cyber threats. The study highlights improvements in latency reduction, system throughput, andoperational efficiency across enterprise workloads. Furthermore, it addresses challenges such as interoperability, dataconsistency, and computational overhead introduced by AI integration. The findings suggest that combining distributedcomputing with intelligent automation significantly enhances enterprise agility and decision-making capabilities. Theframework also supports seamless migration of legacy systems into cloud-native architectures, enabling digitaltransformation at scale. Overall, this research establishes a foundation for next-generation intelligent enterprise systemsthat are secure, adaptive, and highly scalable.
AI-Powered Cyber Defense and Cloud Computing Frameworks for Intelligent Enterprise Applications Systems
Artificial Intelligence (AI)-powered cyber defense and cloud computing frameworks are transforming modern enterprise application systems by enabling intelligent, scalable, and secure digital infrastructures. As organizations increasingly adopt cloud-based platforms for business operations, the demand for advanced cybersecurity mechanisms has grown significantly. Traditional security systems often fail to detect sophisticated cyber threats, resulting in data breaches, financial losses, and operational disruptions. AI-driven cybersecurity solutions utilize machine learning, deep learning, and predictive analytics to identify anomalies, detect threats in real time, and automate incident response processes. Simultaneously, cloud computing frameworks provide flexible, cost-effective, and highly available environments that support intelligent enterprise applications such as big data analytics, Internet of Things (IoT), and enterprise resource planning systems. The integration of AI with cloud computing enhances system performance, improves resource optimization, and strengthens organizational resilience against evolving cyberattacks. This study explores the role of AI-powered cyber defense mechanisms and cloud computing architectures in intelligent enterprise systems. It further examines current technological trends, security challenges, implementation methodologies, advantages, and limitations associated with these technologies. The research emphasizes the importance of secure, intelligent, and adaptive enterprise frameworks capable of supporting future digital transformation initiatives while ensuring data confidentiality, integrity, and availability in dynamic cloud environments.
A Hybrid Low Power Fast Full Adder Using XOR And XNOR Gates
In this paper, novel circuits for XOR/XNOR and simultaneous XOR–XNOR functions are proposed. The proposed circuits are highly optimized in terms of the power consumption and delay, which are due to low output capacitance and low short-circuit power dissipation. Many existing XOR-XNOR cells suffer from non full-swing outputs, high power consumption and low speed issues. In this paper, a new fast, full-swing and low-power XOR XNOR cell, is presented. In this paper, a hybrid 1-bit full adder design employing both complementary metal–oxide–semiconductor (CMOS) logic and transmission gate logic is reported. The design was first implemented for 1 bit and then extended for 32 bit also. We also propose six new hybrid 1-bit full-adder (FA) circuits based on the novel full-swing XOR–XNOR or XOR/XNOR gates. Each of the proposed circuits has its own merits in terms of speed, power consumption, power delay product (PDP), driving ability, and so on. To investigate the performance of the proposed designs, extensive HSPICE and Cadence Virtuoso simulations are performed. The simulation results, based on the 65-nm CMOS process technology model, indicate that the proposed designs have superior speed and power against other FA designs.
A Comprehensive Review on Liver Sepsis and Its Effect on Human Physiology
Sepsis is a common disease which morbidity and mortality rate in the recent years remain high. Sepsis disease is common in growing population and its irregular affects patient with cancer and underlying immunosuppression. Sepsis in its severe form causes multi-organ dysfunctions that produce a state of severe immune dysfunction and catabolism. No specific medication targeting the mediators of sepsis has yet proven effective. The liver acts a double-edged sword in sepsis. It remains a leading cause of death in United States the world health assembly and WHO made sepsis disease a global health priority in recent year. Sepsis irregular effect on organs like liver damage remains imprecise no specific therapy has yet not proven to control this irregular effect. Mortality rates of sepsis and septic shock have not improved in the past decade. The Surviving Sepsis Campaign (SSC) guidelines released in 2017 emphasize early recognition and treatment of sepsis, in an effort to reduce the burden of sepsis worldwide. No single solution is likely to be beneficial, and sepsis continues to be an entity that should receive high priority for the development of precision health approaches for treatment. Review concluded the impact of sepsis severity in the recent year, a diagnostic tool the influence of outcome as well as pathophysiological effects, including the cellular event leading to liver dysfunction. Finally, the role of liver is host immune response to sepsis and therapeutic considerations with the weakness of the relevant specific approach are examined.
Design and Analysis of Pneumatic Clamp for Turn Mill Application
The system which works using compressed air as working fluid is the pneumatic brake system. Pneumatic clamps are used in air actuated mechanisms to operate the clamping action. Pneumatic clamps are the ideal one in quick operation and economical to use in short run jobs. The pneumatic clamp structure should be designed to resist vibration under various performance conditions. The purpose of this work is design and analysis of pneumatic clamp to support milling operation on turn spindle. The 3-Dimensional Modelling of pneumatic clamp is done using Solid works 2016 software and Finite Element Analysis is performed using ANSYS workbench 15.0.7 software. Boundary conditions are applied as the base fixed and moment at the end of the shaft which is clamped to the spindle of lathe. Expected deflection of pneumatic clamps shaft is 20μ, however the deflection from the numerical solution is 15.85μ. Pneumatic clamp working frequency is 0.5 Hz and from the modal analysis, it is found that no frequency is falling near to the working frequency. Therefore, resonance does not occur for these conditions. Analysis results of deflection are compared with theoretical results. It is found that percentage error are in the permissible limits to validate the model.
Phytochemical Screening and Anti-inflammatory activity of Murraya Koenigii & Ficus Lacor roots in Carrageenan, Histamine and Serotonin induced paw edema in albino Wistar rats
Objective: To perform the Phytochemical Screening & Anti-inflammatory activity of Murraya Koenigii & Ficus Lacor roots in Albino Wistar rats. Methods: The different Pharmacognostical parameters were evaluated as per standard procedure. The crude drug was evaluated for organoleptic properties shape, size, color, odor, taste. Preliminary Phytochemical Screening was carried out & finally Anti-inflammatory activity was evaluated by adopting different methods. Results: The extracts showed a marked Anti-inflammatory effect. The FLET fraction from Ficus lacor aerial roots showed maximum inhibition (75%) of Carrageenan induced edema (Table 3.12B), followed by FLPE and MKCF from Murraya koenigii roots (74 % and 70% approx) (p <0.001 for all). The inhibition of inflammation was comparable for all the fractions for any change in extent & percentage of inhibition at 30 min, 1hr, 2hr and 3hr. The anti-inflammatory effect induced via indomethacin gradually increased and reached at higher level (80.8%) after 3 hrs. It was maintained up to six hours. For, MKPE the inhibition was observed to be maximum at the end of 2 hrs and then tapered. MKEA, FLEA and FLCF showed minimum response that was constant throughout and insignificant. MKPE was slightly significant (55.1 %, p<0.05) which was more effective. Conclusion: The results of the study indicate that the extract of Murraya koenigii roots and Ficus lacor possesses strong Anti-inflammatory activity. This study also describes therapeutic effect of Murraya koenigii roots and Ficus lacor aerial roots in inflammation and arthritis which will give a new direction for the future scientific research.
Wireless Sensing Element Network-Based Pet Location Observation System for Domestic Situations
Pets need special care and tracking system to track their path in case of lost. Due to our busy life style, this task is not simple as it used to be. The aim of this work is to design and implement smart pet house and tracking system. The objective of this paper is to allow pets owners to automate simple things like monitoring and tracking of the path of pets. Also lighting, feeding and air-conditioning controls. The proposed work addresses the implementation and analysis of a wireless sensor networks based pet location monitoring system for domestic scenarios. These systems describes, finding the path of lost pet using fitbit flex scale device and also using GPS tracker. Pet begin moving before you get a GPS signal, the tracker will calculate distance using steps and stride length as described above until a GPS signal is found.
Network Security in the Age of Reinforcement Learning
In the internet age, networks are everywhere. The ability to connect on a call, to browse the web, to push your content for viewers online, to send someone a message and to do a billion of other things comes from the availability of networks. As the networks have diversified and have become omnipresent, they have also become vulnerable and exposed. Thus, network security has become a huge priority for commercial and personal users. The concern of data leakage or stealing is not only a question of privacy but many also incur loss of money, property and sometimes even identity. Fortunately, along with the growth of computer networks, computational methods and practices such as machine learning, cryptography, block-chain etc. have also evolved and have become more efficient and applicable to solve real world problems. One such area of computer science, that is just breaking out of its shell is Reinforcement Learning. Reinforcement Learning is a field within Machine Learning which aims to make machine make intelligent and planned decisions. This paper discusses some of the recent works done in the field of network security using Deep Reinforcement Learning.
Determining the Effect of Using the Fordyce Joy Pattern on Stress Anxiety and Depression of Diabetics in the Selected Hospitals of the Faculty of Medical Sciences of Abadan
Background and Aims: Diabetes is one of the most common metabolic diseases with debilitating effects, with a chronic and imperceptible course, and is on the rise, and is a source of stress for people with the disease. In addition to the physical effects of diabetes, stress also has a negative psychological effect, the most important of which is depression. Therefore, the aim of this study was to investigate the effect of using Fordyce happiness pattern on stress, anxiety, and depression in diabetic patients. Method: The present study was a two-group intervention study in two stages before and immediately after the intervention, which was performed on 50 patients with diabetes hospitalized in Abadan educational and medical hospitals in 2019. Samples were randomly selected and then randomly divided into intervention and control groups. Happiness training was given to patients in 3 sessions by the researcher and to collect data, a 21-item DASS questionnaire was used as a pre-test and post-test in both groups. Results: The results of independent t-test showed that the mean stress, anxiety, and depression before the intervention were not significantly different between the two groups (p> 0.05) but in the intervention group between the two groups were statistically different (p <0.05). Intragroup comparisons also showed that the mean stress, anxiety, and depression in the control group after the intervention did not have a statistically significant difference (p <0.05) but in the test group after the intervention there was a statistically significant difference (p <0.05). Conclusion: The results indicate the effectiveness of Forday's happiness model on the level of anxiety, stress, and depression of diabetic patients, so it is recommended that this model be used as a suitable guide and solution in clinics and diabetes centers.
A Survey on Encryption Algorithms – RSA, DES, AES, And SIT Algorithm
Encryption is the way towards scrambling a message with the goal that just the proposed beneficiary can read it. Encryption can give methods for anchoring data. As more data is put away on PCs or conveyed by means of smart gadgets, the need to guarantee that this data is resistant to snooping as well as altering turns out to be more significant. With the quick movement of computerized information trade in an electronic way, Information Security is winding up considerably more essential in information stockpiling and transmission. Data Confidentiality has a noticeable criticalness in the investigation of morals, law and most as of late in Information Systems. With the advancement of human knowledge, the specialty of cryptography has turned out to be more mind-boggling with the end goal to make data more secure. Varieties of Encryption frameworks are being conveyed in the realm of Information Systems by different associations. In this paper, an overview of different Encryption Algorithms is introduced.
Descartes and the paradigm of Western medicine: an essay
This essay is a contribution to the study of the CartesianDualism (separation of mind and body) and Mechanicism (the body is a machine) that have contributed to the paradigm of Western medicine. These Cartesian ideas of separation and fragmentation of the reality have hampered the implementation of the new paradigm in health care in contemporary society, where the patient is seen in an holistic way: soul-mind-body.
Copyright (c) 2018 International Journal of Recent Advances in Science and Technology
A Review Paper on Specimens Size and Shape Effects on the Concrete Properties
Concrete properties are the most essential and critical physical material property when reinforced concrete structures are planned. Because of the development and in types and nature of material used to enhance the concrete innovation, materials and mix proportions, test measure, mixing strategy, and testing condition have noteworthy impacts on highlights of concrete strength, in light of the fact that the control specimen sizes and shapes might be unique in relation from State to State. Testing of mechanical properties (especially compressive strength and tensile strength) of concrete is one of the most urgent stages of development. To control the nature of the concrete, there are different molds used for pouring concrete samples according to different directions in different countries. Many researchers have realized that the specific shapes and sizes of concrete samples can cause different types in the consequences of compressive strength or tensile strength. The relationship between the compressive strength of the concrete cube and the cylinder is complex. The cylinder and concrete cube samples were compared in the pressure test by studying previous research, including the test methodology, factors affecting the cylinder / cube strength ratio, and the coefficients, equations and components of the conversion factors. Previous attempts to determine experimental transformational relationships and conversion factors have proved that it is disturbing (if not impossible) to predict the relationships between the forces of the cylinder and the cube. Previous research has shown that the ratio of the strength of the cylinder / cube is between 0.65 and 0.90, despite the fact that the ratios outside this range have been found similarly. In light of this review of previous research, the test of the cylinder is not initiated by testing the cube. This study is a review of the sample size and shape effect on concrete properties.
The integration of time-based single frequency double differencing carrier phasegps / micro-electro-mechanical system-based INS
The integration of Global Positioning System (GPS)/Inertial Navigation System (INS) is used to overcome the limitations of the two systems providing integrated system better than either on a stand-alone basis. Previous research shows that the integration of stand-alone code GPS/Micro-Electro-Mechanical System (MEMS) based INS has a negative effect on the Inertial Measurement Unite (IMU)gyro measurements and may not add any improvements to the quality of individual GPS solution.Time based single frequency double differences carrier phase (TL1DD) is an accurate velocity estimation and relative positioning method based on L1 stand-alone carrier phase GPS observables. In this paper, the integration of TL1DD/MEMS-INS has been evaluated comparing to simultaneous precise navigation solution. The results show that a precise, high frequency, and reliable low-cost relative navigation solution can be obtained in open-sky areas, which can be used for a wide range of relative positioning engineering applications. The results show also and the high ability of the suggested technique to deal with cycle slips and its limitation in GPS-off areas.