International Journal of Emerging Research in Engineering and Technology
Intelligent IoT-Enabled Deep Learning System for Advanced Cybersecurity Anomaly Detection
The Internet of Things (IoT) is rapidly increasing attack surface of cyber threats, making the existing security measures insufficient to detect complex and dynamic abnormalities. The article presents a plan for a sophisticated system that uses deep learning (DL) and the internet of things (IoT) to identify cybersecurity irregularities using a CNN-BiLSTM architecture. Convolutional Neural Networks (CNNs) easily extract geographic information, whereas Bidirectional Long Short-Term Memory (BiLSTM) networks detect temporal correlations in network data. After label encoding, normalization, and SMOTE-based data balancing, the proposed model is evaluated on the UNSW-NB15 dataset, thereby increasing its consistency. As shown in experiments, the suggested hybrid model has a higher performance rate as it reaches 99.3% testing accuracy (acc), high precisions (prec), recall (rec), and a low FNR, which is significantly better than the traditional machine learning (ML) and standalone models of DL. The system successfully identifies both known and undiscovered cyber threats, making it ideal for real-time IoT applications. This study promotes the idea of scalable, accurate, and intelligent IDS to safeguard modern IoT networks.
Hybrid Indexing for Operationally-Aware Inventory Costing: Simulation and ERP-Based Validation
Dynamic chain of supply with variable service levels and demand volatility must be supported by a mechanism for inventory costs that is adaptive and able to work with enterprise resource planning (ERP). This paper presents a framework for Adaptive Hybrid Indexing for Operationally-Aware Inventory Costing (AHI-OAIC) that combines operational signals, dynamic SKU tiering, and ERP costing structures to provide a responsive inventory costing without the need to move inventory. The framework encompasses simulation-based scenario modelling, weighted cost-to-serve scoring, buffer-driven volatility control and metadata-based cost group assignments that can be used with Oracle EBS R12.2 workflows. The results from experiments on 2,500 synthetic SKUs from promotional surges, carrier disruptions and operational sensitivity scenarios show enhanced cost visibility and profitability differentiation. Results show 61.6% stability in Tier convergence over 30 days, Oracle EBS convergence improvements of 91-97% in respect to flat baseline costing, and convergence on cost differentiation of 2.35× to 4.18× over flat baseline costing. The framework successfully maintained an even margin lift on all operational profiles while ensuring auditability, GAAP compliant cost rollups and ERP-native governance. Findings demonstrate that adaptive hybrid indexing enables scalable, operationally responsive, and financially transparent inventory costing in volatile supply chain environments.
A Robust Machine Learning Architecture for Accurate Malware Classification in Large-Scale Cyber Threat Dataset
Malware is a significant threat to information systems and software security as the technology progresses. As malware continues to evolve, it is essential to have quick and precise detection systems to deal with any potential threats. Machine learning might be a solution, as it is challenging for standard detection technologies to keep up with the rapid evolution of malware. This work introduces the EMBER dataset as the base for a machine learning malware classification system. Data pretreatment approaches and exploratory data analysis are used in order to make the data more consistent and improve the feature representation. Random Forest (RF) and Multi-Layer Perceptron (MLP) classifiers are used to develop and evaluate classifiers based on accuracy, precision, recall, F1-score and Area Under the ROC Curve (AUC). The experimental outcomes reveal that the MLP model exhibits a higher accuracy of 97.63%, recall of 95.43%, and F1 score of 95.44% and AUC of 99.13% compared with RF model with 94.66% accuracy and 92.25% AUC. The effectiveness of the proposed framework is further substantiated through a comparative study with other ML and DL approaches in terms of prediction power and generalization ability. The proposed framework is expected to offer strong malware classification capabilities, enabling cybersecurity decision-making in the evolving threat landscape, and to offer scalable and reliable detection capabilities.
Data-Driven Intrusion Detection Techniques for Secure Wireless Sensor Networks Using Machine Learning
Denial of Service (DoS) attacks are a major concern for wireless sensor networks (WSNs), which are widely used in IoT-based real-time monitoring applications. In this paper, an intrusion detection framework based on data is proposed to secure communication in WSN using machine learning techniques. The WSN-DS dataset is used with LEACH protocol for Flooding, TDMA Scheduling, Blackhole and Grayhole attack detection. Prior to training the model, the dataset is subjected to data preparation methods such as encoding, normalization, balancing with SMOTE, and train–test splitting. Following their implementation, the Decision Tree and K-Nearest Neighbors (KNN) classifiers are assessed using several metrics such as accuracy (ACC), precision (PRE), recall (REC), F1-score (F1), ROC-AUC, confusion matrix, and SHAP analysis. It is clear from the experimental results that the KNN model (with an ACC rate of 99.39% and an F1-score of 99.38%) and the Decision Tree model (with a ROC-AUC of 99.43%) perform well in the intrusion detection task. The proposed models are compared with other existing methods such as CatBoost, Naïve Bayes, SVC, RF and D-GOPA, which have proved that the proposed models outperform the other methods in terms of detection ACC and classification reliability. The proposed framework considerably increases the efficiency of the security that can be provided for the network, the efficiency with which attacks can be detected and it offers an efficient solution for secure, intelligent applications of wireless sensor networks.
A Survey of AI-Driven Techniques for Enhancing Clinical Decision Support in Modern Healthcare Systems
Modern healthcare would not be possible without clinical decision support systems (CDSS), which aid doctors in making quick and accurate choices. By incorporating AI, CDSS has evolved into a sophisticated system that can sift through mountains of patient data, spot trends that might not be immediately obvious, and soffer recommendations supported by evidence. AI-powered CDSS can enhance diagnostic precision, facilitate early disease forecasting, and aid in individual treatment plans. This study suggests a model that combines a Recurrent Neural Network with a Gated Recurrent Unit (RNN+GRU) to improve the accuracy (ACC) of clinical predictions. The model tested on the MIMIC-IV dataset. The proposed hybrid model is successful in capturing the sequential clinical patterns and is able to learn without overfitting. The proposed model showed to be more accurate than the other models used, with a value of 99.71% ACC, which is higher than the ACC of the LSTM model with 82% ACC, Adaboost model with 92.9% ACC, and XGboost model with 95.8% ACC. The comparison shows that the proposed framework has higher prediction performance, reliability and generalization ability. The developed model has the potential to be a powerful tool in the era of modern healthcare, enabling intelligent healthcare analytics, patient risk assessment, and effective clinical decision-making.
AI-Augmented DevSecOps Pipelines for Secure and Efficient Software Delivery in Cloud-Native Platforms
Cloud-native architectures have revolutionized software development, offering businesses the opportunity to achieve scalability, flexibility, and constant innovation. At the same time, distributed systems, microservices, containerized deployments and multi-cloud infrastructures have added a number of security challenges along the entire software development lifecycle (SDLC) process. The major focus of traditional DevOps practices is to speed up software delivery by leveraging automation and Continuous Integration/ Continuous Deployment (CI/CD) pipelines. As cyberattacks grew in number and complexity, however, security has become part of the SDLC and DevSecOps has entered the picture. While DevSecOps provides benefits, it can be a challenge for organizations to successfully integrate security into their deployment processes without slowing down the deployment process, causing the system to operate less efficiently or reducing developer productivity. Artificial Intelligence (AI) has become a game-changing technology that can help solve these problems by boosting automation, threat intelligence, and predictive security analytics. AI-enhanced DevSecOps pipelines integrate machine learning, intelligent orchestration, automated vulnerability scanning, anomaly detection, and predictive risk management to create secure and efficient software delivery pipelines in cloud-native environments. Incorporating AI-powered security tools into the CI/CD lifecycle enables organizations to preemptively detect vulnerabilities, focus resources on remediation, minimize false alarms and track system activity in real-time. In this study, the researchers explore how to design and implement AI-enhanced DevSecOps pipelines for cloud-native 0environments. The framework proposed will combine several capabilities, including AI-based static application security testing (SAST), dynamic application security testing (DAST), container image scanning, Infrastructure-as-Code (IaC) security validation, runtime threat intelligence, and automated compliance monitoring. Besides, the framework uses machine learning algorithms to study software artifacts, deployment patterns and operational telemetry data to detect potential security risks prior to production deployment. It explores the ways in which AI-powered automation can help to minimize vulnerability exposure, speed up deployment timelines, and enhance operational resilience. The results of their experiments show that AI-powered DevSecOps pipelines deliver a markedly higher velocity of accurate vulnerability detection, shorter mean time to remediation (MTTR), fewer deployment failures, and better compliance adherence than traditional DevSecOps approaches. The results show better security incident prevention, deployment efficiency and software quality. The findings highlight the need for embedding intelligent security mechanisms to provide security assurance and delivery agility in cloud-native CI/CD pipelines. The new model provides a flexible and scalable solution for enterprises aiming to achieve fast innovation while maintaining comprehensive cybersecurity in today's cloud-based environment.
A Control Plane Architecture for Secure and Governable AI in Regulated Financial Systems
Artificial Intelligence adoption within regulated financial institutions has outpaced the security frameworks designed to govern it. Existing controls were built around network perimeters, static role assignments, and API-layer enforcement and were never designed for the behavioral unpredictability of large language models (LLMs) and autonomous AI agents. This paper presents a Control Plane Architecture for Secure and Governable AI (CP-SGAI): a purpose-built governance architecture that treats AI inference as a first-class security event requiring identity attestation, policy-bounded prompt execution, and ephemeral privilege scoping, and structured observability. Grounded in field experience operating multi-account AWS environments at a large financial institution, the framework addresses practical gaps that purely theoretical governance models miss, including prompt injection at the enterprise boundary, cross-account data egress through model responses, and the absence of prompt-response lineage in existing SIEM and audit toolchains. Three original constructs are introduced: an AI Interaction Identity (AII) attestation model, an Ephemeral Prompt Security Context (EPSC) lifecycle, and an AI Observability Schema (AOS) aligned with financial regulatory audit requirements.
Optimizing Cost-Efficient Payment Transactions: AI-Driven Routing Strategies for Reducing Payment Costs
The escalation of digital commerce necessitates transaction systems that are both economically viable and highly scalable. Conventional on-chain settlement mechanisms are frequently hindered by substantial costs and latency, shifting focus towards peer-to-peer off-channel solutions. This work introduces WhisperPay, a novel decentralized routing protocol augmented by artificial intelligence, which achieves marked improvements in settlement affordability and speed. Our methodology enhances routing efficacy through a proximity-based pathfinding mechanism, diminishing associated control-plane expenditures by approximately 102 and halving completion intervals relative to standard ledger technologies. By employing adaptive, learning-centric transaction processing, the framework continuously identifies optimal payment corridors for value transfer, preserving confidentiality and integrity. Evaluations confirm that WhisperPay curtails both fee structures and resource consumption, thereby improving the economic sustainability of distributed payment infrastructures. This investigation underscores the potential of intelligent routing paradigms in mitigating expenses and augmenting operational throughput within contemporary financial networks.
An Intelligent AI-Driven Framework for Real-Time ATM Transaction Validation, Fraud Detection and Financial Switching Integrity
The exponential growth of digital financial transactions has placed unprecedented stress on automated teller machine (ATM) networks and the underlying financial switching architectures. Traditional rule-based transaction validation systems are increasingly inadequate for detecting sophisticated, high-velocity fraud vectors while maintaining the strict latency requirements of real-time processing. This paper proposes a comprehensive, intelligent framework that converges Graph Neural Networks (GNNs) with Extreme Gradient Boosting (XGBoost) ensembles to capture both spatiotemporal transaction anomalies and complex relational dependencies across banking nodes. By moving away from standalone models, the proposed architecture dynamically analyzes the topology of transaction requests, significantly enhancing the precision of fraud detection algorithms. Furthermore, the model embeds an architecture-centered lifecycle governance mechanism to ensure seamless deployment, continuous predictive quality assurance and automated performance degradation monitoring. Empirical evaluation using simulated high-frequency financial switch data demonstrates that the proposed framework achieves an F1-score of 0.962 in fraud detection while maintaining a sub-40 millisecond inference latency, ensuring zero disruption to switching integrity. This study contributes a novel, robust methodology for mitigating financial risk, optimizing software lifecycle integration and reinforcing cybersecurity intelligence in distributed financial systems. The framework proves that low-latency operational constraints do not necessitate a compromise in algorithmic complexity, provided that rigorous software engineering paradigms are enforced during deployment.
Self-Learning Audit Orchestration Model for Continuous ASPICE Process Governance
Automotive SPICE (ASPICE) compliance is essential for ensuring quality and safety in automotive software development. Traditional ASPICE assessments are periodic, resource-intensive, and reactive, often leading to compliance drift between official audits. This paper introduces a novel Self-Learning Audit Orchestration Model (SLAOM) for continuous process governance. By leveraging reinforcement learning and natural language processing (NLP), SLAOM continuously ingests artifacts from engineering lifecycles, maps them to ASPICE Process Reference Models (PRM), and orchestrates automated mini-audits. The system self-learns from auditor feedback to minimize false positives, providing a real-time compliance posture. Experimental validation demonstrates an 84% reduction in manual audit preparation time and a 91% accuracy rate in predicting Capability Level compliance.
Fault-Tolerant Architecture for Real-Time Payment Processing in Large-Scale Telecom Billing Systems
Payment processing systems that serve large consumer-facing organizations must handle millions of transactions per day while maintaining consistent reliability. A single point of failure in the payment pipeline can result in measurable revenue loss and customer dissatisfaction. This paper presents a fault-tolerant architecture for integrating CyberSource payment services within a large-scale Internet Service Provider (ISP) billing platform that handles payments for over three million active customer accounts. The system achieves high availability through a combination of idempotent API design, exponential backoff retry logic, a circuit breaker mechanism calibrated for payment gateway behavior, asynchronous event queuing using Apache Kafka, and distributed transaction tracing. The architecture reduces mean recovery time from over eighteen minutes to under one minute, eliminates duplicate charge incidents through idempotency enforcement, and sustains 99.94% transaction success rates during observed periods of CyberSource API instability. This paper describes the design decisions behind each component, the CyberSource REST API integration approach, lessons learned from operating this system in production and observed performance data before and after the fault-tolerant design was adopted.
Self-Adaptive AI Reliability Models for Scalable Enterprise Infrastructure Engineering
The rapid expansion of enterprise-scale digital ecosystems has significantly transformed infrastructure engineering practices across cloud computing, distributed systems, edge computing, hybrid architectures, and intelligent automation platforms. Modern enterprises increasingly rely on highly scalable infrastructures capable of supporting continuous service delivery, massive data processing, dynamic workload balancing, and intelligent decision-making processes. However, the growing complexity of enterprise infrastructure environments introduces major challenges associated with reliability, fault tolerance, adaptive monitoring, resilience engineering, security management, and operational sustainability. Traditional reliability engineering techniques are often unable to dynamically respond to continuously evolving infrastructure conditions because they rely heavily on static thresholds, rule-based monitoring systems, and manually configured fault recovery mechanisms. In response to these limitations, self-adaptive artificial intelligence reliability models have emerged as a transformative paradigm capable of autonomously monitoring, analyzing, predicting, and optimizing enterprise infrastructure performance in real time. This research article investigates the design, implementation, and operational significance of self-adaptive AI reliability models for scalable enterprise infrastructure engineering. The study explores the integration of machine learning, reinforcement learning, predictive analytics, autonomous orchestration, explainable AI, and cognitive reliability frameworks into modern infrastructure reliability management systems. The paper further examines how self-adaptive AI mechanisms can enhance system resilience, reduce downtime, improve infrastructure scalability, optimize resource allocation, and support autonomous recovery in distributed enterprise environments. A comprehensive literature review is conducted to evaluate existing research contributions in AI-driven infrastructure reliability, intelligent fault management, self-healing systems, and adaptive cloud engineering. The proposed research methodology introduces a multilayer adaptive reliability architecture integrating AI-driven telemetry analysis, anomaly detection, dynamic decision engines, and autonomous orchestration modules. Comparative analysis demonstrates that self-adaptive AI reliability models significantly outperform conventional infrastructure reliability frameworks in terms of predictive accuracy, fault recovery time, operational scalability, and service continuity. The findings indicate that self-adaptive AI systems can substantially improve enterprise operational efficiency while enabling intelligent infrastructure governance across multi-cloud, edge, and hybrid enterprise ecosystems. The study concludes that adaptive AI reliability engineering represents a foundational component for the next generation of autonomous enterprise infrastructure platforms.
Next-Generation AI-Driven Cloud Reliability and DevSecOps Optimization Frameworks
The rapid expansion of cloud-native infrastructures, distributed enterprise systems, containerized applications, and intelligent digital ecosystems has transformed the operational foundations of modern enterprises. Organizations increasingly rely on scalable cloud platforms and continuous software delivery pipelines to support mission-critical services, real-time analytics, and global digital operations. However, the growing complexity of multi-cloud architectures, cybersecurity threats, software vulnerabilities, operational failures, and dynamic workload fluctuations has exposed significant limitations in conventional cloud reliability engineering and DevSecOps practices. Traditional monitoring and security mechanisms often struggle to provide adaptive, intelligent, and autonomous operational resilience across heterogeneous enterprise environments. In response to these challenges, next-generation Artificial Intelligence (AI)-driven cloud reliability and DevSecOps optimization frameworks have emerged as transformative technological paradigms capable of enabling intelligent infrastructure orchestration, predictive reliability management, adaptive cybersecurity enforcement, and autonomous operational decision-making. AI-driven frameworks integrate machine learning, deep learning, reinforcement learning, explainable AI, federated learning, and intelligent automation technologies into cloud reliability engineering and DevSecOps ecosystems. These intelligent frameworks enhance system observability, predictive maintenance, workload optimization, anomaly detection, automated threat mitigation, policy compliance monitoring, and continuous deployment reliability. This research article presents a comprehensive investigation of next-generation AI-driven cloud reliability and DevSecOps optimization frameworks through detailed academic analysis, comparative evaluation, and conceptual framework development. The study examines the integration of AI technologies into cloud reliability engineering, cybersecurity operations, infrastructure orchestration, CI/CD automation, and zero-trust security architectures. Furthermore, the research identifies critical research gaps associated with explainability, interoperability, scalability, governance, adversarial AI risks, and ethical AI adoption. The proposed conceptual framework integrates intelligent monitoring systems, predictive analytics engines, autonomous orchestration modules, AI-powered security enforcement, and explainable governance mechanisms to support secure and resilient cloud-native enterprise operations. The findings demonstrate that AI-driven reliability and DevSecOps frameworks significantly improve infrastructure resilience, operational efficiency, cyber defense capabilities, resource optimization, and software deployment stability. However, the study also highlights challenges related to computational overhead, governance complexity, AI trustworthiness, and integration across heterogeneous cloud environments. This article contributes to the academic and industrial understanding of AI-enabled enterprise cloud reliability and DevSecOps optimization by providing comprehensive technical analysis, implementation insights, research gap identification, and future strategic directions suitable for next-generation intelligent enterprise ecosystems.
A Systematic Survey of Autonomous AIOps and Generative AI in Cloud-Native Infrastructure
Cloud-native infrastructures have transformed modern computing through scalable, flexible, and resilient deployment models based on microservices, containers, orchestration, and DevOps practices. Nevertheless, increasing system complexity, distributed workloads, and dynamic operational environments create major challenges in monitoring, fault management, security, and resource optimization. This systematic survey reviews the integration of Autonomous Artificial Intelligence for IT Operations (AIOps) and Generative Artificial Intelligence within cloud-native infrastructure. The study examines cloud-native architectural foundations and explores how artificial intelligence and machine learning enhance operational automation and infrastructure intelligence. Core AIOps capabilities, including automated monitoring, anomaly detection, predictive maintenance, and root cause analysis, are analyzed alongside Generative AI applications in log analysis, incident management, and decision support. Recent literature demonstrates that combining AIOps with Generative AI improves reliability, reduces mean time to recovery, optimizes resource utilization, and minimizes manual intervention. The survey also identifies key challenges, including model drift, explainability limitations, privacy concerns, integration complexity, security risks, and computational demands. Finally, future research directions toward secure, scalable, trustworthy, and self-healing autonomous cloud operations are highlighted for future next-generation digital infrastructure ecosystems.
Securing Databases in the Cloud with RBAC and Encryption Best Practices
The protection of databases that are hosted in cloud facilities, remains an issue that trends within cybersecurity discourse. Secure cloud database management systems face risks of unauthorized access and data leakage, sometimes due to negligent insider threats. Moreover, cloud service providers' compliance with regulatory requirements has become a matter of increasing concern for attestation of security of stored data (Elsayyad et al., 2020). In order to ensure that user privacy is respected and data utility is not compromised as organizations migrate critical information to cloud platforms, confidentiality, integrity, and availability should be observed judiciously (SAXENA & HALDER, 2021). In particular, this paper is interested in the ways in which Role-Based Access Control (RBAC) and cryptography theme through encryption techniques can correspond to one another to fight off the most intractable cyber intrusions to cloud databases. Role-based access control (RBAC) enables administrators to regulate the extent of access to data according to the clear-cut definitions of user roles, thereby guaranteeing that data privacy is maintained, and at the same time data integrity is shared and updated securely. On the other hand, encryption safeguards data that is stored in normal modes of operation and is made available across secure communication channels, by allowing only legitimate users to decrypt and obtain data in a human-readable form (Chen et al., 2020). This study scrutinizes how the employment of RBAC in concert with reliable cryptography standards, such as AES for symmetric encryption and TLS for secured data transfer, may reduce vulnerabilities arising from data leakage and increase protective obligations towards data subjects defined by regulations like GDPR and HIPAA.
Automating Backups and Recovery: Reducing Manual Work by Over 50%
The skyrocketing amount of data enterprises are generating, along with the growing complexity of their application environments and hybrid infrastructures, has turned data protection into a very serious operational challenge for modern organizations. When systems extend over distributed environments, it is increasingly hard to make backups that are consistent, reliable, and timely, but at the same time, the recovery times get shorter due to the growing threats of cyberattacks, system failures, and human errors. Nowadays, backup and recovery are not just additional IT chores anymore; they are essential business capabilities that preserve business continuity, ensure regulatory compliance, and build stakeholder trust. Nevertheless, there are still many companies that implement manual or semi-automated backup methods, which take a lot of time, are prone to errors, and heavily rely on the knowledge of a few individuals. Such methods usually have difficulties in adjusting to the ever-changing workloads and, as a result, there are backups that get missed, recoveries that are delayed, and the operational risks that get increased. This article presents a backup and recovery framework that is automated and policy-driven, which is intended to remove the repetitive manual chores by intelligent orchestration and standardized controls. Automation is deeply integrated within the framework starting from scheduling, validation, and monitoring to recovery workflows and it ensures consistent execution and at the same time, teams can be engaged in more value-adding operational and strategic activities. The essence of the proposed approach is to keep things simple, scalable, and resilient; hence, backup policies can change automatically in accordance with workloads and no human intervention is needed all the time.
Building Custom Monitoring Scripts for Predictive Failure Detection
Developing custom monitoring scripts for predictive failure detection is about creating simple, versatile, and easily deployable solutions that can find equipment failures in advance without the system shutting down. Typically, conventional monitoring systems are obliged to react to problems after the event, whereas this approach focuses on prediction and prevention. By employing tailor-made scripts along with such strategies as statistical modeling, threshold-based anomaly detection, and machine learning algorithms, enterprises are enabled to observe system performance data at all times and thus they can point to the first signs of failure in the system as their reference. This is done to escalate system reliability, execute the minimum of zero unplanned outages, and maximize operational efficiency. The proposed approach includes the development of modular scripts that can be changed for different environments and can be adjusted to different workloads. These scripts can get performance metrics, analyze trends, and even alert based on learned patterns or when there is a deviation from normal behavior. Real-life examples where the identical solutions have been applied demonstrate the decrease in the time that the system is not working as well as the reduction of the costs of maintenance apart from the increase of the continuity of services and the users' satisfaction. In addition to the direct operational advantages, the article presents predictive monitoring as one of the key factors leading to the concept of resilient IT ecosystems by enabling data-driven decision-making and more efficient resource allocation. The paper concludes with the potential that is next for the integration of sophisticated AI models, automated remediation workflows, and self-healing mechanisms resulting in fully autonomous predictive maintenance systems as the follow-up stages.
Techniques and Best Practices in Database Administration: Insights from Salesforce Ecosystem
Cloud-based solutions have dramatically changed contemporary data management, and effective database management is critical in terms of guaranteeing performance, scalability and data integrity. This paper explores key techniques and best practices in database administration with a focus on the Salesforce ecosystem. It discusses fundamental concepts such as data modeling, normalization, indexing, query optimization, and transaction management, which form the basis of efficient database systems. The study further examines database management in Salesforce, including schema design, data lifecycle management, and security mechanisms that support large-scale, multi-tenant environments. Advanced practices such as performance optimization and scalability techniques are analyzed to highlight their role in improving system efficiency. Moreover, the paper outlines key challenges, such as data volume, integration complexity, data quality issues, and governance requirements. New trends such as artificial intelligence, big data integration, low-code development, and cloud-native architecture are also discussed to understand future of database administration. All in all, the paper provides a detailed overview of how contemporary database practices and techniques can be used to improve the performance, reliability and data management of enterprise systems in cloud.
Tracking the Status of Long-Running Apex Methods in LWC
Handling timeout or long waiting operations is a major problem that Salesforce Lightning Web Components (LWC) have. In these systems, a long-running Apex operation is usually handled synchronously, which means that the whole workflow is put on hold and the user interface is not updated until the operation has been completed. Hence, the shared user interface sometimes freezes, slows down, or the user is simply "kicked out" due to timeouts and system limits. The present study focuses on creating a real-time communication channel between LWC and Apex whereby standard users are free from waiting and checking the time they can. It is enabled and maintained by a set of the latest Salesforce asynchronous features, such as Platform Events, Queueable Jobs, and the Continuation framework. The case study reveals that LWCs can now listen to server-sent events and reflect the operation statuses without any input from the users, i.e., maintenance and monitoring become invisible. These "background jobs" have reduced the latency that the user is most aware of and thus user satisfaction has been increased significantly. Monitoring completion rates could be increased by as much as 40% just by this method alone, according to the experimental data. This technique serves as a scalable model for complex Salesforce apps and thus is very beneficial in terms of system throughput and dependability. This research shows how the use of asynchronous methods coupled with real-time progress updates can be a breakthrough to the scalability and user experience of modern Salesforce implementations.
Building a Reusable AI Connection Utility Class
Developers are often confused by the multiple AI model usage situation, making the integration of these models quite tricky. Indeed, each API has its authentication flow, request structure, and response formatting, which are different from the others. Besides, to use these frameworks (OpenAI, Hugging Face, and Google Vertex AI) efficiently, one is obliged to do repetitive and inconsistent connection logic, which is time-consuming and makes the development more complicated. Thus, the objective of a reusable AI connection utility class is to lessen the systems' connections and interactions' complexities to a standard level. Consequently, the developers will not be bothered with the connection complexities; in fact, what will exist is a single modular gateway integrating all AI systems. Compliance with the coding conventions will be highly improved along with interoperability because of this shortcoming. In addition, the scalability of this framework will be improved too since new models or APIs can be easily inserted like plug-and-play. In fact, accountable practices are ensured through centralized handling of credentials as well as controlled access to configuration data. Initially, code duplication was noticeably reduced, and the integration cycles got rapidly executed, besides, there was an effortless interchangeability between AI models for experimentation and deployment. They can securely and efficiently orchestrate their distributed AI workloads if, in the future, they take this idea to multi-cloud or federated AI environments. So by embracing a single reusable architecture for connection handling, organizations stand the chance of streamlining AI adoption, quickening innovation, and making their codebases cleaner and more sustainable in the wide, diversified machine learning ecosystems.
The Wrath of Limitations: Lightning Fields and Their Constraints
Lightning​‍​‌‍​‍‌ fields, in particular, are the large and short-lived displays of pure electrical power by nature that show a variety of complex physical and electromagnetic behaviors and have been a source of fascination for scientists and engineers for a very long time. These fields, which result from the extreme charge separations within the storm systems, are structurally very complicated, having in their nature extremely high voltages, rapid discharge dynamics, and complex spatial patterns that are difficult for precise measurement and prediction. This research explores the boundaries of the phenomena of lightning fields spatial, temporal, and energetic that arise as a consequence of their formation, propagation, and dissipation. By the use of atmospheric modeling, high-speed imaging, and controlled laboratory simulations together, the study attempts to understand how electric field intensity, ionization thresholds, and conductive channel evolution characterize these natural phenomena. The results show that spatial confinement is due to atmospheric inhomogeneity and ion mobility, temporal limitations are a consequence of very fast charge neutralization and dynamic feedback processes, while energetic constraints are linked to breakdown potentials and medium conductivity. These limitations that accompany the natural phenomenon of lightning not only determine its behavior but also have far-reaching consequences for the design of engineering systems, environmental safety, and new energy applications such as lightning-based power harvesting and electromagnetic shielding. Knowing these nature-imposed limits, scientists become capable of lightning–infrastructure interaction prediction with more accuracy, protection system designing, and controlled energy extraction feasibility exploration. The paper ends with a message about the potential of behind-the-scenes research that combines atmospheric physics, materials science, and energy engineering to transcend the existing limitations and to unfold novel scenarios of using and controlling lightning power for technology development in the ​‍​‌‍​‍‌future.
When Identity Decisions Throttle Data Movement
Distributed systems nowadays are very much dependent on continuous data flows among various platforms, services, and storage venues. Currently, companies operate quite elaborate data pipelines through which large amounts of data are transferred between different apps, cloud platforms, microservices, and data lakes for the purpose of analytics, machine learning, and real-time decision-making. Generally, network bandwidth, storage performance, and compute scalability are the factors that have most commonly been regarded as determining performance in data transfer. Yet, identity checking and authorization processes are gradually becoming a significant factor in data transfer performance. From one perspective, it is understandable that these identity confirmations, for instance, take place at various points in the data pipeline, e.g., API gateways, storage layers, service-to-service communication, and policy engines. Actually, these precautions are necessary in protecting sensitive information and are in line with the regulations, but at the same time, they might cause delays and disruptions in the workflows that are not intended. In many distributed environments, data moves at a slower pace without the users even realizing it due to issues such as validation of tokens being performed very often, policy evaluation becoming more complex, and repeated verification of identities. This article is about identity-related data transfer performance losses in modern data infrastructures. The study reveals the influence of IAM decisions on data transfer performance and the different ways security constraints can affect throughput, latency, and pipeline efficiency. This article is based on the architectural review, performance monitoring, and the study of a distributed, cloud-based environment made up of microservices and large-scale data storage. The research points out security enforcement as a bottleneck by identifying identity checkpoints and their effects on data transfer operations. Security-related activities, especially those related to the policy engine and token verification overhead, were identified as the main sources of data pipeline performance degradations associated with high-scale conditions.
The Storage Stack Nobody Draws: Cabling, Panels, and the Illusion of Isolation
Most of us nowadays usually identify storage infrastructures with servers, switches, and storage arrays while the physical cabling and patch panel layouts that connect these components are rarely taken into account in architectural discussions. Due to this neglect of the physical infrastructure, an "illusion of isolation" has been created in which people assume that since logical separation exists, the physical independence is also guaranteed. However, it is through the physically shared cable pathways that interdependencies become visible making performance, reliability, and troubleshooting very much concerned with the hidden interdependencies factor. The paper reveals how physical-layer materials in data centers can have a great impact on the performance of the whole center. It shows that through physical infrastructure, single failure points were formed. It has been found that, although redundant links are commonly found to be going through the same physical paths, the administrators remain unaware of this fact. In addition to this, there is an increase in signal interference, congestion, and extended mean-time-to-repair (MTTR) due to poor cabling practices. Without the physical layer, resilience evaluation is not accurate. Proper infrastructure planning must see the integration of cabling design, clear documentation, and disciplined supervision to avoid the emergence of hidden risks. By putting forward such an issue, this work provides practical guidance for engineers and data center operators who seek to build truly reliable systems by bridging the gap between the theoretical storage architecture and the real-world deployment.
The Geometry of Redundancy: Why Physically Separate Storage Paths Behave as One System
Nowadays,​storage systems are, in most cases, based on the redundancy concept so that physically separate paths, controllers, and devices are considered to produce fault isolation and independent behavior by default. However, in reality, such systems often have failure patterns that are tightly coupled, performance bottlenecks, and recovery behaviors contradicting the physical separation that is visible. This paper reveals that there is a large distance between the storage design perception of redundancy and the systemic behavior witnessed in real-life environments. Therefore, it is argued that redundancy is more of a geometry than a function in most cases. We discuss geometric redundancy, meaning that components are placed in different physical or logical paths, but they are still limited by a common control plane, synchronization mechanisms, queuing dynamics, or workload characteristics, and hence, they behave like one system when being stressed. Though there have been plenty of observations of cascading failures and correlated performance degradation, few have formally analyzed why physically independent paths in storage fail together at the end of the day. In this paper, a method is described that involves architectural decomposition, failure-mode mapping, and workload-driven stress analysis to locate those coupling points that are hidden between different layers of the storage system. The study extends to a real-life example of an enterprise storage environment with dual-fabric connectivity and redundant controllers, where empirical data showed that latency spikes were synchronized, failover was coordinated, and recovery thresholds were shared even though the physical separation was complete. The main points indicate that the efficiency of redundancy depends more on control symmetry, feedback loops, and shared operational geometry than on the physical topology of the system.
JMeter-Driven Performance and Security Validation: A Combined Load Testing and Vulnerability Discovery Methodology for Legacy Java Services
Apache JMeter is widely used for performance and load testing of web applications. It is less widely recognized as a vehicle for security testing, despite its capabilities for site spidering, parameter fuzzing, and stress-driven vulnerability discovery. This paper presents a combined methodology that uses JMeter for both performance validation and security vulnerability discovery on legacy Java services. The methodology arose from production work optimizing legacy product performance, in which response times were improved by approximately twenty-five percent through a combination of targeted optimization informed by JMeter measurements and concurrent identification of resource-exhaustion and input-validation vulnerabilities that the load testing surfaced. The paper covers the test plan design that supports both concerns simultaneously, the assertion patterns that distinguish performance regressions from functional failures from security incidents, the integration with continuous integration pipelines, and the analytical patterns that extract security signal from load test telemetry. The methodology is demonstrated with reference to vulnerability classes including denial-of-service-by-exhaustion, race conditions exposed under concurrency, authentication and rate-limiting weaknesses, and the input-handling failures that load testing exercises by accident. The paper closes with a discussion of the boundaries of the methodology and the role of specialized security tooling that JMeter does not replace.
Shift-Left Security for Decentralized Engineering Organizations: Embedding SAST, DAST, and Penetration Testing Throughout the Software Development Lifecycle in University and Research Computing Environments
Shift-left security in a single product engineering organization is a well-documented practice. Adapting the same patterns to a decentralized engineering organization, in which multiple independently governed teams ship software on heterogeneous timelines using heterogeneous tooling, requires additional attention to consent, autonomy, and the operational realities of cross-team coordination. University research computing environments are a representative instance of this organizational shape. This paper describes the design and rollout of a shift-left security program in a decentralized higher-education engineering setting. The program embeds Static Application Security Testing, Dynamic Application Security Testing, and penetration testing throughout the software development lifecycle, but it does so through invitation and incentive rather than through top-down mandate. The paper covers the rationale for the design choice, the architecture of the central platform components that teams opt into, the developer-facing artifacts that lower the cost of adoption, the integration patterns with the heterogeneous continuous integration systems in use across teams, and the metrics that track program reach and effectiveness over time. The paper closes with a discussion of how the same patterns transfer to other decentralized engineering organizations such as large enterprises with federated product groups, scientific computing collaborations, and open-source project ecosystems. The intent is to document a practical approach that engineering organizations whose authority structure does not support a security mandate can use to nonetheless drive substantive improvements in their security posture.
A Comparative Empirical Study of Messaging Primitives for Enterprise-Scale Event-Driven Microservices: EventBridge, SQS, SNS, and Apache Kafka under a Unified Decision Framework
Event-driven architecture has become a dominant integration style for enterprise microservice platforms because synchronous request-response meshes accumulate latency, failure modes, and coupling that constrain independent service evolution. The selection of an appropriate messaging primitive, however, remains under-studied: vendor documentation tends to advocate single-primitive solutions, while academic treatments often abstract over the operational and economic differences that dominate real deployment choices. This paper presents a comparative empirical study of four messaging primitives commonly composed in enterprise-scale event-driven backbones: Amazon EventBridge for content-based routing, Amazon Simple Queue Service for reliable point-to-point work distribution, Amazon Simple Notification Service for fan-out pub-sub, and Apache Kafka for high-throughput streaming with replay. The contribution is a workload-to-primitive decision framework, calibrated against measurements collected from three representative production deployments spanning regulated enterprise workloads, and an analysis of the dimensions on which the primitives differ in practice. The methodology characterizes workloads along interaction shape, ordering requirement, replay requirement, throughput, and acceptable tail latency, and defines a measurement protocol for end-to-end latency, sustainable throughput, ordering guarantees, and per-million-message cost. The data show that no single primitive dominates: EventBridge minimizes integration cost for low-to-moderate volumes with heterogeneous consumers, Kafka is the only studied primitive that sustains five-figure message rates with replay, and SQS-FIFO remains the most cost-effective choice when strict ordering is required at moderate throughput. The findings have implications for architects designing event-driven backbones in any large enterprise context where multiple interaction shapes must coexist on a shared substrate.
Design Patterns and Empirical Evaluation of Reusable Terraform Modules Encoding Audit-Ready Defaults for Multi-Account AWS Deployments: A Cross-Team Study across EC2, S3, RDS, EKS, IAM, and Cloud Watch
Infrastructure as code (IaC) with Terraform is widely adopted for provisioning multi-account Amazon Web Services (AWS) deployments, yet the quality of the underlying module library determines whether the security, compliance, and operational baselines an organization requires are consistently realized. This paper presents a reference design for a reusable Terraform module library covering Elastic Compute Cloud (EC2), Simple Storage Service (S3), Relational Database Service (RDS), Elastic Kubernetes Service (EKS), Identity and Access Management (IAM), and CloudWatch resource families, together with an empirical evaluation conducted across multiple consumer teams in regulated enterprise environments. We catalog twelve modules instantiated in production deployments, define an evaluation protocol comprising continuous integration (CI) pipeline duration, audit-finding rate, drift incidents, and developer time-to-onboard, and report comparative measurements collected over two consecutive quarters before and after module adoption. We observe a reduction of mean time-to-provision from 38.4 to 4.7 hours, a fall in audit findings per quarter from 19 to 3, a fall in drift incidents per quarter from 26 to 5, and a fall in mean pull-request review time from 71 to 22 minutes. The findings indicate that disciplined module design coupled with a validation pipeline yields measurable improvements in delivery throughput and compliance posture, and the implications for the broader field of cloud platform engineering include a stronger evidentiary basis for treating module libraries as first-class engineering assets.
AI-Driven Blood Glucose Forecasting in Real-World Diabetes Care: Evaluating Wearable-Based Predictive Models
Wearable technology has rapidly advanced the landscape of diabetes self-management by enabling continuous, real-time monitoring of glucose levels in daily life. This paper leverages data collected exclusively through wearable continuous glucose monitors to develop and compare advanced forecasting models for short-term blood glucose prediction in individuals with diabetes. By implementing and benchmarking artificial intelligence-based recurrent neural networks against traditional statistical approaches, the research investigates both generalized and patient-tailored prediction strategies. Outcomes highlight the effectiveness of population-level machine learning models for predicting glucose fluctuations, even with limited user history, and discuss the implications for proactive and personalized intervention. The findings underscore the growing potential of AI-enhanced wearables to deliver actionable insights, optimize insulin dosing, and mitigate acute events, signaling a new era of digitally-empowered diabetes management through wearable technologies.
Enabling Real-Time Multi-System Synchronization through No-Code Integration Platforms: A Salesforce–Workato Implementation Approach
This​‍​‌‍​‍‌ is a study about the use of no-code integration platforms for synchronous real-time data sharing among various enterprise systems and is mainly centered on the implementation model of Workato and Salesforce. Basically, companies nowadays are operating on several applications which include CRMs, ERPs, marketing tools, and analytics platforms, but these applications do not communicate to each other smoothly. The main disadvantage of this is that the initiatives end with data silos causing duplication, latency, and inconsistent information across the departments, which is the root of the problem of decision-making and customer experience being limited. The current integration methods that necessitate custom codes and manual maintenance are costly, time-consuming, and hard to scale. This work shows how Workato, a popular no-coding automation platform, can bring about the resolution of the problem of real-time synchronization by offering pre-built connectors, event-driven workflows, and API-based orchestration without the need for deep programming skills. The example of the integration of the local procedures of Salesforce and Workato illustrated the laser focus on how data could be updated in real-time across systems—e.g., by customer information, opportunity records, and service ticket synchronization. This way not only operational efficiency but also data accuracy was improved. The research has found that automation recipes by Workato greatly shortened the time of integration and the dependence on the IT department, while at the same time the quality of data has been improved and its visibility across different teams increased. In addition, the real-time synchronization led to an enhancement of the customer-facing process by speeding up the response and decreasing human errors.