International Journal of Sustainability and Innovation in Engineering
The International Journal of Sustainability and Innovation in Engineering (IJSIE) is a multidisciplinary forum for disseminating research-based engineering related to all aspects of engineering design, including the fields of computer science and artificial intelligence (AI). We publish peer-reviewed technical papers, technical notes, and case studies that explore the integration of sustainability and innovation in engineering practices. All submissions are encouraged to have design or regulatory implications that contribute to advancing the profession. The journal welcomes manuscripts describing original research, short communications, and reviews in the domains of engineering sustainability, innovation, computer science, and AI. The Editorial Board of IJSIE comprises esteemed professors from various countries worldwide, representing diverse expertise in these fields.
Adaptive Data Pipeline Architectures for Evolving Fraud Patterns Using Graph ML
The growing sophistication of financial fraud has intensified alongside digital transformation, real-time payment systems, and the expansion of online financial services, resulting in substantial financial losses and operational challenges. Organizations combat these evolving threats by deploying advanced analytics and machine learning models capable of detecting unusual patterns across large volumes of transactional and behavioral data. Yet this approach creates tension: the financial data required for effective fraud detection—particularly personally identifiable information and transaction records—is inherently sensitive and subject to significant privacy protections. This paper introduces a comprehensive framework for integrating machine learning into ETL pipelines while preserving privacy, enabling real-time financial fraud detection that is both data-driven and secure. The architecture embeds privacy, security, and regulatory compliance throughout every pipeline stage—from initial data ingestion and transformation through model training to real-time fraud alert generation. Our approach combines multiple privacy-enhancing technologies, including differential privacy, homomorphic encryption, federated learning, and secure multi-party computation, allowing organizations to perform collaborative analytics without exposing raw or sensitive data to unauthorized parties. The system incorporates temporal and behavioral modeling alongside external data enrichment and automated fraud registry capabilities, enhancing its ability to identify sophisticated fraud patterns as they evolve. Pipeline orchestration ensures scalability and near real-time processing, delivering timely fraud risk assessments. Experimental results indicate substantial improvements in both detection accuracy and processing speed relative to conventional approaches. Performance gains are further amplified through dimensionality reduction techniques. This framework enables data processing systems to scale dynamically in response to changing demands while preserving operational efficiency and resilience. The resulting ML-enhanced ETL pipeline equips financial institutions with an effective mechanism for minimizing fraud losses while maintaining both operational agility and regulatory compliance.
Copyright (c) 2026 International Journal of Sustainability and Innovation in Engineering
Advanced Diagnostic Automation Techniques for Resilient AI Hardware in Fragmented Supply Chains
The supply chain fragmentation, geopolitical limitations and the fast rates of component obsolescence in the contemporary AI-hardware ecosystem require the strong diagnostic automation structures. This paper outlines a new architecture to integrate in-line hardware diagnostics, federated anomaly detection and automated remedial workflows to augment resilience of AI-specific hardware assets (e.g. neural-accelerator boards, memory modules) in the context of distributed supply chains. The suggested system leverages embedded sensors and telemetry based on board-level power/thermal/failure-event logs (instrumented through the use of IoT gateways) and consolidated through the use of secure edge-cloud pipelines. The federated learning module is used to train localized anomaly-detection models (e.g., variational autoencoders) at all nodes of the supply chain without losing data sovereignty. On the occurrence of a diagnostic signature, e.g. an increase in leakage current, non-standard thermal gradient across AI accelerator units, or failure during repeated operation in a stack, the system initiates automated correction measures: dynamically re-routed hardware units, non-standard scoring of suppliers through blockchain-based traceability. Simulations of supply chains in the form of digital twins are used to execute what-if risks of disruption of components and hardware diagnostics to focus on risk mitigation plans. The diagnostic system combines a real-time repair-automation code (e.g., firmware rollback, self-healing microcontrollers) with procurement procedures to minimize the mean time to detect (MTTD) and mean time to recover (MTTR). A synthetic supply-chain testbed evaluation demonstrates a 45 percent reduction in defect isolation time and a 30 percent decrease in hardware lead-time upheavals in a multi-tier supplier failure condition. The study paves the way towards the intersection of AI hardware diagnostics, supply-chain automation, and resilience engineering, which offers operators of distributed manufacturing networks a blueprint of integrating autonomous diagnostic loops into fragmented supply-chain settings.
Security-Oriented Microservices Deployment Leveraging Python Analytics and Java Frameworks
This paper investigates security-oriented microservice deployment by integrating Python analytics with Java frameworks, aiming to provide a comprehensive understanding of how hybrid solutions strengthen distributed architectures. The primary purpose of the study is to examine the effectiveness of predictive intrusion detection, anomaly-based data flow monitoring, secure API gateway enforcement, and resilient authentication mechanisms when implemented through Python and Java technologies. To achieve this, a secondary research method was employed, drawing from peer-reviewed journals, technical white papers, and industry-specific reports. This approach ensured reliable, cost-effective, and broad-based insights into technical implementations without requiring direct experimental testing. The findings indicate that Python-based predictive analytics enhances intrusion detection by using machine learning models to anticipate potential attacks before execution. Similarly, anomaly detection models built with Python improve monitoring by identifying irregular traffic behaviors and hidden data manipulation attempts across microservice transactions. On the other hand, Java-centric API gateways enforce traffic regulation, encryption, token validation, and rate limiting, thereby preventing unauthorized access and ensuring secure communication between services. Resilient authentication mechanisms supported by Java frameworks, including OAuth2 and JWT, further strengthen identity verification and role-based access control. Collectively, these results demonstrate that Python and Java offer complementary strengths, delivering proactive defense, scalability, and adaptability within containerized and distributed environments. However, challenges such as false positives, gateway bottlenecks, and integration complexity highlight the need for careful implementation. The study concludes that hybrid security approaches combining predictive analytics and strong authentication provide robust microservice protection while requiring balanced trade-offs between performance, usability, and operational efficiency.
Copyright (c) 2024 International Journal of Sustainability and Innovation in Engineering
Scalable Data Archival & Purging Mechanism for HighVolume Applications
With IoT, multimedia, financial, and healthcare data, data is exponentially increasing in modern applications, and storage scalability, cost, and privacy compliance are monumental hindrances to contemporary applications. The paper proposes an automatic policy-based scalable framework of data archival and purging in high-volume applications, resolving life cycle operations in real time with support of tiered storage, metadata-based decision-making, and distributed coordination to scale to petabyte scale. This architecture is a decomposition of ingestion, metadata indexing, policy evaluation, and execution, and isolates faults and allows work to be scaled horizontally. It implements a hybrid indexing strategy where the write-heavy metadata ingestion and search data are stored in wide-column stores, and the complex policy queries are made on search engines, and it also introduces configurable retention policies by using data age, access frequency, data size, and business-specific tags. The algorithms used in candidate selection are a rule-based heuristic, a derivative classifier based on supervised learning based on past access patterns, and a combination of the two methods. Synthetic and real-world e-commerce workloads show that decision throughputs can be as high as 20,000 requests per second, and policy latency can be well below a second, with storage costs reduced by an order of magnitude or more, and stay compliant with safe-delete pipelines that have audit trails and rollback capabilities. The analysis shows the trade-offs between latency and cost savings made throughout strategies and identifies the elasticity of the framework as it empowers loads of petabytes. The outcomes confirm the theoretical capabilities of automated, adaptive management of the lifecycle of cloud-native and on-premises infrastructures, which can provide a credible solution to data management requirements of modern solutions and additional operational resiliency.
API Gateway Threat Prevention in Large-Scale Applications
WAFs and API gateways are the lowest common denominator in every transaction and, therefore, the logical place to have the backend integrate threat prevention that is made scalable. This paper focuses on proactive, synchronous measures: driver block, driver challenge, rate-limit, sandbox, and step-up reauthentication, as opposed to detection procedures. It proposes an end-to-end design that combines on-path risk ratings with a deterministic policy engine and strictly enforced tail-latency budgets. This evaluation is capable through the use of a privacy-preserving, multi-region month-long corpus (~10B requests) in the form of gateway logs, auth events, WAF flags, and honeypot hits. Only O(1) online operations are supported: the presence of the header, the existence of specific trie paths, the length and entropy of tokens and parameters, rarity statistics, and sliding-window counters based on hashed client and tenant identifiers. The available serving options include in-process WebAssembly or a gRPC scorer with stringent deadlines; isotonic calibration and per-endpoint thresholds (including hysteresis) are available that map risk space to action. Idempotent GET caching, fail-open/closed defaults based on endpoint criticality, and signed audit logs can be used to provide reliability and governance. Offline experiments include traditionally separated splits PR-AUC and recall at specific false-block rates; ablations measure feature and model contributions. Online shadow/canary trials can be used to decrease malicious acceptance rate without increasing latency p95/p99 by more than ≤5-10ms. With 10^5-10^7 requests per second, between tenants and across regions, the strategy achieves SLOs and signed, hot-reloaded policy/model bundles. The artifacts consist of a public schema, feature definitions, a synthetic generator, policy DSL examples, rollback playbooks, config, and release scripts to provide reproducible deployment.
Tokenization in Healthcare: A Pathway to Secure Patient Data Communication
Tokenization has become a central data security strategy in healthcare focused on the feeder of data breaches, fraud, and non-compliance. The process involves replacing sensitive patient data with a non-sensitive placeholder, or token, which behaves exactly as the original data did but without any exploitable value. Tokenization in healthcare systems allows integration of that which helps mitigate the risks of cyber-attacks and data theft by eliminating the storing of sensitive information (personal health details and financial records) in its original form. Tokenization is used by healthcare organizations to dramatically increase privacy and patient trust and decrease the risk of compliance violations concerning regulations like HIPAA and GDPR. On the other hand, tokenization is more secure than encryption, which needs key management and can be susceptible to reverse engineering attacks, where tokens cannot be reverse-engineered without approved access to a secure vault. It also eases the security infrastructure, reduces operational costs, and facilitates regulatory compliance. Tokenization in health care systems would only work if IT infrastructure integration is careful, adheres to the industry’s standards, and has staff trained. The paper covers tokenization advantages over other data protection methods, real applications, and the future of tokenization in healthcare as it develops alongside the technology. Tokenization is critical in ensuring that data is transmitted safely and securely in a digital world.
HIPAA (Health Insurance Portability and Accountability Act)
Delta Lake Optimization Techniques for Scalable Lakehouse Architectures
This paper provides a validated, tested playbook to tune Delta Lake on cloud object store to a terabyte-level scale. It is posed as a multi-objective control problem that improves the read latency, write cost, maintenance cost, and reliability against bursty ingestion and mixed workloads. These methods include metadata (checkpoint cadence, log compaction, conservative VACUUM), file hygiene (bin-packing to 512-1024 MB, small file prevention), partitioning and skew (time+entity, salting), clustering to achieve selectivity, and read/write performance (AQE, dynamic file pruning, broadcast caps, shuffle parallelism, selective caching). Guardrails, re-writable budgets, cooldowns, canaries, small-file ratio, fragmentation, pruning hit rates, conflicts, coldstart timings, and visibility into these allow closed-loop reactions. TPC-DS-like and telemetry datasets (0.510 TB) have been applied on evaluation using back ETL, streaming CDC upserts, ad-hoc selective queries, and BI scans by repeatedly running the tests and building interval confidence. Compaction decreases the overhead of planning; combined with workload-driven clustering, it provides the most significant performance boost on narrow predicates: for median and P95 tail latency, it increases 44-58 percent and 37-49 percent, respectively, at low to medium selectivity (0.1-5 percent). Staged upserts purges candidate files batch and file-level statistics are cut in bytes, shuffled by 2741 percent, and P95 times are sunk on MERGE by 2436 percent. This is because daily compaction maintains a small-file ratio at ~8% compared to hourly schedules with minimal to no viable read advantage (write amplification increases ~1.6 x). AQE truncates long tails; past two nesting columns, the returns vanish, and effectiveness has a broad 512 MB to 1 GB file-size peak.
Ethical and Social Responsibility in Procurement for Electric Vehicle Components
Electric Vehicle (EV) industry growth proceeds rapidly, with associated ethical and social challenges in obtaining key components, both batteries and rare earth metals. This study investigates how they can combine corporate social responsibility (CSR) goals into their procurement practices to confront these problems. It emphasizes the need to build quality jobs, source responsibly, and minimize the environmental impact of EV production by recycling second-life batteries for them. The study shows how blockchain technology could help with supply chain transparency and verifying the legal sourcing of raw materials in supply chains. Adopting a sustainable and transparent strategy is the best way for EV OEMs to keep their brand image intact, disregard regulations, and damage sustainability in the long term. Social aspects of procurement are also considered, including material procurement from countries with weak labor laws. Auditing and checking ethical standards that prevent workers' rights from being violated and fair labor practices is necessary. This study also suggests that a more sustainable and ethical future for the EV industry lies in developing CSR practices that will help create a cleaner, more equitable transition towards electric mobility. The study lays down the responsibility of the industry to carry out astute collaboration and innovation in technological areas to support responsible sourcing, promote environmental outlay, and uphold human rights
Data Quality as a Service (DQaaS): A Paradigm Shift in Enterprise Data Management
The data environments found in enterprises continue to be plagued by incompleteness, inconsistency, duplication, staleness, and distributional drift, which directly impact decision-making, the performance of machine learning, and regulatory compliance. Conventional data-quality strategies that may be as narrow as semi-static rules or as inefficient as manually cleaning data cannot address the speed and variety of modern pipelines. This paper suggests Data Quality as a Service (DQaaS). This paradigm shift redefines quality as a provision-managed, cloud-native capability that provides through APIs, contracts, and measurable service level objectives (SLOs). DQaaS incorporates declarative rules, statistical anomaly detectors, and machine learning models under a common multi-tenant platform and delivers round-the-clock monitoring, lineage-enabled diagnostics, and remediation as a service. The contributions that this work has can be classified in four ways. It is first to present a reference architecture that has control and data planes in both the streaming and batch pipelines. It formalizes measures of service level indicators (SLIs), SLOs, and error budgets on the critical dimensions of completeness, validity, timeliness, and accuracy. It makes contracts and schema evolution operational in CI/CD pipelines, in a compatible and accountable way between producers and consumers. It also tests DQaaS using enterprise datasets across ERP, CRM, and streaming data, clearly highlighting improvements in reliability, incident recovery times, and business performance with very little latency overhead. The results show how DQaaS can turn ad hoc quality activities into a scalable and bureaucratically enforceable service that is economically sustainable, with technical assurance, governance, and organizational objectives.
The Role of Data Governance in Enabling Secure AI Adoption
Artificial Intelligence (AI) has rapidly evolved into a cornerstone of digital transformation, revolutionizing decision-making, operational efficiency, and innovation across industries. Yet, as enterprises accelerate adoption, risks related to data privacy, integrity, and security are escalating. AI systems rely on vast volumes of sensitive data often personal, regulated, or business-critical that must be managed responsibly to prevent breaches, misuse, and ethical violations. At the same time, regulatory frameworks such as GDPR, HIPAA, and CCPA impose strict requirements around lawful processing, data minimization, and accountability. This dual challenge underscores the urgent need for robust data governance as an enabler of secure AI adoption. Data governance establishes the policies, processes, and standards for managing data across its lifecycle. When applied to AI ecosystems, it ensures the quality, provenance, and lawful use of training data, while embedding security and compliance at every stage of the model lifecycle. Unlike purely technical cybersecurity controls, governance provides a socio-technical framework that aligns people, processes, and technology to build trust in AI outcomes. It enables organizations to mitigate risks such as adversarial data poisoning, model bias, or unauthorized access to sensitive datasets. This paper examines how data governance frameworks integrate with cybersecurity to secure AI adoption. We review existing literature, highlight governance gaps, and propose a Secure AI Governance Model (SAIGM) consisting of four pillars: data integrity, privacy and compliance, access and control, and continuous oversight. Case studies demonstrate how effective governance translates into trusted AI outcomes, regulatory compliance, and business resilience.
Privacy and compliance (GDPR/HIPAA/CCPA)
Copyright (c) 2025 International Journal of Sustainability and Innovation in Engineering
Smart Manufacturing and the Operator’s Digital Double: Modeling Cognitive Load Through a Psychosocial Digital Twin
Digital twin technology, a virtual model that replicates real-world machines, has become a key component of modern manufacturing, enabling companies to predict problems before they occur and enhance operational efficiency. Yet, most of these systems are built around equipment, overlooking the human operators who play a crucial role in the production process. To address this gap, we propose the Psychosocial Digital Twin (PDT), a framework designed to create a real-time virtual model of a worker’s cognitive state. Unlike traditional monitoring tools, the PDT combines multiple data sources to track and predict stress and workload as they unfold. To test this idea, we created a virtual factory environment using VR and conducted an experiment with 70 experienced factory workers. Participants were split into two groups: one used the new PDT system, while the other relied on conventional monitoring methods. The PDT combined information from several streams, including machine performance data (such as speed and error rates), environmental conditions (like noise and lighting), and non-invasive physiological measures (such as heart rate variability, electrodermal activity, and eyetracking). All of this was processed by an AI model that produced a Cognitive Load Index (CLI), a score showing the worker’s real-time mental stress levels. Supervisors in the PDT group could then run “what-if” simulations to test how proposed changes might affect workers before applying them on the floor. Results demonstrate that the PDT enhanced both worker experience and operational stability. The system predicted stress events with 87.4% accuracy, reduced reported stressful episodes by 42%, and cut task-related errors by 28% compared with the control group. Supervisors also proactively altered or canceled 65% of stress-intensive tasks based on simulations. Overall, the PDT represents a shift from reactive human factors analysis toward proactive, simulation-driven design. This study contributes to understanding human behavior in cyber-physical environments by modeling how cognitive load dynamically influences performance and decision-making in AI-augmented workplaces. By making workers’ wellbeing visible, measurable, and optimizable, this framework provides a scalable method for balancing productivity and safety, thereby enhancing performance in Industry 4.0 environments.
MLOps and Data Engineering: Integrating ETL into the ML Lifecycle
In this paper, MLOps and data engineering are discussed with a focus on the role of ETLs throughout the ML pipeline. With the growing adoption of AI solutions in organizations, the—to be used—integration of sound data management has become a crucial success factor influencing its effectiveness, reliability, and value. The paper overviews the architectural strategies for integrating ETL into MLOps methodologies, introduces methods of automated feature engineering, and discusses main issues like data drift detection and versioning. Through the analysis of the current trends and technologies, this paper outlines how integrated ETL is in the process of moving traditional ML projects from proving grounds to scalable production systems that are defined to deliver tangible business value.
Feature Pipeline Automation Introduction
Integrating Circular Business Models in Construction: A Framework for Design and Planning to Enhance Sustainability
This paper develops a practical framework for implementing circular business models (CBMs) in the design and planning phases of the construction industry. By synthesizing circular economy literature, industry case studies, and a survey of sector stakeholders, the study shows how modular construction, reclaimed materials, and service-based models can optimize resource efficiency and minimize waste throughout the built environment lifecycle. The framework features actionable tools such as material flow mapping and a circular economy model canvas to facilitate stakeholder collaboration and embed circularity at the earliest project stages. Results reveal substantial barriers to CBM adoption, including policy gaps, investment needs, and limited technical expertise, but highlight major potential for reducing environmental impacts, enhancing economic resilience, and aligning with global sustainability goals. The research concludes with recommendations for policy support, skill development, and future investigation into digital solutions, providing guidance for professionals aiming to drive sustainable transformation in construction.
Analytical Study of Insulation, Air-Sealing, and High-Efficiency HVAC in Affordable Housing for Energy Affordability in ColdClimate region (Western PA)
The economy of energy has become a sufficiently significant, but less extensively researched, factor of housing stability, especially in housing areas with cold temperatures, such as where the LMI households reside. Traditional Housing affordability factors have assumed that it is mostly rent or mortgage payments rather than rising utility bills. This study discusses the impact of escalating costs of energy in residential housing in Pittsburgh and Western Pennsylvania, whereby residential housing costs are normally jeopardized by utility costs. The research was a case study design of a vacant single-family living room undergoing renovation through specific energy-saving measures that were dense-packed insulation, full air sealing, and high-performance heat pump HVAC systems. The study that was under evaluation was one that assessed the amount of energy both at pre-rehabilitation and post-rehabilitation by using the U.S. Department of Energy Home Energy Score and estimating a reduction in utility costs. The performance on the energy has been credited as having brought about a reduction of 44% in the energy consumption and over $470 saved annually in utility costs. The research proves that energy retrofits minimize the redundant energy factored to address LMI households, leading to less challenging illness of housing affordability and reduced displacement trepidation. The findings indicate that the economic feasibility of energy must be considered as part of the housing policy, as the cost of energy must be factored, as this is necessary in the creation of long-term stability of housing. Future research should extrapolate those findings to other climates and socio-economic settings to ensure that the housing solutions are applied on a broader scale to the vulnerable populations.
Low- and ModerateIncome (LMI) Households
AI Enabled Pharmaceutical Serialization Using SAP QM for FDA 21 CFR Part 11 Compliance
Pharmaceutical serialization is a critical regulatory mandate aimed at ensuring product traceability, combating counterfeiting, and safeguarding patient safety. Within the United States, compliance with FDA 21 CFR Part 11 (Berry & Martin, (Eds.). (Year)) imposes stringent requirements on electronic records and signatures, positioning serialization as a pivotal compliance activity rather than a mere logistical function. Traditional serialization approaches, often characterized by manual validations and fragmented data management, present significant challenges including increased audit risks and inefficiencies. This study addresses the evident gap in leveraging artificial intelligence (AI) within SAP Quality Management (QM) systems to enhance serialization processes and ensure robust compliance with FDA regulations. Employing a mixed-methods methodology, the research developed an AIenabled serialization framework integrated with SAP QM and SAP Advanced Track and Trace for Pharmaceuticals (ATTP) modules (Parmaksiz, Pisani, & Kok, 2020) within a controlled sandbox environment. AI techniques such as predictive analytics, anomaly detection, and intelligent automation were incorporated via SAP Business Technology Platform (BTP) services to augment data integrity, automate quality checks, and facilitate comprehensive audit trail generation. Validation involved simulating FDA 21 CFR Part 11 compliance scenarios, cross-referencing system outputs with regulatory checklists, and conducting expert interviews with SAP consultants and pharmaceutical compliance officers. Empirical findings demonstrate that AI integration significantly improves serialization accuracy by reducing manual errors by over 40%, enables real-time detection of serialization anomalies, and streamlines audit readiness by decreasing preparation time by approximately 60%. Furthermore, the synergy between SAP QM, ATTP, and AI services enhances crossfunctional visibility and governance, fostering a scalable and audit-ready compliance framework. These results suggest that AI-enabled SAP QM serialization not only fortifies regulatory adherence but also delivers operational efficiencies and risk mitigation benefits (Ojha & Jaiswal, 2023). Consequently, this research contributes a novel, intelligent compliance paradigm that transforms serialization into a proactive quality intelligence system, thereby supporting pharmaceutical manufacturers in navigating complex regulatory landscapes with greater agility and confidence.
Delta Lake for Collaborative AI: A ServiceNow Case Study
This study examines how the concept of Delta Lake can be connected with the business value of ServiceNow to accelerate the collaborative work with AI, especially in terms of applying key Delta Lake functionality such as ACID transactions, automatic data streaming, and governance status. The study highlights the potential offered by the Delta Lake to allow real-time data ingestion and an uninterrupted teamwork of AI teams and IT operations to result in quantifiable enhancements of AI model performance and operational measures. Delta Lake provides consistency in AI models trained on ServiceNow data via time travel as a version control feature and continuous updates to keep these models relevant and generate better predictions and decisions, using Change Data Capture (CDC). The study also emphasizes how Unity Catalog facilitated data governance and how Delta Sharing helped to secure the collaboration of data among teams to make the AI workflow more productive. The author reports noteworthy findings with an 80% reduction in the time taken to create a resolution note, 14% lower deflection rates on a self-service level, as well as 2-minute time period savings of an AI-driven search. These findings also emphasize the use of Delta Lake to remove time delays in implementing AI models and their effectiveness, as well as the development of the field of Data governance, and thus, organizations have the keys to unlock the full value of their Data in ServiceNow to implement new AI models jointly.
AI-Optimized Kubernetes Scheduling: Node Affinity for Java Microservices
This study investigates Kubernetes scheduling that is optimized with AI through Node Affinity to maximize the deployment and performance of Java microservices. It also reviews the drawbacks of conventional, rule-based schedulers —the lack of adaptability in workloads and their resource requirements that tend to cause inefficient node assignment, resource partitioning, and performance issues. Given that the impending integration of machine learning algorithms into the core Kubernetes codebase will support only supervised learning of the resource requirements and reinforcement learning of adaptive schedules, the proposed framework will enhance the native scheduler in Kubernetes with the ability to make data-derived decisions. A custom scheduler plugin makes all of them use historical and real-time scale data as well as metrics, such as CPU, memory, I/O, pod latency, throughput, predictive node scoring, and affinity-based pod placement. The experiments conducted in an isolated Kubernetes cluster to test the AI-optimized scheduling reveal a 12% decrease in the mean absolute error when forecasting the required resources, a 25% throughput improvement in microservices, an 18% increase in CPU and memory utilization, and a 15% decline in response time as compared to the default scheduler. The placement efficiency of the Java microservices also increases by 22%, which can affirm successful matching of the microservice specifications to the node capabilities through the framework. These findings indicate high performance, scalability, and cost-effectiveness and provide recommendations to help industries incorporate AI models into production Kubernetes practices. Work in the future will include deep learning advanced architectures, continuous ad-hoc model retraining, and the extension to heterogeneous workloads in the cloud. This framework eliminates the manual overhead of configuration. It can be continuously optimized, shifting the need for resilient and efficient operations as part of the larger-scale Kubernetes cluster and the ability to scale economically in any location around the globe. This research affirms the power of AIbased scheduling, which can drive the container orchestration industry that focuses on optimizing object or node affinity-based selection decisions, as seen in Java microservices.
Real-Time Jira Analytics: Integrating JQL with Power BI/Snowflake for Predictive Agile Metrics
This document integrates it with real-time analytics tools like Power BI and Snowflake for Predictive Agile metrics. Agile methodologies are supported by the project management tool Jira because Jira provides important metrics such as sprint velocity, cycle time, and burndown, all of which help monitor project performance. As the data produced by large teams grows, finding actionable knowledge from data becomes more important. The realtime Jira analytics uses advanced reporting features and predictive analytics to predict unplanned delays in the project, resource allocation problems, and risks and provide teams with proactive choices. For the raw Jira data to become comprehensive and real-time insights, it is essential to integrate Jira Query Language (JQL) with Power BI and Snowflake. JQL can report how the work is being done, how much progress has been made, and how performance will be, with a fraction of the effort required using Jira's reports. Interactive dashboards can be created using Power BI, and Snowflakes gives it a scalable solution to store and process large datasets in the cloud. When combined, this allows Agile, ITSM, and DevOps teams to optimize their workflows, improving collaboration and predicting future outcomes. It talks about the technical suitability of these integrations in terms of their help in improving decision-making and predictive analytics trends in the future of Agile project management. In the future, as AI and automation continue to develop, there will be the capabilities of using them to integrate them into everything and allow companies to predict and manage project timelines more effectively and accurately to deliver more efficient and faster outcomes in very dynamic environments.
Choosing the Right NoSQL Database: MongoDB vs. Aerospike for Enterprise Applications
The paper provides an in-depth analysis of the main differences between MongoDB and Aerospike as representatives of NoSQL database solutions in enterprise applications, focusing on their architectural differences, performance, and adaptability to various workloads. As a document-oriented database, MongoDB has schema flexibility, rich querying, good integration with analytics pipelines, and is well-suited to content management, e-commerce, and customer data platform use cases. Aerospike is a highperformance key-value store focusing on ultra-low latency, deterministic scalability, and throughput, with real-time analytics, financial services, advertising technology, and IoT among the use cases it excels at. The comparison analyzes data models, scaling mechanisms, consistency models, and benchmark performances, showing a comparison where MongoDB excels in complex queries and schema evolution flexibility, compared to Aerospike excelling with predictable sub-millisecond responses and high throughput operations functions. Applications in the industry and the purpose demonstrate the capabilities of each of these systems with MongoDB and its flexibility, and Aerospike and its stability in high-stakes tasks. Future work is then discussed in the form of NoSQL advances that include enhancements to distributed transactions, storage, predictive auto scaling, and implementing adequate security functions. The paper is also able to spot the prospects in hybrid constructions based on the use of both systems, further integration with AI, ML, and big data platforms, and the prospects of performance optimization, geodistributional fault tolerance, and energy-efficient systems. Findings conclude that data requirements dictate the use of databases: when flexibility, analytics binding, and schema flexibility are oriented towards MongoDB, and when latency, probing inner transactions, and Aerospike are better. This comparison can give enterprise architects and decisionmakers practical data in helping enterprises align database capabilities with operational and strategic goals.
Helm Charts for Kubernetes Deployments: Simplifying Application Deployment and Management
Helm charts function as the principal subject of examination in this study regarding their role in simplifying Kubernetes application deployment and management procedures. Developers find it difficult to deploy Kubernetes applications through container orchestration even though Kubernetes offers scalable platforms and fault tolerance due to its complex nature for those without configuration experience. Through its package manager role, Helm provides reusable charts known as "charts," which solve deployment issues by streamlining the installation process. Developers can automate Kubernetes deployments through Helm charts because these tools create standardized packages that organize application configurations together with their dependencies. An automated Helm package system removes difficult-to-handle YAML configuration files, establishing uniform deployments and minimizing mistakes from human operators. Helm charts give developers three main advantages: user-friendly deployment through charts alongside version management features and rollback protocols and automatic environment deployment configuration capabilities. The paper analyzes Helm's position in Kubernetes management through discussions about its CI/CD pipeline integration and GitOps workflow adoption. The paper examines future developments in Kubernetes and Helm mainly through the lens of enhanced multi-cluster management and serverless architecture growth. The paper supports Helm charts because they enable organizations to achieve faster deployment times, enhanced practice management, and decreased Kubernetes environment complexity. Helm charts within Kubernetes deployment methods produce simplified processes and grant application managers complete control to operate across different infrastructure types.
Low-Latency DDoS Mitigation: Arista DANZ vs. Cisco Tetration
This study assesses the performance of low-latency protection against DDoS attacks in contemporary leaf-spine and hybrid-cloud datacenters, where queuing delays of milliseconds can violate SLO and broadcast attacks. It introduces a side-by-side approach to compare Arista DANZ and its use of DANZ Monitoring Fabric, sFlow/IPFIX, ERSPAN, and gNMI with Cisco Tetration (Secure Workload), which measures hosts using kernellevel sensors, and micro-segmentation can be enforced. The cure-to-end is examined: from the first anomalous telemetry window to the first verified drop/redirect. A more controlled testbed is used with PTP-synchronized hardware timestamps, synthetic (TRex/MoonGen) and replay first-trace replay, export scaling of 100-500 ms, and 1:512-1:2048 sampling. The features are comprised of SYN/ACK divergence, flow/packet rates, inter-arrival variance, source/port entropy, and five-tuple fan-in/out. Performance metrics have been detection time, enforcement time, p99-p99.9 one-way and overall-delay deltas, falsepositive and false-negative rates, TCAM occupancy, controller/collector CPU and API throughput; verification has been coupled with mirrored packet captures and device counter information and policy acknowledgments. Fabric-first mitigation reduces time-to-mitigate in volumetric L3/L4 floods through ACLs, policers, BGP Flowspec or RTBH, whereas host-centric enforcement is more effective in app-layer low-and-slow traffic and intraVLAN flood jumping; a hybrid trigger path offers the strongest blast-radius shortening and accuracy. Contributions encompass a vendor-neutral control-loop budget, a repeatable harness and failure-injection regimen, operational SLOs and rollback playbooks, and practical recommendations on sampling and export cadence and rule-churn limits; deployment recommendations are delivered. The scope is enterprise datacenters and hybrid clouds; forensics and external scrubbing will remain out of scope at the moment.
Streaming telemetry (sFlow/IPFIX/NetFlow, gNMI)
Addressing power efficiency challenges in AI hardware through verification
AI accelerators already run with constrained energy and thermal budgets, and small inefficiencies are amplified across an entire fleet, resulting in increased costs and emissions. This work redefines power efficiency as a checkable requirement and not a back-silicon addition. It specifies power intent in IEEE 1801 (UPF), encodes protocol noncorrectness with SystemVerilog/PSL assertions, and quantifies progress with power-state, transition, and cross coverage (DVFS X workload phase X thermal bin). The reproducible dataset schema integrates time with microarchitectural counters, voltage, frequency, temperature, and power, measured across real workloads (ResNet, BERT, and attention/GEMM) in simulation, emulation, and instrumented silicon. Telemetry input is synchronised using triggers and PTP/NTP; rails calibrated and error budgets quoted. The continuous integration gates are merged on quantitative thresholds (e.g., >2% p95 energy/inference regression), and dashboards auto-bisect offending changes. Experiments show a hybrid analytical-plus-ML estimator of 3.8-6.1% MAPE at millisecond latency with 30- 60x emulation throughput compared to simulation and mid-single-digit energy reductions due to verification-driven fixes. Case studies involve preventing standby leakage through restored isolation, smoothing a DVFS table to eliminate 10-15 ms oscillations, and fixing compiler schedules that caused incorrect L2 miss models and increased DRAM data traffic. This yields a realistic, start-to-finish pipeline UPF, ABV/formal, emulation/FPGA, calibrated rigs, and CI to bring watts into the top echelon of test metrics and achieve long-lasting efficiency improvements in GPU/NPU/ASIC accelerators. The full scope includes training and inference across 14-5 nm nodes adhering to rigorous safety, ethics, and licensing practices.
Dynamic Voltage and Frequency Scaling (DVFS)
Affordable Housing: The Growing Gap in Rental Markets
The housing deficit in the United States has become alarming, with rents increasing faster than incomes, making millions of people live in haste while in search of affordable shelter. This article discusses some of the causes of this crisis, such as the lack of houses in specific areas, a problem financially due to reduced wages, a problem due to increased costs of construction, and an issue due to gentrification. Low-income earners, African American households, senior citizens, single-breadwinner families, and frontline workers are the most affected by this widening rent increase relative to income. The article also focuses on individual instances of the problem in large cities such as New York, Los Angeles, and Miami. The following recommendations include federal appropriations for housing assistance programs, enlarging the critical Housing Choice Voucher program, strengthening the Low-Income Housing Tax Credit (LIHTC), and liberalizing exclusionary zoning rules. By such specific approaches, it is possible to reduce the number of people who are homeless and increase the chances of homeownership for everyone.
Copyright (c) 2023 International Journal of Sustainability and Innovation in Engineering
Optimizing Sales Headcount for Revenue Growth in the US Rentals Market Using Machine Learning (2023)
In the context of the American rental market, it is essential to underline that managing sales teams is one of the critical factors affecting the revenue increase for property management software providers. In this article, the author discusses issues of sales head management in SaaS firms, how to increase sales headcount and some barriers the firms themselves experience due to variations in demand, differences in the regions, and client segmentation. Existing sales labour models rely on historical data and must be more adequate to capture the fluctuating landlord market. ML's best solution is based on the given data to help firms predict the variability in demand, distribute workload efficiently, and minimize churn rates. Organizations in the SaaS segment can now classify their sales labour models with the Random Forest algorithm to anticipate the need for hiring and cutting costs for truly sustainable growth. This article also shows how they use machine learning to solve problems such as seasonal fluctuation, lead quality, managing sales territories, and employee turnover. This paper confirms how the implementation of machine learning models supports more tangible and accurate workforce planning, cuts costs, and optimizes the sales team's performance in a highly saturated market.
The Role of Hedge Funds in the Residential Real Estate Market
US-based hedge funds have adjusted their investment targets to include the residential real estate market, influencing the availability and affordability of homes. After the 2008 financial crisis, hedge funds moved from operation in stocks and derivatives to real estate as this area had more stability and probable high yields. This paper analyzes the approaches adopted by hedge funds, the buying and extensive conversion of single curb houses and single facilities into rental houses, the use of financial assets, and house swapping. These activities have also contributed to this paradigm shift, affecting housing prices and rent charges in metropolitan regions. The massive capital and flow of strategic properties by hedge funds have increased property prices and rents, bringing homeownership dreams out of reach for middle-income earners and first-time homeowners. These investments introduced positive consequences, including the revitalization of properties and production of profits through property tax, and negative impacts, including displacement and reduced opportunities for small investors to access property investments. The concentration of ownership has tilted markedly towards institutional investors and rentals, especially with the heavy involvement of hedge funds in high-demand locations. The paper demonstrates how and with what effects these investment practices are at work locally, for housing markets and the economy more generally. This article reasons for moderate legislation in that it seeks to reduce undesirable effects and tap into the economic advantages of hedge funds in real estate. Considering different responses and legislation measures, the study reveals that policies should be formulated to create better housing options that do not favor one individual while maintaining market stability. This includes, for example, rejecting large quantities of purchases, improving standards of tenants while improving standards of accommodation, and lastly, investing in affordable housing projects. The study results indicate that hedge funds have positively affected local communities. However, their leadership in the housing market requires closer monitoring to avoid adverse effects on the locals and their viability in the long run
PUNCHING SHEAR BEHAVIOR AND STRENGTH OF FLAT SLABS
Prestressed and reinforced concrete flat plates are susceptible to failure due to punching shear. Modeling the punching shear strength of prestressed concrete slabs poses conceptual challenges, as it involves various factors, including the bonding of tendons, stress in tendons during punching failure (which can be indeterminate), tendon banding, and the presence of draped tendons that yield a beneficial vertical component of the prestressing force. Additionally, prestressed concrete slabs typically require supplementary bonded reinforcement. The objective of this research is to comprehensively investigate the physical behavior of each test specimen, considering parameters such as deflections, crack patterns, crack widths, mode of failure, and to compare the punching shear resistance of interior slab-column connections with various code provisions. The study involved testing a total of five interior slab-column connections, each measuring 1600x1600 mm in size, 80 mm in thickness, with square columns of 180x180 mm and a height of 300 mm.
Effect of Fire on Concrete Contribution to Shear Resistance of GFRP RC Beams
The use of fiber-reinforced polymer (FRP) bars is becoming commonplace in various civil engineering applications due to the several advantages they offer over traditional materials. However, limited knowledge about some aspects of the behavior of FRP bars, particularly when subjected to severe fires, is hampering their widespread use. This paper presents the results of a research program to investigate the shear behavior of glass-FRP (GFRP)-reinforced beams exposed to severe fires. Three reinforced concrete (RC) beam specimens with a cross-sectional width and height of 150 mm and 300 mm, respectively, and with a total length of 1700 mm were constructed and tested under four-point bending load up to failure. The main test variables were the reinforcement ratio and the fire. The experimental results show that all beams failed as a result of diagonal tension cracking. The shear resistance and stiffness of the RC beams decreased when the beams are exposed to fire at the same reinforcement ratio.
Analyzing the Nonlinear Finite Element Behavior of FRP Composite Electrical Structures in Flexural Loading
Finite Element Analysis of Flexural Behavior in Full-scale Tapered FRP Pole Structures: Influence of Fiber Orientations, Circumferential Layers, and Carbon Fiber Substitution. In this study, we present a finite element modeling analysis of the nonlinear behavior of laterally loaded full-scale tapered fiber-reinforced polymer (FRP) pole structures. The study explores the impact of various parameters, including fiber orientations in longitudinal and circumferential layers, the number of circumferential layers, and the substitution of glass fiber with carbon fiber in the FRP pole compositions. The FRP poles in question were manufactured using the filament winding technique, with E-glass fiber and epoxy resin as the primary materials. Our analysis results exhibit a significant correlation between the finite element analysis and experimental data, emphasizing the critical role of fiber orientation in determining flexural behavior. The findings underscore the advantages of incorporating circumferential layers and highlight that enhanced strength can be achieved by incorporating both outer and inner circumferential layers alongside longitudinal layers. Moreover, substituting carbon fiber for glass fiber in the FRP poles results in notable improvements, with increased total load capacity and stiffness as the percentage of carbon fibers rises.
Effect of SHEAR SPAN-TO DEPTH RATIO OF Concrete Beams Reinforced with GFRP bars on Shear under Fire Exposure
ABSTRACT: Glass bars for concrete reinforcement, known as glass fiber-reinforced polymers (GFRPs), are new natural inorganic materials with distinct mechanical properties that have been used recently in the construction field. Generally, the FRPs bars have no yield before the brittle failure as steel bars and their behavior, when exposed to fire, is still under investigation, this paper presents an experimental study to get more knowledge about the characteristics and the fire resistance of a concrete beam reinforced using GFRPs. Three-scale concrete beams were constructed and tested up to failure under direct fire at 500 ◦C for an hour. Shear span-to depth ratio is the main parameter of this study. Beam (B1) had distance from face of support 670 mm ,beam (B2) had distance from face of support 250 mm and beam (B3) had distance from face of support 500 mm .All beams had 30 mm concrete cover , exposed to fire for an hour and three GFRPs reinforcement bars were used. The results are discussed in terms of load capacity, cracking behaviour, and failure modes. Moreover, the experimental results are compared with theoretical calculations according to the ACI code and other codes. The results show that when distance from face of support over effective depth greater than 2.5 the beams act as slender beams while distance from face of support over effective depth less than 2.5 the beams act as arch action. Our results also showed that when distance from face of support over effective depth 2.02 the failure load incress by percent 60.3% and beam failure as shear failure while distance from face of support over effective depth 1.01 the failure load incress by percent 221.7% and beam failure as flexural failure . More studies are needed to justify our observations in details and determine their applicabilities under different conditions.
Shear Contribution of GFRP Reinforced Concrete Beams Mixed with Seawater, Freshwater, and Chopped Glass Fiber
The behavior and shear strength of concrete beams reinforced with longitudinal GFRP bars mixed with seawater are the subject matter of experimental research discussed in this paper. Seven beams were tested in bending to determine how much concrete contributes to shear resistance. The beams were longitudinally reinforced with glass fiber-reinforced polymer bars and were similar in size and concrete strength; however, they were not even shear-reinforced. The 3,100 mm long, 400 mm deep, and 200 mm wide beams underwent thorough testing until the failure. The test parameters included chopped fiber content (0, 0.5, 2, and 3 kg/m3), longitudinal reinforcement ratios (1.0, 1.4, and 2.0%), and mixing water type (freshwater and seawater). The test findings showed that Increasing the reinforcement ratio raised the neutral-axis depth, enabled the formation of more closely spaced cracks, and minimized the loss of flexural stiffness after cracking, according to the test results. The contribution of aggregate interlock as well as the contribution of uncracked concrete are both enhanced through increasing the area of concrete in compression. Additionally, increasing the reinforcement ratio enhances the dowel action, which reduces the tensile stresses produced in the surrounding concrete.