Journal of International Crisis and Risk Communication Research
The Journal of International Crisis and Risk Communication Research (JICRCR) is the first scholarly journal dedicated to human and mediated communication issues associated with crises, risks, and emergencies around the world. The journal is supported by an international editorial board comprised of top crisis and risk communication scholars. JICRCR is a biannual peer-reviewed, open access publication, which means that all content is freely available without charge to the user or institution. ISSN: 2576-0017 E-ISSN:2576-0025 Indexed in ProQuest, Scopus, EBSCO, Informit, Google Scholar, Ulrichsweb Global Serials Directory, and other databases.
Enhancing Mean Time To Resolution (MTTR) In High-Frequency Financial Platforms: A Dual-Stage Retrieval-Augmented Generation (RAG) Approach With Metadata-Aware Re-Ranking
Financial systems operating in high-frequency trading and real-time settlement environments face critical challenges in incident response, where rapid diagnosis directly impacts financial exposure and regulatory compliance. Traditional diagnostic workflows require engineers to manually correlate millions of log entries with proprietary code and documentation under extreme time pressure, resulting in extended resolution cycles. While Large Language Models offer powerful reasoning capabilities, parametric models hallucinate when lacking domain-specific knowledge, and conventional Retrieval-Augmented Generation systems fail to differentiate between data sources of varying epistemic fidelity. This introduces a Dual-Stage RAG architecture with Metadata-Aware Re-Ranking that addresses the knowledge heterogeneity problem by explicitly prioritizing transactional ground-truth logs over general documentation. The architecture implements query parsing to extract transaction identifiers, enabling filtered retrieval of operational data, combined with a weighted re-ranking function that assigns source credibility weights based on evidential hierarchy principles. Experimental validation using the RAGAS framework demonstrates substantial improvements: the Hybrid system achieves a significant reduction in diagnostic latency, improves Context Precision substantially, and achieves strong Faithfulness scores while reducing hallucination rates significantly. The system successfully concentrates high-fidelity transactional logs in top retrieval positions, ensuring LLMs receive better-targeted evidence that enables precise root cause identification with explicit temporal and quantitative evidence citations, offering actionable remediation guidance for production incident response teams.
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The Role Of Enterprise Devops Engineering In Modern Society: Societal Impact Of High-Reliability Software Systems
Modern society increasingly relies on large-scale software transfers to facilitate financial transactions, online commerce, and safe information exchange. Enterprise DevOps engineering is a field of practice that ensures the continuous availability, security, and reliability of critical digital infrastructure at scale. Although productivity and efficiency are used as the common operational definitions of DevOps, the overall impact of the concept on society is not yet well examined in the academic sources. This article explores the social application of enterprise Devops engineering, specifically in [1]high-reliability financial systems that underpin daily economic operations. The presentation positions DevOps as a crucial digital infrastructure, comparable to standard utilities, where disruptions can have significant effects on society, potentially impacting millions of people simultaneously. The reliability of the system is directly linked to economic stability and trust among the population, which makes DevOps practices critical instruments for ensuring the confidence of digital services. Compliance with regulations and consumer responsibility is an increasingly important area, and global standards provide guidelines for the application of DevOps in a controlled setting. Chaos engineering techniques and resilience engineering practices are important in ensuring system tolerance to failure, especially in the event of a crisis, to provide continuity to society. Enterprise DevOps engineering has developed into a kind of digital stewardship, which carries enormous implications for society, well beyond the organizational scope.
AI-Driven Climate Risk Intelligence As Critical Infrastructure For Society
Climate change imposes operational complexities that require measurable risk intelligence at the residential, business, and governmental levels. Conventional approaches to climate evaluation are unaffordable, restricted, and unstandardized. The article demonstrates the effectiveness of cloud-native, artificial intelligence-powered climate risk solutions as digital public infrastructure. The solutions synthesize climate data feeds, catastrophe history, satellite imagery, and climate models to produce continuously refreshed geospatial risk analyses. The system maps environmental datasets to actionable intelligence, empowering preemptive resilience strategies instead of post-event crisis response. Their contributions lie in democratizing access to advanced climate analysis, promulgating risk quantification paradigms, and stabilizing financial markets via portfolio-level risk assessment of exposure. Evaluation illustrates their societal effectiveness through fair distribution of risk intelligence, data-driven policy design, and clear capital allocation practices. Designed to handle tens of millions of geospatial queries in under a second, without compromising analytic fidelity to floods, fires, wind, heat, and drought risk models, the system demonstrates effectiveness in migrating high-barricade, proprietary solutions to public infrastructural status, thereby innovating novel paradigms to prospect and respond to climate risks in the areas of urban planning, financial, and disaster resilience solutions.
Resilient Heterogeneous Network Architecture: A Deep Reinforcement Learning And Cryptographic Provenance Approach For Performance Optimization Under Cascading Failures
Cascading failures in telecommunications infrastructure during large-scale disasters (bushfires, hurricanes) isolate emergency operations centers from field personnel, preventing real-time situational awareness required for coordinated response. This paper presents a heterogeneous network (HetNet) architecture integrating terrestrial cellular (LTE, Public Safety Band), satellite backhaul (Ku-band, L-band emergency systems), land mobile radio (VHF/UHF), and mobile ad-hoc networks (IEEE 802.11p mesh) with AI-driven dynamic resource allocation to maintain communications resilience during cascading infrastructure failures. The proposed architecture employs: (1) multi-path routing optimization to maximize redundancy across heterogeneous links, (2) spectrum sensing algorithms (MIMO, adaptive modulation and coding) to maintain signal quality in degraded RF environments, (3) cryptographic audit trails enabling post-event forensic analysis, and (4) machine learning-based traffic classification for priority-based resource allocation. Mathematical modeling establishes network reliability as a function of redundancy parameter k, with significant packet delivery ratio (PDR) improvement from legacy cellular-only to proposed HetNet configurations under cascading failure scenarios. Simulation results using NS-3 demonstrate rapid system recovery time across all frequency bands and latency maintenance for critical emergency dispatch despite substantial infrastructure outage conditions. The design principles apply to telecommunications resilience requirements in public safety networks, essential communications infrastructure, and disaster response operations.
While Large Language Models demonstrate capabilities in reasoning, creativity, and task automation, they remain unable to reliably execute high-precision enterprise tasks due to inherent constraints. This article explores strategies for overcoming these constraints through tool integration, retrieval systems, and structured workflows specifically addressing issues related to static training data, computational costs, limited context windows, and hallucinations inherent to the modeling approach. Experiments show that Retrieval-Augmented Generation yields 10-percentage-point improvements in accuracy on knowledge-intensive datasets, that multi-stage prompting yields 83.5 percentage point improvements in compositional reasoning datasets, and that scaling the number of parameters from 62 billion to 540 billion yields 7.6- to 12.2 percentage point improvements on different metrics of complex reasoning. Human-AI collaboration frameworks show 20-35% productivity gains in software engineering tasks and 40-60% data efficiency gains in interactive machine learning methods. By combining retrieval methods, experimental agent architectures, fine-tuning methods, and human-in-the-loop strategies, systems have been built that use language models closely as components of a larger pipeline. This augmented intelligence model can flexibly meet enterprise-grade requirements for precision, latency, and reliability and can also accommodate continued advances across a wide variety of operational contexts.
Refunding The Border: Duty Drawback Rules In Preferential Trade And Wto Law
This paper evaluates how duty drawback—refunds of import duties on inputs embodied in exports or re-exported in the same state—is treated across regional trade agreements and whether such regimes cohere with WTO disciplines. Grounded in the rise of global value chains where intermediates dominate trade and imported content in exports is substantial, the analysis situates drawback as a pivotal policy tool for pricing inputs at world levels while maintaining border protection for domestic markets. Using doctrinal legal analysis, the study interprets MFN obligations under GATT Article I, limits on quantitative restrictions under Article XI, the conditions for regional agreements under Article XXIV, and the Enabling Clause for developing countries to assess RTA drawback provisions. It finds that common no-drawback rules in RTAs can disadvantage non-parties and lack firm justification under WTO law, recommending a shift toward more flexible, WTO-consistent allowance of drawback. The discussion clarifies how proper remission up to duties actually paid interacts with anti-dumping and subsidy rules, and distinguishes design choices such as full, partial, and substitution drawback. Policy guidance favors moving from blanket bans to calibrated permission with safeguards, reducing distortions and administrative burdens while aligning preferential regimes with multilateral norms.
Transforming Enterprise Operations At National Scale: Engineering Real-Time Distributed Event Systems For 100,000+ Frontline Workers
Contemporary business organizations are confronted with the severe issues of providing real-time operational information to mobile workforces that are located across geographical boundaries and functioning under extreme resource limitation conditions. Early broadcast-based designs are ineffective at scale, causing disastrous battery usage and network bandwidth waste when supporting tens of thousands of simultaneous mobile connections. This article introduces a full event-based architecture that fundamentally reinvents the operation data flow of an operational distributed system to mobile devices. The proposed subscription-based event filtering pattern enables mobile clients to explicitly express their interest in a given event stream, resulting in server-side event filtering that significantly reduces the transmission of irrelevant data. The architecture includes three special communication channels designed for specific operational needs, a flexible connection management system that changes based on the application's status, and a detailed monitoring system that helps improve performance before issues arise. Optimization methods for the battery use a mix of adjusting how communication works and improving how the client processes data and compresses information. The methodology of implementation focuses on rolling out and intensive monitoring, gradual geographical broadening, and refining through telemetry. The framework reveals the wide applicability to sectors that need high-frequency event streams for resource-constrained mobile gadgets.
Ai, Iot, And Cloud Convergence In Sap Ecosystems: Driving The Smart Factory Of The Future
Artificial Intelligence, Internet of Things, and cloud computing intersecting in the SAP realm is a disruptive paradigm that is transforming contemporary manufacturing processes. The Industry 4.0 technologies are no longer theoretical theories but have transformed into reality and are being applied to promote the development of the smart factories in global production connections. The IoT infrastructure provides the base layer of sensing, which collects real-time operational information of the machinery, materials, and the external environment conditions across the manufacturing plants. This flow of constant data is processed by AI-based analytics platforms, which are able to extract actionable insights by using machine learning algorithms, predictive models, and computer vision systems. Cloud architecture is the scalable computing base that allows these intelligent systems to scale to manufacturing scale, provide elastic resources, distributed processing facilities, and support easy integration between hybrid and multi-cloud environments. The converging technologies of SAP are integrated into operational structures by their digital ecosystem, which includes S/4HANA, Digital Manufacturing, Business Technology Platform, and Product Compliance. Predictive maintenance algorithms minimize the amount of time spent in equipment downtime by detecting failure patterns in advance. The production scheduling is optimized using AI to balance the complex constraints to achieve the highest throughput and on-time delivery. Deep learning in automated quality assurance systems can detect defects and do so with accuracy and as quickly as humans. Energy management applications determine where there are optimization opportunities that can be used to save money and, at the same time, save the environment. Its implementation has high barriers, such as data governance challenges, the challenge of cybersecurity in connected settings, skill gaps in the workforce, the complexity of integrating legacy systems, and regulatory obligations in different jurisdictions. Companies that successfully negotiate through such difficulties find themselves in a place where they can enjoy operational maturity, competitive differentiation, and enduring performance benefits in ever-smarter manufacturing environments.
Integrated Clinical Systems For 360-Degree Patient Care: An Industry-Specific Cloud Architecture
The digital platform is becoming an important part of healthcare delivery (helping in the diagnosis, treatment, and care coordination), but in healthcare organisations, fragmentation of clinical systems remains the main factor that undermines patient safety and quality of care. Integrated clinical systems constitute a unique product category of industry-specific cloud applications with engineering choices having direct consequences for human health outcomes, unlike enterprise software. The architectural structure above puts seamless clinical integration as essentially an operational engineering challenge that needs solid reliability practices, real-time data coordination, and governance systems that are in line with clinical workflow criteria. The cloud-native structures that support consolidated sources of data, improved collaboration among groups of care providers, and complete provision of patient data need to meet the high-energy requirements of interoperability, compliance with regulations, availability of data, and auditing capabilities that are above the normal data requirements of enterprise applications. These include event-driven architectures, terminology management services, and purpose-optimised persistence layers that can naturally provide unified patient views that can support clinical interpretation, though still with the semantic fidelity required by safe decision support. Reliability engineering should restructure the conventional measures of availability into a clinical workflow dependency and patient safety implications, understanding that system failure in healthcare setting has a significantly different implication than downtime in a more typical business setup. The framework offers realistic recommendations to the design of platforms that will simultaneously meet technical performance goals and clinical effectiveness outcomes that will promote safe, ongoing, and patient-centred care delivery.
Anatomy Of Real-Time Ad Delivery Systems: Behind The Scenes Of Modern Advertising Infrastructure
The key infrastructure of ad delivery systems in the digital advertising age is real-time systems, which integrate advanced engineering and machine learning to drive monetization on both web and mobile platforms. These multifaceted systems must operate within strict latency limits, with responsiveness measured in milliseconds, and evaluate hundreds of possible advertisements based on a set of targeting parameters. This article reviews the complex design of such high-performance systems, including routing of requests with load balancers, and specialized subsystems that deal with eligibility, ranking, budgetary, and creative selection. It examines how multi-layered caching and parallel processing can be used to support sub-millisecond performance at large scale, and the analytical pipelines that promote continual improvement. The article also discusses the major engineering issues that these systems encounter, such as global scale pressures, distributed consistency problems, optimisation of cold-start issues, and changing privacy policies. The knowledge of these system design patterns and limitations can provide useful learning to the engineers in other fields of high performance, other than the advertising technology.
Automation And Its Evolutions In Various Domains
The automation of tasks and activities can take place with very little or no supervision by human beings due to the expansion of technology through the advancement of machines from mechanical through the use of electronics and now through the use of computers. Initially, the machines used to automate tasks were basic mechanical devices that were developed during the Industrial Revolution. As a result of the development of electric and electronic devices, automation has expanded greatly. After computers were introduced, programmable controls were developed as well as more complex algorithms, therefore allowing for further automation and increased automation's overall efficiency. With the development of cyber-physical systems, all of the computational processes and the physical processes can now be connected. With the development of PLCs as a method of controlling machines in the industrial setting, automation in the industrial setting has changed dramatically. CAD and CAM have changed the way that production has been conducted. The growth of the IIOT has created a network of devices that can connect with one another within industrial manufacturing ecosystems. HRC has also made it possible for workers and robots to work cooperatively and safely as joint operators. Advanced process controls have increased efficiency and optimization of chemical manufacturing processes. The processes of continuous integration and continuous deployment allow for the efficient use of resources when developing software applications. The use of machine learning to automate the processes of business intelligence and data analysis will allow for companies to make better use of the data that they acquire. Robotic surgery represents a highly advanced level of surgical precision and capability. Automation within financial institutions has changed how we conduct banking and make payments. Future trends in automation will centre around AI integration and autonomous systems. The implications of continuous technical advancements on the workforce must also be considered. Ethical dilemmas, such as privacy and algorithmic biases, must be addressed and solved. Potential technical challenges of integrating many automation technologies, such as complexity and weaknesses within today's cyber-security systems, make regulations necessary for companies as they begin to utilize new technologies for automation.
AI-Driven Zero Trust For Healthcare And Enterprise Networks: A Unified Framework For Intelligent Security Architecture
The growth of interconnected digital systems in healthcare and enterprise settings has established new security risks that demand intelligent and adaptive protection systems that have never been applied before. This converged framework is a synthesis of artificial intelligence and Zero Trust security principles to fulfill the requirements of a changing cyber threat by using behavioral analytics, anomaly detection, and micro-segmentation approaches. The suggested architecture includes four layers that are linked to each other: network telemetry collection, machine learning-based intelligent processing, dynamic policy implementation, and healthcare-specific augmentation modules. Machine learning algorithms such as isolation forests, deep neural networks, and ensemble classifiers can be used to identify more complex attack patterns with low false positive rates due to contextual awareness and behavioral profiling. The implementation strategies can deal with both the technical and operational issues with the help of phased implementation, identity-based access control, and management of network segments with the help of software definition. Experimental validation shows significant enhancement in threat detection accuracy, incident containment effectiveness, and operational efficiency without causing a disruption in clinical workflow and regulatory compliance. The framework has managed to be secure without compromising user experience and is available enough to be used in critical systems without causing perceptible performance degradation. Augmentation that is specific to healthcare incorporates clinical context into security decision-making, minimizing false alarms in cases of emergencies and ensuring a high degree of security of sensitive patient information. This intersection of artificial intelligence and Zero Trust architecture signifies a necessary change in cybersecurity approach to preventive measures that organizations must use to manage sensitive data in the context of a distributed and heterogeneous network.
Trust-Aware Generative Conversational AI: Mitigating Hallucinations In LLM-Powered Chatbots
Large language models (LLMs) have shown impressive levels of capability in machine-generated human-like conversational responses, but they frequently generate incorrect or fake information, the so-called hallucinations. This discourages the trust of the user and restricts the use of AI-based chatbots in high-stakes uses, like healthcare, finance, and customer care. This paper presents a Trust-Aware Generative Conversational AI model that will help reduce hallucinations in chatbots with LLMs. The proposed architecture incorporates the knowledge-infused language modeling (KILM), contextual validation systems, and the trust score system to evaluate the accuracy of the generated answers. In particular, the system integrates structured knowledge, so-called curated knowledge bases, into the LLM, cross-checks the results with various sources, and gives a trust rating to each answer to instruct the chatbot to give the correct and contextually accurate answers. Our testing was performed based on benchmark datasets, such as ConvAI2 and a corpus of domain-specific and factual knowledge. Measures were taken of quantitative variables (like factual accuracy, hallucination rate, and user trust scores). In the experimental study, the suggested trust-aware system has demonstrated a reduction in incidences of hallucinations by 42 percent over the baseline LLM chatbots, and an improvement in user-perceived reliability by 37 percent. Qualitative analysis also demonstrates consistency of context and correctness of facts in different conversation situations. This study indicates that the concept of knowledge infusion and verification in generative conversational AI helps a great deal to increase trustworthiness without stereotyping dialogue naturalness. The results are a basis to construct credible, stakes high chatbot applications and emphasize on the significance of trust-aware design in the next generation AI communication system.
AI-Driven Automation In Enterprise Systems: A Technical Overview
The integration of artificial intelligence into enterprise automation represents a radical redefinition of the latter, as opposed to lifeless, rule-based systems, and adaptable, intelligent structures that can compute more complex patterns and react dynamically to changing operational environments. Old-fashioned deterministic forms of automation exhibit serious deficiencies in flexibility and necessitate massive preparations to adapt, and can hardly keep up with the fast-changing business environment. Modern AI-based automation ensures these restraints by utilizing hybrid decision engines, where deterministic business policies are combined with probabilistic machine learning models, allowing organizations to have human-understandable policy specifications and continually optimize decisions based on operational information learning. Cloud-native infrastructures provide fundamental building blocks for implementing AI at scale, utilizing containerized model runtimes, event-based architectures, and elastically scalable microservices that enable real-time inference with minimal latency. Advancing technologies, like reinforcement learning to improve processes, semantical retrieval by vectors to store knowledge, and explainable AI to foster its transparency, allow for advanced automation in such fields as fraud detection and prevention, resource-based systems, and tailored customer experience. Orchestration structures maintain the structures of complex workflows involving data ingestion, feature engineering, model invocation, and retraining pipelines. Human-in-the-loop systems, on the other hand, trade off automation performance with expert judgment by selectively routing risk decisions to human raters. This technical development puts AI-powered automation as the basic potential of contemporary companies that want to turn manual operations into intelligent and adaptive systems, which can be scaled with ease.
Sustainable Supply Chains As Catalysts For Global Environmental Responsibility: An Analysis Of United States Industrial Transformation
The operations of the supply chain in America have undergone a paradigm shift in recent years. Conventional models were focused on cost reduction and speed maximization. The current practice incorporates environmental responsibility as one of the main principles. Federal climate laws, corporate responsibility demands, and pressure from stakeholders have enhanced this change. As an example, the Inflation Reduction Act allocates $369 billion for energy security and climate programs. Together with changing corporate policies, this law makes supply chains the key infrastructure for achieving environmental objectives without compromising the competitiveness of industries. The transportation and logistics sector accounts for approximately 29 percent of the total greenhouse gas emissions in America. The solution to this is the adoption of electric fleets, renewable fuels, and efficiency programs, such as the SmartWay Transport Partnership. Methods of Life Cycle Assessment and circular economy models give a structured methodology for measuring environmental impacts of value chains. These tools assist managers in balancing the ecological factor with operational demands. The goals of the National Recycling Strategy aim for a 50 percent recycling rate by 2030, which represents a systematic attempt to shift away from linear production trends. Instead, the emphasis is placed on regenerative systems that reuse materials and increase resource security, and reduce environmental burdens. The technologies of artificial intelligence, blockchain, and the Internet of Things provide continuous visibility throughout complex supply chains. Sustainability is not a periodic compliance reporting, but it becomes a daily reality of operations. By 2022, the clean energy and energy efficiency industries had employed 8.1 million Americans. The transformation of the environment yields physical economic benefits, including job creation, manufacturing expansion, and competitive advantages in new markets. Sustainable supply chain management is a twofold task, whereby an organization fulfills environmental requirements while contributing to economic growth. This raises questions about the premise of an inherent conflict between being ecologically responsible and financially successful. Industrial systems can operate within natural limits and provide prosperity.
Standardizing Escalation Management In Cloud Support: A Global Framework For Efficiency And Accountability
This article examines how the implementation of standardized escalation frameworks in large-scale cloud environments improves operational efficiency, accountability, and customer satisfaction. Using the case of a global cloud service organization, it highlights how process unification, automation, and leadership alignment reduced resolution times and improved visibility across regions. The discussion outlines how an integrated escalation management tool streamlined collaboration among support, engineering, and leadership teams. It also explores lessons learned in balancing automation with human oversight and provides a roadmap for replication in other large enterprises.
Challenges And Innovations In Synthetic Data Generation: Toward Context-Aware, Privacy-Preserving, And High-Utility AI Data
The dramatic increase in the number of artificial intelligence applications requires huge data sets that are balanced in terms of fidelity, privacy, and utility. Synthetic data generation has become a paramount remedy to privacy regulations, lack of data, and regulatory hurdles in the medical, financial, and autonomous domains. The classical generative models have inherent problems of distributional precision, mode collapse, privacy assurance, and computing efficiency. The Context-Aware Distribution-Adaptive Synthetic Generator framework deals with these shortcomings by jointly optimizing distributional consistency, privacy, and downstream utility. It is a combination of Wasserstein distance-based distribution matching, adaptive noise injection, covariance preservation, and hybrid GAN-VAE optimization. Context-aware caching schemes provide the opportunity of distributional modeling at fine-grained demographic, time-based, and operational segments with a guarantee of differential privacy. Experimental evaluation on standard tabular datasets shows that there are significant gains in distributional fidelity, downstream task performance, privacy preservation, and computational efficiency over standard generative methods. The framework provides building blocks to scalable, production-grade synthetic data pipelines that can be deployed to regulated, privacy-sensitive systems where optimization of many competing goals simultaneously is needed in order to have the functionality to be practically viable.
A Framework For Privacy-Preserving Artificial Intelligence In Modern Financial Services
The financial services are being changed by Artificial Intelligence, allowing for improved fraud detection, credit scoring, and customer personalization, and introducing serious privacy issues. This privacy-preserving AI (PPAI) in financial services systems presents a stacked architecture that allows financial institutions to utilize AI without compromising the privacy or security of their data. Its framework uses the most modern approaches, such as Federated Learning, Differential Privacy, Homomorphic Encryption, and Secure Multi-Party Computation, to allow cooperation without exposing sensitive financial information. The article describes the powerful implementation model based on the cloud-edge hybrid strategy and containerized technologies, the gradual implementation strategy, the systematic design principles, and the strategic positioning of various stakeholders within the financial ecosystem. Next generation directions include quantum-safe cryptography integration, decentralized AI marketplaces where models can be exchanged, and cross-border privacy systems that negotiate thorny regulatory environments. The framework is a strategic dictum to financial institutions looking to strike a balance between data intelligence and data privacy amidst a highly regulated landscape.
Human-Readable Machine Intent: Designing Visual And Auditory Interfaces For Autonomous Systems
As autonomous systems increasingly integrate into public spaces, conveying machine intent to humans has emerged as a critical safety and usability concern. This article examines multimodal approaches to machine-to-human communication, with focus on visual and auditory interfaces that effectively bridge the gap between algorithmic decision-making and human understanding. Findings from extensive user testing demonstrate significant improvements in human-machine interaction when adaptive intent displays are implemented. A comprehensive framework for developing inclusive intent communication systems accommodates diverse user needs, including those with sensory impairments. The work underscores the necessity of transparent machine intent as a foundational element in fostering trust and safety in human-autonomous system interactions.
Architectural Strategies For Memory Systems In Agentic Applications: Structured Approaches To Context Management
Contemporary computational agents execute sophisticated operations spanning workflow automation, decision support, and inter-system coordination activities within organizational environments. The transition from isolated interaction handlers to continuous operations makes memory organization a fundamental performance determinant. Inadequate architectural decisions produce systems exhibiting either context loss or maladaptive retention of deprecated information. These structural weaknesses generate unreliable automation that diminishes stakeholder confidence and operational effectiveness. This article presents methodological frameworks for structuring temporary and permanent memory components in agent-based systems. The article emphasizes a purposeful distinction between ephemeral thread-local states and persistent cross-interaction knowledge stores. Coordination between architectural choices and real-world deployment requirements, encompassing institutional workflow standards and processing guidelines, permits systems to sustain interaction-level consistency alongside temporal behavioral stability. Memory operations are not unified abstractions; instead, they are layered technical requirements. Transient mechanisms allow for turn-by-turn dialogue coherence, while permanent mechanisms keep user customization and institutional alignment going beyond individual sessions.
Architecting National-Scale Regulatory Reporting Platforms: A High-Availability Infrastructure Framework For Compliance-Critical Systems
National-level regulatory reporting tools are critical infrastructure because the failure of these systems would threaten the stability of the financial system. Trusting regulations is still difficult: customary enterprise architectures cannot satisfy all demands because of availability, accuracy, auditability, and constantly rising regulatory datasets. As frequencies reduce between reports, this document presents a high-availability architecture for national-scale platforms based on the cloud and hybrid platform engineering model, where the infrastructure architecture provides regulatory accuracy. Real implementations achieve major improvements in processing throughput. The system significantly enhances the latency between reporting cycles. Data accuracy improves, while unplanned downtime vanishes during peak periods. The framework acknowledges the importance of infrastructure design in achieving compliance and does not treat it as a second-class concern. The platform enforces this determinism and resilience, ensuring structural enforcement of regulation, not just operational assurance. Federal agencies and enterprise-wide regulatory spaces, such as banking supervision, securities reporting, or insurance regulation, can utilize it.
Next-Generation Resiliency: Evaluating Ai-Augmented Self-Healing Automation Frameworks
Modern software development practices require constant revalidation of changing UIs, but customary test automation frameworks are brittle in the face of such dynamic change. Maintenance of the test suite is the most costly part of any automation project. Customary automation patterns used by the industry tend to utilize static locators, which require meaningful manual maintenance if the underlying UI changes. Generally, machine learning on the automation frameworks set the basis for self-healing characteristics through smart object recognition models and self-adaptive algorithms that heal broken locators automatically. Empirical studies have shown the decreased maintenance costs with machine learning over the customarily used statically defined locators and the need for agentic automation architecture. For screenshots, visual pattern recognition techniques are used to control the program by simulating keyboard and mouse events. For DOM elements, multi-property analysis techniques extract a set of structural similarity and behavioral properties and use them to generate a weighted score. Reinforcement learning models are used for finding optimal corrections using experience replay learning and a deep Q-network architecture across UI, API, and database layers. Fault exposing potential can also be used in defect predictive systems for test case prioritization based on efficiency while keeping the defect detection coverage. The reliability of maintainability prediction can be examined through historical software metric measurements towards predictive systems that ascertain locator fragility before an execution-cycle failure.
Ai For Customer Support: Scaling Tier 0 Agents With Intelligent Automation
The fast pace of integrating artificial intelligence (AI) into enterprise customer care has enabled the introduction of so-called Tier 0 agents, or automation systems that do not interact with a human operator and instead address frontline inquiries. This article scrutinizes the necessary technical and operational frameworks for the successful implementation of high-performance Tier 0 agents. The topic of reinforcement fine-tuning with golden datasets and the usage of Retrieval-Augmented Generation (RAG) to reduce the number of factual errors. The article looks at important measures of success, including confidence scoring and the ability to resolve issues without emotions, along with economic indicators that show productivity can improve by up to 35 percent for certain worker groups. Finally, the article presents a safety-focused architecture that utilizes the NIST AI Risk Management Framework and iterative machine learning best practices. The results indicate that effective Tier 0 implementation will convert the customer support from a reactive cost center to a proactive, scalable strategic asset.
Harnessing Cloud Applications And Artificial Intelligence For Collaborative Supply Chain Transformation In Large Enterprises
Mounting global complexities paired with swift market disruptions push large enterprises toward embracing cloud-native platforms merged with artificial intelligence to build collaborative and resilient supply chains. Cloud applications form a shared digital infrastructure, permitting suppliers, logistics partners, manufacturers, and customers to interact using a unified data architecture. These arrangements yield instantaneous visibility, seamless information exchange and collective decision-making processes spanning enterprise-scale operations. Artificial intelligence layered upon cloud foundations empowers enterprise networks to forecast precisely, optimize continually and orchestrate supply chain operations instantaneously. Recent innovations show cloud-based AI agents managing supply chain control towers that simultaneously track demand patterns, inventory levels, shipment movements and supplier risk factors using singular interfaces. These technological breakthroughs reshape supply chain collaboration using three principal mechanisms: shared platforms tear down information silos dividing internal divisions from external partners; AI-generated insights enable proactive optimization, replacing reactive adjustments; and instantaneous workflow orchestration guarantees stakeholder coordination throughout disruption scenarios. Effective large-enterprise implementations blend cloud scalability with API-driven connectivity and AI-enhanced workflows, converting supply chains from linear value sequences into intelligent networked ecosystems. Organizations consequently achieve enhanced responsiveness, diminished operational expenditures, elevated supplier transparency, and fortified resilience. Merging cloud applications with AI permits large enterprises to shift from isolated logistics operations toward collaborative data-driven supply chain networks exhibiting autonomous adaptation to opportunities and risks.
Artificial Intelligence Integration In Electronic Quality Management Systems For Life Sciences
The pharmaceutical industry faces a growing problem. Old-fashioned quality management methods simply cannot keep up with today's massive data volumes and ever-changing regulatory demands. The present study takes a hard look at how artificial intelligence can transform electronic quality management systems in drug manufacturing. A total of 47 published studies were reviewed using well-established quality assessment tools, including the Mixed Methods Appraisal Tool and ROBINS-I framework. Two reviewers independently rated each study, achieving strong agreement with Cohen's kappa of 0.78. The analysis covers how machine learning, natural language processing, and predictive analytics are being used across the industry. The review examined deviation classification, document analysis, risk assessment, and complaint handling. The results are encouraging. AI consistently outperforms traditional rule-based approaches across nearly all quality management functions. The research also proposes something new: an Adaptive Multi-Dimensional Risk Quantification Framework. The framework functions as a smart system that pulls together risk signals from multiple sources and learns from mistakes over time. The technical backbone includes sigmoid normalization, temporal difference learning, and eligibility trace mechanisms. Testing on 2,847 real production batches showed F1 score improvements of 23-31% compared to standalone machine learning models. False alerts dropped significantly, and quality teams could focus efforts where results mattered most. That said, some troubling gaps exist in current research. Most studies do not address how to explain black-box model decisions to regulators. Few tackle the bias that can creep into training data. Long-term performance data spanning multiple years is hard to find. Such issues matter because FDA 21 CFR Part 11, ISO 13485, and ICH Q9 all have expectations that organizations understand how AI systems make decisions. The bottom line is straightforward: AI integration represents a genuine step forward for pharmaceutical quality assurance. But success requires more than just good algorithms. Organizations need solid data governance, trained staff who understand both AI and quality systems, and validation approaches that satisfy regulators. When done right, AI enables a shift from reactive firefighting to proactive quality management. That shift ultimately protects patients and ensures medicines work as intended.
Dental Handpiece Contamination: A Systematic Review Of Reprocessing Protocols And Interprofessional Responsibilities
Background: Dental handpieces are critical instruments in routine and surgical dental procedures, yet their complex internal mechanisms make them vulnerable to microbial contamination. Inadequate reprocessing may result in cross-contamination, biofilm persistence, and occupational exposure risks for dental personnel. Despite existing infection prevention guidelines, variability in reprocessing protocols and compliance remains a concern. Objective: To systematically review the evidence on dental handpiece contamination and evaluate the effectiveness of reprocessing protocols, while clarifying interprofessional responsibilities within dental healthcare teams. Methods: A systematic review was conducted following PRISMA guidelines. Electronic databases were searched for studies evaluating contamination levels and reprocessing effectiveness of dental handpieces. Eligible studies included experimental, observational, and clinical assessments of cleaning, lubrication, sterilization, and storage protocols. Data were extracted on contamination outcomes (e.g., microbial counts, residual protein, ATP levels) and workflow components. Risk of bias was appraised using design-appropriate tools. Results: Evidence indicates that contamination may persist particularly within internal turbine chambers when cleaning is insufficient or protocols deviate from manufacturer instructions. Automated cleaning systems combined with validated sterilization cycles demonstrated more consistent decontamination outcomes compared with manual-only approaches. Variability in compliance and documentation was commonly reported. Conclusion: Effective dental handpiece reprocessing requires standardized, validated protocols and clearly defined interprofessional responsibilities to ensure patient and occupational safety.
Dr. Nahar Abdullah Alqahtani, Ms.Lubna Huddied Alaslani, Ms.Salma Mohammed Babaidhan, Salwa Abbas Idrees, Eman Bader Alshoteri, Azizah Mohammed Almenhali
Strategic Crisis Communication On Social Media During Public Health Emergencies: A Comparative Analysis Of Government Responses
The research question presented in this study is how government crisis communication affects social media in times of a health crisis, and how the Perceived Credibility of Social Media Platforms, Government Transparency in Crisis Communication, and Social Media Engagement affect the Public Trust in Government Crisis Communication on Social Media. A quantitative survey design was used to collect data on 200 respondents and statistical analyses on the data were done using SPSS, correlation, regression and reliability test. The findings indicate that government transparency and the use of social media positively impact the level of public trust the most, whereas the credibility of the platform, still being significant, has a less important impact. These results emphasise the importance of governments using transparent and interactive communication approaches in situations of the national health crisis as the means of creating and sustaining the trust of the population. The present research paper adds to the existing body of research on crisis communication as it illustrates the relative significance of these elements and provides a way of informing future studies on the specifics of the government communication strategy that should be employed in the event of an emergency.
Real-Time Data Integration IN Hybrid Cloud Environments: Leveraging Striim AND Gcp FOR Enterprise Transformation
Hybrid cloud environments—spanning on-premises systems and public cloud services—introduce complexity in data integration, consistency, and governance. Enterprises pursuing modernization must deliver real-time data to both operational applications and analytical platforms without sacrificing reliability or compliance. This article presents an end-to-end architecture for low-latency, fault-tolerant integration using change data capture technologies, streaming backbones, and GCP-native processing and serving. Source capture strategies minimize disruption by reading database logs and applying in-flight transformation, filtering, enrichment, and routing logic in streaming applications. Transport patterns connect on-premises and cloud messaging systems using connectors or custom relays, taking into account how to split data for efficiency, maintain order for consistency, set rules for data storage and replay, and ensure Processing uses cloud-based stream processing with features that manage event timing, group data into windows, and remove duplicates, while schema registries and compatibility rules protect users from changes. Serving combines multiple database technologies for near-real-time analytics, globally consistent transactions, and high-performance relational needs. Operational excellence manifests through observability with standardized dashboards for lag, throughput, error taxonomies, dead-letter queues, and replay windows, along with alerting for schema drift and automated runbooks for recovery. Reliability design includes checkpointing, idempotent sink writes, backpressure controls, and rolling upgrades to ensure continuity during maintenance. A multi-domain case study demonstrates batch windows collapsing from tens of minutes to seconds, enabling fresher dashboards and real-time downstream decisions. The findings demonstrate that real-time integration transcends mere transport problems, representing a holistic architectural and operational capability requiring governance, standards, and disciplined execution. This architecture and set of practices equip enterprises to accelerate modernization, minimize risk, and deliver reliable real-time data products across hybrid landscapes, turning integration from a bottleneck into a strategic advantage.
Automated Cloud-Native PLM Deployment Architecture: AWS Cloudformation And Chef Integration Framework
The deployment of Product Lifecycle Management applications such as Teamcenter has traditionally been characterized by manual, time-intensive processes that introduce configuration inconsistencies, limit scalability, and constrain organizational agility in responding to dynamic business requirements. This article presents a comprehensive cloud-native deployment framework that integrates AWS CloudFormation for declarative infrastructure provisioning with Chef configuration management to automate the complete PLM deployment lifecycle. The framework adopts a layered architecture implementing Infrastructure-as-Code principles, enabling consistent and repeatable provisioning of multi-tier PLM infrastructure while eliminating manual configuration steps through automated cookbook-based application installation and configuration. Implementation validation conducted within a large-scale manufacturing enterprise environment demonstrates substantial improvements in deployment efficiency, with dramatic reductions in deployment cycle times and near-complete elimination of manual intervention requirements. The article reveals superior consistency, predictability, and scalability characteristics of automated deployments relative to traditional manual approaches, while cost analysis establishes compelling return on investment through personnel time savings and operational expense optimization. The framework addresses critical security, compliance, and governance requirements through layered security architectures, encryption implementations, and comprehensive audit logging capabilities. Beyond immediate deployment efficiency gains, the article establishes foundational competencies in cloud-native practices that support broader digital transformation initiatives and position organizations for accelerated innovation cycles. The article identifies critical success factors, including executive sponsorship, cross-functional collaboration, and phased implementation approaches, while acknowledging limitations related to network connectivity dependencies, regulatory constraints, and application-specific hardware requirements that may constrain applicability in certain contexts.
HBM4 Integration In AI/HPC Chiplet Architectures: Co-Design And Telemetry-Driven Optimization
The explosive growth of artificial intelligence and high-performance computing workloads has exposed fundamental scalability limitations in traditional monolithic system-on-chip designs, driving industry adoption of chiplet-based architectures that decompose complex systems into modular dies for heterogeneous integration. High Bandwidth Memory generation 4 promises substantial improvements in aggregate bandwidth and energy efficiency, yet integrating HBM4 stacks with chiplet processors introduces multifaceted challenges spanning die-to-die interconnect design, physical layer robustness, package-level signal and power integrity, thermal management, and runtime system control. This article presents a comprehensive methodology for chiplet-HBM4 integration that harmonizes protocol-level optimizations with adaptive physical layer techniques, thermal-aware package design, hierarchical power delivery networks, and telemetry-driven runtime adaptation. A unified verification framework bridges digital performance models with analog signal integrity and thermal simulations to ensure pre-silicon predictions align with post-silicon measurements, enabling first-pass silicon success. Experimental evaluation across representative AI training, inference, and HPC workloads demonstrates that cross-layer co-optimization combined with intelligent runtime control delivers substantial gains in latency reduction, energy efficiency, and operational availability under realistic environmental variations. The article establishes practical design principles and reusable methodologies for multi-terabyte-per-second memory systems targeting deployment in next-generation AI accelerators and scientific computing platforms.
Feedback-Driven Autonomy: Learning Safe Automation Boundaries In Large-Scale Decision Systems
While automated decision systems are increasingly deployed at unbounded scales, autonomy is necessary for performance and availability. Autonomy is controlled only through thresholds, manual tuning, or infrequent human review, leading to automation boundaries that drift out of alignment with real-world risk once systems are in production. While previous work considers when to enable autonomy at runtime, there is relatively little work on how autonomy should adapt after deployment, during ongoing production operation. Feedback-driven autonomy is a system-level model where the boundaries of autonomy are adjusted in production not only based on prediction accuracy but also on observed operational performance. The control plane also manages an autonomy learning loop, which consumes asynchronous and aggregated feedback (e.g., rollback costs, incident correlation, human interventions) from live systems and adjusts the autonomy level of the system over time. Rather than learning an agent's decision-making like a reinforcement learning algorithm would do, the governance layer learns how autonomous the system should be in its current operational context. Examples in recommendation, offer vending, and notification systems inform the exploration of feedback-driven autonomy as a strategy for safe, scalable, and attribution-tolerant automation in high-throughput production environments. Uncovering autonomy as a learned property of the system provides a practical focus for designing adaptive, resilient, automated decision systems that can respond to a dynamic, real-world operational context after deployment.
Security Challenges In Healthcare Cloud Apis: A Systematic Review
Cloud computing has a substantial influence on the healthcare ICT infrastructure because of its flexible, scalable, and cost-effective features; it also plays an important role in electronic health record management, clinical workflow, and the interoperability of disparate healthcare systems. The use of cloud-based Application Programming Interfaces in a healthcare system raises security and compliance risks due to sensitive protected health information and strict data protection requirements. This systematic review describes the various privacy and security challenges and vulnerabilities associated with healthcare cloud application programming interfaces and identifies the most important security areas that need consideration, including authentication protocols, data encryption protocols, and secure data transmission protocols. This article also explains the fundamental building blocks of healthcare cloud APIs and reviews their unique privacy and security challenges for real-time access and interoperability, as well as informed consent workflows. The article analyzes the potential attack surfaces for healthcare cloud APIs, such as man-in-the-middle attacks, distributed denial of service, and unauthorized access, and their impact on healthcare operations and patient safety. Additional threats include issues with verifying and managing medical device access (like credential management, MFA, token-based authentication, role-based access control, and attribute-based access control), using encryption for stored and transmitted data, managing encryption keys, and how end-to-end encryption affects performance in complex systems. Existing security standards include general and sector-specific recommendations for security hygiene, such as security securityby- design, continuous security monitoring, incident reporting, and regulatory compliance. Emerging security concerns for APIs include artificial intelligence for threat detection, the adoption of zero-trust architecture, and the use of quantumresistant encryption mechanisms in light of quantum computing developments. Recommendations are made for healthcare organizational leadership, cloud service providers, and policymakers to improve API security posture, prompt security innovation and address security challenges while maintaining operational efficiency and privacy compliance.
Deploying Model Context Protocol Servers in Serverless Environments
The Model Context Protocol has created a standard way for Large Language Models to connect to external data sources and tools. This has allowed AI agents to go from simple chat interfaces to fully autonomous entities that can interact with complex operational systems. This article presents a comprehensive technical framework for implementing MCP servers within serverless computing environments, specifically examining the integration of FastAPI, FastMCP adapters, and AWS Lambda to create scalable, cost-effective AI infrastructure. The article explores the three-layer architectural model comprising protocol adaptation, application logic, and runtime management, while addressing critical implementation considerations including automated API-to-protocol transformation, cold start mitigation strategies, and stateless execution patterns. Serverless architectures enable MCP servers to consume computing resources only during active AI agent interactions. This reduces costs associated with idle infrastructure while enabling automatic horizontal scalability. The article provides detailed analysis of deployment optimization techniques, security architecture incorporating zero-trust principles and identity-based access control, comprehensive observability frameworks for monitoring distributed AI tool invocations, and governance mechanisms ensuring safe operation of AI-accessible capabilities. Through investigation of architectural trade-offs, performance optimization strategies, and operational best practices, this study establishes a production ready blueprint for enterprises seeking to build resilient, fiscally responsible AI infrastructure that aligns protocol driven AI interactions with modern cloud native computing paradigms.
Cross-Channel Data Integration And Customer Identity Resolution For Revenue Optimization
In the contemporary framework of marketing ecosystems, customer contacts are fragmented, with contact happening via email platforms, direct mail campaigns, targeted display advertising, and social media networks. Correct identity identification is still the basis for correlating these discrete streams of data to aligned customer images. Deterministic matching uses precise identifiers (e.g., email addresses and customer account numbers) to create absolute linkages. Probabilistic methods use statistical algorithms to discover links between partially overlapping sets of data. Attribution models will attribute revenue credit to particular channels in accordance with the contribution to conversion events, allowing the marketer to measure the performance of each touchpoint. Clickstream information records finer behavioral messages that display the path in navigation, content preference, and purchase intent messages. Channel sequencing strategies are the best way of deciding the timing and sequence of marketing messages to ensure maximum engagement velocity. Joint campaigns demonstrate measured increases in customer response rates and transaction values. The idea of revenue optimization appears due to the accurate distribution of investments in marketing channels and has been proven in controlled experiments.
Dynamic Zoning And API Integration In Robotic Fulfillment Systems: A Case Study In Omni-Channel Warehouse Optimization
The transformation of warehouse operations through robotic fulfillment optimization represents a critical evolution in contemporary supply chain management, driven by the convergence of dynamic zoning algorithms, API integration frameworks, and innovative picking methodologies. This case demonstrates how strategic integration of collaborative robots with legacy Radio Frequency-based warehouse systems addresses the persistent challenges of omni-channel order fulfillment while maintaining operational continuity. The implementation encompasses a multi-layered integration architecture employing RESTful API protocols to enable real-time data exchange between warehouse management platforms and robotic systems, incorporating blind verification processes, tote validation mechanisms, and automated exception routing capabilities. Dynamic zoning logic operates through multi-factor optimization algorithms that continuously evaluate order volumes, demand patterns, picker locations, and storage density to generate zone reconfiguration recommendations. Single Line Single Unit batch picking methodology analyzes order composition at the individual line-item level, identifying consolidation opportunities that reduce redundant travel and maximize picker productivity. Empirical outcomes demonstrate substantial improvements in walk path reduction, order cycle completion acceleration, picking error rate decline, and pick density enhancement, collectively generating significant annual cost savings across multiple facility locations. Cross-facility adoption revealed critical success factors, including API integration calibration, comprehensive workforce training programs, and pilot-based implementation approaches. The integration architecture made it successful when scaling to a large number of distribution centers, and the financial implications that the implementation had were not limited to direct cuts in operational costs, but also included improved customer satisfaction indicators and increased service level capacity.
The Future Of Next-Generation HPC At Scale: The Transformative Impact Of PCI Express 8.0
High-Performance Computing and Artificial Intelligence are getting even closer, at unprecedented scales, building the computational models that exascale and the next generation zettascale architectures will require. PCI Express 8.0 is the next disruptive evolution of the de facto standard for high bandwidth, low latency interconnect that will help heterogeneous computing overcome the very limits that have historically restricted its ascendance. The specification also includes Pulse Amplitude Modulation four-level (PAM4) signaling, improved Forward Error Correction (FEC), and improved protocol stacks, which result in meaningful improvement of the effective data rate. Modern computers adopt more and more diversified accelerators such as Graphics Processing Unit (GPU), Field-Programmable Gate Array (FPGA), or domain-specific accelerated processors with complex interconnect topologies. PCIe 8.0's high bandwidth and low latency empower system architectures to efficiently keep nodes well fed with data to achieve the desired system performance. These system architectures may utilize cache-coherent memory fabrics, disaggregated pools of resources, and different peer-to-peer communication modes to power the next-generation systems enabled by PCIe 8.0. Furthermore, the implementation of such approaches at scale encounters difficulties related to power delivery network design, thermal management, and channel implementation in order to maintain the integrity of the transmitted information. Such implementations have been shown to provide varying benefits to applications such as streaming data processing and machine-learning workloads in distributed systems. In latency-sensitive scientific computing applications, the speed advantage is less impressive, and system integration considerations such as new cooling methods, power management, and signal conditioning become major success factors. As a vendor-neutral and open industry specification, PCIe 8.0 benefits from interoperability and multivendor support across the broad PCI Express ecosystem. The PCIe 8.0 specification will support the next generation of computing systems from the present generation of exascale systems to future systems based on zettascale technology.
The Convergence Of Operational Technology (OT) And Information Technology (IT) In Smart Terminals: A Technical Review
Digital transformation is fundamentally reshaping terminal operations in process industries globally, with customary automation systems becoming clever and connected ecosystems of digital technologies. Smart terminals form the nexus of industrial supply chains, and convergence between OT and IT forms ecosystems of integrated digital technologies. These are integrated platforms from the field to the enterprise. Modern terminals utilize IoT-enabled sensors for real-time monitoring and visibility of assets. Advanced analytics provide real-time insights into operational data feeds. Cloud-edge architectures can deliver scalable systems and remote management. AI predictive maintenance relies on the prediction of equipment degradation, and autonomous loading of material occurs with little human intervention. Digital twin technology can be used for operations simulation, optimization, and planning. Safety automation can help to detect and address hazards. Product tracking through supply chains is easier, telemetry reduces the number of field workers in dangerous environments, operational systems are integrated with the enterprise platform, reducing data silos, and product traceability is extended through the supply chain. Automated data collection improves billing accuracy. Real-time visibility across facilities improves supply chain coordination. Cybersecurity, interoperability, and change management are known problems with digital transformation in healthcare. Structured frameworks to guide the digitalization process can alleviate some of these challenges. When multiple dimensions are taken into account, convergence can improve safety, sustainability, and terminal performance.