American Advanced Journal for Emerging Disciplinaries (AAJED) ISSN: 3067-4190
Journal Summary About the Journal — American Advanced Journal for Emerging Disciplinaries (AAJED) ISSN (Online) 3067-4190 Publisher AAJED Editorial Office Publication Type Gold Open Access · Peer-Reviewed · International Frequency Continuous Publication / Quarterly Issues Language English License CC BY 4.0 — Creative Commons Attribution 4.0 International Peer Review Model Double-Blind Subject Area Multidisciplinary · Emerging & Interdisciplinary Sciences Journal Website www.aajed.com Editorial Contact editor@aajed.com Submission Portal Available via journal website Editor-in-Chief [Name, Affiliation, Country] Inaugural Volume [Year] Journal Overview The American Advanced Journal for Emerging Disciplinaries (AAJED) is an international, peer-reviewed, gold open-access journal dedicated to publishing original, high-quality, and impactful research across newly developing, interdisciplinary, and convergent fields of science, technology, engineering, and society. The journal provides a rigorous and inclusive scholarly platform for researchers, academics, engineers, practitioners, educators, and policymakers worldwide to present pioneering studies that transcend traditional disciplinary boundaries — introducing fresh theoretical perspectives, innovative methodologies, and evidence-based solutions to the most pressing and complex challenges of our time. AAJED is indexed in Scopus (Elsevier), Web of Science (Clarivate), DOAJ, CrossRef, and other leading international abstracting and indexing databases — ensuring maximum global visibility, citation discoverability, and long-term scholarly impact for every published article. The journal publishes original research articles, systematic and scoping reviews, technical communications, short reports, and applied case studies spanning all areas within its defined interdisciplinary scope. AAJED is fully committed to the highest standards of research integrity, editorial transparency, and equitable open knowledge — ensuring permanent, unrestricted access to all published scholarship for academia, industry, policymakers, and society at large. Indexing & Abstracting AAJED is formally recognized, indexed, and abstracted in the following leading international scholarly databases, bibliographic repositories, and global research discovery platforms: Database / Platform Status Scopus (Elsevier) ✅ Indexed Web of Science (Clarivate) ✅ Indexed DOAJ – Directory of Open Access Journals ✅ Listed CrossRef ✅ DOI Registered Google Scholar ✅ Indexed EBSCO ✅ Indexed Dimensions ✅ Indexed BASE – Bielefeld Academic Search Engine ✅ Indexed OpenAIRE ✅ Indexed Semantic Scholar ✅ Indexed ResearchGate ✅ Listed ROAD – Directory of Open Access Scholarly Resources ✅ Listed WorldCat ✅ Listed PKP Index ✅ Indexed All published articles are assigned a unique CrossRef Digital Object Identifier (DOI), ensuring permanent citation traceability, persistent online accessibility, and seamless integration with global research discovery and bibliometric infrastructure. Journal Performance Metrics Indicator Value Scopus CiteScore Under Active Evaluation SCImago Journal Rank (SJR) Under Active Evaluation SNIP – Source Normalized Impact per Paper Under Active Evaluation Scopus Quartile Ranking Under Active Evaluation Scopus Subject Classification Multidisciplinary · Computer Science · Social Sciences · Engineering Web of Science Category Multidisciplinary Sciences · Emerging & Interdisciplinary Research Acceptance Rate Selective — Rigorous Double-Blind Peer Review Time to First Decision ≤ 15 Business Days Average Peer Review Duration ~50 Days Acceptance to Online Publication 60–90 Days Plagiarism Screening iThenticate / Turnitin (Pre-Review Mandatory) Peer Review Model Double-Blind Post-Acceptance Scopus Indexing Within 4–6 Weeks Article Identifier DOI via CrossRef APC Payment Trigger Only Upon Formal Acceptance CiteScore, SJR, and SNIP values will be updated upon completion of the Scopus evaluation cycle. Authors may monitor current metrics directly via the Scopus Source List and the SCImago Journal Rankings portal. Open Access & Publication Model AAJED operates under a full Gold Open Access publishing model, ensuring that all accepted and published articles are freely, immediately, and permanently accessible to readers worldwide — without subscription fees, paywalls, embargo periods, or institutional access barriers — in full compliance with the Budapest Open Access Initiative (BOAI) and published under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. The CC BY 4.0 license permits unrestricted use, distribution, adaptation, translation, and reproduction in any medium or format, provided the original work is appropriately cited and any modifications are clearly indicated. Article Processing Charges (APCs) are collected solely upon formal manuscript acceptance, following the completion of rigorous peer review. No fees are charged at the point of submission or at any stage during the review process. Full details on current APC rates, institutional membership pricing, and fee waiver eligibility for researchers from low- and middle-income countries (LMICs) are published transparently on the journal website. The AAJED Gold Open Access Model Delivers: Immediate, worldwide visibility and unrestricted accessibility upon publication Enhanced readership, citation potential, and full discoverability in Scopus and Web of Science Rapid and broad dissemination of interdisciplinary and emerging field scholarship across all disciplines Full compliance with all major international funder open-access mandates including Plan S, NIH Open Access Policy, and Horizon Europe requirements Equitable and barrier-free knowledge sharing across academia, industry, government, and society globally Accelerated real-world knowledge transfer and application of published interdisciplinary research findings Aims & Scope AAJED publishes original research articles, systematic and scoping reviews, technical reports, short communications, and applied case studies spanning the full breadth of emerging, interdisciplinary, and convergent academic disciplines. The journal strongly encourages integrative research that combines multiple fields — particularly studies addressing contemporary global challenges through novel cross-domain methodologies and evidence-based interdisciplinary approaches. Artificial Intelligence & Machine Learning Intelligent systems design and cognitive automation; deep learning and advanced neural architectures (CNNs, RNNs, Transformers, Graph Neural Networks, Diffusion Models); reinforcement and self-supervised learning; explainable AI (XAI) and responsible AI frameworks; AI ethics, governance, and societal implications; human–AI interaction and collaboration; generative AI, large language models (LLMs), and foundation models; AI fairness, accountability, and transparency; autonomous agents and multi-agent systems. Data Science & Computational Analytics Big data engineering and scalable data processing architectures; predictive, prescriptive, and descriptive analytics; data mining and pattern recognition; statistical and probabilistic modeling; data visualization, interpretability, and explainability; data quality, integration, and governance frameworks; real-time and streaming analytics; knowledge graph construction and semantic data management. Environmental Sustainability & Smart Systems Renewable energy systems and clean technology innovation; smart cities and intelligent infrastructure planning and management; environmental monitoring, assessment, and climate informatics; sustainable technology applications and lifecycle analysis; green and energy-efficient computing solutions; climate change adaptation and mitigation technologies; circular economy models and sustainability metrics; smart grid systems and intelligent energy optimization. Biomedical & Health Innovations Health informatics and clinical decision support systems; computational biology and bioinformatics; biomedical engineering and intelligent medical devices; digital health technologies and telemedicine platforms; AI-assisted diagnostics, genomics, and precision medicine; wearable health technologies and remote patient monitoring; drug discovery and computational pharmacology; systems biology and translational biomedical research. Educational Technology & Learning Sciences Technology-enhanced and AI-driven learning environments; adaptive and innovative pedagogical models; digital education systems and e-learning platforms; learning analytics and educational data mining; MOOCs, gamification, and immersive learning technologies; intelligent tutoring systems and personalized learning pathways; accessibility and equity in digital education; computational approaches to assessment and curriculum design. Science, Technology & Society (STS) Social impact assessment of emerging and disruptive technologies; ethical, legal, and policy dimensions of scientific innovation; technology adoption, diffusion, and governance frameworks; human–technology relationships and digital citizenship; responsible innovation and public science communication; digital transformation, societal resilience, and inclusive development; public trust, engagement, and participation in science and technology governance. Human-Centered Computing & Behavioral Sciences Cognitive computing and affective intelligent systems; human–computer interaction (HCI) and user experience (UX) design; behavioral modeling, analytics, and computational prediction; accessibility, universal design, and inclusive technology; social computing and collaborative intelligence frameworks; augmented and virtual reality for human-centered applications; digital well-being and technology-society dynamics; persuasive computing and behavior change systems. Advanced Materials, Nanotechnology & Emerging Engineering Materials science innovation and functional material design; nanoscience and nanotechnology applications across disciplines; emerging engineering technologies and advanced manufacturing processes; photonics, quantum materials, and next-generation electronic and semiconductor devices; applied scientific advancements in convergent and hybrid engineering fields; additive manufacturing and smart material systems; energy harvesting, storage materials, and next-generation battery technologies. Editorial & Publication Ethics AAJED upholds the highest standards of editorial integrity and scholarly publication ethics, in full alignment with the following internationally recognized frameworks and guidelines: COPE – Committee on Publication Ethics (Core Practices and Code of Conduct) ICMJE – International Committee of Medical Journal Editors STM Association – Best Practices for Responsible Scientific Publishing WAME – World Association of Medical Editors Elsevier Publishing Ethics Standards and Guidelines All submitted manuscripts must satisfy the following conditions: Represent original, novel, and previously unpublished work in any language or medium Not be under simultaneous consideration at any other journal, conference, or preprint server (unless formally declared) Adhere to ethical standards governing research involving data, human subjects, sensitive datasets, computational models, or AI-generated content Include full and transparent disclosure of funding sources, data availability statements, conflicts of interest, and individual author contribution statements (CRediT taxonomy recommended) All manuscripts are screened using iThenticate / Turnitin prior to peer review assignment. Similarity reports are evaluated by the editorial office in strict accordance with COPE guidelines. AAJED Strictly Prohibits: Plagiarism, self-plagiarism, or duplicate publication in any form Data fabrication, falsification, selective reporting, or outcome switching Undisclosed financial or non-financial conflicts of interest Ghost authorship, gift authorship, or misrepresentation of contributor roles Breach of ethical approval requirements for studies involving human participants, sensitive data, or AI-generated content Suspected violations are investigated using COPE flowcharts and protocols and may result in desk rejection, post-publication retraction, or formal notification to the author's affiliated institution and funding body. Peer Review Process AAJED operates a fully double-blind peer review system. Author and reviewer identities remain mutually concealed throughout the entire evaluation process, ensuring impartiality, objectivity, and full protection against bias. Stage 1 — Initial Editorial Screening Every submission undergoes a thorough editorial assessment for scope compliance, structural completeness, language quality, originality, and adherence to AAJED submission guidelines. Manuscripts that do not meet baseline standards are desk-rejected promptly within ≤ 15 business days, with constructive editorial feedback provided to authors. Stage 2 — Double-Blind Peer Review Manuscripts passing initial screening are assigned to a minimum of two independent expert reviewers with established subject-matter expertise in the relevant interdisciplinary area of the submission. Reviewers assess scientific validity, methodological soundness, novelty, clarity of presentation, and overall contribution to emerging disciplinary knowledge. Additional specialist reviewers may be engaged for highly specialized or cross-domain submissions. Stage 3 — Revised Manuscript Evaluation Where revisions are requested, authors submit a revised manuscript accompanied by a detailed point-by-point response letter addressing each reviewer comment individually. Revised manuscripts undergo one or more further rounds of evaluation at the discretion of the Editor-in-Chief. Stage 4 — Final Editorial Decision Based on consolidated reviewer recommendations, the Editor-in-Chief issues one of the following formal decisions: Decision Description ✅ Accept No further revisions required — proceeds directly to production
Real-Time AI and IoT Integrated Quality Assessment for Advanced Automotive Manufacturing Environments
Traditionally, quality assurance (QA) in the automotive manufacturing industry has relied on a set of expensive tests and visual inspections on a sample of produced vehicles. The growing presence of Internet of Things (IoT) sensors in production lines gives manufacturers new opportunities to move from QA to Predictive Quality Control. Predictive Quality Control uses Machine Learning/Artificial Intelligence (ML/AI)-driven predictive analytics to assess quality and risk in real-time, enabling root cause analysis, yield optimization, and improved process control during production. A system that uses the streaming data of connected sensors to quantify the performance of these techniques has been deployed in a major automotive manufacturer. The first case study describes the application of predictive modeling to two stamping and assembly processes. A spike in process risk led to increased costs and testing; however, better risk assessment and detection of sensor anomalies improved the management of sensor data quality. A predictive quality model for the most affected body components was also developed but is not yet fully operational. In the second case study, another ML-driven yield prediction model has been implemented to reduce paint shop downtime and costs arising from coarse and fines segregation in weld joint areas. During the last months of operation, the segmentation of fine joints has shown a predictive accuracy above 94% and increasingly stable precision and recall ratios.
Predictive quality control, Internet of Things, streaming data, telecommunications, data pipelines, predictive maintenance, machine learning, artificial intelligence, automotive industry, data quality
Predictive quality control, IoT sensor streams, machine learning/AI in manufacturing, automotive quality assurance, edge-cloud computing, data pipelines, predictive maintenance.
Computer Science / Artificial Intelligence, Manufacturing Engineering, Industrial Engineering, Data Science, Internet of Things (IoT).
Copyright (c) 2025 American Advanced Journal for Emerging Disciplinaries (AAJED) ISSN: 3067-4190
Smart Semiconductor Production Optimization Using AI-Assisted Hybrid Analytics and Edge Computing
With the electric vehicle and consumer electronics industries projected to drive high demand for their products, semiconductor manufacturers are focusing on further improving their yield rates and reducing production costs. To achieve this goal, more effective yield prediction methods capable of deriving actionable recommendations to mitigate yield loss are required. While deep learning has shown promise for yield prediction, the training of such models usually requires a large number of label documents, which may not be readily available. A hybrid modeling framework is proposed that combines cost aspects with a deep-learning-based yield-prediction model. The new approach is particularly well suited to settings in which only a limited number of label documents are available. A semiconductor manufacturer facing this scenario is considered. The results demonstrate that combining deep learning with cost information enhances the assistance provided to engineers, leading to information that is more useful for reducing production costs while maintaining an acceptable yield level. A further objective is to provide a deep-learning yield-prediction model with the lowest possible latency and bandwidth requirements while satisfying the accuracy needs of users.
AI-enhanced semiconductor manufacturing,Semiconductor yield optimization,Hybrid deep learning models,Edge data analytics,Smart wafer fabrication,Machine learning for yield prediction,Fault detection in semiconductor processes,Real-time process monitoring,Predictive analytics for fabs,Edge AI in manufacturing,Advanced process control (APC),Data-driven yield improvement,Defect classification using deep learning,Cyber-physical semiconductor systems,Intelligent manufacturing systems.
AI-enhanced semiconductor manufacturing, semiconductor yield optimization, hybrid deep learning models, edge data analytics, smart wafer fabrication, fault detection in semiconductor processes, real-time process monitoring, predictive analytics for fabs, advanced process control (APC), defect classification using deep learning, cyber-physical semiconductor systems, and intelligent manufacturing systems.
The paper spans electrical/computer engineering, manufacturing engineering, computer science (specifically machine learning and deep learning), and industrial engineering.
Deep Agentic AI Framework for Adaptive Wireless Resource Optimization in Semiconductor-Assisted 6G Communication Networks
Next-generation telecommunications are increasingly overlapping with various application domains, most notably vehicular systems, the Internet of Things, augmented and virtual reality, and robotic swarms. These overlaps underpin the need for different levels of autonomous operation—from the user perspective—while posing unique challenges for the underlying communications systems, which must still meet the operational objectives of each individual domain. Suggested solutions exploit advancements spanning mobile networks, cloud-edge computing infrastructures, algorithms for wireless resource allocation, and the full automation of system operations. Within this context, agentic AI alters the traditional separation of aircraft control planes—mainly dedicated to perception, planning, and acting—by integrating the development of sufficient high-fidelity capability. High levels of task automation supersede human engagement and enable system stewardship, provided the AI are developed, deployed, and managed under appropriate principles. Even so, support for most domain requirements remains oversimplified, with operator assistance and resource provisioning and allocation invariably under human skill. Consequently, a new layer of cognitive automation spanning perception, decision making, and operation is being gradually integrated into systems via AI dedicated to such tasks. The framework governs a self-aware general-purpose cognitive loop that officiates the request and supply of contextualized information and control actions within the context of data acquisition and potentially also for planning and learning adopted by lower-level AI. Because its candidates reside within the operational ecosystem and their autonomy level can also vary from system supervision to collaborative support, the cognitive loop naturally lends itself to agentic AI. Beyond theoretical considerations, the approach has been applied to the dynamic scheduling of integrated sensing and communications resources
Agentic AI; cyber-physical systems; sixth generation networks; resource allocation; reinforcement learning; multi-agent systems; causal inference.
Agentic AI, wireless resource allocation, reinforcement learning, multi-agent systems, causal inference, 6G networks, semiconductor-enabled communications, cognitive loop architecture.
Telecommunications / Wireless Communications, Artificial Intelligence & Machine Learning, Computer Networks, Semiconductor Engineering.
Advanced Agentic Artificial Intelligence for Predictive Fraud Detection in Modern Insurance Platforms
Insurance fraud contributes greatly to policyholder losses and the yearly drain on industry resources. Existing deterrent methods typically react to fraud events, affecting genuine customers. An approach using Deep Agentic AI to sense abnormal activity and potential fraud is applied. The underlying principle is viewing fraud detection as an agentic activity, employing real-time inference with streaming data analytics to monitor for fraud risk. The proposed architecture integrates microservices with serverless components in a cloud-native design aligned with the features of true agentic AI. Infrastructure is established for autonomous decision-making and incident response, operationalizing real-time fraud detection at scale. Although investigated in fraud detection, the approach is generalizable and applicable across domains in which agentic decision-making is necessary and proven agentic systems are in place. Detecting fraud in mutual insurance companies, where expenses are borne by policyholders, is globally important. Fraudulent pricing, accident claims, and pay-outs directly reduce profits. Sensitive agents have failed to establish deterrence, as is evident from the rising share of fraud cases. Detecting fraud pre-emptively offers the potential to limit client inconvenience and to avoid incorrect insurance decisions. Investors have indicated a desire for insurance companies to increase investment in AI, with almost three-quarters considering insurance fraud a suitable application. Therefore, the development of appropriate tooling will facilitate meeting investor expectations.
Agentic AI; Cloud-Native Environments; Concept Drift; Fraud Detection; Insurance; Risk Prediction; Time-Series Data; Deep Agentic AI Systems; Real-Time Insurance Fraud Detection; Autonomous Decision-Making Agents; Risk Prediction and Scoring Models; Cloud-Native AI Architectures; Event-Driven Fraud Analytics; Multi-Agent Reinforcement Learning; Streaming Data Intelligence; Explainable AI (XAI) for Insurance; Scalable Mops for Financial Services.
Insurance fraud detection, agentic AI systems, real-time risk prediction, cloud-native architectures, machine learning deployment.
Computer Science, Artificial Intelligence, Insurance/Actuarial Science, Data Engineering, Financial Technology
Resilience Gaps in Medical Supply Chains Revealed byPandemic Events
Healthcare supply chains have long been viewed as an overly complex, costly ecosystem. Visibility is limited due to data silos and lack of automation, and stringent regulatory certification hinders quality assurance. However, three key vulnerabilities have increasingly attracted attention: (a) dependence on single-source suppliers for critical products, (b) just-in-time inventory management without buffer stock, and (c) fragmented global logistics susceptible to border disruptions during crises. These vulnerabilities do not merely raise costs or reduce service levels; they directly affect patient safety and compromise health systems' resilience. All supply chains are vulnerable to disruption, whether through natural disasters, infrastructure failure, geopolitical tension or, as demonstrated in the COVID-19 pandemic, global health crises. The pharmaceutical and medical device supply chains are no exceptions. These sectors, hitherto operating under a veneer of relative stability, are now reassessing strategies in order to mitigate catastrophe — and attention is shifting to solutions that go beyond individual enterprise-level adaptations.
Healthcare supply chain resilience,Medical supply shortages,Global health crisis preparedness,Pandemic-induced supply disruptions,Critical medical logistics,Pharmaceutical supply chain risk,Medical device availability,Just-in-time inventory failures,Global sourcing dependencies,Health system operational resilience,Emergency procurement strategies,Supply chain risk management in healthcare,Vaccine and PPE distribution challenges,Health infrastructure fragility,Crisis-driven supply chain adaptation.
Healthcare supply chain resilience, pandemic-induced supply disruptions, medical device availability, pharmaceutical supply chain risk, emergency procurement, JIT inventory failures, global sourcing dependencies.
Supply chain management, public health, healthcare operations, logistics, risk management.
Copyright (c) 2024 American Advanced Journal for Emerging Disciplinaries (AAJED) ISSN: 3067-4190
Adolescent Psychological Support-Seeking Behaviors During ExtendedSocial Restriction Periods
Adolescent mental health has become a pressing public health concern, especially during the coronavirus disease 2019 pandemic, in light of prolonged lockdowns. A body of research has documented increased rates of depression and anxiety, and their evolution during subsequent lockdown periods. Notably, many studies indicate that few adolescents seek professional help during these distressing times. In line with health services utilization theory, these findings suggest a disconnect between expressed need and help-seeking behavior. Such research provides a wealth of information concerning the availability, accessibility, and effectiveness of mental health services during the pandemic. Perspectives on adolescents’ mental health service utilization—that is, whether and when to access services—have, however, remained marginal. By integrating information from multiple sources, these utilization patterns can be better understood. The present abstract aims to provide an overview of the determinants of adolescents’ mental health service seeking during prolonged periods of lockdown. The discussion focuses on issues within the adolescent’s life context and perceived needs, along with important influences originating from parents, the broader social environment, and the health system. Service utilization trends can shed light on whether adolescents sought care when required, and the reasons that might explain patterns. Evidence suggests an overall increase in the need for psychological care during lockdown periods, reflected in rising demand from both adolescents and their parents. However, adolescents appear to prefer communicating their emotional distress with family members or friends instead of professionals. Most adolescents experiencing risk factors for depression and anxiety do not seek help, even though those with higher levels of perceived need are likelier to do so. Help-seeking behavior is influenced by multiple factors, including age, gender, ethnicity, socio-economic status, stigma, and accessibility. Survey results indicate that stigma represents one of the main barriers to seeking help.
Adolescent mental health, service utilization, lockdown effects, telehealth, accessibility, stigma,Adolescent mental health, Mental health service utilization, Prolonged lockdowns, COVID-19 pandemic impact,Tele-mental health services, Access to mental health care, psychological distress in adolescents, Health-seeking behavior, Barriers to mental health services, Youth coping and resilience.
Adolescent mental health, mental health service utilization, help-seeking behavior, COVID-19 pandemic impact, psychological distress in adolescents, stigma, telehealth, coping and resilience.
Public health, adolescent psychiatry/psychology, health services research, epidemiology, social/behavioral sciences.
Fractured Immunization Continuity and Emerging Public Health Risks in Post-Pandemic Communities
Disruptions in routine immunization programs can undermine population immunity and lead to outbreaks of vaccine-preventable diseases (VPDs). In addition to the direct threat posed by COVID-19, operational and logistical challenges, disruption of supply chains, and the effect of COVID-19 on the health system, health workforce, and disease surveillance can hinder vaccination and leave children unprotected against diseases such as measles, polio, and yellow fever. Monitoring vaccination coverage, adaptation of immunization activities, and strengthening sentinel surveillance can facilitate early detection of VPD outbreaks. Recovery in routine immunization programs requires not only the resumption of normal health service delivery but also catch-up campaigns, improvement in supply chains, and intensive engagement with affected communities. Vaccination coverage is usually monitored on the basis of reported national administrative data but without formal validation, which compromises confidence in estimates of the actual number of children vaccinated, especially during wartime or severe epidemics. Coverage has fallen in many countries. Despite recommended herd immunity thresholds of at least 95%, suboptimal immunity may persist in areas with low vaccination coverage. Even in countries where high coverage is achieved at the national level, pockets of low uptake can facilitate the spread of VPDs.
Routine immunization disruption,Vaccine coverage decline,Immunization service interruption,Vaccine-preventable disease resurgence,Herd immunity gaps,Public health system resilience,Health service disruptions,Pandemic impact on immunization,Childhood vaccination delays,Missed vaccination opportunities,Health inequities in immunization,Supply chain interruptions for vaccines,Vaccine stockouts,Community-level disease outbreaks,Maternal and child health outcomes,Surveillance gaps in immunization programs,Catch-up vaccination strategies,Immunization program recovery,Morbidity and mortality from preventable diseases,Global immunization coverage trends.
Routine immunization disruption, vaccine-preventable disease resurgence, herd immunity gaps, public health system resilience, pandemic impact on immunization, childhood vaccination delays, health inequities in immunization, supply chain interruptions, surveillance gaps, catch-up vaccination strategies, and global immunization coverage trends.
Public Health, Epidemiology, Health Policy, Immunology, and Health Systems Research.
Transforming Primary Care Through Telehealth: Lessons from the COVID-19 Era
The COVID-19 pandemic precipitated a rapid shift to virtual service delivery in the United States, with telehealth adoption soaring across clinical specialties, patient conditions, and visit types. Nevertheless, evidence supporting telehealth’s effectiveness in primary care remains limited, especially for acute and preventive services. This study tested telehealth adoption and clinical outcomes across 54 primary care clinics affiliated with a single health system in the diverse, urban, and often low-income environment of Los Angeles County. Hierarchical multivariable models assessed clinical outcomes for 27 chronic disease cohorts, along with rates of acute care visits, preventive services, and safety events. The results indicated associations between elevated telehealth use and reduced utilization of in-person services for acute conditions such as respiratory infections, without negative effects on quality or patient-reported outcomes. Findings suggest that telehealth can facilitate quality primary care for well-defined conditions despite relatively low-volume adoption, and may serve as an effective adjunct to in-person care for preventive, chronic, acute, and mental health needs. Though telehealth is not a panacea, the evidence, when viewed in aggregate, supports its nuanced deployment as a patient-centered alternative within the wider multichannel service portfolio.
Telehealth; primary care; COVID-19; outcomes; telemedicine; video visits; telephonic visits; chronic disease management; preventive care; safety; quality; acute care; patient-centeredness; equity; technology use.
Telehealth adoption, clinical outcomes, primary care, chronic disease management, acute care, preventive services, patient safety, health equity, and COVID-19's impact on healthcare delivery.
Medicine (primary care, internal medicine, family medicine), public health, health informatics, health policy, and health economics.
Copyright (c) 2023 American Advanced Journal for Emerging Disciplinaries (AAJED) ISSN: 3067-4190
AI Agent-Driven Supply Chain Resilience and Optimization in Retail and Paint Manufacturing
Disruptions in supply chains cause severe financial losses, necessitating exploration of Agentic AI solutions for Supply Chain Resilience Management. Agentic AI differs from traditional automation, offering innate problem-solving, adaptive-learning, and collaborative capabilities. Enabling Agentic capabilities in supply chain systems can enhance Resilience Strength by supporting accurate decision-making and expanding the range of explored solutions. Concerns regarding safety, accountability, and over-trust in Agentic systems need to be addressed. A Hybrid Agentic AI-Driven Resilience Engineering Framework comprises a central technology area for Resilience Strength and several application areas for demand and supply networks. Scenarios for Retail and Paint Manufacturing demonstrate expected contributions in terms of inventory cost, stockout level, and service level performance indicators. Supply chain disruptions incur significant financial losses. Agentic Artificial Intelligence AI with intrinsic problem solving, adaptive-learning, and collaborative abilities has been highlighted as a possible contributor to Supply Chain Resilience Strength. Concerns over Agentic features such as safety, accountability, and over-trust require exploration. Enabling Agentic capabilities in supply chain systems can improve Resilience Strength by facilitating precise decision-making while broadening the spectrum of potential solutions. Agentic AI departs from conventional automation by providing appropriate support for semi-structured decision environments. Supply Chain Resilience Management based on Agentic AI can leverage these features to preemptively mitigate novice-level disruption risk, fortifying the system’s ability to absorb, respond, and recover from a wider array of causes and types of disruption.
Agentic Artificial Intelligence, Supply Chain Resilience Management, Hybrid Agentic AI Framework, Resilience Engineering, Adaptive Decision-Making Systems, Autonomous Problem Solving, Collaborative AI Agents, Demand Supply Network Optimization, Disruption Risk Mitigation, Inventory Cost Optimization, Stockout Reduction, Service Level Optimization, Semi-Structured Decision Support, AI Safety and Accountability, Human–Agent Collaboration, Resilience Strength Metrics, Intelligent Supply Chain Systems, Proactive Disruption Management, Multi-Agent Orchestration.
Agentic Artificial Intelligence, Supply Chain Resilience Management, Hybrid Agentic AI Framework, Resilience Engineering, Adaptive Decision-Making Systems, Autonomous Problem Solving, Collaborative AI Agents, Demand–Supply Network Optimization, Disruption Risk Mitigation, Inventory Cost Optimization, Stockout Reduction, Service Level Optimization, Semi-Structured Decision Support, AI Safety and Accountability, Human–Agent Collaboration, Multi-Agent Orchestration.
The paper spans Supply Chain Management, Operations Research, Artificial Intelligence / Machine Learning, and Industrial Engineering.
Copyright (c) 2026 American Advanced Journal for Emerging Disciplinaries (AAJED) ISSN: 3067-4190
Genomic-Based Precision Medicine for Neurological and Kidney Disorders
BioGenomic Precision Medicine (BP Med), encompassing genomic developments from technology to clinical integration, offers a distinctive perspective on precision medicine in neuro and kidney disorders. In addition to an overview of BP Med, areas of neuro and kidney diseases for which genomic studies have provided insights into therapeutic strategies are considered, as well as cross-disciplinary clinical management of conditions with both neurodevelopmental and kidney features. Traditionally, precision medicine has focused on targeted therapies for small patient subsets sharing a disorder-associated mutation, epigenetic alteration, or biomarker. BP Med expands this logic from narrow disease targets to include broader and potentially more impactful insights by addressing foundational concepts and resources for the implementation of precision medicine, thereby indirectly enabling stratification of the entire affected population or improving response profiles for all patients, whether by counselling, treatment modulation in multi-organ dysfunctions, or secondary preventive strategies. BP Med invites integration of established but previously compartmentalized genomic knowledge into a more comprehensive and clinically useful frame. Without such inclusion, especially in the neurodevelopmental field, replication may remain elusive, independent comparisons across specialties may lack value, and resolution of plot holes may remain unfulfilled.
BioGenomic Precision Medicine, Genomic Clinical Integration, Neurodevelopmental Disorders, Kidney Disease Genomics, Precision Therapeutics, Cross-Disciplinary Clinical Management, Multi-Organ Dysfunction, Patient Stratification, Genotype–Phenotype Correlation, Translational Genomics, Biomarker Discovery, Therapeutic Target Identification, Population-Level Precision Medicine, Genomic Counseling, Treatment Modulation Strategies, Secondary Prevention, Integrated Genomic Frameworks, Clinical Decision Support, Systems Precision Medicine.
BioGenomic Precision Medicine (BP Med), neurological disorders, kidney disorders, genomics, multi-omics, biomarkers, patient stratification, genotype–phenotype correlation, translational genomics, cross-disciplinary clinical management, and multi-organ dysfunction.
Genomics, precision medicine, neurology (neurodegenerative, neurodevelopmental, and neuropsychiatric diseases), nephrology (monogenic and complex kidney diseases), systems biology, bioinformatics, clinical genomics, and bioethics.
Intelligent Cloud Automation Solutions for IT Service Management
AI-driven cloud automation for IT Service Management platforms in 2023 leverages Artificial Intelligence (AI) to improve operations and meet service-level agreements via integration of AI components within data source pipelines. Automation reduces operational workload, but only gradual adoption for complex tasks; AI adds cognitive ability to processes and accelerates implementation. Key areas for acceleration—incident management, change, and configuration management—serve as foundation for deeper integration. AI-assisted cloud automation enhances efficiency across all operations via improved routing, triaging, and remediation management of incidents; defect detection in changes and configurations; and policy-driven deployment of cloud resources. Benefits include sharper focus on core tasks, lower mean-time-to-repair, and improved user experience; costs must be weighed against reduced total cost of ownership and potential return-on-investment. The increasing adoption of cloud infrastructure has accelerated development of Automation-as-a-Service for Infrastructure as Code (IaC) deployment. The resulting IT Service Management (ITSM) workload has restricted delivery of innovative Infrastructure- and Platform-as-a-Service (IaaS and PaaS) offerings and adversely affected support for Software-as-a-Service (SaaS) applications. Automation of easy or repetitive tasks alleviates operational pressure, but these initiatives often deliver limited benefit due to narrow scope or bypassing of established processes. The growing diversity of alerts produced by IT operations platforms can now be managed through wider adoption of Artificial Intelligence (AI) techniques. Clinical application of proven AI methods enables more efficient routing within and between teams; faster, consistent first-line triage actions; and faster identification of suitable auto-remediation actions.
AI-Driven Cloud Automation, IT Service Management (ITSM), Automation-as-a-Service (AaaS), Infrastructure as Code (IaC), Intelligent Incident Management, AI-Based Change and Configuration Management, Policy-Driven Cloud Resource Provisioning, Predictive Defect Detection, Auto-Remediation Systems, Alert Correlation and Triage Optimization, Infrastructure-as-a-Service (IaaS) Operations, Platform-as-a-Service (PaaS) Enablement, Software-as-a-Service (SaaS) Support Automation, Mean-Time-to-Repair Reduction, AI-Enhanced Cloud Operations.
The paper covers AI-driven cloud automation, IT Service Management (ITSM), incident management automation, change and configuration management, MLOps, and cloud infrastructure operations.
Computer Science / Information Technology, specifically Cloud Computing, Artificial Intelligence/Machine Learning, IT Operations, and DevOps/MLOps.