Multidisciplinary Innovations & Research Analysis
Multidisciplinary Innovations & Research Analysis is a dynamic, peer-reviewed open-access journal dedicated to advancing innovative research that transcends traditional disciplinary boundaries. Welcoming theoretical inquiries, applied case studies, systematic reviews, and experimental investigations, the journal fosters rigorous scholarship across science, technology, engineering, social sciences, humanities, education, and beyond . Through a swift double‑blind review process and prompt online publication, it aims to facilitate the global exchange of transformative ideas and solutions that tackle contemporary challenges . By promoting transparency, ethical standards, and broad accessibility—with no paywalls—the journal empowers researchers from diverse backgrounds to share high-impact findings, cross-pollinate disciplines, and drive forward societal, environmental, and technological progress across the world.
Reimagining Educational Leadership in Minority Contexts: Pathways to Equity and Student Advancement
Educational leadership has become increasingly central to debates on equity, inclusion, and student success. In minority contexts, where schools frequently operate within conditions marked by socioeconomic disadvantage, cultural marginalization, and unequal access to resources, leadership assumes heightened significance. Traditional administrative approaches are often insufficient for addressing the layered challenges faced by students in such environments. Reimagined leadership must therefore combine instructional effectiveness with social responsibility, cultural responsiveness, and strategic advocacy. This paper investigates how educational leadership can create pathways to equity and student advancement in minority settings. The study adopts a qualitative research design based on semi-structured interviews and document analysis involving school principals, teachers, and policy practitioners working in minority-serving institutions. The objective is to identify leadership practices that improve academic outcomes while strengthening inclusion, belonging, and long-term opportunity. Attention is given to how leaders respond to structural barriers, support student development, and build partnerships with families and communities. Findings indicate that effective leadership in minority contexts is characterized by five interrelated dimensions: equity-oriented resource allocation, high expectations with targeted support, culturally affirming school climates, student empowerment, and future-focused skill development.
Explainability and Trust in AI-Driven Public Health Decision Making: Barriers to Adoption Among Policymakers and Frontline Health Officials
Artificial intelligence systems for infectious disease surveillance and outbreak response have demonstrated substantial technical capability, yet their adoption among public health policymakers and frontline health officials remains limited and uneven. This paper examines the barriers to adoption from a human and organizational perspective, drawing on human-computer interaction research, public health governance literature, and case evidence from deployed AI surveillance systems. We identify six primary adoption barriers spanning opacity, automation bias, accountability ambiguity, regulatory vacuum, cultural resistance, and alert fatigue. We propose a structured taxonomy of explainable AI techniques matched to the needs of two distinct actor groups and present a set of governance and design interventions grounded in published evidence. The analysis demonstrates that technical explainability is necessary but insufficient: sustained adoption requires simultaneous investment in governance standards, training, and institutional accountability frameworks.
Predictive Maintenance as a Circular Economy Enabler: Extending Asset Lifetime, Reducing Replacement Demand, and Lowering Embodied Carbon in Capital-Intensive Industries
Predictive maintenance, enabled by Internet of Things sensor networks, machine learning health scoring, and digital twin technology, is one of the most underappreciated circular economy interventions available to capital-intensive industries. By extending the operational life of high-value physical assets, predictive maintenance directly reduces the frequency of replacement manufacturing, which carries substantial embodied carbon and virgin material consumption. This paper presents a cross-sector analysis of predictive maintenance as a circular economy enabler, examining the embodied carbon profiles of industrial assets across five capital-intensive sectors, characterizing the technology components that enable condition-based life extension, and quantifying the circular economy benefits through a comparative analysis of reactive versus predictive maintenance outcomes. Evidence from digital twin-enabled implementations demonstrates that predictive maintenance achieves a 58% reduction in maintenance downtime, reduces defect rates by 57%, and extends asset operational lives by 20 to 40% beyond design life expectations. Applied to the embodied carbon intensities characteristic of industrial assets in steel, power generation, oil and gas, and manufacturing sectors, these outcomes translate to substantial avoided manufacturing emissions over multi-decade asset portfolios. The EU Ecodesign Regulation, which establishes product durability and repairability requirements, and the ISO 55000 asset management standard together provide the governance framework for institutionalizing predictive maintenance as a mandatory element of circular economy compliance in capital-intensive industries.
Exogenous Melatonin as a Protective Agent Against Heavy Metal Stress: Mechanisms of Tolerance and Growth Enhancement in Plants
Heavy metal contamination in agricultural soils has emerged as a critical environmental constraint affecting plant growth, productivity, and food safety worldwide. Exogenous melatonin, a multifunctional signaling molecule, has gained increasing attention as a promising protective agent that enhances plant tolerance to heavy metal stress. This paper reviews the physiological, biochemical, and molecular mechanisms through which melatonin mitigates heavy metal toxicity in plants. Melatonin improves antioxidant defense systems, regulates metal uptake and transport, enhances photosynthetic efficiency, and stabilizes cellular homeostasis under stress conditions. Additionally, it modulates gene expression associated with stress signaling pathways and chelation processes, thereby reducing metal-induced oxidative damage. The application of exogenous melatonin also contributes to improved plant growth, biomass accumulation, and yield stability under contaminated environments. Overall, melatonin represents an eco-friendly and effective strategy for improving plant resilience against heavy metal stress and ensuring sustainable agricultural productivity.
Functional and Glyconutritional Properties of Moringa oleifera Leaf Extract: Food Quality, and Nutritional Security
Moringa oleifera leaf extract has gained increasing attention as a functional feed additive due to its rich nutritional composition and bioactive compounds, including glyconutrients, antioxidants, vitamins, and essential amino acids. This study explores the functional and glyconutritional properties of Moringa oleifera leaf extract and evaluates its impact on broiler productivity, meat quality, and broader implications for nutritional security. The extract demonstrates significant potential in enhancing growth performance, feed conversion efficiency, immune response, and gut health in broiler chickens. Additionally, its incorporation into poultry diets improves meat quality by increasing protein content, reducing lipid oxidation, and enhancing shelf life. From a glyconutritional perspective, the presence of bioactive polysaccharides supports cellular communication and metabolic functions, contributing to improved physiological health in poultry. The integration of Moringa oleifera into animal nutrition systems offers a sustainable, cost-effective strategy to improve food production while addressing global challenges related to malnutrition and food security. This paper highlights the multifaceted benefits of Moringa oleifera leaf extract and underscores its potential as a natural alternative to synthetic growth promoters in poultry production systems.
AI-Driven Vulnerability Management and Intelligent Cyber Risk Mitigation Frameworks
The rapidly evolving threat landscape in cyberspace has significantly increased the complexity of securing modern information systems. Traditional vulnerability management approaches, while effective to an extent, often struggle to keep pace with the scale and sophistication of contemporary cyberattacks. This paper introduces an AI-driven perspective on vulnerability management and cyber risk mitigation, highlighting how artificial intelligence and machine learning techniques can enhance the identification, prioritization, and remediation of security weaknesses. By integrating predictive analytics, automated threat detection, and intelligent risk scoring mechanisms, organizations can transition from reactive defense strategies to proactive and adaptive security models. This study explores a range of AI-enabled tools, frameworks, and methodologies that support continuous monitoring and real-time decision-making in cybersecurity environments. Furthermore, it emphasizes the role of data-driven insights in improving vulnerability assessment accuracy and optimizing resource allocation for risk mitigation.
AI Safety Systems and Risk Analytics for High-Hazard Engineering Systems
Engineering systems with high hazards (nuclear power plants, oil and gas installations, aerospace platforms, and large-scale chemical facilities) are used under the conditions when a failure may have disastrous outcomes. The conventional safety assurance and risk evaluation techniques, although functional, tend to be constrained by the fact that they use static modeling assumptions and that they respond slowly to dynamic, complex operating conditions. This paper evaluates how artificial intelligence (AI) safety systems and more sophisticated risk analytics can be used to improve the prevention, detection, and mitigation of hazards in engineering fields with high safety standards. It examines how machine learning, probabilistic reasoning and data-driven anomaly detection can come together with the classic risk assessment models, such as fault tree analysis, HAZOP and reliability engineering models, to allow predictive and adaptive safety management. The abstract also brings to the fore AI-enabled system architectures that utilize real-time sensor information, digital twins, and intelligent decision support systems to enhance situational awareness and operational resilience. The critical issues concerning the model validation, explainability, human-AI interaction, and regulation compliance are also discussed. Altogether, the paper highlights how AI-based safety and risk analytics can be used to shift the engineering systems characterized by high risks toward the reactive safety paradigm to the proactive and anticipatory risk governance paradigm.
Data Science Approaches to Asset Integrity Management in Offshore and Onshore Oil and Gas Operations
Asset integrity management is important in addressing safety, reliability and economic sustainability of offshore and onshore oil and gas operations and especially in the ageing infrastructure and in more complex operating environments. The increasing access to large volumes of high-velocity, high-volume operational and inspection data has put data science in a transformative enabling role of integrity assurance throughout the asset lifecycle. This paper considers the use of data science methods in asset integrity management and how sophisticated analytics, machine learning, and statistical modeling methods can be used to detect abnormalities, anticipate degradation and failure, and assist in risk-based decision-making. Significant data sources such as sensor measurements, inspection history, maintenance history, and operation history are examined in terms of their contribution to the predictive maintenance and risk-based inspection models. The paper also explains the implementation issues that are unique to offshore and onshore, citing the differences in exposure to the environment, accessibility, and reliability of the data. Combining information-based insights with the current system of program management, operators will have an opportunity to optimize proactive maintenance initiatives, minimize unplanned downtime, and increase the overall asset efficiency. The results reinforce the importance of data science in the strategy of resilient, efficient, and safety-driven asset integrity management in the oil and gas industry.
Ventilator Pressure Prediction Using Recurrent Neural Network: An Integrated Multimodal Deep Learning Framework for Adaptive and Real-Time Healthcare Applications
Ventilator pressure prediction plays a crucial role in modern intensive care units, where accurate and adaptive control of respiratory support directly influences patient outcomes. Traditional ventilator systems often rely on static or rule-based approaches, which lack the flexibility required to handle dynamic patient-specific respiratory patterns. This study proposes a novel deep learning framework based on recurrent neural networks (RNNs) for accurate and real-time prediction of ventilator pressure. The proposed model integrates Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures with an attention mechanism to effectively capture temporal dependencies in respiratory signals. In addition, a multimodal feature fusion strategy is employed to incorporate both physiological and contextual information, enhancing the model’s predictive capability. The framework is further optimized using advanced training strategies, including hybrid loss functions and adaptive optimization techniques. Experimental results demonstrate that the proposed approach outperforms traditional machine learning models and baseline deep learning architectures in terms of Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). The model shows strong generalization ability and robustness, making it suitable for real-world healthcare applications. The findings of this research highlight the potential of integrating sequential modeling, attention mechanisms, and multimodal learning to develop intelligent and adaptive healthcare systems. The proposed framework provides a scalable and efficient solution for ventilator pressure prediction and lays the foundation for future advancements in AI-driven critical care technologies.
A Hybrid Deep Learning Framework for Intelligent Prediction and Recommendation Using Multimodal Data and Adaptive Learning
The rapid advancement of artificial intelligence has enabled the development of intelligent systems capable of addressing complex challenges across multiple domains, including healthcare, finance, transportation, and digital media. However, most existing approaches focus on isolated tasks such as prediction, recommendation, or detection, limiting their effectiveness in real-world, data-rich environments. This paper proposes a hybrid deep learning framework that integrates recurrent neural networks for temporal prediction, graph neural networks and large language models for recommendation, and an enhanced YOLOv8 architecture for object detection within a unified system. The framework further incorporates a multimodal fusion mechanism to combine heterogeneous data sources and a reinforcement learning-based optimization strategy to enable adaptive and continuous learning. Experimental results across diverse datasets demonstrate that the proposed model significantly outperforms baseline methods in terms of prediction accuracy, recommendation quality, and detection performance. The findings highlight the effectiveness of integrating multiple artificial intelligence paradigms into a single scalable framework, providing a robust solution for intelligent decision-making in dynamic environments.
Artificial Intelligence for Predictive Analytics and Intelligent Decision Systems Across Domains
Artificial Intelligence (AI) has emerged as a transformative technology for predictive analytics and intelligent decision-making across diverse application domains. This study presents a unified multi-domain AI framework that integrates advanced deep learning models to address complex prediction tasks in healthcare, financial systems, intelligent transportation, and recommendation platforms. The proposed architecture combines domain-specific modeling techniques, including sequential learning for time-series prediction, hybrid feature interaction models for financial forecasting, real-time object detection for transportation systems, and sequence-aware recommendation mechanisms for personalized user experiences. The framework is designed to handle heterogeneous datasets through a structured pipeline consisting of data acquisition, preprocessing, model training, and a unified decision layer. Experimental analysis demonstrates that the proposed system achieves high predictive accuracy, robustness, and scalability across all domains. The integration of multiple AI models within a single architecture enables efficient cross-domain knowledge utilization and supports real-time decision-making in dynamic environments. Furthermore, the study highlights key challenges, including computational complexity, data dependency, and the need for model interpretability. Future directions focus on enhancing explainability, optimizing model efficiency, and enabling secure and scalable deployment in real-world applications. The proposed framework contributes to the advancement of next-generation AI systems and provides a comprehensive foundation for multi-domain predictive analytics research.
Towards Zero-Trust Enabled Intelligent Intrusion Detection in Software-Defined Networks: A Multi-Layered Deep Learning Defense Framework
This study proposes a Zero-Trust Enabled Intelligent Intrusion Detection Framework for Software-Defined Networks (SDN) by integrating zero-trust security principles with an attention-based Convolutional Neural Network (CNN) model. While SDN enhances programmability and centralized network control, it simultaneously introduces critical security vulnerabilities, particularly within the controller and inter-layer communication interfaces. Existing zero-trust SDN implementations focus primarily on static identity verification and micro-segmentation, whereas deep learning-based intrusion detection systems (IDS) operate independently of access control logic. This architectural separation limits adaptive real-time threat mitigation. To address this limitation, the proposed framework introduces a multi-layer security architecture that embeds zero-trust enforcement across control, data, and application planes while integrating an attention-enhanced CNN-based IDS within the SDN controller. The model was implemented using a Mininet simulation environment with a Ryu controller and evaluated using the NSL-KDD and UNSW-NB15 benchmark datasets. A dynamic trust scoring mechanism recalibrates access privileges based on behavioral analysis outputs from the IDS. Experimental results demonstrate that the proposed ZT-CNN framework achieves 98.7% detection accuracy with a significantly reduced false positive rate of 0.021, outperforming traditional IDS and standalone CNN models. The findings confirm that synchronizing detection intelligence with programmable policy enforcement enhances SDN resilience, reduces lateral attack propagation, and enables adaptive trust validation. This research contributes a unified and intelligent defense framework suitable for next-generation programmable and cloud-integrated networks.
Credit risk assessment for SMEs in developing economies
SMEs are important in the development of the economy, creation of jobs and innovation in the developing economies. Irrespective of the importance, SMEs also experience great problems when they seek formal credit because of information asymmetry, lack of collateral, and poor financial records. Effective credit risk evaluation is thus critical to the financial institution intending to finance SMEs with minimal risk of default. This paper looks at the determining factors and methodology applied in evaluation of credit risk among SMEs in the third-world economies. The studies consider the conventional methods of credit evaluation that include financial ratio analysis and statistical credit scoring models, as well as the analysis of the emerging methods of credit evaluation that include machine learning, fintech-based credit scoring, and alternative data analytics. The research indicates that a combined approach of financial indicators and non-financial and behavioral variables enhances the precision in the credit risk prediction. The results indicate that hybrid credit risk assessment models that involve integration of conventional banking models and innovative data-driven approaches may play a vital role in improving the lending decision-making and improving access to credit by SMEs. The research also highlights the need to enhance credit information infrastructure and financial infrastructure to enable more inclusive and effective financing of SMEs.
Income inequality and education outcomes: A regression-based approach
The issue of income inequality has been a constant problem to both social and economic performance in the global arena. This paper aims at exploring the connection between education outcomes and income inequality through the regression-based decomposition model. The study finds that household-level data on income, as well as data on educational attainment indicate the major factors, which lead to disparities in education among different socioeconomic groups. The results indicate that income of households, education of parents and geographical inequalities are major factors that affect inequality in education. The regression approach to the methodology gives a detailed insight into the role of income-related factors in determining access to education and education outcomes. Policy implications imply that the negative impacts of income inequality on education can be addressed by means of focused measures, including equal school funding, income redistribution, and conditional educational support measures.
Advancing Food Security in the United States: A Multidimensional Analysis of Policy, Innovation, and Community-Driven Solutions
Despite being one of the world’s most developed nations, food insecurity remains a persistent and troubling reality in the United States. In 2023, approximately 13.5% (18 million) of U.S. households experienced food insecurity, revealing deep and enduring disparities in access to nutritious food. This study examines how a multifaceted approach, grounded in policy reform, technological innovation, and grassroots initiatives can address this ongoing crisis. Drawing on recent data from the U.S. Department of Agriculture and peer-reviewed research, we analyze the impacts of federal nutrition programs such as the Supplemental Nutrition Assistance Program (SNAP) and the Women, Infants, and Children (WIC) program, assess the potential of emerging tools including precision agriculture and AI-driven farming, and evaluate the role of community-led solutions like urban gardens and food policy councils. Our analysis indicates that no single intervention is sufficient; sustainable progress requires coordinated strategies that empower communities, leverage technological advances, and strengthen national food policies to reduce disparities across race, income, and geography. Achieving food security in the United States demands collaboration, innovation, and equity-focused reform.
Secure Knowledge Sharing in Intelligent Systems through Blockchain-Based Distributed Ledgers
The rapid expansion of intelligent systems across domains such as healthcare, finance, transportation, and smart cities has significantly increased the need for reliable mechanisms for knowledge sharing. These systems rely on distributed data sources and collaborative learning processes to make accurate decisions. However, sharing knowledge across multiple systems introduces critical concerns related to trust, data integrity, privacy, and security. Traditional centralized architectures often fail to address these issues effectively due to single points of failure and limited transparency. Blockchain technology, with its decentralized architecture and immutable ledger structure, provides a promising solution for enabling secure knowledge exchange among intelligent systems operating in distributed environments. This research explores a blockchain-based distributed ledger framework designed to facilitate secure and trustworthy knowledge sharing among intelligent systems. The proposed architecture integrates blockchain mechanisms with machine learning-based intelligent systems to ensure tamper-resistant data exchange, traceability of knowledge contributions, and secure validation of shared information.
Mathematical Optimization of Fiscal Policy under Budget Constraints
Fiscal policy formulation increasingly requires analytically rigorous tools capable of addressing complex trade-offs under binding budget constraints. Mathematical optimization offers a structured framework for designing fiscal policies that maximize economic and social objectives while respecting revenue limitations, borrowing rules, and institutional constraints. This study develops a formal optimization-based approach to fiscal policy analysis, integrating welfare maximization, expenditure allocation, and fiscal sustainability considerations within a unified modeling framework. The analysis demonstrates how linear, nonlinear, and robust optimization techniques can be applied to public budgeting problems to improve allocative efficiency, enhance policy coherence, and support evidence-based decision-making. By incorporating fiscal space limitations and institutional realities, the framework enables systematic evaluation of alternative expenditure and revenue configurations and highlights the trade-offs between growth, stability, and equity. The findings underscore the value of mathematical optimization as a practical decision-support instrument for governments operating in constrained fiscal environments and contribute to the advancement of quantitative methods in public finance policy design.
Confidential Computing in the Cloud: Cryptographic Isolation for Trusted Execution
Confidential computing has emerged as a transformative paradigm in cloud security, enabling sensitive workloads to be processed in encrypted form even while in use. Unlike traditional cloud security models that protect data at rest and in transit, confidential computing introduces hardware-backed trusted execution environments (TEEs) that isolate computation from the host operating system, hypervisor, and cloud provider administrators. This paper presents a comprehensive study of confidential computing architectures, cryptographic isolation mechanisms, and their implications for multi-tenant cloud platforms. We analyze modern TEE implementations, including Intel SGX and TDX, AMD SEV-SNP, and ARM TrustZone, and examine how cryptographic primitives, remote attestation, and secure memory encryption jointly establish trustworthy execution domains. A prototype evaluation is conducted on representative confidential virtual machine and enclave-based deployments to quantify performance overhead, scalability, and resistance to privileged adversaries. Experimental results demonstrate that while cryptographic isolation introduces moderate latency and memory overhead, careful system design and workload-aware optimization substantially mitigate these costs, making confidential computing practical for data analytics, machine learning inference, and regulated workloads. Finally, we discuss open research challenges, including side-channel resistance, programmability, orchestration at scale, and the integration of confidential computing into emerging confidential AI pipelines.
Blockchain-Integrated Cryptographic Security Models for Cloud Service Providers
Cloud computing has become the backbone of modern digital infrastructure, yet its rapid adoption continues to surface fundamental concerns around data confidentiality, integrity, access control, and trust between tenants and service providers. Traditional cryptographic protections—while mathematically sound—are often embedded in centralized management architectures that introduce single points of failure, opaque audit processes, and limited accountability. In parallel, blockchain technology has matured into a viable distributed ledger system capable of enforcing immutability, decentralized consensus, and programmable trust. This paper investigates the integration of blockchain mechanisms with advanced cryptographic models to construct next-generation security frameworks for cloud service providers. We present a layered architecture that combines attribute-based encryption, homomorphic computation, secure key escrow, and blockchain-anchored audit trails. Through controlled experimentation on a simulated multi-tenant cloud environment, we evaluate security resilience, access transparency, operational overhead, and latency trade-offs. The results demonstrate that blockchain-integrated cryptographic models can substantially improve tamper resistance and cross-organizational trust while introducing manageable performance costs, suggesting a practical pathway for deploying trustworthy cloud infrastructures at scale.
Strategic API-Driven Enterprise Integration for Digital Transformation: Architectures, Governance, and Emerging Trends
Enterprise integration has become a fundamental requirement for organizations undergoing digital transformation, particularly in environments characterized by distributed systems, hybrid cloud infrastructures, and rapidly evolving business demands. Application Programming Interfaces (APIs) have emerged as a strategic mechanism for enabling seamless interoperability, system modularity, and scalable enterprise connectivity. Recent studies emphasize that structured API strategies facilitate not only technical integration but also organizational agility, innovation capacity, and operational efficiency. This paper examines the evolving role of API-driven enterprise integration frameworks, focusing on architectural models, governance mechanisms, security considerations, and future technological trajectories. Drawing upon contemporary academic research and industry developments, the study proposes a strategic perspective on enterprise API ecosystems that integrates technical architecture with organizational transformation objectives. The analysis highlights the importance of governance maturity, lifecycle management, and cloud-native integration approaches in achieving sustainable enterprise interoperability. The findings suggest that organizations adopting comprehensive API strategies are better positioned to enhance digital resilience, optimize system performance, and support continuous innovation. Furthermore, emerging technologies such as artificial intelligence, automated monitoring, and hybrid cloud orchestration are expected to further reshape enterprise integration paradigms in the coming years.
Enterprise Integration and API Strategy: Advancing Scalable Digital Ecosystems in Modern Enterprises
Enterprise integration has become a critical determinant of organizational agility, operational efficiency, and digital transformation success. The rapid evolution of distributed systems, cloud computing, and platform-based business models has intensified the need for robust Application Programming Interface (API) strategies that enable seamless connectivity across enterprise applications, partners, and customers. This paper examines contemporary enterprise integration paradigms and highlights the strategic role of APIs in enabling scalable, secure, and interoperable digital ecosystems. The study synthesizes recent developments in integration architectures, including microservices-based design, hybrid cloud environments, and event-driven frameworks, to evaluate how API-led connectivity enhances system interoperability while maintaining governance and security standards. Furthermore, it discusses challenges such as API lifecycle management, performance optimization, organizational alignment, and security risks that enterprises encounter when implementing large-scale integration strategies. The findings suggest that organizations adopting structured API strategies alongside modern integration architectures demonstrate improved operational flexibility, faster innovation cycles, and stronger digital resilience. The paper concludes by proposing a strategic integration framework aimed at guiding enterprises toward sustainable digital transformation through effective API governance and integration planning. This research contributes to ongoing academic and industry discourse by providing a contemporary perspective on enterprise integration strategy aligned with emerging technological trends expected to shape enterprise systems beyond 2026.
Architectural Identity in Transit Infrastructure: Branding vs Functionality
Transit infrastructure serves not only as a functional network for urban mobility but also as a canvas for architectural identity. This study investigates the interplay between branding and functionality in transit architecture, examining how aesthetic expression can coexist with operational efficiency. Through analysis of case studies and design strategies, the research highlights the tensions and synergies between visually distinctive designs and practical requirements such as passenger flow, safety, accessibility, and maintenance. The findings suggest that successful transit infrastructure achieves a balance where architectural branding enhances user experience without compromising functional performance. Recommendations are provided for architects, urban planners, and transit authorities seeking to integrate identity and efficiency in contemporary transit design.
Human-Centered Design in Underground Transit Environments
Human-centered design is an increasingly important method for improving user experience in underground transit environments, where a variety of variables make for an unpleasant travel experience. Some of these environmental variables include: tight spaces, absence of natural light, and high numbers of passengers. This research explores the application of human-centered design principles to underground transit station architecture in four specific areas: lighting, wayfinding, accessibility, and psychological comfort. The research examines the relationship between adaptive lighting systems and improved visibility, safety perception, and circadian alignment; the relationship between intuitive wayfinding strategies and improved spatial legibility and reduced user stress; and the role of inclusive design in providing equal access to a diverse variety of users. The study also emphasizes the use of environmental psychology to address the issues of disorientation, anxiety, and discomfort that often occur within underground spaces through the use of materiality, acoustics, artistic expression, and biophilic design elements. Overall, the research indicates that the integration of human-centered design strategies can create improved passenger well-being, support navigation efficiencies, and create safer, more resilient, and socially responsible underground transit systems..
Dynamic 3D Assessment of Root Canal Morphological Changes Following Endodontic Instrumentation Using Sequential CBCT Imaging
Accurate evaluation of root canal morphology is critical for successful endodontic treatment. This study aimed to dynamically assess three-dimensional morphological changes in root canals following endodontic instrumentation using sequential cone-beam computed tomography (CBCT) imaging. Extracted human teeth underwent standardized instrumentation protocols and were imaged before and after treatment. Sequential CBCT scans were reconstructed to generate 3D models, allowing quantitative analysis of changes in canal volume, surface area, and curvature. Results demonstrated significant alterations in canal morphology, highlighting variations associated with different instrumentation techniques. This approach provides a precise, non-destructive method for monitoring root canal modifications, offering insights to optimize clinical protocols and improve treatment outcomes.
Artificial Intelligence–Assisted Interpretation of CBCT Scans for Early Periapical Pathology Detection: A Comparative Endodontic Study
Early detection of periapical pathologies is critical for successful endodontic treatment and preservation of dental structures. Cone-beam computed tomography (CBCT) provides detailed three-dimensional imaging, yet interpretation can be time-consuming and subject to observer variability. This study evaluates the efficacy of artificial intelligence (AI) in assisting the interpretation of CBCT scans for early periapical lesion detection, comparing its performance with that of experienced endodontists. A set of CBCT scans from patients with suspected periapical pathology was analyzed using a trained AI model and independently by clinicians. Diagnostic accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were assessed. Results demonstrate that AI-assisted interpretation achieves comparable or superior accuracy to human evaluation, with potential to reduce diagnostic time and improve early intervention outcomes. Integration of AI into endodontic imaging workflows may enhance diagnostic consistency and support clinical decision-making.
REAL-TIME IO T-ENABLED CONTROL OF STORMWATER ASSETS: REDUCING RUNOFF PEAKS AND POLLUTANT LOADS
Urbanization and industrial expansion have intensified stormwater runoff volumes and pollutant transport, placing significant pressure on conventional drainage systems. Static stormwater infrastructure, typically designed for historical conditions, often lacks the flexibility required to respond to dynamic rainfall patterns and evolving land use. Real-time control supported by Internet of Things technologies has emerged as a promising approach for improving stormwater system performance. This article examines the role of IoT-enabled real-time control in managing stormwater assets, with particular emphasis on sensor integration, telemetry, and actuators. A representative case study from an urban or industrial context is discussed to illustrate system operation and outcomes. Performance metrics related to peak flow attenuation and pollutant load reduction are analyzed, demonstrating the capacity of smart stormwater systems to enhance resilience, efficiency, and environmental protection.
FRAUD DETECTION IN DIGITAL PAYMENTS USING AI
The rapid adoption of digital payment systems has revolutionized financial transactions, offering convenience and speed to consumers and businesses alike. However, this growth has also led to an increase in sophisticated fraudulent activities, posing significant challenges to the security and trustworthiness of digital payments. Traditional rule-based fraud detection methods often struggle to keep pace with evolving attack patterns, highlighting the need for more intelligent solutions. Artificial Intelligence (AI) has emerged as a powerful tool for enhancing fraud detection by analyzing large volumes of transactional data in real time, identifying anomalies, and adapting to new threats. This article examines the application of AI techniques, including machine learning and deep learning models, in detecting fraudulent transactions, evaluating their performance, and addressing the associated challenges such as data imbalance, privacy concerns, and computational constraints. The findings underscore the potential of AI-driven approaches to improve detection accuracy, minimize financial losses, and strengthen the overall security of digital payment ecosystems, while offering insights for future advancements in secure financial technology.
Implementing AI Governance Frameworks in Hospitals: From Ethical Principles to Operational Practice
The use of artificial intelligence (AI) in hospitals has a potential beneficial impact on clinical choice, operational effectiveness, and patient outcomes. Nevertheless, there are severe ethical, legal and operational problems, which the fast adoption of AI technologies poses. This paper addresses the manner in which AI governance structures can be applied in hospitals, particularly on the ways in which the ethics principles of transparency, accountability, fairness, and patient safety can be put into practice. It examines the existing regulatory requirements, system of organization, integration plans of workflow, identifying the quality of reality of the problems and the opportunities of efficient governance. This work is possible through the analysis of the risk-based strategy, the stakeholder engagement strategy, and monitoring systems that could be offered to the hospital administrators, policy makers and clinicians interested in the provision of the safe, credible and accountable use of AI. The findings indicate the significance of the well-designed system of governance in order to develop patient trust and advance the ethical use of AI in healthcare.
Sleep Health as an Economic Asset: Evaluating Roles of adequate sleep in global labor efficiency
The economic impact of sleep health is poorly studied, whereas it is becoming a key factor defining human capital. This paper is an exploration of how adequate sleep can lead to better labor productivity in the world, both at an individual and macroeconomic level. Based on the evidence provided in sleep science, research on cognitive performance, and research in labor economics, the paper emphasizes the role of enough sleep in enhancing productivity, decreasing errors, and alleviating absenteeism and presenteeism. Insomnia, on the other hand, is a costly contributor to loss of economy due to poor performance in the workforce and high healthcare expenditure. The study also addresses the policy and organizational interventions that can maximize the health of sleep, which makes it a strategic economic asset. The concept of sleep health, when understood and introduced in the labor and economic planning, can be a quantifiable benefit to the efficiency of the workforce and economic stability.
Cybersecurity in Fintech and Digital Payment Systems
The high development of fintech and digital payment systems has revolutionized the field of financial services as it makes them more accessible, efficient, and convenient. Nevertheless, this digital transformation has also come with major cybersecurity issues such as fraud, digital breaches, malwares, and new AI-based threats. This study explores the importance of cybersecurity in the security of fintech platforms and digital payment systems. It discusses most common cyber threats, considers existing security solutions used, including encryption, multi-factor authentication, and blockchain-based solutions, and reviews regulatory frameworks that regulate financial technology security. Moreover, the research determines operational and technical issues that fintech businesses encounter to retain effective cybersecurity and to secure their smooth user experiences. These results indicate that more complex technologies and risk management plans should be incorporated to address cyber risks. The study will help increase awareness of security, inform policy-making and help develop the further evolution of digital financial services sustainably.