The integration of blockchain and artificial intelligence (AI) technologies offers a transformative approach to data integration in smart healthcare systems. As healthcare systems generate vast amounts of sensitive and complex data, efficient and secure integration is critical to improving patient outcomes, optimizing medical workflows, and ensuring data privacy. Blockchain technology provides a decentralized and immutable data-sharing platform that ensures the security, integrity, and privacy of medical records. Concurrently, AI plays a pivotal role in (...) processing and analyzing these vast datasets to derive meaningful insights, automate diagnostics, and predict patient outcomes. This paper proposes an advanced data integration framework for smart healthcare systems by leveraging the strengths of both blockchain and AI technologies. The blockchain component ensures secure and transparent data exchange between healthcare providers, patients, and researchers, while AI algorithms are employed to process the integrated data for predictive analysis, diagnostics, and personalized treatment recommendations. Through a review of current approaches to data integration and an exploration of emerging technologies, this paper demonstrates how blockchain and AI can be combined to address challenges related to healthcare data privacy, interoperability, and efficiency. The proposed framework offers a comprehensive solution to advancing smart healthcare systems through secure data integration and intelligent decision-making. (shrink)

Remote patient monitoring (RPM) increasingly relies on mobile platforms for continuous health data capture, yet many Android-based mHealth applications remain inaccessible to users with disabilities due to fragmented interaction design and insufficient integration of multimodal data sources. This paper presents an Android-centric data integration and accessibility framework that unifies adaptive user interfaces, voice-driven interaction, and wearable sensor data pipelines to support inclusive remote patient care. First, a domain-specific accessibility barrier taxonomy is developed to characterize (...) challenges across perceptual, motor, cognitive, and communicative dimensions in Android mHealth environments. Second, a data integration architecture is proposed that combines real-time wearable data ingestion, voice interaction services, and context-aware UI adaptation driven by a user capability model. Third, an Android-based prototype implementation is evaluated across multiple disability cohorts, demonstrating significant improvements in task completion rates, interaction efficiency, and error reduction compared to baseline applications. Results show improvements of over 30–50% in task success rates and substantial reductions in interaction time, highlighting the importance of tightly coupling data integration with adaptive interface design. The findings establish that accessibility in Android mHealth systems is not solely a compliance requirement but a system-level design objective, where integrated data pipelines and adaptive interaction models are essential for delivering reliable and inclusive healthcare services. (shrink)

Relational databases remain the primary operational store for enterprise records, but users increasingly expect search behavior that tolerates unfamiliar schemas, synonymy, missing joins, and mixed textual and structured evidence. This paper presents Semantic-Relational Search Integration (SRSI), a framework for adding semantic search and lightweight data integration to existing relational sources without replacing the database engine. SRSI builds a semantic overlay consisting of schema correspondences, attribute phrases, entity links, and query-rewriting templates. Queries are interpreted through lexical, structural, and (...) semantic signals, then compiled into ranked SQL plans and optional ontology-based access paths. The framework is intended for 2020-era deployments: relational systems, SPARQL-to-SQL rewriting, schema matching, record linkage, word and contextual text representations, and conservative human review of low-confidence mappings. A prototype study over product, publication, and service-ticket datasets shows that SRSI improves top-10 answer accuracy from 0.61 to 0.78 relative to keyword-only relational search, reduces manual mapping effort by 42%, and keeps median query latency below 1.8 seconds for the tested workloads. The results suggest that semantic search and relational integration are most practical when semantics are treated as a governed overlay over relational data rather than as a full replacement for relational storage and query processing. (shrink)

The healthcare sector is undergoing a transformative shift towards smart healthcare, driven by advancements in technology, including Artificial Intelligence (AI) and Blockchain. As healthcare systems generate vast amounts of data from multiple sources, such as electronic health records (EHRs), medical imaging, wearable devices, and sensor-based monitoring, the challenge lies in securely integrating and analyzing this data for real-time, actionable insights. Blockchain technology, with its decentralized, immutable, and transparent framework, offers a robust solution for securing data integrity, privacy, (...) and sharing across healthcare networks. AI, on the other hand, enables advanced data analytics, providing healthcare professionals with predictive insights, early diagnosis, and personalized treatment plans. This paper explores the integration of Blockchain and AI for advanced data management in smart healthcare. We propose a system that utilizes Blockchain for secure and transparent data storage, ensuring that healthcare data from various sources is tamper-proof and can be shared across multiple stakeholders. AI algorithms, particularly machine learning and deep learning models, are applied to the integrated data to detect patterns, predict disease progression, and optimize treatment plans. By leveraging Blockchain, healthcare data can be securely shared across providers and patients, ensuring privacy and regulatory compliance, while AI technologies provide the intelligence required for enhancing clinical decision-making. This integrated system not only addresses the growing concerns surrounding data security and privacy but also promotes interoperability and collaboration within the healthcare ecosystem. The paper discusses the challenges, opportunities, and future directions of Blockchain and AI integration in smart healthcare systems, highlighting how this convergence can lead to more efficient, secure, and personalized healthcare services. (shrink)

Modern enterprises integrate heterogeneous data from operational databases, SaaS applications, event streams, and semi-structured sources to power analytics and machine learning workloads. Despite advances in distributed processing engines and lakehouse architectures, data integration remains labor-intensive and error-prone due to schema drift, semantic ambiguity, brittle transformation logic, and slow incident diagnosis. Recent large language models (LLMs) offer new capabilities for semantic reasoning over schemas, SQL code, pipeline logs, and documentation, enabling automated assistance in mapping, rule generation, and failure (...) analysis. This paper proposes a governed architecture for LLM-assisted data integration that combines unified ingestion and lakehouse-style storage zones with an LLM co-pilot for schema mapping and transformation synthesis, and an observability-driven repair loop for fault localization and targeted recomputation. We introduce a confidence scoring framework that integrates semantic similarity with empirical sample evidence and constraint validation to assess mapping reliability. A human-in-the-loop and continuous integration workflow ensures regression safety and reproducibility before promotion to production pipelines. An illustrative evaluation demonstrates reductions in onboarding time and mean time to repair compared with conventional rules-only integration approaches, while maintaining data quality guarantees. The results indicate that LLMs are most effective when deployed as a planning, validation, and debugging layer grounded in empirical evidence and governance controls, augmenting deterministic data processing systems rather than replacing them. (shrink)

Cloud-native architectures deployed on Amazon Web Services (AWS) increasingly rely on microservices and Internet of Things (IoT) platforms to support scalable, real-time, and data-intensive applications. While these paradigms improve agility and elasticity, they introduce significant challenges in secure data integration, governance, and regulatory compliance due to highly distributed data flows and expanded attack surfaces. This paper presents a secure cloud-native data integration architecture on AWS that unifies microservices-based application workloads, IoT data ingestion pipelines, (...) and database-level protection using Oracle 19c Transparent Data Encryption (TDE). The proposed architecture maps established design patterns such as service meshes, edge and fog computing, and streaming-based ETL to AWS-native services, enabling encrypted data exchange in transit and robust protection of data at rest without application modifications. A prototype implementation using Amazon EKS, AWS IoT Core, AWS Glue, and Oracle 19c demonstrates scalable ingestion, low-latency processing, and minimal performance overhead from encryption. Experimental results validate that the architecture achieves improved scalability and tail-latency characteristics while maintaining strong data-at-rest security and compliance readiness. The paper further identifies research gaps in AI-driven automation, unified observability, and adaptive security frameworks for future cloud-native and multi-cloud data integration environments. (shrink)

Rapid advancement of urbanization has necessitated the creation of "smart cities," where information and communication technologies (ICT) are used to improve the quality of urban life. Central to the smart city paradigm is data integration—connecting disparate data sources from various urban systems, such as transportation, healthcare, utilities, and public safety. This paper explores the role of Artificial Intelligence (AI) in facilitating data integration within smart cities, focusing on how AI technologies can enable effective urban governance. (...) By examining the current landscape of data integration and AI in smart cities, we highlight key challenges, solutions, and future trends. We conclude that AI-driven data integration is essential for optimizing city operations, fostering sustainability, and enhancing decision-making for urban governance. (shrink)

This paper presents a predictive analytics system for loan default prevention in community banking that combines explainable AI techniques with comprehensive socioeconomic data integration to provide early warning capabilities while maintaining fairness and transparency in lending decisions. The proposed Community Banking Predictive System (CBPS) addresses unique challenges faced by community banks including limited data availability, the need for personalized customer relationships, and regulatory requirements for fair lending practices. Our methodology integrates traditional credit data with alternative (...) class='Hi'>data sources including local economic indicators, employment statistics, demographic trends, and community development metrics to create more comprehensive risk assessments. The system employs explainable machine learning algorithms that provide clear, understandable reasons for risk predictions, enabling loan officers to make informed decisions while maintaining customer relationships. We introduce a novel fairness-aware feature selection algorithm that automatically identifies and mitigates potential bias in lending decisions while preserving predictive accuracy. The predictive component can identify customers at risk of default up to 12 months in advance, enabling proactive intervention strategies such as loan restructuring, financial counseling, or payment plan modifications. Our implementation includes automated early warning alerts, customer communication tools, and intervention tracking capabilities. Experimental validation using anonymized community bank datasets shows 73% improvement in default prediction accuracy while maintaining fair lending compliance and reducing actual default rates by 45% through proactive intervention programs. (shrink)

This paper presents a machine learning-based framework for real-time short-term snowfall forecasting by integrating atmospheric and topographic data. The model uses real-time meteorological data such as temperature, humidity, and pressure, along with terrain data like elevation and land cover, to predict snowfall occurrence within a 12-hour forecast window. Random Forest (RF) and Support Vector Machine (SVM) models are employed to process these multi-source inputs, demonstrating a significant improvement in prediction accuracy over traditional methods. Experimental results show that (...) the RF model achieved a root mean square error (RMSE) of 3.2 cm, while traditional regression models achieved an RMSE of 5.1 cm. The proposed framework provides a more reliable and accurate tool for real-time snowfall prediction, especially in mountainous regions, and can be deployed for operational weather forecasting applications. (shrink)

Ontologies, as the term is used in informatics, are structured vocabularies comprised of human- and computer-interpretable terms and relations that represent entities and relationships. Within informatics fields, ontologies play an important role in knowledge and data standardization, representation, integra- tion, sharing and analysis. They have also become a foundation of artificial intelligence (AI) research. In what follows, we outline the Coronavirus Infectious Disease Ontology (CIDO), which covers multiple areas in the domain of coronavirus diseases, including etiology, transmission, epidemiology, pathogenesis, (...) diagnosis, prevention, and treatment. We emphasize CIDO development relevant to COVID-19. (shrink)

In recent years, the combination of Artificial Intelligence (AI) and Blockchain technology has garnered significant attention, especially in the healthcare domain. With the increasing reliance on biomedical signal processing for disease diagnosis and treatment, ensuring the security, privacy, and integrity of data has become paramount. Biomedical signals, including electrocardiograms (ECG), electroencephalograms (EEG), and other physiological data, often contain sensitive information. AI models have shown great promise in processing and interpreting these signals, enabling accurate disease detection and personalized healthcare. (...) However, the potential for data tampering, unauthorized access, and privacy concerns pose significant challenges. Blockchain, with its decentralized, tamper-resistant, and secure framework, offers a solution to these challenges. By integrating Blockchain with AI for biomedical signal processing, data can be securely stored, verified, and accessed, ensuring that healthcare providers can rely on the integrity and accuracy of the signals used in AI-based diagnostics. This paper explores the role of Blockchain-enhanced AI solutions for secure biomedical signal processing and data integration, addressing both the technical and ethical challenges in this space. We propose a system that leverages Blockchain to manage the secure transfer and storage of biomedical signals while utilizing AI to analyze and interpret these signals in real time. Blockchain provides an immutable ledger for auditing and validating data provenance, ensuring that the data remains secure throughout the healthcare pipeline. The integration of these two technologies not only enhances the security of biomedical data but also enables more trustworthy AI-driven diagnostics and decision-making in healthcare. (shrink)

We describe a strategy that is being used for the horizontal integration of warfighter intelligence data within the framework of the US Army’s Distributed Common Ground System Standard Cloud (DSC) initiative. The strategy rests on the development of a set of ontologies that are being incrementally applied to bring about what we call the ‘semantic enhancement’ of data models used within each intelligence discipline. We show how the strategy can help to overcome familiar tendencies to stovepiping of (...) intelligence data, and describe how it can be applied in an agile fashion to new data resources in ways that address immediate needs of intelligence analysts. (shrink)

This Chapter explores the integration of Blue- Green Infrastructure (BGI) into urban development using data- driven approaches enhanced by AI- powered ETL (Extract, Transform, Load) systems. As cities face increasing challenges due to climate change, sustainable urban planning practices such as BGI—which combines natural (green) and water management (blue) elements—are critical for resilience. However, the complexity of urban environments demands sophisticated data processing techniques to assess, design, and implement BGI solutions effectively. By adopting AI models within ETL (...) processes, this paper presents a framework that automates the analysis of incoming environmental data, optimizes the planning process, and provides adaptive decision- making tools. The study highlights how AI- augmented ETL systems can process large volumes of geospatial, environmental, and infrastructure data, offering a more efficient, scalable, and intelligent approach to urban BGI integration. Case studies of smart city initiatives employing this technology are discussed, showcasing the benefits of data- driven BGI solutions in enhancing sustainability, urban resilience, and quality of life. (shrink)

The increasing diversity of data generated across enterprise, cloud, and distributed computing environments has created significant challenges for integrating heterogeneous data sources into unified analytical platforms. Differences in data models, schema structures, and semantic representations often limit the effectiveness of conventional extract-transform-load (ETL) approaches. This paper presents ASIF (A Semantic Integration Framework), a scalable architecture designed to support semantic integration across structured, semi-structured, and unstructured data sources. The framework combines ontology-driven schema reconciliation, entity resolution, (...) and distributed consistency mechanisms to enable reliable integration in large-scale data environments. ASIF was evaluated using datasets from government open-data repositories, healthcare interoperability benchmarks, and e-commerce transaction systems comprising approximately 4.2 billion records. Experimental results demonstrate that the framework significantly reduces schema conflicts while achieving high entity-matching accuracy across heterogeneous data sources. The proposed architecture also supports scalable ingestion and query processing in distributed environments. These findings indicate that semantic integration approaches can effectively address structural and semantic heterogeneity while maintaining the performance requirements of modern big data platforms. The framework provides a practical foundation for enterprise and cloud-based data integration systems operating at scale. (shrink)

We describe a strategy for integration of data that is based on the idea of semantic enhancement. The strategy promises a number of benefits: it can be applied incrementally; it creates minimal barriers to the incorporation of new data into the semantically enhanced system; it preserves the existing data (including any existing data-semantics) in their original form (thus all provenance information is retained, and no heavy preprocessing is required); and it embraces the full spectrum of (...) data sources, types, models, and modalities (including text, images, audio, and signals). The result of applying this strategy to a given body of data is an evolving Dataspace that allows the application of a variety of integration and analytic processes to diverse data contents. We conceive semantic enhancement (SE) as a lightweight and flexible process that leverages the richness of the structured contents of the Dataspace without adding storage and processing burdens to what, in the intelligence domain, will be an already storage- and processing-heavy starting point. SE works not by changing the data to which it is applied, but rather by adding an extra semantic layer to this data. We sketch how the semantic enhancement approach can be applied consistently and in cumulative fashion to new data and data-models that enter the Dataspace. (shrink)

This fieldnotes article documents the design and implementation of a Philosophy for Children (P4C) lesson aimed at introducing children aged 8–12 to the ethical dimensions of Artificial Intelligence (AI), with a particular focus on data privacy. Drawing on the Community of Philosophical Inquiry (CoPI) methodology— specifically the “Plain Vanilla” model associated with Thomas Jackson and disseminated by the IAPC (Yarmel & Gregory, 2023)—the session employed an illustrated narrative stimulus, “Liam’s Secret Base,” followed by student-generated dialogue and a collaborative “Safe (...) Sharing Scenario” activity. These components were designed to foster critical, creative, and caring thinking (Lipman, 2003) about digital life. This article demonstrates that engaging young learners in ethical inquiry about AI technologies is both timely—given the global call for age-appropriate AI-ethics education (UNICEF, 2021; Dabbagh et al., 2025)—and feasible, since most children already interact with AI-mediated platforms. Classroom reflections showed that the lesson supported higher-order reasoning, metacognitive awareness, and collaborative dialogue in a supportive environment, outcomes consistent with reported benefits of P4C (Adams et al., 2023; Ab Wahab et al., 2022). Rather than prescribing a list of “don’ts,” educators used open-ended questions and realistic digital-life scenarios to nurture children’s autonomy in ethical decision-making, echoing participatory design approaches from the MIT Media Lab’s AI + Ethics curriculum (Wu, 2020; Williams et al., 2022). We conclude that P4C—co-founded by Matthew Lipman and Ann Margaret Sharp (Gregory & Laverty, 2017)—provides an effective pedagogical framework for integrating digital-ethics learning into childhood education, enabling students to become thoughtful, informed, and responsible digital citizens capable of navigating an increasingly data-driven world (Floridi, 2023/2024; Turner, 2019). (shrink)

The integration of information resources in the life sciences is one of the most challenging problems facing bioinformatics today. We describe how Language and Computing nv, originally a developer of ontology-based natural language understanding systems for the healthcare domain, is developing a framework for the integration of structured data with unstructured information contained in natural language texts. L&C’s LinkSuite™ combines the flexibility of a modular software architecture with an ontology based on rigorous philosophical and logical principles that (...) is designed to comprehend the basic formal relationships that structure both reality and the ways humans perceive and communicate about reality. (shrink)

DBT (Data Build Tool) has revolutionized data transformation workflows, enabling data engineers to model, test, and document data within cloud data warehouses. When coupled with Git for version control, DBT enables more efficient collaboration, reproducibility, and error tracking in data engineering teams. This paper explores how integrating DBT with Git can streamline the development and deployment of data pipelines. The research focuses on the advantages of using Git for managing DBT projects, ensuring collaborative (...) workflows, maintaining data pipeline versions, and automating deployments. We discuss best practices for integrating DBT with Git to improve data pipeline efficiency, reduce errors, and ensure a smoother CI/CD process in modern data engineering environments. (shrink)

Integrative structure modeling is a method for using information from multiple sources to compute structural models of biomolecular systems. It proceeds via four steps: (i) defining the model representation, which determines the variables whose values will be computed; (ii) constructing a function for scoring alternative models according to how well they accommodate input information; (iii) searching a space of candidate models for acceptable models; and (iv) analyzing acceptable models to evaluate their fit with input information. These steps are iterated until (...) a model adequate for addressing biological questions is found. In this paper, we draw lessons from integrative modeling about effective integration and about modeling. We describe what it means to integrate information from multiple sources: Integration amounts to distributing information among the four steps of integrative modeling. Theory and data alike can be sources of information; this process thus generates models of information, rather than models of theory or models of data. We then propose heuristics for distributing information and designing multiple iterations of modeling. Effective iteration requires prioritizing the most reliable information and minimizing the time required to obtain an adequate model. Rather than being constructed from theory and assessed using data, models are constructed from any available information and assessed in a coherentist manner. (shrink)

Questions of data privacy in Africa are imbued with complexity. We examine a slice of this complexity by putting the concept of contextual integrity into dialogue with Africa's plural-legal contexts to explore data privacy within Africa's emerging digital landscape. The conceptual insights are empirically illustrated based on a case study in Ghana, involving content analysis of policy documents and interviews with a sample of residents, cultural leaders (among the Akan ethnic group), subject matter experts, and digital entrepreneurs (e.g. (...) fintech firms). Our findings illuminate a nuanced and contextually rooted understanding of privacy, focusing on the complementarities and tensions around data anonymization for privacy; the multiplicity of information spheres that result in a complicated terrain of privacy breaches; and the individuality, mutuality, and collectivity of privacy harms and remedies thereof. Our findings challenge prevailing discourses in the literature that either suggests (a) misalignments between locally-rooted privacy norms and European-imported statutory regulations on privacy in African countries or (b) privacy myopia among Africans—underestimating their personal information and the need to protect such information. We suggest shifting away from such privacy discourses from above (meta-narratives, generalizations, and inherent assumptions on African states and societies) to conversations from below. That is, refocusing on the particularities of place, culture, and norms that complicate meta-narratives on privacy. This requires centering the everyday privacy norms that result from co-existing, and sometimes oppositional, customary-informed and state-stipulated information norms within Africa's emerging digital landscapes. (shrink)

Successful biomedical data mining and information extraction require a complete picture of biological phenomena such as genes, biological processes, and diseases; as these exist on different levels of granularity. To realize this goal, several freely available heterogeneous databases as well as proprietary structured datasets have to be integrated into a single global customizable scheme. We will present a tool to integrate different biological data sources by mapping them to a proprietary biomedical ontology that has been developed for the (...) purposes of making computers understand medical natural language. (shrink)

This integral investigation explored phenomenological and neurophysiologic, individual and collective dimensions of Christian Trinitarian meditation experiences in a volunteer, convenience sample of 10 practicing Christians, 6 men and 4 women, with a mean age of 48 years and an age range from 21 to 85 years. Participants meditated for a minimum period of 15 minutes, during which neurophysiologic data in the form of electroencephalographic (EEG), electromyographic (EMG), blood volume pulse (BVP) and respiratory activity were recorded. A phenomenological analysis indicated (...) that the meditation process generally involved a movement from body to mind to spirit as evident in reports of an increasingly relaxed, contented and focused state of consciousness characterised by Christian Trinitarian imagery, wonder, surrender, peace, bliss, openness and formlessness. The neuropsychological findings indicated significant increases, from baseline to meditation recordings, in the alpha and beta range, accompanied by increasing mean trends in the theta and gamma range, and decreasing mean trends in the delta range, EMG, BVP and respiration. Integrative findings indicated the practical theological value of small doses of Christian Trinity meditation to enhance spiritual life for those forms of waking, thinking, conscious behaviour needed in everyday world involvement and healing. Findings were discussed in relation to further integrative investigations and interventions with practical theological implications. (shrink)

Abstract: Sorting is a fundamental operation in computer science, critical for data organization, retrieval, and analysis. Traditional algorithms like QuickSort and MergeSort excel in smaller datasets but struggle in big data contexts, prompting the need for innovative solutions. Artificial Intelligence (AI) has emerged as a transformative force, enabling adaptive, intelligent sorting mechanisms that significantly outperform conventional methods in efficiency and scalability. This paper examines the integration of AI into sorting algorithms, highlighting innovative approaches, comparative analyses, and implications (...) for big data. By exploring machine learning, neural networks, and genetic algorithms, this research reveals the potential of AI-driven sorting to enhance performance in diverse environments. The findings emphasize the importance of hybrid AI-traditional models for addressing computational costs and scalability challenges, underscoring the need for continued research. (shrink)

The rapid urbanization of cities globally has brought about significant challenges in terms of resource management, sustainability, and quality of life for residents. To address these issues, the concept of Smart Cities has emerged, where technologies like the Internet of Things (IoT) are harnessed to integrate various sensors and data systems to enhance urban services and infrastructure. Smart Cities leverage IoT devices such as sensors, actuators, and connected systems to collect and analyze real-time data, which enables efficient decision-making, (...) optimized resource allocation, and improved services for citizens Incorporating IoT into city infrastructure allows for better management of essential services such as water, electricity, waste management, transportation, and healthcare. This integration fosters urban sustainability by reducing energy consumption, minimizing waste, and enhancing the quality of life through the application of data-driven solutions. The data collected through IoT systems also plays a crucial role in predictive maintenance, risk management, and climate adaptation strategies, ensuring that cities become more resilient to the challenges of climate change and population growth. This paper explores the integration of IoT in Smart Cities, focusing on the role of sensors and data collection for urban sustainability. The paper provides an overview of the technological framework that supports IoT in Smart Cities and discusses how these technologies are transforming urban life. It also highlights key challenges, such as data privacy, security concerns, and infrastructure costs, while proposing strategies for overcoming these challenges. Furthermore, the paper discusses successful case studies and outlines future trends in IoT deployment for urban sustainability. (shrink)

As organizations face increasing pressure to protect personal and sensitive data, privacy by design has emerged as a critical approach to data protection. Privacy by design integrates privacy considerations directly into business operations, technologies, and processes from the outset, rather than as an afterthought. This proactive approach ensures that privacy is embedded into the core of business practices and reduces the risk of data breaches, legal non-compliance, and reputational damage. This paper explores the principles of privacy by (...) design, how it can be implemented in business operations, and its benefits for both organizations and consumers. Additionally, it examines relevant regulations such as the General Data Protection Regulation (GDPR) and offers practical steps for organizations to adopt privacy-focused strategies across their operations. Ultimately, by embracing privacy by design, businesses can enhance their data governance practices, build trust with consumers, and stay ahead of evolving data privacy regulations. (shrink)

In today's increasingly digital and interconnected business environment, organizations face significant challenges in managing multiple systems, applications, and data streams. SAP plays a central role in integrating these diverse systems and enabling seamless data flow across various platforms. Through solutions like SAP S/4HANA, SAP Cloud Platform, and SAP Integration Suite, businesses can create a unified digital ecosystem that connects onpremise, cloud-based, and third-party applications. These integration capabilities are critical for enhancing operational efficiency, improving decision-making, and fostering (...) innovation. This paper explores the role of SAP as a foundation for enterprise integration, highlighting its technologies, benefits, challenges, and real-world use cases. Additionally, it discusses how organizations can leverage SAP solutions to bridge the gap between legacy systems, modern applications, and emerging technologies in the digital age. (shrink)

Modern enterprises increasingly depend on unified data platforms that integrate data ingestion, cloud computing, AI/ML pipelines, and business intelligence across heterogeneous environments. This paper analyzes multi-cloud data integration architectures spanning AWS, Azure, GCP, BigQuery, and Snowflake, evaluating their suitability for healthcare, FinTech, and retail analytics workloads across four dimensions: performance, governance, portability, and cost efficiency. We propose a layered reference architecture connecting ingestion, governed storage, analytics, and AI consumption; define practical evaluation metrics for cost efficiency, (...) class='Hi'>data freshness, and portability overhead; and compare platform ecosystems in terms of workload placement trade-offs. Our analysis demonstrates that no single platform dominates across all requirements. Instead, effective enterprise data platforms combine governed storage, reusable transformation layers, flexible ML services, and domain-specific consumption patterns. Optimal architecture depends on workload volatility, data residency constraints, team capabilities, and the balance between cross-cloud portability and platform-native optimization. These findings motivate a hybrid placement strategy governed by shared metadata, stable data contracts, and clear domain ownership rather than full-stack standardization on any single cloud provider. (shrink)

Abstract: Sorting algorithms have always been the primary focus of data organization and have been the same since they were discovered. They play a vital role in reducing work and maximizing efficiency as well as accuracy. This paper aims at comparing and examining traditional sorting algorithms, usually applied by programmers as opposed to AI-based methods like Decision Trees and Neural Networks. The performance of these sorting techniques would be evaluated on time over such data executions considering memory requirement (...) and accuracy on different datasets. Most traditional methods could sort structured data even of a smaller dataset, but they cannot work well when it comes to scaling and unstructured data [4]. AI-based algorithms, however, seem to perform better in terms of accuracy, which is superior in its implementation to complex and unstructured datasets, at a great cost of and accuracy; thus, making possible insights into the practical implementations. These findings show the potentials of hybrid methods regarding modern data sorting application challenges and pave the way for further future optimization and real-world implementation [8]. (shrink)

As data-driven decision-making becomes integral to various industries, ensuring data integrity has never been more critical. With the growing reliance on data across sectors, the need for transparent and auditable data pipelines is essential. This paper explores the concept of AI-driven data lineage within the context of Federated Learning, Artificial Intelligence, and Trusted Execution (FATE) integrity. We propose a robust framework that leverages AI technologies to trace, verify, and maintain data integrity through the entire (...) lifecycle, ensuring trustworthiness in machine learning systems. This framework addresses challenges of ensuring data provenance, authenticity, and transparency in decentralized data environments. We present the design, implementation, and potential impacts of this approach, focusing on its application to Federated Learning and its synergy with FATE principles. (shrink)

While calls to integrate ethics into computer science education go back decades, recent high-profile ethical failures related to computing technology by large technology companies, governments, and academic institutions have accelerated the adoption of computer ethics education at all levels of instruction. Discussions of how to integrate ethics into existing computer science programmes often focus on the structure of the intervention—embedded modules or dedicated courses, humanists or computer scientists as ethics instructors—or on the specific content to be included—lists of case studies (...) and essential topics to cover. While proponents of computer ethics education often emphasize the importance of closely connecting ethical and technical content in these initiatives, most do not reflect in depth on the variety of ways in which the disciplines can be combined. In this paper, I deploy a framework from cross- disciplinary studies that categorizes academic projects that work across disciplines as multidisciplinary, interdisciplinary, or transdisciplinary, depending on the degree of integration. When applied to computer ethics education, this framework is orthogonal to the structure and content of the initiative, as I illustrate using examples of dedicated ethics courses and embedded modules. It therefore highlights additional features of cross-disciplinary teaching that need to be considered when planning a computer ethics programme. I argue that computer ethics education should aim to be at least interdisciplinary—multidisciplinary initiatives are less aligned with the pedagogical aims of computer ethics—and that computer ethics educators should experiment with fully transdisciplinary education that could transform computer science as a whole for the better. (shrink)

Gender-affirming healthcare interventions are medical or surgical interventions that aim to allow trans and non-binary people to better affirm their gender identity. It has been argued that rights to GAH must be grounded in either a right to be cured of or mitigate an illness—gender dysphoria—or in harm prevention, given the high rates of depression and suicide among trans and non-binary people. However, these grounds of a right to GAH conflict with the prevalent view among theorists, institutions and activists that (...) trans and non-binary people do not have a mental illness and that one can be trans and entitled to GAH without being depressed or suicidal. This paper challenges the orthodoxy that a right to GAH must be grounded in either of these ways and instead argues for a right to GAH grounded in a right to live and act with integrity. The standard view, which this paper explains, is that our rights to live and act with integrity ground a right to religious accommodation in many cases such as a right to not be denied social security due to one’s refusal to work a job on a holy day. This paper argues that if our rights to live and act with integrity can ground prima facie rights to religious accommodation, our rights to live and act with integrity ground prima facie rights to GAH. There are no data in this work. (shrink)

This research explores the design-to-production computational framework of a Timber Dowel System, emphasizing an integrated computational workflow informed by fabrication and assembly feedback. This workflow incorporates contextual and fabricability parameters, enabling a parametric framework optimized for performance, adaptability, and scalability. The initial design process utilized a modular plug-and-play approach, employing manually controlled surfaces and poly-surfaces to explore flexible geometries and structural configurations. Two full-scale prototypes were developed using AR-guided assembly and robotic milling, addressing production challenges such as dowel placement precision, (...) material limitations, and assembly feasibility. Feedback from these prototypes informed refinements in the computational framework, translating into practical parameters to improve structural integrity and procedural efficiency. A horizontal orientation was prioritized due to its standalone stability, while multiple extensions of the initial basket prototype were analysed to evaluate and study the system performance under varied geometric configurations. By integrating community-driven requirements and contextual considerations, this research lays the groundwork for a multi-criteria framework that supports informed design variation and facilitates collaborative co-production workflows. (shrink)

BioPortal is a Web portal that provides access to a library of biomedical ontologies and terminologies developed in OWL, RDF(S), OBO format, Protégé frames, and Rich Release Format. BioPortal functionality, driven by a service-oriented architecture, includes the ability to browse, search and visualize ontologies (Figure 1). The Web interface also facilitates community-based participation in the evaluation and evolution of ontology content.

Referent Tracking (RT) advocates the use of instance unique identifiers to refer to the entities comprising the subject matter of patient health records. RT promises many benefits to those who use health record data to improve patient care. To further the adoption of the paradigm we provide an illustration of how data from an EHR application needs to be decomposed in order to make it accord with the tenets of RT. We describe the ontological principles on which this (...) decomposition is based in order to allow integration efforts to be applied in similar ways to other EHR applications. We find that an ordinary statement from an EHR contains a surprising amount of “hidden” data that are only revealed by its decomposition according to these principles. (shrink)

This paper presents a concise analytical review of selected case studies on the integration of multiphysics methods in aerospace structural design, with emphasis on rocket and high-speed vehicle applications. The review synthesizes peer-reviewed publications (2022–2025) indexed in Web of Science and Scopus that address coupled Computational Fluid Dynamics—Finite Element Method (CFD–FEM) analyses, fluid–structure interaction (FSI), conjugate heat transfer (CHT), ablation and thermal protection modeling, and multidisciplinary design optimization (MDO) incorporating surrogate and machine-learning accelerants. Source selection and evaluation prioritized methodological (...) transparency, relevance to structural design tasks, and applicability of results to engineering practice. The surveyed studies indicate that fully coupled CFD–FEM and FSI formulations are capable of reproducing key interactions among aerodynamic, thermal and structural fields, while posing significant numerical and computational demands related to mesh compatibility, stiffness of governing equations and temporal integration. CHT approaches yield improved estimates of interfacial heat fluxes for nozzles and TPS elements when turbulent flow models are combined with wall conduction models. Multidimensional ablation models provide mechanistic insight into material removal processes but require careful treatment of thermochemical coupling and validation against experimental data. Recent developments in multifidelity optimization and gradient-enhanced surrogate models demonstrate potential to reduce computational cost in MDO workflows without discarding essential physics. Given the limited scope inherent to a concise analytical review, conclusions are presented as informed recommendations rather than exhaustive generalizations. Recommended practical measures for engineering application include: explicit consistency of boundary and initial conditions across disciplinary solvers; use of adaptive local mesh refinement in regions of strong gradients; adoption of hybrid coupling strategies (e.g., weak/iterative coupling, multiscale and multifidelity frameworks) to balance accuracy and cost; and integration of surrogate or data-driven models into optimization loops with rigorous uncertainty quantification and targeted validation for TPS, nozzle and thin-walled components. These recommendations aim to support practitioners and researchers seeking to improve predictive capability and computational efficiency in multiphysics design procedures for aerospace systems. (shrink)

This paper explores the integration of multiple intelligences and artificial intelligence (AI) in language learning, focusing on its potential to enhance personalization and engagement. Drawing from existing research and studies conducted in various contexts, including the Philippines, this study aims to contribute to the understanding of the benefits, challenges, and effectiveness of this integration. The paper begins with an introduction that highlights the background and significance of integrating multiple intelligences and AI in language learning, identifying research gaps, objectives, (...) research questions, and the theoretical framework. A literature review provides an overview of multiple intelligences theory by Howard Gardner, the role of AI in language learning, and identifies gaps in the existing literature. The methodology section outlines the research design and approach, participant selection, data collection methods, validity and reliability measures, and ethical considerations. Findings and results are presented through the analysis of qualitative data, exploring emergent themes and patterns. The discussion section critically examines the identified research gaps, discusses the validity and reliability of the study, addresses the scope and limitations, and explores the implications of the findings for theory, practice, and future research. The conclusion summarizes the key findings and contributions of the study, reflects on the achievement of research objectives, offers recommendations for further research, and provides final remarks tying together the main points of the study. This paper contributes to the existing body of knowledge by providing insights into the integration of multiple intelligences and AI in language learning and its impact on personalization, engagement, and language learning outcomes. (shrink)

The visuospatial system integrates inner and outer functional processes, organizing spatial, temporal, and social interactions between the brain, body, and environment. These processes involve sensorimotor networks like the eye–hand circuit, which is especially important to primates, given their reliance on vision and touch as primary sensory modalities and the use of the hands in social and environmental interactions. At the same time, visuospatial cognition is intimately connected with memory, self-awareness, and simulation capacity. In the present article, we review issues associated (...) with investigating visuospatial integration in extinct human groups through the use of anatomical and behavioral data gleaned from the paleontological and archaeological records. In modern humans, paleoneurological analyses have demonstrated noticeable and unique morphological changes in the parietal cortex, a region crucial to visuospatial management. Archaeological data provides information on hand–tool interaction, the spatial behavior of past populations, and their interaction with the environment. Visuospatial integration may represent a critical bridge between extended cognition, self-awareness, and social perception. As such, visuospatial functions are relevant to the hypothesis that human evolution is characterized by changes in brain–body–environment interactions and relations, which enhance integration between internal and external cognitive components through neural plasticity and the development of a specialized embodiment capacity. We therefore advocate the investigation of visuospatial functions in past populations through the paleoneurological study of anatomical elements and archaeological analysis of visuospatial behaviors. (shrink)

This thesis presents Graduated Panpsychism with Information-Integration Spectrum (GP-IIS), a novel theoretical framework that addresses fundamental critiques of both traditional panpsychism and Integrated Information Theory (IIT) while providing empirically testable predictions. GP-IIS resolves IIT's non-uniqueness problem through deterministic mathematical formulations, calibrates consciousness thresholds against empirical Perturbational Complexity Index (PCI) data, and generates specific predictions that distinguish it from competing theories. The framework introduces computable measures of consciousness based on information cascade formation, with a Critical Integration Threshold (CIT) (...) that scales with network properties and maps to the empirically validated PCI threshold of 0.31. Through adversarial testing against recent COGITATE study results and comprehensive prediction tables, GP-IIS demonstrates superior empirical alignment while maintaining theoretical coherence. The framework provides solutions to traditional panpsychist problems including the combination problem and grain problem, while offering practical applications for clinical consciousness assessment and artificial intelligence development. (shrink)

The rise of the Metaverse as a virtual, interconnected world has captured significant attention in recent years. As businesses increasingly recognize the potential of the Metaverse to transform industries, Microsoft Azure stands out as a leading platform for integrating and scaling Metaverse solutions. This paper explores how Azure's cloud infrastructure, advanced computing capabilities, and digital transformation tools enable businesses to integrate with the Metaverse, opening new opportunities for collaboration, customer engagement, and innovation. Additionally, the paper discusses the challenges associated with (...) implementing Metaverse solutions, such as data privacy, interoperability, and infrastructure scalability. Through a comprehensive analysis of Azure's role in supporting the Metaverse, this paper presents key considerations for enterprises looking to build and sustain their ecosystems in this new digital frontier. (shrink)

Modern enterprises increasingly depend on unified data platforms that integrate data ingestion, cloud computing, AI/ML pipelines, and business intelligence across heterogeneous environments. This paper analyzes multi-cloud data integration architectures spanning AWS, Azure, GCP, BigQuery, and Snowflake, evaluating their suitability for healthcare, FinTech, and retail analytics workloads across four dimensions: performance, governance, portability, and cost efficiency. We propose a layered reference architecture connecting ingestion, governed storage, analytics, and AI consumption; define practical evaluation metrics for cost efficiency, (...) class='Hi'>data freshness, and portability overhead; and compare platform ecosystems in terms of workload placement trade-offs. Our analysis demonstrates that no single platform dominates across all requirements. Instead, effective enterprise data platforms combine governed storage, reusable transformation layers, flexible ML services, and domain-specific consumption patterns. Optimal architecture depends on workload volatility, data residency constraints, team capabilities, and the balance between cross-cloud portability and platform-native optimization. These findings motivate a hybrid placement strategy governed by shared metadata, stable data contracts, and clear domain ownership rather than full-stack standardization on any single cloud provider. (shrink)

-/- Integrating Angelito Malicse’s Universal Formula as the Governing Logic of a Resource-Based Economy -/- Abstract This paper explores the integration of Angelito Malicse’s universal formula, which emphasizes natural laws and balance, into a Resource-Based Economy (RBE). The application of Malicse’s formula offers a cohesive framework for managing resources, guiding ethical decision-making, and achieving sustainability by aligning economic systems with ecological limits and human well-being. The transition from profit-driven systems, which often result in environmental degradation and inequality, to an (...) RBE based on feedback loops, systemic thinking, and natural law, is discussed. This paper presents a practical pathway for implementing these principles into governance, technological systems, education, and social frameworks. -/- I. Introduction -/- Throughout history, human economic systems have been structured around profit maximization and capital accumulation, often at the expense of social equity and environmental sustainability. Modern capitalist economies, relying on fiat currency and market competition, have produced negative externalities, such as resource depletion, pollution, and inequality (Korten, 2006). This economic model also generates unsustainable growth, a pursuit that does not align with the finite nature of Earth’s resources (Daly, 1991). -/- A Resource-Based Economy (RBE) offers an alternative where human needs are met within the planet’s carrying capacity. The allocation of resources is managed through technology, scientific principles, and ethical decision-making rather than through monetary systems (Fresco, 2012). This paper argues that Angelito Malicse’s universal formula—which is grounded in balance, feedback mechanisms, and natural laws—can serve as the governing logic of an RBE, providing both the moral and scientific foundation necessary to guide sustainable governance and resource management. By examining the universal formula’s principles and their application to RBE, this paper outlines how such a society can operate effectively, minimizing harm and enhancing human well-being. -/- II. The Universal Formula and Resource-Based Economy: Key Principles -/- 1. The Law of Balance in Nature -/- Central to Malicse’s universal formula is the law of balance in nature, which asserts that all systems, whether natural or artificial, must function in harmony with their environment. This principle calls for humans to operate within the boundaries of ecological sustainability, ensuring that resource use does not exceed regenerative capacities. In capitalist systems, resource extraction and consumption are often dictated by the demand for profit, resulting in environmental degradation and resource depletion (Meadows et al., 2004). This imbalance leads to environmental crises, such as climate change, biodiversity loss, and ocean acidification (Rockström et al., 2009). -/- In an RBE, the law of balance would guide the sustainable use of resources. Every decision, from production to distribution, would be evaluated in terms of its environmental impact and its ability to sustain the system’s ecological health over the long term. The goal is to manage resources within the planetary boundaries while ensuring that the needs of all humans are met (Steffen et al., 2015). -/- 2. Feedback Mechanisms in Decision-Making -/- Malicse’s universal formula emphasizes the importance of feedback mechanisms in decision-making processes. This concept aligns with cybernetic theory, where systems are viewed as self-regulating through continuous feedback loops (Wiener, 1948). In the context of an RBE, feedback mechanisms ensure that resource management and societal functions are responsive to both human and ecological needs. -/- Technological systems, such as smart grids, environmental sensors, and data-driven agricultural systems, can collect real-time data on resource use, environmental conditions, and societal needs. This information feeds into decision-making processes, enabling real-time adjustments in resource allocation and use. By utilizing technology to monitor environmental and societal conditions, feedback ensures that the RBE operates in harmony with its ecological and social context, preventing wasteful practices and correcting imbalances as they occur. -/- 3. Decision-Making Governed by Natural Laws -/- The third key principle of Malicse’s universal formula is that decision-making must be grounded in natural laws, particularly the law of balance and the cause-and-effect principle. In a traditional market economy, decisions are often driven by profit motives and speculative financial interests, which can lead to unsustainable practices and social inequality. These systems disregard the long-term consequences of their actions, which leads to environmental damage, economic instability, and social unrest (Korten, 2006). -/- In an RBE governed by Malicse’s formula, all decisions—whether they concern resource allocation, infrastructure development, or social programs—must adhere to the principles of natural law. This ensures that all actions are aligned with ecological sustainability and social well-being. For instance, resource extraction decisions would not be made based on short-term profit but based on ecological science and long-term sustainability, ensuring that the Earth’s natural capital is preserved for future generations. -/- III. Integrating the Universal Formula into RBE Governance -/- 1. Role of the Universal Formula as a Constitutional Principle -/- In an RBE, Malicse’s universal formula would serve as the core constitutional principle that guides all governance structures. Just as national constitutions enshrine fundamental values such as justice, liberty, and equality, the universal formula would enshrine principles of ecological balance, social equity, and ethical decision-making. All public policies, economic models, and technological innovations would be evaluated through the lens of Malicse’s formula to ensure they contribute to a sustainable, just, and balanced society. -/- For instance, urban development projects would be designed to minimize environmental impact, incorporate renewable energy sources, and meet the needs of all community members. Policies would be implemented to ensure that economic activities are in line with the carrying capacity of the planet, and that human activities do not exceed ecological thresholds (Rockström et al., 2009). -/- 2. The Universal Formula in AI and Technological Systems -/- AI and technological systems would play a critical role in an RBE governed by natural laws. Under Malicse’s formula, AI systems and automated processes would be programmed to prioritize the principles of balance and feedback. For example, resource allocation algorithms could ensure that resources are distributed equitably and sustainably, while environmental monitoring systems could adjust production practices based on real-time data about resource availability and ecological health. -/- This concept is supported by cybernetic theory, where systems are designed to optimize based on feedback from their environment. By using AI and sensors, the RBE would be able to respond dynamically to changing ecological and societal conditions, continuously adjusting policies and practices to maintain balance. -/- 3. The Universal Formula as the Educational Foundation -/- For the principles of Malicse’s universal formula to be successfully implemented, they must first be ingrained in educational systems. Current educational frameworks often prioritize competition, individualism, and economic growth—values that are incongruent with the principles of sustainability and collective well-being (Orr, 1992). In an RBE, however, systems thinking and ecological literacy would form the foundation of education. -/- Students would be taught not only about scientific principles and natural laws, but also about the ethical responsibility humans have to maintain balance with the Earth’s ecosystems. This foundational education would produce future leaders, technocrats, and citizens who understand that decision-making must align with the long-term health of both society and the planet. By instilling these values from an early age, society can create a population that is equipped to address the complex challenges of living sustainably within ecological limits (Davidson, 2017). -/- IV. Transitioning from Capitalism to a Resource-Based Economy -/- 1. Educational and Technological Transformation -/- The transition from a capitalist model to an RBE based on Malicse’s formula would require both educational and technological transformations. First, the educational system would need to be restructured to teach systems thinking, ecological balance, and ethical decision-making. Schools, universities, and training programs would focus on sustainability, cooperation, and resource management to prepare individuals for their roles in a resource-based society. -/- Parallel to educational reform, technology would play a crucial role in managing resources efficiently. The technological infrastructure of an RBE would rely on feedback-driven systems, such as automated resource allocation, smart infrastructure, and sustainability-monitoring technologies. These technologies would ensure that resource use is optimized and that the economy operates within ecological limits. -/- 2. Governance and Policy Reform -/- In terms of governance, the transition to an RBE would involve policy reform that aligns with the principles of balance, feedback, and natural law. Governments would be responsible for ensuring that policies foster sustainability, equity, and environmental health. Policies would also ensure that social programs, such as healthcare, education, and public services, are accessible to all and are aligned with ecological sustainability. -/- Additionally, governance structures would need to move away from profit-driven politics and toward a more ethical, cooperative, and feedback-based decision-making system. The goal would be to create a political system that prioritizes the well-being of all people and the health of the planet (Raworth, 2017). -/- V. Conclusion -/- The integration of Angelito Malicse’s universal formula into a Resource-Based Economy offers a robust framework for creating a society that operates within ecological limits, promotes human well-being, and adheres to natural laws. By emphasizing balance, feedback, and natural law, this approach provides an alternative to the profit-driven, unsustainable economic systems that dominate the world today. The successful implementation of this framework requires education reform, technological innovation, and policy change to align societal practices with the values of sustainability and cooperation. Ultimately, the universal formula offers a pathway to a just, sustainable, and equitable future, where human activities are harmonized with the natural world. -/- References -/- Daly, H. E. (1991). Steady-State Economics: The Economics of Biophysical Equilibrium and Moral Growth. -/- Davidson, C. (2017). Ecological Literacy: Education and the Transition to a Postmodern World. -/- Fresco, J. (2012). The Venus Project: The Redesign of a Culture. -/- Korten, D. C. (2006). The Great Turning: From Empire to Earth Community. -/- Meadows, D. H., Meadows, D. L., & Randers, J. (2004). Limits to Growth: The 30-Year Update. -/- Raworth, K. (2017). Doughnut Economics: Seven Ways to Think Like a 21st-Century Economist. -/- Rockström, J., et al. (2009). A Safe Operating Space for Humanity. Nature, 461(7263), 472-475. -/- Wiener, N. (1948). Cybernetics: Or Control and Communication in the Animal and the Machine. -/- . (shrink)

In the modern business landscape, effective Human Capital Management (HCM) is pivotal for organizational success. The integration of HCM tools into existing HR systems is a strategic endeavor that can enhance workforce management, streamline HR processes, and foster employee engagement and productivity. This paper explores best practices for integrating HCM tools, highlighting key considerations such as system compatibility, data migration, user training, and change management. Through an extensive literature review and analysis of real-world case studies, the study demonstrates (...) how successful integration can lead to significant improvements in HR operations and overall organizational performance. Additionally, the paper identifies common challenges encountered during the integration process and proposes strategies to mitigate them. The findings underscore the importance of a structured approach to HCM tool integration, emphasizing the role of leadership, stakeholder engagement, and continuous evaluation. Future research directions are suggested to further enhance the integration frameworks, incorporating advancements in artificial intelligence (AI) and machine learning (ML) to drive more intelligent and adaptive HCM systems. The insights provided aim to equip HR professionals and organizational leaders with the knowledge to navigate the complexities of HCM tool integration, ultimately fostering a more agile, efficient, and engaged workforce. (shrink)

Persistent reports from the West African Examinations Council (WAEC) and the National Examinations Council (NECO) reveal that secondary school students in Nigeria struggle to grasp abstract concepts in Biology, particularly in areas such as genetics, ecology, and cellular respiration. At the same time, students in South- East Nigeria exhibit a profound, practical understanding of local flora and fauna (for instance, the medicinal applications of Vernonia amygdalina (bitter leaf), Xylopia aethiopica (uda), and Ocimum gratissimum (scent leaf)), which they have acquired through (...) Indigenous Knowledge Systems (IKS). Nevertheless, this ethnobiological knowledge is largely disconnected from formal educational settings. This study aimed to explore the degree to which the incorporation of Igbo ethnobiological knowledge into the teaching of challenging Biology topics enhances students' conceptual understanding, engagement, and long-term retention. A quasi-experimental pretest-posttest non-equivalent control group design was utilized. The participants consisted of 186 senior secondary school year two (SS2) students from four co-educational public schools located in Enugu and Anambra States (two experimental groups and two control groups). The experimental groups underwent a 6-week IKS-integrated Biology instruction, which included culturally contextualized lessons, indigenous classification tasks, ethno-medicinal plant analysis, and local ecological observations, while the control groups received traditional lecture-based instruction. The instruments used for assessment included the Biology Conceptual Understanding Test (BCUT, comprising 35 multiple-choice and 15 short-answer items, r=0.87), the Engagement Inventory (EI, a 5-point Likert scale, α=0.91), and the Delayed Retention Test (identical to the BCUT, administered 6 weeks after the intervention). Qualitative data were gathered from 12 focus group discussions to complement the quantitative findings. The results of the ANCOVA indicated that students in the IKS-integrated group significantly outperformed those in the control group in terms of conceptual understanding (mean experimental=72.4%, SD=9.6; control=48.3%, SD=11.2; F(1,183) =146.3, p<0.001, η²=0.44). Qualitative findings indicated that students perceived the integration of Indigenous Knowledge Systems (IKS) as "meaningful," "easier to recall," and "linking school to home." The incorporation of ethno-biology notably enhances the understanding of abstract concepts in Biology, boosts student engagement, and aids in the retention of knowledge over the long term. The study advocates for the establishment of a formal ethno-biology curriculum for senior secondary Biology, the professional development of teachers in IKS integration, and the acknowledgment of Indigenous knowledge as a valid pedagogical framework in policy. (shrink)

-/- The Integration of Angelito Malicse’s Universal Formula with Quantum Computer Design, AGI Algorithmic Design, and Education -/- In the pursuit of developing intelligent systems, the realms of quantum computing, artificial general intelligence (AGI), and educational frameworks face the significant challenge of balancing complex feedback mechanisms, ethical decision-making, and system stability. The universal formula developed by Angelito Malicse provides a pioneering approach to understanding free will, human behavior, and decision-making. His three laws, deeply rooted in the concept of natural (...) balance, offer valuable insights into addressing problems related to technological systems, such as quantum computers and AGI. This essay explores how Malicse’s universal formula can be integrated into quantum computer design, AGI algorithmic development, and educational models, emphasizing the importance of feedback loops, balance, and ethical decision-making as critical components in these fields. -/- The Universal Formula: An Overview -/- Angelito Malicse’s universal formula consists of three key laws: -/- 1. The Law of Karma (Cause and Effect): This law posits that every action leads to a consequence. It emphasizes that systems—whether natural or man-made—must function without defects to ensure they operate effectively and safely. Any imbalance or disruption in the system causes dysfunction, whether the system is biological, mechanical, or computational. -/- 2. The Homeostasis Principle: This principle asserts that systems—be they biological, technological, or social—must maintain equilibrium to function correctly. When an imbalance occurs, the system seeks to restore balance. This is a critical process in all functional systems, from the human body to technological networks. -/- 3. The Ethical Decision-Making Law: This law underscores the importance of ethical decision-making within systems. It suggests that choices must promote balance and avoid disrupting the natural harmony of the system. If decisions violate this balance, they result in negative consequences, reflecting the idea that imbalance leads to dysfunction in both societal and technological systems. -/- These principles, while addressing human behavior and societal functioning, also have profound implications for the development of quantum computing and AGI, areas that are critical to future advancements in technology. -/- Quantum Computing and the Universal Formula -/- Quantum computing represents a revolutionary leap in computational power, utilizing quantum mechanics principles—such as superposition, entanglement, and interference—to solve problems in parallel. Quantum systems, however, are highly sensitive to errors and disturbances, which could affect their ability to function reliably. Thus, understanding and applying the universal formula to quantum computer design is essential. -/- Quantum Causal Inference and the Law of Karma -/- Quantum computing is built on probabilistic models where each quantum state evolves based on prior interactions, an approach that mirrors the concept of cause and effect. Quantum systems follow causal inference, meaning that each quantum operation is influenced by the previous one. This aligns directly with the Law of Karma—every operation in a quantum computer has consequences, which can either lead to a stable state or introduce error. If the system is defective or disrupted, it results in instability, just as a defective system in nature or society fails to function correctly. -/- Quantum algorithms, such as those used in quantum machine learning, rely heavily on cause-effect relationships to guide the computation. The delicate balance between quantum states and operations mirrors the need for balance in natural systems: if a quantum system is not free from errors, its performance will degrade, reflecting Malicse’s principle that systems must be free of defects to operate properly. -/- Quantum Neural Networks (QNNs) and Feedback Mechanisms -/- Quantum Neural Networks (QNNs) aim to replicate the neural network structure of the human brain using quantum bits (qubits) and quantum gates. These networks utilize feedback loops, where the system continuously adapts and updates based on prior outputs, enhancing its learning process. This closely aligns with Malicse’s feedback loop principle, which emphasizes the importance of continuous adjustment based on environmental feedback. -/- QNNs utilize quantum feedback to optimize learning. However, just like biological systems, they require feedback that operates in balance, ensuring that each decision made by the system aligns with previous learnings and the larger goal of the system. For a quantum system to achieve reliable performance, the feedback mechanism must be balanced and precise, aligning with Malicse’s view that all systems—whether biological or technological—must act to restore balance when disrupted. -/- Homeostasis in Quantum Systems -/- The ability of quantum systems to maintain homeostasis—to return to a balanced state after a disturbance—is another vital aspect of quantum computing. Quantum error correction algorithms play a central role in ensuring that quantum states do not deviate significantly from their intended positions due to noise or errors. These mechanisms allow quantum systems to maintain internal equilibrium despite external interference, mirroring the concept of homeostasis in biological systems. -/- Malicse’s Homeostasis Principle is directly applicable here: just as biological systems restore balance to ensure survival, quantum systems require constant error-correction and recalibration to maintain balance. This principle ensures that quantum computations are accurate and that imbalances (such as errors or instabilities) are corrected swiftly, maintaining the overall stability of the quantum system. -/- AGI and the Universal Formula -/- The field of AGI seeks to develop machines capable of generalizing knowledge and solving complex problems across multiple domains. As AGI systems evolve, the challenge lies in ensuring they adhere to ethical principles and maintain balance within their decision-making processes. The universal formula offers guidance on how AGI can be structured to make decisions that reflect the principles of natural balance. -/- Feedback Mechanisms and Ethical Decision-Making -/- One of the core challenges of AGI is ensuring that it can make decisions ethically, even when faced with complex, ambiguous situations. AGI systems learn from feedback, adapting their decisions based on both the data they receive and the ethics embedded within their algorithms. The feedback loop in AGI is similar to that of human decision-making—data inputs (environmental feedback) are processed, and decisions are refined to optimize outcomes. -/- Malicse’s Ethical Decision-Making Law can be applied to AGI systems to ensure that their actions promote balance and avoid harm. For example, an AGI system designed for healthcare must balance patient needs with resource availability, ensuring that decisions benefit society while maintaining fairness and equity. If the AGI violates this balance—say, by favoring one group over another—it could result in negative consequences, such as social inequality or resource misallocation. -/- Homeostasis in AGI Systems -/- Much like quantum systems, AGI must strive for homeostasis to maintain long-term operational stability. AGI should be able to detect imbalances in its decision-making process and correct them accordingly. For instance, an AGI operating in a financial system must adjust its strategies based on market feedback, ensuring that it consistently contributes to the stability of the market, without causing disruptive cycles. -/- Integrating Malicse’s Homeostasis Principle in AGI ensures that these systems are capable of continuous adaptation, balancing inputs and outputs in a way that prevents harmful outcomes. In decision-making, this means the AGI will always strive for balance, ensuring that no decision undermines the overall health of the system, whether it’s societal, economic, or environmental. -/- The Role of Education in Promoting Natural Balance -/- Educational systems are fundamental to shaping the leaders and innovators of the future. By incorporating the principles of natural balance, ethical decision-making, and feedback-based learning, we can produce individuals capable of understanding the complexities of quantum systems and AGI. -/- Malicse’s universal formula should be integrated into the educational curriculum at all levels, fostering a generation of thinkers who can balance cause and effect, understand feedback loops, and navigate the ethical challenges of technology. Through this holistic education, students will be better equipped to understand and contribute to the design of future technologies, ensuring that they maintain harmony with natural systems and societal needs. -/- Conclusion -/- Angelito Malicse’s universal formula offers a profound framework for understanding the fundamental principles of decision-making, both in human and computational systems. By integrating these principles into the design of quantum computers and AGI systems, we can create technologies that adhere to the natural laws of balance and ethics, preventing harmful outcomes and ensuring stable, sustainable progress. -/- In the realms of quantum computing and AGI, the application of the Law of Karma, Homeostasis, and Ethical Decision-Making will ensure that these advanced systems work in harmony with their environments, just as natural systems do. Further, by embedding these principles into educational curricula, we can train future generations to harness the power of these technologies in a way that benefits society, promotes fairness, and sustains the natural balance of the universe. -/- By applying Malicse’s universal formula across these domains, we can foster a future where technology and humanity work together in a sustainable, ethical, and balanced way. -/- . (shrink)

This article presents a comprehensive framework for building a strong foundation in blockchain technology and data analytics, two rapidly evolving fields that are increasingly intertwined in today's digital landscape. We propose a structured approach that combines theoretical knowledge acquisition with practical skill development, designed to equip aspiring professionals with the necessary tools to succeed in this dynamic sector. The framework encompasses key areas such as blockchain fundamentals, smart contracts, cryptography, data analysis techniques, and advanced machine learning concepts including (...) neural networks, deep learning, and artificial intelligence. We emphasize the importance of leveraging diverse educational resources while highlighting the critical role of hands-on projects in reinforcing learning outcomes. By detailing strategies for developing blockchain applications and conducting advanced data analytics, this article bridges the gap between academic understanding and practical application. We explore real-world use cases demonstrating the synergy between blockchain and AI in areas such as financial crime prevention, supply chain management, and healthcare. Our findings suggest that this integrated approach not only enhances technical proficiency but also cultivates the problem-solving and analytical skills essential for advancing towards professional roles in blockchain and data analytics. This research contributes to the growing body of literature on technology education and provides a roadmap for individuals seeking to establish a robust foundation in these interconnected fields. (shrink)

Water is an essential resource for domestic, agricultural and industrial activities, especially in areas where surface water supply is scarce. But growing population, climate change and inadequate subsurface information has resulted in borehole failures and ineHicient use of groundwater resources in places like Obukpa watershed, Enugu State. The research seeks to solve the issues of uncertain borehole selection due to insuHicient knowledge of the hydrogeology and singular methods of groundwater exploration. This research is warranted by the need to increase the (...) success rate of groundwater exploration through an integrated geoelectric and hydrogeological modelling technique that will minimise uncertainties and optimise sustainable water resource development. The research objectives were to map the lithology, aquifers, estimate hydrogeological parameters, classify aquifer productivity, integrate a model, and establish borehole drilling depths. Primary data sources were Vertical Electrical Sounding (VES) using the Schlumberger array, while secondary data included hydrogeological information. Methods applied were log₁₀ resistivity transformation, regression analysis, rule-based classification, productivity index modelling and model diagnostics. This identified a layered subsurface with resistivity values between 538.74-5387 Ω·m, and a highly productive saturated sand unit at 203.43-253.43 m with resistivity of 1315.8 Ω·m (log₁₀ = 3.119), and productivity index of 1.0995, which corresponds to an excellent aquifer. The regression model explained 92.09% variability (R²) of the data but was not significant at p = 0.1163. The best drilling depth was found to be between 198-213 m (average 205.5 m). The research concludes that integrated modelling enhances aquifer identification and borehole productivity. It calls for mandatory pre-drilling geophysical studies and groundwater databases to manage the resource. (shrink)

This paper presents a comprehensive review of artificial intelligence (AI) applications in food safety and quality control, focusing on emerging technologies including generative AI, agentic AI systems, and automated compliance solutions. This review synthesizes current research and industry applications, highlighting how AI-driven systems are transforming food safety protocols, enhancing regulatory compliance, and improving overall food quality management. We examine various AI implementations, from optical imaging for bacterial detection to intelligent compliance agents and generative AI for supply chain optimization. This paper (...) synthesizes current research and industry applications across multiple domains: automated visual inspection systems that detect contaminants with precision exceeding human capabilities; predictive quality analytics that forecast potential safety issues before they manifest; AI-driven regulatory compliance systems that continuously monitor and interpret complex regulatory requirements; and autonomous agentic systems that make real-time decisions without human intervention. The review also addresses significant technological innovations, including the FDA's development of AI tools for regulatory operations, generative AI applications for scenario planning and documentation, and cloud-based AI architectures deployed across major platforms. Critical challenges are examined, including data quality requirements, regulatory validation frameworks, system integration complexities, and ethical considerations. The paper concludes with policy recommendations for government implementation, proposing structured approaches to AI validation, data sharing incentives, regulatory modernization, research support, and ethical oversight. A dedicated response to FSIS-2025-0145 demonstrates how AI technologies can address overtime inspection fee challenges through remote monitoring and automated compliance. (shrink)

This paper provides a comprehensive review of Artificial Intelligence (AI) integration in K-12 education, examining current implementations, policy frameworks, and emerging challenges. We analyze over 40 recent publications (2024-2025) from academic journals, government reports, and industry whitepapers to identify key trends in AI adoption across primary and secondary education systems. This paper presents a comprehensive review of Artificial Intelligence (AI) integration in K-12 education, examining its pedagogical, technical, and policy dimensions. Through an analysis of recent literature, we highlight (...) Generative AI as the most widely adopted paradigm in educational settings, with Agentic AI emerging as a significant secondary focus. The review identifies key trends in architectural approaches while noting underrepresented technical frameworks. Our review reveals three critical dimensions of AI in education: (1) pedagogical applications including personalized learning and administrative automation, (2) policy and ethical considerations at federal and state levels, and (3) infrastructure requirements for successful implementation. We highlight the rapid growth of Generative AI (GenAI) tools in classrooms alongside persistent concerns about equity, data privacy, and teacher preparedness. We summarize a conceptual framework for evaluating educational AI systems that balances pedagogical value with implementation considerations. This systematic review examines Artificial Intelligence (AI) integration in K-12 education through pedagogical, technical, and policy lenses. (shrink)