Although the idea of the Anthropocene originated in the earth sciences, there have been increasing calls for questions about the Anthropocene to be addressed by pan-disciplinary groups of researchers from across the natural sciences, social sciences, and humanities. We use data analysis techniques from corpus linguistics to examine academic texts about the Anthropocene from these disciplinary families. We read the data to suggest that barriers to a broadly interdisciplinary study of the Anthropocene are high, but we are also (...) able to identify some areas of common ground that could serve as interdisciplinary bridges. (shrink)

This approach significantly enhances patient care by minimizing delays in response and improving overall health outcomes. The system's architecture, based on big data frameworks, supports scalable and efficient data processing. The study demonstrates how the integration of predictive models and data visualization tools can revolutionize health alert systems, making them more responsive and adaptive to individual patient needs. Future enhancements will focus on incorporating machine learning models for more personalized predictions and extending the system's capabilities to remote (...) patient care. (shrink)

By embracing AI-driven HR analytics, organizations can anticipate market shifts, prepare their workforce for future challenges, and stay ahead of the competition. This study outlines the essential components of AI-driven HR analytics, demonstrates its impact on workforce agility, and concludes with potential future enhancements to further optimize HR functions. Key words: Predictive Workforce Analytics, Talent Optimization, Machine Learning in.

Chronic Kidney Disease (CKD) is a significant global health issue, often leading to kidney failure and requiring costly medical treatments such as dialysis or transplants. Early detection of CKD is essential for timely intervention and improved patient outcomes. This project aims to develop a machine learning-based predictive model for diagnosing CKD at an early stage. By utilizing a range of clinical features such as age, blood pressure, blood sugar, and other relevant biomarkers, we employ machine learning algorithms, including Decision Trees, (...) Random Forests, and Support Vector Machines (SVM), to predict the likelihood of a patient developing CKD. (shrink)

Data increasingly drive our lives. Often presented as a new trajectory, the deep immersion of our lives in data has a history that is well over a century old. By revisiting the work of early pioneers of what would today be called data science, we can bring into view both assumptions that fund our data-driven moment as well as alternative relations to data. I here excavate insights by contrasting a seemingly unlikely pair of (...) early data technologists, Francis Galton and W.E.B. Du Bois. Galton, well known for his contributions to eugenics, was first and foremost a tinkering technician of measure. There are numerous domains of science over which Galtonian conceptions retain considerable influence, presumably without his pride in racial inequality. A more viable, because more egalitarian, alternative for the present can be found in the early data work of Du Bois. (shrink)

The concept of “life” certainly is of some use to distinguish birds and beavers from water and stones. This pragmatic usefulness has led to its construal as a categorical predicate that can sift out living entities from non-living ones depending on their possessing specific properties—reproduction, metabolism, evolvability etc. In this paper, we argue against this binary construal of life. Using text-mining methods across over 30,000 scientific articles, we defend instead a degrees-of-life view and show how these methods can contribute to (...) experimental philosophy of science and concept explication. We apply topic-modeling algorithms to identify which specific properties are attributed to a target set of entities (bacteria, archaea, viruses, prions, plasmids, phages and the molecule of adenine). Eight major clusters of properties were identified together with their relative relevance for each target entity (two that relate to metabolism and catalysis, one to genetics, one to evolvability, one to structure, and—rather unexpectedly—three that concern interactions with the environment broadly construed). While aligning with intuitions—for instance about viruses being less alive than bacteria—these quantitative results also reveal differential degrees of performance that have so far remained elusive or overlooked. Taken together, these analyses provide a conceptual “lifeness space” that makes it possible to move away from a categorical construal of life by empirically assessing the relative lifeness of more-or-less alive entities. (shrink)

The dataset used in this study includes medical records of patients with various kidney conditions, and preprocessing techniques such as normalization and missing data handling are applied to ensure the model’s robustness. The performance of the model is evaluated using metrics such as accuracy, precision, recall, and F1-score to ensure reliable predictions. This approach not only aims to improve diagnostic accuracy but also provides a data-driven solution to assist healthcare professionals in making informed decisions. The outcome of (...) this project can contribute to better management of CKD, ultimately helping to reduce the burden on healthcare systems and improving patient care. (shrink)

Machine learning (ML) has emerged as a powerful tool for transforming data-driven decision-making across various industries. By leveraging large volumes of data and advanced algorithms, machine learning models can uncover insights, make predictions, and enable businesses to make more informed decisions. This paper explores how machine learning is revolutionizing decision-making processes, enhancing efficiency, accuracy, and predictive capabilities. It also examines the key challenges, opportunities, and future directions for the integration of machine learning into decision-making frameworks.

Cho and Martinez-Martin provide a wide-ranging analysis of what they label “digital simulacra”—which are in essence data-driven AI-based simulation models such as digital twins or models used for i...

As the integration of Artificial Intelligence (AI) and cloud computing transforms the insurance industry, it is undergoing a major breakthrough. With these technologies, insurers can modernize operations, improve the customer experience and make better decisions using real time data and predictive analytics. This paper aims to explore why AI and cloud play such critical roles in shifting insurance practice from legacy systems modernization, to data governance and regulatory compliance to workforce readiness. Today, world-class AI powered tools and cloud (...) platforms such as AWS and Snowflake provide hyper personalization, one-touch automated claims, fraud detection, etc. IoT data can be used to perform dynamic risk assessment for insurers and even to offer products that start with low code and no code solutions to make technology available across an organization. Edge computing, as well as sustainability across insurance products, shows that there is more innovation potential. Yet, these technologies cannot be fully integrated. However, the industry must figure out how to navigate evolving regulations, employ explainability in AI-driven decisions, and address vendor dependency issues. Governance frameworks need to be strengthened;the workforce needs to be upskilled, collaborate with InsurTech startups, and so on. The paper ends with a forward-looking view, offering potential expansion into underserved markets, more advanced predictive analytics, and better customer contact through omnichannel support via AI enabled risk coaching. Embracing these advances, insurers can chart a new course and lead the digital transformation era by providing more value to stakeholders in an evolving environment. (shrink)

This paper explores the challenges and innovations in optimizing data science workflows within cloud computing environments. It begins by highlighting the critical role of data science in modern industries and the pivotal contribution of cloud computing in enabling scalable and efficient data processing. The primary focus lies in identifying and analyzing the key challenges encountered in current data science workflows deployed in cloud infrastructures. These challenges include scalability issues related to handling large volumes (...) of data, resource management complexities in optimizing computational resources, cost management strategies to balance performance with expenses, and ensuring robust data security and privacy measures. The manuscript then delves into innovative solutions and techniques aimed at addressing these challenges. It discusses advancements such as workflow automation tools and frameworks that streamline repetitive tasks, containerization technologies like Docker and Kubernetes for efficient application deployment and management, and the utilization of serverless architectures to enhance scalability and reduce operational costs. Additionally, it explores the benefits of parallel processing frameworks such as Apache Spark and Hadoop in optimizing data processing tasks. The integration of machine learning algorithms for dynamic workflow optimization and effective data management strategies in cloud environments are also examined. Through detailed case studies and application examples across various domains, the manuscript illustrates the practical implementation and outcomes of these optimization strategies. Furthermore, it discusses emerging trends in cloud technologies, the role of AI-driven automation in enhancing workflow efficiencies, and ethical considerations surrounding data science operations in cloud computing. The manuscript concludes with a summary of findings, practical recommendations for organizations seeking to enhance their data science workflows in the cloud, and insights into future research directions to address evolving challenges. (shrink)

In modern data centers, managing the distribution of workloads efficiently is crucial for ensuring optimal performance and meeting Service Level Agreements (SLAs). Load balancing algorithms play a vital role in this process by distributing workloads across computing resources to avoid overloading any single resource. However, the effectiveness of these algorithms can be significantly enhanced through the integration of advanced optimization techniques. This paper proposes an SLA-driven load balancing algorithm optimized using methods such as genetic algorithms, particle swarm optimization, (...) and simulated annealing. By focusing on both resource utilization and SLA compliance, the proposed approach aims to reduce latency, improve throughput, and maximize overall system efficiency. The research introduces a novel framework that incorporates real-time monitoring, dynamic resource allocation, and adaptive threshold settings to ensure consistent SLA adherence while optimizing computing performance. (shrink)

Recently, it has been argued that the use of Big Data transforms the sciences, making data-driven research possible and studying causality redundant. In this paper, I focus on the claim on causal knowledge by examining the Big Data project EXPOsOMICS, whose research is funded by the European Commission and considered capable of improving our understanding of the relation between exposure and disease. While EXPOsOMICS may seem the perfect exemplification of the data-driven view, I show (...) how causal knowledge is necessary for the project, both as a source for handling complexity and as an output for meeting the project’s goals. Consequently, I argue that data-driven claims about causality are fundamentally flawed and causal knowledge should be considered a necessary aspect of Big Data science. In addition, I present the consequences of this result on other data-driven claims, concerning the role of theoretical considerations. I argue that the importance of causal knowledge and other kinds of theoretical engagement in EXPOsOMICS undermine theory-free accounts and suggest alternative ways of framing science based on Big Data. (shrink)

The research introduces a novel framework that incorporates real-time monitoring, dynamic resource allocation, and adaptive threshold settings to ensure consistent SLA adherence while optimizing computing performance. Extensive simulations are conducted using synthetic and real-world datasets to evaluate the performance of the proposed algorithm. The results demonstrate that the optimized load balancing approach outperforms traditional algorithms in terms of SLA compliance, resource utilization, and energy efficiency. The findings suggest that the integration of optimization techniques into load balancing algorithms can significantly enhance (...) the operational efficiency of data centers, paving the way for future advancements in autonomous and self-optimizing data centers. (shrink)

In Husserl’s Missing Technologies, Don Ihde urges us to think deeply and critically about the ways in which the technologies utilized in contemporary science structure the way we perceive and understand the natural world. In this paper, I argue that we ought to extend Ihde’s analysis to consider how such technologies are changing the way we perceive and understand ourselves too. For it is not only the natural or “hard” sciences which are turning to advanced technologies for help in (...) carrying out their work, but also the social and “human” sciences. One set of tools in particular is rapidly being adopted—the family of information technologies that fall under the umbrella of “big data.” As in the natural sciences, big data is giving researchers in the human sciences access to phenomena which they would otherwise be unable to experience and investigate. And like the former, the latter thereby shape the ways those scientists perceive and understand who and what we are. Looking at two case studies of big data-driven research in the human sciences, I begin in this paper to suggest how we might understand these phenomenological and hermeneutic changes. (shrink)

The exponential growth of data storage requirements has become a pressing challenge in hybrid cloud environments, necessitating efficient data deduplication methods. This research proposes a novel Smart Deduplication Framework (SDF) designed to identify and eliminate redundant data, thus optimizing storage usage and improving data retrieval speeds. The framework leverages a hybrid cloud architecture, combining the scalability of public clouds with the security of private clouds. By employing a combination of client-side hashing, metadata indexing, and machine learning-based (...) duplicate detection, the framework achieves significant storage savings without compromising data integrity. Real-time testing on a hybrid cloud setup demonstrated a 65% reduction in storage needs and a 40% improvement in data retrieval times. Additionally, the system employs blockchain for immutable logging of deduplication activities, enhancing transparency and traceability. This study concludes with an evaluation of the deduplication framework's impact on cost efficiency, system performance, and potential scalability. Future enhancements aim to integrate multi-cloud interoperability and advanced compression algorithms to further refine storage management. (shrink)

In this paper, I critically evaluate several related, provocative claims made by proponents of data-intensive science and “Big Data” which bear on scientific methodology, especially the claim that scientists will soon no longer have any use for familiar concepts like causation and explanation. After introducing the issue, in Section 2, I elaborate on the alleged changes to scientific method that feature prominently in discussions of Big Data. In Section 3, I argue that these methodological claims are (...) in tension with a prominent account of scientific method, often called “Inference to the Best Explanation”. Later on, in Section 3, I consider an argument against IBE that will be congenial to proponents of Big Data, namely, the argument due to Roche and Sober Analysis, 73:659–668, that “explanatoriness is evidentially irrelevant.” This argument is based on Bayesianism, one of the most prominent general accounts of theory-confirmation. In Section 4, I consider some extant responses to this argument, especially that of Climenhaga Philosophy of Science, 84:359–368,. In Section 5, I argue that Roche and Sober’s argument does not show that explanatory reasoning is dispensable. In Section 6, I argue that there is good reason to think explanatory reasoning will continue to prove indispensable in scientific practice. Drawing on Cicero’s oft-neglected De Divinatione, I formulate what I call the “Ciceronian Causal-nomological Requirement”, which states, roughly, that causal-nomological knowledge is essential for relying on correlations in predictive inference. I defend a version of the CCR by appealing to the challenge of “spurious correlations,” chance correlations which we should not rely upon for predictive inference. In Section 7, I offer some concluding remarks. (shrink)

How a senior researcher from a developing country can build an organic academic enterprise. Drawing from childhood experience with nature, past works with the business sector, and philosophy of data-driven research, the essay presents a compelling case of letting young graduates work on big database-building projects: one on Vietnamese social sciences; the other is more than 80 years of the pioneer science in Vietnam—mathematics. Two national databases have enabled meaningful data-driven interactions with scientific policymakers.

The Emotion Ontology is an ontology covering all aspects of emotional and affective mental functioning. It is being developed following the principles of the OBO Foundry and Ontological Realism. This means that in compiling the ontology, we emphasize the importance of the nature of the entities in reality that the ontology is describing. One of the ways in which realism-based ontologies are being successfully used within biomedical science is in the annotation of scientific research results in publicly available databases. (...) Such annotation enables several objectives, including searching, browsing and cross-database data integration. A key benefit conferred by realismbased ontology is that suitably annotated research results are able to be aggregated and compared in a fashion that is based on the underlying reality that the science is studying. This has the potential of increasing the power of statistical analysis and meta-analysis in data-driven science. This aspect has been fruitfully exploited in the investigation of the functions of genes in molecular biology. Cognitive neuroscience uses functional neuroimaging to investigate the brain correlates of areas of mental functioning such as memory, planning and emotion. The use of functional neuroimaging to study affective phenomena such as the emotions is called ‘affective neuroscience’. BrainMap is the largest curated database of coordinates and metadata for studies in cognitive neuroscience, including affective neuroscience (Laird et al., 2005). BrainMap data is already classified and indexed using a terminology for classification, called the ‘Cognitive Paradigm Ontology’ (CogPO), that has been developed to facilitate searching and browsing. However, CogPO has been developed specifically for the BrainMap database, and the data are thus far not annotated to a realism-based ontology which would allow the discovery of interrelationships between research results across different databases on the basis of what the research is about. In this contribution, we describe ongoing work that aims to annotate affective neuroscience data, starting with the BrainMap database, using the Emotion Ontology. We describe our objectives and technical approach to the annotation, and mention some of the challenges. (shrink)

In modern data centers, managing the distribution of workloads efficiently is crucial for ensuring optimal performance and meeting Service Level Agreements (SLAs). Load balancing algorithms play a vital role in this process by distributing workloads across computing resources to avoid overloading any single resource. However, the effectiveness of these algorithms can be significantly enhanced through the integration of advanced optimization techniques. This paper proposes an SLA-driven load balancing algorithm optimized using methods such as genetic algorithms, particle swarm optimization, (...) and simulated annealing. By focusing on both resource utilization and SLA compliance, the proposed approach aims to reduce latency, improve throughput, and maximize overall system efficiency. (shrink)

This research explores the transformative role of artificial intelligence (AI) in strategic decision-making and business development, highlighting its capacity to enhance strategy execution, optimize operations, and foster innovation through advanced methodologies such as machine learning, predictive analytics, and natural language processing. By employing a mixed-methods approach that combines deductive and inductive research designs, crosssectional case analysis, and a review of empirical literature, the study underscores AI’s critical role in delivering datadriven insights, accurate forecasting, and robust simulations, positioning it as a (...) cornerstone of competitive advantage in dynamic global markets. However, key challenges such as ethical concerns, data quality issues, and disparities in organizational AI readiness are identified, emphasizing the need for robust governance frameworks and interdisciplinary collaboration to ensure responsible adoption. The findings provide actionable guidance for businesses, policymakers, and researchers, advocating for the alignment of AI implementation with strategic goals and ethical standards to harness its full potential for sustainable growth and innovation. (shrink)

In today’s rapidly evolving business landscape, organizations must continuously adapt to stay competitive. AI-driven human resource (HR) analytics has emerged as a strategic tool to enhance workforce agility and inform decision-making processes. By leveraging advanced algorithms, machine learning models, and predictive analytics, HR departments can transform vast data sets into actionable insights, driving talent management, employee engagement, and overall organizational efficiency. AI’s ability to analyze patterns, forecast trends, and offer data-driven recommendations empowers HR professionals to make (...) proactive decisions in hiring, skill development, performance management, and retention. (shrink)

As urbanization accelerates, diminishing green spaces pose growing public health challenges, exacerbating pollution exposure, stress, and chronic illnesses. This narrative review synthesizes research on the biological and psychological pathways through which urban plants promote human health. Biologically, urban greenery enhances air quality by filtering pollutants, strengthens immune function by increasing microbial diversity, and regulates stress physiology via endocrine mechanisms. Psychologically, nature exposure restores cognitive function, reduces stress, and fosters emotional resilience, as evidenced by neuroimaging and epidemiological studies. The findings suggest (...) that integrating green infrastructure into urban environments yields measurable public health benefits, including lower mortality rates, reduced cardiovascular and respiratory disease prevalence, and improved mental well-being. However, critical research gaps remain regarding the optimal “doses” of nature for health, cultural differences in nature perception, and the potential role of incorporating insights from quantum and informational theories to study cognitive-environment interactions (e.g., Granular Interaction Thinking Theory). Addressing these gaps through interdisciplinary collaboration, artificial intelligence integration, and data-driven urban planning can help cities evolve into healthier, more sustainable ecosystems. (shrink)

In today's data-driven world, the efficient management of network resources is crucial for optimizing performance in data centers and large-scale networks. Load balancing is a critical process in ensuring the equitable distribution of data across multiple paths, thereby enhancing network throughput and minimizing latency. This paper presents a comprehensive approach to load balancing using advanced optimization techniques integrated with multipath routing protocols. The primary focus is on dynamically allocating network resources to manage the massive volume of (...) data generated by modern applications. By leveraging algorithms such as Genetic Algorithms (GA) and Particle Swarm Optimization (PSO), the proposed method efficiently distributes data across multiple paths, ensuring balanced network utilization. The combination of these algorithms with multipath routing significantly reduces congestion and improves overall network performance. (shrink)

What is philosophy of science? Numerous manuals, anthologies or essays provide carefully reconstructed vantage points on the discipline that have been gained through expert and piecemeal historical analyses. In this paper, we address the question from a complementary perspective: we target the content of one major journal of the field—Philosophy of Science—and apply unsupervised text-mining methods to its complete corpus, from its start in 1934 until 2015. By running topic-modeling algorithms over the full-text corpus, we identified 126 key (...) research topics that span across 82 years. We also tracked their evolution and fluctuating significance over time in the journal articles. Our results concur with and document known and lesser-known episodes of the philosophy of science, including the rise and fall of logic and language-related topics, the relative stability of a metaphysical and ontological questioning (space and time, causation, natural kinds, realism), the significance of epistemological issues about the nature of scientific knowledge as well as the rise of a recent philosophy of biology and other trends. These analyses exemplify how computational text-mining methods can be used to provide an empirical large-scale and data-driven perspective on the history of philosophy of science that is complementary to other current historical approaches. (shrink)

: In recent years, artificial intelligence (AI) has become a critical enabler of innovation in supply chain and logistics management. By leveraging AI capabilities, enterprises can automate key processes, optimize operations, and make data-driven decisions that lead to enhanced efficiency, reduced costs, and improved customer satisfaction. Microsoft Azure provides a comprehensive suite of AI-driven tools and services designed to streamline and automate various aspects of supply chain and logistics operations. This paper explores how Azure's AI tools are (...) reshaping the landscape of supply chain management, focusing on key areas such as demand forecasting, inventory management, route optimization, predictive maintenance, and real-time monitoring. Furthermore, the paper delves into the benefits, challenges, and implementation strategies for integrating Azure AI solutions within enterprise ecosystems, with realworld case studies demonstrating successful AI-powered transformations. (shrink)

The Science of Balanced Leadership and Competition: The Role of AI Technology as a Guide -/- Introduction -/- Leadership and competition are two fundamental forces that shape human societies, economies, and institutions. However, their effectiveness depends on how they are managed. When leadership is imbalanced, it leads to corruption, authoritarianism, or inefficiency. When competition is unregulated, it creates inequality, exploitation, and instability. The science of balanced leadership and competition is an approach that integrates principles of natural balance, ethical (...) decision-making, and systemic thinking to ensure sustainable and just progress. -/- In the modern era, artificial intelligence (AI) technology serves as a powerful tool to assist leaders in maintaining this balance. AI can analyze vast amounts of data, predict potential crises, and offer insights for decision-making. Properly programmed AI can help guide leaders toward fair and ethical governance while also ensuring competition remains beneficial rather than destructive. -/- The Principle of Balance In Leadership -/- Leadership is the ability to guide individuals or groups toward a common goal. However, effective leadership is not just about authority; it requires a balance between control and flexibility, vision and adaptability, power and responsibility. -/- 1. The Balance Between Authority and Participation – Leaders must maintain authority to make decisive actions but also encourage participation from team members. Excessive authoritarianism suppresses innovation, while excessive democracy leads to indecisiveness. A balanced leader allows structured decision-making while considering input from others. -/- 2. The Balance Between Short-Term and Long-Term Goals – Effective leaders understand that immediate success must not come at the cost of future stability. Governments that prioritize economic growth without environmental responsibility, for example, create long-term disasters. Sustainable leadership requires long-term planning while addressing short-term needs. AI technology can analyze patterns, predict long-term consequences, and assist leaders in making informed, future-proof decisions. -/- 3. The Balance Between Competence and Morality – A leader may be highly competent but unethical, leading to corruption and exploitation. Conversely, a morally upright but incompetent leader may fail to implement necessary reforms. Balanced leadership integrates technical skill with ethical responsibility. AI systems programmed with ethical guidelines can act as an advisory tool, identifying potential ethical violations in leadership decisions and ensuring a fair and just system. -/- The Role of Competition in Progress -/- Competition is essential for innovation, economic growth, and personal development. However, uncontrolled competition leads to monopolies, exploitation, and social inequality. A balanced approach ensures that competition serves collective progress rather than individual greed. -/- 1. Healthy Competition vs. Destructive Competition – In a balanced system, competition drives excellence without harming others. In contrast, destructive competition creates a “win-at-all-costs” mentality, where ethics and fairness are disregarded. Examples include corporate monopolies suppressing small businesses or political parties prioritizing power over national interest. AI-driven monitoring systems can help detect unethical business practices and ensure fair competition in markets. -/- 2. Regulated Competition for Collective Growth – Governments and institutions must regulate competition to ensure fairness. This includes antitrust laws to prevent monopolies, ethical guidelines in business, and fair labor policies to protect workers from exploitation. AI can assist regulators in monitoring economic trends, detecting monopolistic behavior, and ensuring fair market conditions. -/- 3. Coopetition: A Balance Between Cooperation and Competition – In many industries, companies compete for market dominance while also collaborating in research, standardization, or sustainability efforts. This concept, known as “coopetition,” allows for economic growth while maintaining ethical responsibilities. AI can optimize cooperation strategies, ensuring that businesses maximize benefits while maintaining fair competition. -/- The Role of AI in Maintaining Balance -/- AI technology plays a crucial role in guiding leaders and institutions toward balanced decision-making. It can be used in the following ways: -/- 1. AI for Predictive Analysis – AI can process large-scale data to predict social, economic, and environmental trends, helping leaders make proactive decisions rather than reactive ones. -/- 2. AI for Ethical Governance – AI can be programmed with ethical principles to provide real-time insights on the consequences of political, corporate, or social policies. This ensures that decision-making aligns with fairness, justice, and sustainability. -/- 3. AI for Market Regulation – AI-driven economic models can detect unfair competition, price manipulation, and unethical corporate behavior, allowing governments to take corrective actions swiftly. -/- 4. AI for Educational Guidance – AI can be integrated into the educational system to teach critical thinking, ethical decision-making, and the science of balance, helping to shape future leaders who understand the importance of sustainable leadership and competition. -/- Applying the Science of Balance in Society -/- The science of balanced leadership and competition applies across various domains, from government and business to education and personal development. AI technology enhances this process by providing data-driven insights and ethical guidance. -/- 1. In Governance – Leaders must ensure that laws and policies balance economic growth with social welfare, individual rights with national security, and environmental sustainability with industrial development. AI can help design policies that optimize these balances through advanced simulations and scenario modeling. -/- 2. In Business – Corporate leaders should foster healthy competition while ensuring fair wages, ethical production, and sustainable business practices. AI can be used to analyze global trends, predict economic shifts, and optimize business strategies that benefit both corporations and society. -/- 3. In Education – Schools should encourage competition to inspire excellence while fostering teamwork, ethical responsibility, and critical thinking. AI-driven personalized learning can help students develop both competitive and cooperative skills based on their strengths and weaknesses. -/- 4. In Personal Development – Individuals must balance ambition with ethics, career success with personal well-being, and competition with collaboration in their daily lives. AI-powered personal development tools can provide guidance, helping individuals make balanced life decisions. -/- Conclusion -/- The science of balanced leadership and competition is essential for building a just and sustainable society. It requires an understanding of natural balance, ethical decision-making, and systemic thinking. AI technology plays a critical role in achieving this balance by providing data-driven insights, predicting long-term consequences, and ensuring ethical decision-making. -/- When leadership is balanced, it fosters progress without oppression. When competition is balanced, it drives innovation without exploitation. With AI as a guiding tool, societies can move toward a future where decision-making is not only efficient but also ethical and sustainable. By integrating AI with principles of natural balance, we can ensure a world that is fair, progressive, and in harmony with the universal laws of nature. -/- . (shrink)

Science and engineering rely on the accumulation and dissemination of knowledge to make discoveries and create new designs. Discovery-driven genome research rests on knowledge passed on via gene annotations. In response to the deluge of sequencing big data, standard annotation practice employs automated procedures that rely on majority rules. We argue this hinders progress through the generation and propagation of errors, leading investigators into blind alleys. More subtly, this inductive process discourages the discovery of novelty, which remains (...) essential in biological research and reflects the nature of biology itself. Annotation systems, rather than being repositories of facts, should be tools that support multiple modes of inference. By combining deduction, induction and abduction, investigators can generate hypotheses when accurate knowledge is extracted from model databases. A key stance is to depart from ‘the sequence tells the structure tells the function’ fallacy, placing function first. We illustrate our approach with examples of critical or unexpected pathways, using MicroScope to demonstrate how tools can be implemented following the principles we advocate. We end with a challenge to the reader. (shrink)

In the era of big data and distributed ecosystems, understanding the origin, flow, and transformation of data across complex infrastructures is critical for ensuring transparency, accountability, and informed decision-making. As data-driven enterprises increasingly rely on hybrid cloud architectures, data lakes, and real-time pipelines, the complexity of tracking data movement and transformations grows exponentially. Traditional data lineage solutions, often based on static metadata extraction or rule-based approaches, are insufficient in dynamically evolving environments and fail (...) to provide granular, context-aware insights. This research introduces an AI-augmented, cognitive graph-based framework for autonomous data lineage, designed to enhance data traceability in large-scale and heterogeneous data ecosystems. The framework leverages cutting-edge machine learning (ML), natural language processing (NLP), and graph-based reasoning techniques to enable intelligent discovery, semantic interpretation, and continuous monitoring of data assets throughout their lifecycle. The core of the solution lies in the construction of a dynamic cognitive graph that represents relationships between datasets, processes, systems, and users, enriched with contextual annotations and temporal dimensions. Our architecture incorporates self-learning mechanisms, enabling adaptive lineage discovery and automated anomaly detection. By applying reinforcement learning and stream analytics, the framework not only maps data flows in real time but also evolves with system changes, schema variations, and business logic updates. It provides both forward and backward traceability, supports impact analysis, and enhances compliance auditing capabilities. Furthermore, our system is capable of processing both structured and unstructured metadata, employing advanced NLP models to extract implicit lineage information from data dictionaries, SQL queries, and documentation. The result is a holistic, intelligent, and scalable data lineage solution that reduces manual intervention, mitigates operational risks, and supports regulatory compliance frameworks such as GDPR, HIPAA, and SOX. Experimental evaluations conducted in hybrid cloud environments using tools such as Apache Kafka, Neo4j, Spark, and Python-based ML libraries demonstrate a significant improvement in lineage coverage, anomaly detection accuracy, and system scalability. Compared to conventional lineage tools, our AI-augmented framework delivers a 30% increase in traceability precision and a 40% reduction in manual effort required for lineage tracking and governance. This research lays the foundation for a new paradigm in data governance, where AI not only enhances observability but enables autonomous cognition within data infrastructure. The framework is poised to play a critical role in enabling data democratization, operational agility, and enterprise-wide data literacy. (shrink)

Property and Casualty (P&C) insurers are adopting multi-cloud environments as a strategic imperative because of their increasing data volumes and complexities in regulatory compliance, customer expectations and technological advancements. This paper discusses how Snowflake’s cloud-agnostic, unified platform enables insurers to create resilient, efficient, and compliant multi cloud data strategies. Using Snowflake’s elastic scalability, real-time analytics, secure data sharing and seamless cloud interoperability, insurers can optimize claims processing, augment fraud detection, and support customer engagement. The study offers core (...) design principles for multi cloud data architectures and sheds light on problems in data fragmentation, latency, and security risks. It points to how Snowflake enables data resilience, including data replication, encryption, GDPR, and CCPA compliance. Details are provided. Practical implementation strategies of phased data migration ETL pipelines and integration with existing enterprise system to ensure a smooth deployment. Snowflake is seen as a clear win through a cost-benefit analysis with metrics like RTO and RPO that define how well Snowflake performs disaster recovery and business continuity. Additionally, the paper also looks at the emergence of tech like AI, blockchain and edge computing which could additionally bolster multi-cloud strategies even further. Finally, Snowflake enables the journey for P&C insurers through cloud ecosystem pitfalls, enabling operational resilience, regulatory compliance, along competitive advantage in a data-driven industry. (shrink)

: Financial institutions are required to ensure data accuracy, integrity, and compliance when reporting to regulatory authorities. Reports such as FR 2052a (Liquidity Monitoring), Y-14Q (Stress Testing) necessitate robust data reconciliation (Recon) strategies to maintain regulatory compliance and mitigate risks. This paper explores technical and functional aspects of data reconciliation, highlighting key automation techniques, AI-driven solutions, and statistical methodologies for optimizing financial compliance processes. We analyze data integration challenges, anomaly detection models, and best practices in (...) recon automation to enhance efficiency. Furthermore, case studies demonstrate how leveraging machine learning, cloud computing, and robotic process automation (RPA) can significantly improve financial data integrity. The future of reconciliation strategies is discussed, focusing on real-time monitoring, AI-driven decision intelligence, and blockchain-based audit trails. By implementing these strategies, financial institutions can reduce compliance costs, improve operational efficiency, and enhance regulatory transparency. (shrink)

In today’s rapidly evolving business landscape, organizations must continuously adapt to stay competitive. AI-driven human resource (HR) analytics has emerged as a strategic tool to enhance workforce agility and inform decision-making processes. By leveraging advanced algorithms, machine learning models, and predictive analytics, HR departments can transform vast data sets into actionable insights, driving talent management, employee engagement, and overall organizational efficiency. AI’s ability to analyze patterns, forecast trends, and offer data-driven recommendations empowers HR professionals to make (...) proactive decisions in hiring, skill development, performance management, and retention. (shrink)

: In today's data-driven world, the efficient management of network resources is crucial for optimizing performance in data centers and large-scale networks. Load balancing is a critical process in ensuring the equitable distribution of data across multiple paths, thereby enhancing network throughput and minimizing latency. This paper presents a comprehensive approach to load balancing using advanced optimization techniques integrated with multipath routing protocols. The primary focus is on dynamically allocating network resources to manage the massive volume (...) of data generated by modern applications. By leveraging algorithms such as Genetic Algorithms (GA) and Particle Swarm Optimization (PSO), the proposed method efficiently distributes data across multiple paths, ensuring balanced network utilization. The combination of these algorithms with multipath routing significantly reduces congestion and improves overall network performance. Simulations conducted on various network scenarios demonstrate the effectiveness of this approach, showcasing improvements in data throughput, reduced packet loss, and enhanced quality of service (QoS). (shrink)

In today's data-driven world, the efficient management of network resources is crucial for optimizing performance in data centers and large-scale networks. Load balancing is a critical process in ensuring the equitable distribution of data across multiple paths, thereby enhancing network throughput and minimizing latency. This paper presents a comprehensive approach to load balancing using advanced optimization techniques integrated with multipath routing protocols. The primary focus is on dynamically allocating network resources to manage the massive volume of (...) data generated by modern applications. By leveraging algorithms such as Genetic Algorithms (GA) and Particle Swarm Optimization (PSO), the proposed method efficiently distributes data across multiple paths, ensuring balanced network utilization. The combination of these algorithms with multipath routing significantly reduces congestion and improves overall network performance. (shrink)

We investigate the application of Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, and a hybrid CNN-LSTM model for forecasting air pollution levels based on historical data. Our experimental setup uses real-world air quality datasets from multiple regions, containing measurements of pollutants like PM2.5, PM10, CO, NO2, and SO2, alongside meteorological data such as temperature, humidity, and wind speed. The models are trained, validated, and tested using a split dataset, and their accuracy is evaluated using performance metrics (...) like Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared. (shrink)

In today's data-driven business environment, organizations increasingly rely on advanced analytics and decision support systems to gain a competitive edge. Azure AI and Machine Learning (ML) provide powerful tools for predictive analytics, enabling businesses to forecast trends, optimize processes, and make more informed decisions. By leveraging the capabilities of Microsoft Azure, businesses can integrate AI and ML into their decision-making processes, enhancing productivity and improving strategic outcomes. This paper explores how Azure's AI and ML tools can be applied (...) to predictive analytics and decision support systems, with a focus on real-time decision-making, risk mitigation, and operational efficiency. We will discuss the key Azure services like Azure Machine Learning, Azure Cognitive Services, and Azure Databricks, as well as real-world use cases that highlight their impact on business performance. The paper will also highlight the challenges associated with implementing these technologies and suggest best practices for organizations aiming to integrate AI and ML into their systems. (shrink)

With the applications like MalGAN, such cyberattacks enhanced with artificial intelligence (AI) in a broad way across cyber-defense lifecycles successfully take the vulnerabilities of systems at advantage, which are many as these are evading defenses nowadays. Therefore, this methodology proposed a new method which presents the approach of data fingerprinting and visualization for AI-Enhanced Cyber-Defense Systems (AIECDS) for efficiency in detection. AIECDS approach is built combining dynamic reinforcement learning, feature extraction and visualization with Hilbert curves and tornado graphs, real-time (...) data processing. Experimenting using the UNSW-15 dataset shows that even using very small sample sizes it's possible to differentiate malicious sessions from benign ones, meaning a large advancement in AI-driven solutions to cybersecurity, by being more adept at identifying complex threats with simplified machine learning models. (shrink)

The research methodology involves data preprocessing, feature engineering, model training, and performance evaluation. We employ optimization methods such as Genetic Algorithms and Grid Search to fine-tune model parameters, ensuring robust and generalizable models. The dataset used includes patient medical records, with features like age, blood pressure, cholesterol levels, and lifestyle habits serving as inputs for the ML models. Evaluation metrics, including accuracy, precision, recall, F1-score, and the area under the ROC curve (AUC-ROC), assess the model's predictive power.

Accelerated computing leverages specialized hardware and software techniques to optimize the performance of computationally intensive tasks, offering significant speed-ups in scientific, engineering, and data-driven fields. This paper presents a comprehensive study examining the role of accelerated computing in enhancing processing capabilities and reducing execution times in diverse applications. Using a custom-designed experimental framework, we evaluated different methodologies for parallelization, GPU acceleration, and CPU-GPU coordination. The aim was to assess how various factors, such as data size, computational complexity, (...) and task concurrency, impact processing efficiency. (shrink)

The integration and security of healthcare data are crucial challenges in the modern healthcare system, driven by the increasing digitization of medical records, diagnostic data, and patient health information. The need for secure, interoperable, and efficient data management systems has become essential, especially as healthcare providers strive to offer better care and reduce operational inefficiencies. Traditional systems often suffer from issues like data breaches, lack of interoperability, and inefficient data handling processes, resulting in compromised (...) data integrity and patient privacy. (shrink)

Each new decade brings ‘advances’ in technology that are more capable of collecting, aggregating, organizing, and deploying data about human practices. Where we go, what we buy, what we say online, and the people with whom we connect, are captured with ever more sophistication by governmental and corporate institutions. Data are increasingly being sold to schools to help them ‘manage’ teaching and administration tasks. Of course, at the same time, schools, teachers, and students are generating data that (...) further advances the goals of data aggregators. After some discussion regarding data’s philosophical dimensions, this chapter examines the benefits and drawbacks of surveillance and datafication in present educational contexts. In addition, this chapter raises ethical and pragmatic questions related to how datafication often reshapes or redefines agency, choice, personal relationships, and individuality. In response to some of the ethical qualms raised by these questions, a vision (imaginary) is proposed to help conserve some of the educational values threatened by datafication, including values related to genuine interest, sociality, and transformative, and qualitative experiences. -/- CITATION: Hildebrand, D.L. (2024). What are Data and Who Benefits?. In: Buch, A., Lindberg, Y., Cerratto Pargman, T. (eds) Framing Futures in Postdigital Education. Postdigital Science and Education . Springer, Cham. doi: 10.1007/978-3-031-58622-4_5 -/- . (shrink)

The reproducibility crisis in social sciences has revealed significant weaknesses in conventional research practices, including selective publication, questionable statistical methods, and opaque peer review processes. This paper introduces Granular Interaction Thinking Theory (GITT) as a novel framework for understanding the plausibility of scientific findings, conceptualizing knowledge validation as a structured entropy-reduction process. Within this framework, open science practices—such as open data, open review, and open dialogue—initially increase informational entropy by exposing inconsistencies. However, through iterative refinement, they ultimately enhance (...) the robustness and plausibility of scientific knowledge. To systematically assess a study’s plausibility, we propose the Scientific Plausibility Index (SPI)—an entropy-based metric that integrates data and method transparency, replication success rates, and community engagement. Additionally, leveraging artificial intelligence (AI) and natural language processing (NLP), a dynamic plausibility-tracking system could be developed to detect unreliable claims early and accelerate scientific self-correction. This shift—from closed, one-time assessments to continuous, community-driven evaluation models—ensures that scientific knowledge remains rigorously tested and updated. We advocate for shifting from closed, one-time assessments to continuous, community-driven evaluation models, ensuring that scientific knowledge remains rigorously tested and updated. Ultimately, aligning incentives with entropy-reducing mechanisms—such as rewarding replication efforts and fostering open discourse—can cultivate a research culture that prioritizes robustness, transparency, and cumulative knowledge-building. (shrink)

The increasing computational demands of artificial intelligence (AI) workloads have significantly escalated energy consumption in data centers. AI-driven applications, including deep learning, natural language processing, and autonomous systems, require substantial computing power, primarily provided by Graphics Processing Units. These GPUs, while enhancing computational efficiency, contribute to significant power consumption and heat generation, necessitating advanced cooling strategies. This study provides a quantitative assessment of AI-specific hardware power usage, focusing on the NVIDIA H100 GPU. The analysis compares AI data (...) center energy consumption to the average US household power usage, demonstrating that a single AI rack consumes approximately 39 times the energy of a typical household. Additionally, a scalability analysis estimates that approximately 87 new hyper-scale data centers consume the electricity as much as consumed by New York City. This emphasizes that with rapid growth of AI Data Centers, the large-scale deployment could lead to an unprecedented rise in global energy demand. Furthermore, the study evaluates the impact of heat dissipation on cooling requirements, highlighting the need for energy- efficient cooling solutions, including liquid and immersion cooling techniques. Future research directions include energy- efficient AI models, renewable energy integration, sustainable AI accelerator designs, and intelligent workload optimization to mitigate the environmental impact of large-scale AI adoption. This research provides critical insights for designing more sustainable AI-driven data centers while maintaining high-performance computing efficiency. (shrink)

This comprehensive exploration of data engineering examines the rapid transformation of the field driven by emerging trends and cutting-edge technologies. The article discusses the exponential growth of data generation, projected to reach 175 zettabytes by 2025, and its implications for data management practices. It delves into key trends such as adopting cloud-native technologies, the rise of DataOps, advancements in real-time data processing, and the impact of artificial intelligence and automation on data engineering workflows. The (...) article also investigates the growing importance of serverless computing, hybrid, and multi-cloud environments and the critical role of data privacy and compliance in shaping modern data architectures. The paper aims to provide organizations with insights to leverage data as a strategic asset in the increasingly competitive digital economy by analyzing these developments. (shrink)

This research paper presents an in-depth analysis of advanced artificial intelligence (AI) algorithms designed to automate data preprocessing in the healthcare sector. The automation of data preprocessing is crucial due to the overwhelming volume, diversity, and complexity of healthcare data, which includes medical records, diagnostic imaging, sensor data from medical devices, genomic data, and other heterogeneous sources. These datasets often exhibit various inconsistencies such as missing values, noise, outliers, and redundant or irrelevant information that necessitate (...) extensive preprocessing before being analyzed by machine learning or statistical models. Traditional data preprocessing methods, which are largely manual and time-consuming, can result in errors that affect the quality of the data and, subsequently, the performance of predictive and diagnostic models. Thus, there is a growing need for intelligent, automated systems that can enhance data quality, streamline the preprocessing pipeline, and reduce the time and effort required by healthcare professionals and data scientists. The study begins by outlining the specific challenges associated with healthcare data, including its high dimensionality, incompleteness, and variability across different data sources and formats. These issues not only complicate the preprocessing stage but also hinder the ability to develop robust models capable of making accurate predictions or diagnoses. The paper then explores how AI algorithms—particularly those based on machine learning (ML), deep learning (DL), and reinforcement learning (RL)—can automate key data preprocessing tasks such as data cleaning, feature selection, normalization, and transformation. These algorithms are designed to identify patterns in data, detect anomalies, and automatically apply corrections or transformations based on predefined rules or learned behaviors, thereby minimizing human intervention. The paper also delves into specific AI techniques that have been successfully applied to healthcare data preprocessing. For instance, supervised learning models, such as decision trees and support vector machines (SVMs), have been utilized to perform imputation of missing data by predicting the most likely values based on the available information. Similarly, unsupervised learning methods, such as clustering algorithms, have been employed to group similar data points and remove outliers that could distort the performance of analytical models. Moreover, deep learning techniques, particularly autoencoders and generative adversarial networks (GANs), have demonstrated remarkable effectiveness in transforming high-dimensional medical data into lower-dimensional representations, enabling more efficient and accurate model training. In addition to the discussion of these algorithms, the paper emphasizes the role of natural language processing (NLP) in automating the preprocessing of unstructured healthcare data, such as clinical notes and diagnostic reports. NLP techniques, including named entity recognition (NER) and word embeddings, are instrumental in extracting relevant information from unstructured text, standardizing terminologies, and converting textual data into structured formats suitable for downstream analysis. Furthermore, AI-based feature selection algorithms are explored, which aim to identify the most relevant features in the dataset, thereby reducing its dimensionality and improving the computational efficiency of predictive models. The study goes on to highlight the significant reduction in processing time achieved by AIdriven automation of preprocessing tasks. In conventional settings, data preprocessing accounts for a substantial portion of the time spent on building healthcare models, often requiring expert intervention to manually inspect and clean the data. By employing AI algorithms, not only can this process be expedited, but the accuracy of the resulting data is also enhanced, which translates into better model performance. The paper provides a detailed comparative analysis of manual preprocessing methods versus automated AI-driven approaches, demonstrating the substantial time savings and improvements in data quality brought about by automation. (shrink)

The heightened digitization of the healthcare industry has led to an exponential increase in sensitive patient data, which requires robust security models to prevent breaches, unauthorized access, and cyber attacks. Traditional security protocols are inadequate, and this has made it imperative to explore Artificial Intelligence (AI) and Blockchain as novel solutions. AI enhances healthcare cybersecurity by facilitating real-time anomaly detection, predictive analysis, and automated threat response, while blockchain offers decentralization, immutability, and secure data sharing. However, blockchain technology faces (...) major challenges for scalability and performance, as represented by lengthy transaction processing durations and high storage demands, elements that could deter its widespread adoption across the healthcare industry. To help counter these challenges, researchers are exploring Layer 2 scaling solutions, hybrid blockchain architectures, and off-chain storage strategies. In addition, cleanroom technology provides a controlled and secure environment for handling sensitive healthcare data, protecting privacy while also supporting AI-driven analytics and research collaboration. This article critically examines the intersection of blockchain and AI for healthcare security, in terms of challenges, use cases, and direction. Leveraging these technologies, health organizations can set up future-proof models of security that address data integrity, regulatory requirements, and resistance to future threats more effectively. (shrink)

Tracking technology has been heralded as transformative for animal ecology. In this paper I examine what changes are taking place, showing how current animal movement research is a field ripe for philosophical investigation. I focus first on how the devices alter the limitations and biases of traditional field observation, making observation of animal movement and behaviour possible in more detail, for more varied species, and under a broader variety of conditions, as well as restricting the influence of human presence and (...) observer bias. I reconstruct these as shifts in scope, objectivity, accuracy and fruitfulness. The second transformation is slightly less obvious but equally significant for animal ecology. Tracking devices generate complex data that demands both statistical and biological expertise, which has led to increasingly frequent and intensive collaborations between statisticians and biologists. Based on interviews, I examine how researchers in these interdisciplinary collaborations negotiate the collection, analysis and interpretation of movement data, integrating research interests, methodological constraints, previous field observations, and background theory. Tracking technology is therefore also shifting which disciplinary considerations are brought to bear on research into animal movement and behaviour and how this research is conducted. (shrink)

Traditional encryption is safe but slows data recovery, especially for keyword searches. Secure, fine-grained access control and quick keyword searches over encrypted data are possible using attribute-based keyword search (ABKS). This study examines how ABKS might optimize search efficiency and data security in cloud storage systems. We examine index compression, query processing improvement, and encryption optimization to decrease computational cost and preserve security. After a thorough investigation, the article shows how these methods may boost cloud storage system (...) performance, security, and usability. Tests show that improved ABKS speeds up search searches and lowers storage costs, making it a viable cloud storage alternative. (shrink)

This entry summarizes an emerging subdiscipline of both empirical bioethics and experimental philosophy (“x-phi”) which has variously been referred to as experimental philosophical bioethics, experimental bioethics, or simply “bioxphi”. Like empirical bioethics, bioxphi uses data-driven research methods to capture what various stakeholders think (feel, judge, etc.) about moral issues of relevance to bioethics. However, like its other parent discipline of x-phi, bioxphi tends to favor experiment-based designs drawn from the cognitive sciences – including psychology, neuroscience, and behavioral economics (...) – to tease out why and how stakeholders think as they do. (shrink)

I’m often asked why, as a philosopher of science, I study archaeology. Philosophy is so abstract and intellectual, and archaeology is such an earth-bound, data-driven enterprise, what could the connection possibly be? This puzzlement takes a number of different forms. In one memorable exchange in the late 1970s when I was visiting Oxford as a graduate student an elderly don, having inquired politely about my research interests, tartly observed that archaeology isn’t a science, so I couldn’t (...) possibly be writing a dissertation in philosophy of science on archaeology. At job interviews a few years later a standard opening gambit was to ask: “so what’s philosophical about the work you do on archaeology” or, more querulously, “what could possibly be of philosophical interest in archaeology”? And from archaeologists, weary of the “theory wars” that raged through 1980s and 1990s, the standard question was, “What’s the point?! Better to step away from the wrangling about philosophical ‘isms’ and get on with the empirical work that needs doing.”. (shrink)