A Question of Leverage (Vietnam Investment Review; March 24, 1997).

Abstract: Artificial Intelligence (AI) has rapidly evolved, offering transformative capabilities for business decision-making. This paper explores how AI can be leveraged to enhance strategic decision-making in business contexts. It examines the integration of AI-driven analytics, predictive modeling, and automation to improve decision accuracy and operational efficiency. By analyzing current applications and case studies, the paper highlights the opportunities AI presents, including enhanced data insights, risk management, and personalized customer experiences. Additionally, it addresses the challenges businesses face in adopting AI, such (...) as data privacy concerns, integration issues, and the need for skilled personnel. The paper concludes with recommendations for effectively implementing AI solutions to maximize their benefits while navigating potential pitfalls. (shrink)

Analytic semantics got its start when Frege pointed out differences in cognitive content between sentences that in some good sense “say the same.” Frege put cognitive content (in the form of sense) at the heart of semantic content. Most prefer nowadays to see cognitive contents as generated by semantic contents in context; a sentence's cognitive significance is an aspect rather of the information imparted by its use. I argue for a particular version of this idea. Semantic contents generate cognitive contents (...) by operating levers in a larger piece of machinery, involving also the presupposition P one is leaning on and the subject matter M under discussion. A sentence's cognitive content is what its literal-content-restricted-to-P-worlds says about M. It's by triggering different presuppositions and/or different subject matters that coreferential names make their distinctive mark on a sentence's felt information value. (shrink)

In today’s digital landscape, businesses face increasing pressure to maintain service continuity and data availability, especially in the face of potential service disruptions or disasters. Traditional single-cloud deployments can be vulnerable to outages, data loss, and geographical limitations. As a result, multi-cloud strategies have emerged as a solution for achieving higher resilience, disaster recovery (DR), and business continuity. By leveraging multiple cloud service providers (CSPs), organizations can architect redundancy, improve service availability, and ensure faster recovery in the event of (...) a failure. This paper explores the importance of multi-cloud strategies for resilience and disaster recovery, focusing on key components like data replication, geographic distribution, failover mechanisms, and backup strategies. We discuss architectural patterns, best practices, and challenges of implementing multi-cloud disaster recovery solutions. Additionally, we present case studies that highlight how multi-cloud strategies enhance business resilience and improve operational continuity during disruptions. (shrink)

A critical review of Collin Rice's book, Leveraging Distortions: Explanation, Idealization, and Universality in Science.

The increasing prevalence of mental health issues, particularly stress, has necessitated the development of data-driven, interpretable machine learning models for early detection and intervention. This study leverages multimodal data, including activity levels, perceived stress scores (PSS), and event counts, to predict stress levels among individuals. A series of models, including Logistic Regression, Random Forest, Gradient Boosting, and Neural Networks, were evaluated for their predictive performance. Results demonstrated that ensemble models, particularly Random Forest and Gradient Boosting, performed significantly better compared to (...) Logistic Regression. Random Forest achieved an accuracy of 73%, while Gradient Boosting delivered a balanced precision-recall tradeoff with an accuracy of 72%. Gradient Boosting outperformed in identifying high-stress instances, achieving a recall of 70%, making it the most reliable model for stress prediction. Explainable AI (XAI), which refers to the ability of machine learning models to provide transparent and understandable explanations for their predictions, was employed in this study using SHAP (SHapley Additive exPlanations). SHAP values revealed that Perceived Stress Score (PSS) had the most significant impact on predictions, followed by event count and activity inference. Higher PSS scores strongly correlated with high-stress predictions, while increased event counts and activity levels were associated with lower stress. These findings underscore the importance of incorporating behavioral patterns in stress diagnostics and highlight the utility of explainable models in improving transparency, trust, and usability for clinical decision-making. This research establishes a robust foundation for deploying interpretable machine learning systems to support mental health diagnostics and enhance clinician decision support. (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)

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)

Designing a virtual environment within a digital game occupies a large part of the design procedure, requiring holistic attention and a broad arrangement of the game constituents. Considering other design disciplines, they occupy a unified design methodology; however, a comprehensive literature review reveals the lack of the intended design methodology in the digital game domain's virtual environment development, despite a currently proposed theoretical methodology trying to dissolve the issue. Hence, this research aims to determine the industry's requirements and provide a (...) set of assets included in current digital games as an initial step of providing such a design methodology for the domain. In this regard, the researchers reverse-engineered ten selected digital games, understanding the current condition of digital games via adopting the mentioned currently available design methodology. This dataset reveals a lack in the assets of the story layer in the recent digital games, despite their focus on being story-based. This dilemma leads to long text or speech conversations between game characters, disrupting the players while following the game. The current design focuses on environmental resources only, however, as a virtual landscape, the story needs to be reinforced to be a balanced and well-designed game. Hence, increasing the ratio of the assets in this layer will advance the games' interactivity. Also, as future work, this data set could pave the way for a digital game industry design tool regarding the virtual environment. (shrink)

In this paper, I explore the Guam Department of Education's (GDOE) decolonization efforts and the potential role of Philosophy for Children (P4C) as a strategic tool for its advancement. I begin with a discussion of Guam's colonial context and its implications for contemporary education on the island. While the GDOE's current attempts to decolonize Guam's public education emphasize the need for an "official body of knowledge," many CHamoru scholars and activists have argued that it is not enough. This paper agrees (...) that such content-based reforms are insufficient and advocates for a more nuanced approach to education through P4C. Drawing on the P4C literature and decolonial education, I argue that while P4C alone may not bring about structural change, its pedagogical framework offers a promising alternative when adapted to indigenous contexts. The paper proposes that P4C can serve as a "Trojan Horse" within public schools, fostering decolonial acts of resurgence by elevating indigenous knowledge, welcoming indigenous masters, and empowering CHamoru students within a Westernized educational setting. (shrink)

Recent scholarship on climate and conflict has increasingly examined the dynamic relationship between environmental scarcities and geopolitical tensions. Overall, climate-related shocks often lead to escalated disputes over natural resources, thus positioning climate hazards not only as an environmental issue but also as a catalyst that intensifies existing geopolitical and social frictions. The discourse on climate and conflict has mostly centred on its direct effects on natural resources and environmental conditions. However, its role in precipitating conflicts underscores the critical need to (...) incorporate climate considerations within broader security and policy frameworks. In recent decades, one of the most compelling developments in this field is the inclusion of environmental issues within the security paradigm. Environmental degradation, global warming, and climate change are now recognized as critical threats that transcend national borders, impacting personal, national, and international security . These factors have broadened our comprehension of the relationships among environmental transformation, conflict, and vulnerability, and have highlighted the importance of conservation and sustainable development in fostering peace, stability, and human security. Despite the increased attention on climate in conflict and security studies, the mechanisms through which an environmental catastrophe, or the threat of it, gets securitized in conflict remain under investigated. Understanding how these threats are framed and leveraged by conflicting parties can provide critical insights into the intersection of environmental risks and geopolitical strategies, thereby enriching the discourse on environmental security and conflict dynamics. Most case studies focus on the securitization of natural resources, such as water in the Nile and Indus basins, concentrating on resource scarcity rather than potential catastrophes. Additionally, research on the Israeli-Palestinian conflict and domestic politics in the United Kingdom and the Netherlands shows how framing environmental threats as national security issues can influence power dynamics and foster global action. However, these cases suggest that previous research has predominantly investigated how states strategically employ environmental narratives within the framework of national security for political and economic advantages, often in contexts removed from direct conflict scenarios. Moreover, much of the discussion on environmental securitization has focused on state-centric perspectives and inter-state conflicts. This emphasis on state actions, both in domestic and international arenas, has left a significant gap concerning the weaponization of environmental threats by non-state actors within ongoing conflicts. In that sense, the contribution of my thesis is twofold. First, it analyzes the weaponization of a looming environmental catastrophe within a conflict setting. Second, it focuses on a case where non-state actors, specifically the Houthis, are primary securitization actors. More specifically, my thesis addresses the existing gap in the literature by analysing the case of the FSO Safer, a deteriorating oil storage tanker moored off the coast of Yemen. Before the United Nations’ successful offloading plan, the tanker held over one million barrels of crude oil, posing a significant risk of massive ecological and economic damage due to its poor condition and lack of maintenance since the onset of the Yemeni conflict. Despite the imminent threat of an ecological disaster, the conflicting parties in Yemen – the Houthis and the internationally recognized government – have used the Safer as a bargaining chip rather than prioritizing immediate remedial action. This has put the entire region in a state of suspense, with the potential for disastrous oil leakage that could impact the Red Sea's marine ecosystems and the broader geopolitical stability. The FSO Safer crisis has directly inspired the research question of this study, highlighting the need to understand the strategic use of environmental threats in geopolitical conflicts. This crisis vividly illustrates how impending ecological disasters are not solely managed as environmental or humanitarian issues but are intricately woven into the fabric of international security and conflict negotiation strategies. It prompts critical inquiries into how and why such existential threats are leveraged by involved parties to advance their geopolitical agendas. Therefore, the research question formulated for this study is: How are environmental threats utilized as strategic tools in geopolitical conflicts? (shrink)

We often claim to know about what is good or bad, right or wrong. But how do we know such things? Both historically and today, answers to this question have most commonly been rationalist or sentimentalist in nature. Rationalists and sentimentalists clash over whether intellect or affect is the foundation of our evaluative knowledge. This paper is about the form that this dispute takes among those who agree that evaluative knowledge depends on perceptual-like evaluative experiences. Rationalist proponents of perceptualism invoke (...) intellectual experiences, while sentimentalist proponents invoke affective experiences. The goal of this paper is to offer a fresh strategy for adjudicating between intellectual and sentimental perceptualism. I argue that the perceptualist’s hand will be forced either in the direction of intellectual or sentimental perceptualism once she decides between two views about the modal status of our basic evaluative knowledge. I close with an argument that the more plausible of the two options is the one which fits best with sentimental perceptualism. The argument, then, is that perceptualists ought to be sentimentalists. (shrink)

Healthcare fraud detection is a critical task that faces significant challenges due to imbalanced datasets, which often result in suboptimal model performance. Previous studies have primarily relied on traditional machine learning (ML) techniques, which struggle with issues like overfitting caused by Random Oversampling (ROS), noise introduced by the Synthetic Minority Oversampling Technique (SMOTE), and crucial information loss due to Random Undersampling (RUS). In this study, we propose a novel approach to address the imbalanced data problem in healthcare fraud detection, with (...) a focus on the Medicare Part B dataset. Our approach begins with the careful extraction of the categorical feature "Provider Type," which allows for the generation of new, synthetic instances by replicating existing types to enhance diversity within the minority class. To further balance the dataset, we employ a hybrid resampling technique, SMOTE-ENN, which integrates the Synthetic Minority Oversampling Technique (SMOTE) with Edited Nearest Neighbors (ENN) to generate synthetic data points while removing noisy, irrelevant instances. This combined technique not only balances the dataset but also helps in mitigating the potential adverse effects of imbalanced data. We evaluate the performance of the logistic regression model on the resampled dataset using common evaluation metrics such as accuracy, F1 score, recall, precision, and the AUC-ROC curve. Additionally, we emphasize the importance of the Area Under the Precision-Recall Curve (AUPRC) as a critical metric for evaluating model performance in imbalanced scenarios. The experimental results demonstrate that logistic regression achieves an impressive 98% accuracy, outperforming other methods and validating the efficacy of our proposed approach for detecting healthcare fraud in imbalanced datasets. (shrink)

This paper explores a novel methodology for utilizing artificial intelligence (Al), specifically large language models (LLMs) like ChatGPT, as an external cognitive augmentation tool. By integrating recursive self-analysis, structured thought expansion, and Al-facilitated selfmodification, individuals can enhance cognitive efficiency, accelerate self-improvement, and systematically refine their intellectual and psychological faculties. This approach builds on theories of extended cognition, recursive intelligence, and cognitive bias mitigation, demonstrating Al’s potential as a structured self-engineering framework. The implications extend to research, strategic decision-making, therapy, and personal (...) development. (shrink)

This is the first section of a five part essay dealing with economics from the perspective of Ernst Cassirer's cultural philosophy. In this first section, I try to demonstrate how the human concern with meaning is distorted in our commercial society into a purely economic sense of reality that displaces other forms of meaning. This distortion and displacement are at the heart of the social, cognitive and spiritual dysfunctions that currently plague us. The "Leverage of Meaning" will prepare the ground (...) for the formulation of the economic as a symbolic form in order to hopefully find some guidance in Cassirer's thought for restoring economy to its rightful cultural function. (shrink)

The research examines the way artificial intelligence can be used to identify fraud in insurance. It assesses the accuracy of various machine learning models with specific emphasis on their capability of recognizing fake claims. It is discovered that the model is successful in determining authentic claims. It is not particularly excellent at detecting fraudulent activity, indicating the need for development. Mitigating data imbalance and investigating hybrid AIrule-based systems are recommended to enhance model accuracy. It also highlights the need for developing (...) comprehensible models that can be trusted and relied on by all stakeholders in the time of using AI in fraud detection for the insurance sector. (shrink)

Each of the five principles can be utilized separately or as a combination depending on the situation of the users (e.g., goals and existing conditions).

The use of machine learning to medical image classification has seen significant development and implementation in the last several years. Computers can learn to identify patterns, make predictions, and use data to inform their judgements; this capability is known as machine learning, a branch of Artificial intelligence (AI). Classifying images according to their contents allows us to do things like identify the type of sickness, organ, or tissue depicted. Medical picture classification and interpretation using machine learning algorithms has greatly improved (...) the speed and accuracy of diagnosis and treatment. Machine learning algorithms can aid radiologists and doctors in diagnosing patients using medical pictures from X-rays, positron emission tomography (PET), computed tomography (CT), magnetic resonance imaging (MRI), and other scans. (shrink)

The proliferation of Internet of Things (IoT) technologies presents a promising avenue for fostering inclusive societies through community development and education initiatives. This paper explores the potential of leveraging IoT to address societal inequalities and empower marginalized communities. Through a multidisciplinary lens, the paper examines the intersection of IoT, community development, and education, elucidating how IoT-enabled solutions can contribute to building more resilient, connected, and equitable societies. By harnessing IoT devices, sensor networks, and data analytics, community development efforts can (...) be augmented, enabling better resource allocation, infrastructure management, and service delivery. Additionally, IoT-enabled educational interventions offer opportunities to enhance learning experiences, improve access to educational resources, and promote lifelong learning for individuals of all backgrounds. However, alongside the promise of IoT, ethical considerations surrounding data privacy, security, and digital divide must be addressed to ensure equitable access and participation. Through case studies and best practices, the study highlights successful examples of IoT implementations in community development and education, while also identifying key challenges and opportunities for future research and practice. Finally, the study advocates for a collaborative and inclusive approach to leveraging IoT for community development and education, emphasizing the importance of empowering individuals and communities to participate actively in shaping their futures. Keywords: Internet of Things, Community development, Education, Inclusive societies, Sensor. (shrink)

Cloud-native architectures and serverless computing have revolutionized application deployment, improving scalability, reducing costs, and increasing operational flexibility. Optimizing performance, resource allocation, and automating workflows continues to be a difficult challenge. This paper discusses the latest developments in serverless computing, focusing on its importance for building scalable applications. Key developments in container-based platforms, workflow execution, and function choreography are explored. Furthermore, we evaluate the combination of serverless computing with AI, scientific workflows, and high performance computing. The research study uncovers performance limitations (...) including cold start latency and shortcomings in resource allocation, whilst also examining methods for streamlining workflows and redistributing workload. Additionally, it considers issues such as cold start latency and resource provisioning inefficiencies. This study synthesizes existing research to offer insights into the advantages, obstacles, and potential future paths for improving cloud-native and serverless infrastructure designs. (shrink)

Abstract: This research explores the application of artificial neural networks (ANNs) in predicting cancer using a synthetically generated dataset designed for research purposes. The dataset comprises 10,000 pseudo-patient records, each characterized by gender, age, smoking history, fatigue, and allergy status, along with a binary indicator for the presence or absence of cancer. The 'Gender,' 'Smoking,' 'Fatigue,' and 'Allergy' attributes are binary, while 'Age' spans a range from 18 to 100 years. The study employs a three-layer ANN architecture to develop a (...) predictive model. The achieved accuracy of 97.24% and a low loss value of 0.02 indicate promising performance. Further analysis involves a comprehensive examination of the confusion matrix, precision, recall, and F1 score metrics, along with the ROC curve and AUC score, to provide a detailed understanding of the model's strengths and weaknesses. Additionally, considerations for cross-validation, hyperparameter tuning, and interpretability are discussed. It is emphasized that while the synthetic nature of the dataset allows for controlled experimentation, results should be cautiously interpreted and validated on authentic clinical data before extrapolation to real-world medical scenarios. This research serves as a valuable contribution to the exploration of predictive models for cancer detection, encouraging continued investigation and development in the field. (shrink)

Article title: A Question of Leverage Author: Vuong Quan Hoang Affiliation: World Bank / IFC Magazine: Vietnam Investment Review Date: March 24, 1997.

Civil disobedience, despite its illegal nature, can sometimes be justified vis-à-vis the duty to obey the law, and, arguably, is thereby not liable to legal punishment. However, adhering to the demands of justice and refraining from punishing justified civil disobedience may lead to a highly problematic theoretical consequence: the debilitation of civil disobedience. This is because, according to the novel analysis I propose, civil disobedience primarily functions as a costly social signal. It is effective by being reliable, reliable by being (...) costly, and costly primarily by being punished. My analysis will highlight a distinctive feature of civil disobedience: civil disobedients leverage the punitive injustice they suffer to amplify their communicative force. This will lead to two paradoxical implications. First, the instability of the moral status of both civil disobedience and its punishment to the extent where the state may be left with no permissible course of action with regard to punishing civil disobedience. Second, by refraining from punishing justified civil disobedience, the state may render uncivil disobedience—illegal political activities that fall short of the standards of civil disobedience—potentially permissible. (shrink)

The COVID-19 pandemic has intensified the extent to which economies in the developed and developing world rely on gig workers to perform essential tasks such as health care, personal transport, food and package delivery, and ad hoc tasking services. As a result, workers who provide such services are no longer perceived as mere low-skilled laborers, but as essential workers who fulfill a crucial role in society. The newly elevated moral and economic status of these workers increases consumer demand for corporate (...) social responsibility regarding this stakeholder group – specifically for practices that increase worker freedom and rewards. We provide algorithmic tools for online labor platforms to meet this demand, thereby bolstering their social purpose and ethical branding while better protecting themselves from future reputational crises. To do so, we advance a managerial strategy rooted in moral self-awareness theory in order to leverage customers’ virtuous self-perception and increase gig-worker freedom. (shrink)

Preparedness inadequacy for the retirement transition is one of the critical reasons for age reduction. This chapter examines retirement planning as a leveraging strategy and vibrant remedy for age reduction using quantitative data obtained from formal and informal sector workers. The findings show that retirement plans are categorized into short-term, medium, and long-term plans. Collectively, these yield income, health, housing, social networks, leisure, and paid work beyond pensions. Post-retirement income is a significant determinant of the retirement decision. Retirement planning (...) can be used to leverage the lack of preparedness for retirement, which in turn reduces the activity of age reduction. This averts dependency in old age and creates the avenue for successful ageing, which is often heralded by the retirement transition. Further, not planning for retirement and the associated act of age reduction is an indication of the non-acknowledgment of the notion of active ageing. Noteworthy age reduction is a portrayal of old age as a shameful loss of youthfulness. This chapter argues that planning for retirement may be the best alternative strategy for combating age reduction from an economic outcome dimension. Retirement planning is leveraged without which labor cannot achieve old age oriented social protection at the individual level. (shrink)

As enterprises compete in increasingly dynamic digital ecosystems, real-time decisioning has become critical to sustaining meaningful customer engagement. Pega’s adaptive decisioning engine, powered by artificial intelligence (AI) and machine learning (ML), enables organizations to personalize experiences at scale. This study investigates the theoretical underpinnings and practical applications of adaptive decisioning in Pega, with a focus on its use in industries such as insurance and telecommunications. Through the lens of decision theory and dynamic learning models, the paper analyzes how adaptive decisioning (...) systems influence key business metrics like churn, upselling, and satisfaction. The framework highlights the feedback loop between behavioral data, predictive modeling, and contextual relevance, positioning Pega’s approach as a model for future customer-centric engagement. (shrink)

The Life Insurance industry is undergoing a profound digital transformation, transitioning from traditional, static methodologies to agile, data-intelligent ecosystems capable of adapting to dynamic customer demands and stringent regulatory complexities. At the forefront of this shift is the adoption of digital twins—high-fidelity virtual replicas of physical systems, processes, and assets—specifically for testing and optimizing Life Insurance platforms. This paper provides a deep dive into the role of Digital twins in the Life Insurance domain, exploring their powerful applications in underwriting, policy (...) lifecycle management, and claims processing. By accurately simulating complex, real-world scenarios, Digital twins enable predictive testing environments that refine system resilience, preemptively identify risk exposures, and ensure operational continuity. Through this sophisticated digital infrastructure, insurers gain a transformative tool for enhancing precision, boosting reliability, and driving efficiency in core insurance processes. This research investigates the architecture, applications, and strategic trajectory of Digital twins as next generation testing mechanisms reshaping Life Insurance. (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)

The link between seen and unseen, matter and spirit, flesh and soul was always presumed, but never clarified enough, leaving room for debates and mostly controversies between the scientific domains and theologies of a different type; how could God, who is immaterial, have created the material world? Therefore, the logic of obtaining a result on this concern is first to see how religions have always seen the ratio between divinity and matter/universe. In this part, the idea of a world personality (...) is implied by many, so that nature itself was transformed into a person ; others have seen within the universe/the world a Spirit ruling all, connecting all and bending all to God’s commands. In a way or another, every culture has gifted the universe/nature with the capability of ruling all, seeing everything and controlling, even determining facts by connecting all together with a Great Spirit. What is this Great Spirit of all and where it resides? With the analogy of human body in relation to his Spirit we will try to figure out a place or vehicle for the Spirit to dwell the body, and the Great Spirit the matter. The Christianity names this linkage between God and matter as ‘the created grace of God’, which indwells matter and helps the Creator move and transform things. Is there any scientific argument to sustain such assertion? Can we argue somehow that God’s voice makes matter vibrate from within the way it can recombine primer elements into giant stars to the human body? If so, what should be the ratio between theology and science on this issue and with these assertions? How could God command to matter to bring things and beings out of it and what were the material leverages that was supposed to be operated to accomplish His will? However, if we can assume that God resides in the universe – as a whole, His body, or as in its very fabric – can we also figure out how is this even possible, without transforming our explanation into a pantheistic and immanent exclusive one? Through these ‘divine leverages within matter’ theory, there is no need for questioning evolutionism, creationism, pantheism, deism and many other cosmological hypotheses any longer. (shrink)

This article examines the successful implementation of Pega's Healthcare Customer Service and Care Management solutions at a major healthcare provider, demonstrating how digital transformation can revolutionize patient care delivery while optimizing operational efficiency. The implementation, which focused on integrated care coordination, patient case management, and compliance frameworks, resulted in significant improvements, including a 20% reduction in hospital readmissions, a 30% reduction in administrative costs, and a 25-35% reduction in operational costs. The organization achieved enhanced patient outcomes, streamlined operations, and improved (...) regulatory compliance through comprehensive data integration, automated workflows, and user-centric design. The article highlights how strategic digital transformation initiatives, aligned with industry best practices and supported by robust change management, can successfully address healthcare's complex challenges while positioning organizations for future innovation in care delivery. (shrink)

Indigenous territories (ITs) are vital to preserving the Amazon’s biodiversity and carbon stocks. Yet, conventional conservation approaches often overlook the intricate cultural and political connections that unify these lands [2]. Esbach et al. [3] introduce the concept of Archipelagos of Indigenous Territories (AITs)—networks of spatially distinct but culturally and politically linked ITs—as a critical framework for advancing conservation efforts in the region.

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)

The rapid urbanization of modern cities necessitates innovative approaches to data collection and integration for smarter urban management. With the Internet of Things (IoT) at the core of these advancements, the ability to efficiently gather, analyze, and utilize data becomes paramount. Generative Artificial Intelligence (AI) is revolutionizing data collection by enabling intelligent synthesis, anomaly detection, and real-time decision-making across interconnected systems. This paper explores how generative AI enhances IoT-driven data collection in smart cities, focusing on applications in transportation, energy, public (...) safety, and environmental monitoring. By addressing challenges such as data privacy, scalability, and ethical considerations, the study highlights how generative AI transforms urban governance and paves the way for sustainable and citizen-centric development. Key trends, case studies, and future research directions are discussed, showcasing the potential of generative AI as a cornerstone of smart city initiatives. (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)

The polarities of democracy framework is used to achieve human emancipation by simultaneously managing multiple paradoxes by employing Johnson’s polarity management as the conceptual framework. Although Johnson’s framework may be appropriate for managing other tension-dependent pairs, it is less suitable for managing multiple democratic values when the goal is human emancipation and sustainable democratic social change. Managing multiple polarities is exacerbated by the problem-shifting and problem-creation effect inherent in a tension-driven framework. The aim was to develop a constructivist grounded theory (...) to answer the research question: How can the first principles of democracy be used to reduce dynamic tension and achieve human emancipation? This gap has been filled by a parsimonious conceptual framework based on three democratic principles: human dignity, fairness, and knowledge. The principles of democracy conceptual framework can synthesize the tension between democratic values in order to manage, plan, and evaluate democratic social change initiatives. (shrink)

As global manufacturing industries evolve, the demand for smarter, more efficient, and predictive production systems is intensifying. The integration of analyzer instrumentation with Artificial Intelligence (AI) in manufacturing environments presents a transformative opportunity to optimize processes, enhance product quality, and reduce operational costs. This research explores the convergence of analyzer technologies and AI-driven automation to create predictive manufacturing ecosystems. Through the use of smart sensors, real-time data analytics, and machine learning algorithms, modern manufacturing setups are becoming increasingly self-aware, adaptive, and (...) proactive. This paper reviews current advancements in analyzer instrumentation—such as gas chromatographs, spectrometers, and thermal analyzers—coupled with AI applications like predictive maintenance, quality forecasting, and anomaly detection. It also investigates key case studies from industries such as pharmaceuticals, automotive, and petrochemicals, highlighting how real-time monitoring and AI-based decision-making are reshaping production efficiency. The study utilizes a qualitative methodology, including a literature review and selected case analysis, to assess the benefits and challenges of these technologies. Findings reveal that predictive process optimization leads to significant improvements in throughput, energy consumption, and defect reduction. However, successful integration relies heavily on data quality, sensor accuracy, and workforce adaptability. The paper concludes by proposing a roadmap for industries aiming to transition toward nextgen manufacturing through AI-integrated analyzer instrumentation, emphasizing strategic planning, cross-disciplinary collaboration, and continuous learning. (shrink)

This article explores the transformative potential of improvisational techniques in reshaping interdisciplinary and transdisciplinary (ITD) learning environments offering art-based exercises and tools for this work. By integrating active research with improvisational methods from theatre and music, we propose a pedagogical shift that transcends traditional academic roles and disciplinary boundaries, fostering a culture of co-creation, mutual learning and innovation. This approach aims to tackle the inherent challenges of ITD research and thus enhance ITD research groups’ ability to address complex societal ‘Grand (...) Challenges’. We argue that improvisation within both ITD research and educational communities serves as a crucial catalyst for nurturing trust, embracing failure as a growth opportunity, and redefining success. Embodied practices based on improvisation help bridge the gap between theoretical knowledge and practical applications, enabling academics to navigate the complexities of collaboration and engage in shared learning experiences. This article introduces techniques from improvisational theatre aimed at fostering trust and collaboration in transdisciplinary research and educational settings. Drawing on over 25 years of combined research experience, we show how these tools enhance mutual understanding and collective problem-solving among students and research teams. Ultimately, we advocate integrating conventional knowledge delivery models with a framework characterised by regenerative practices, care and explorative processes. This integrated approach would offer new opportunities for addressing the intertwined wicked problems our world faces today, promoting a more inclusive, participatory and creatively fulfilling academic community. (shrink)

We investigate the association between controlling shareholders' ownership (CS_Own) and firms' leverage decisions in the Singaporean context. We examine whether the impact of ownership concentration on leverage differs across excess and lower control. We report that shareholders with excess control prefer leverage financing for an optimal capital structure and focus on value maximisation rather using leverage as a tool of minority shareholders' expropriation. Our analysis shows that firms capital structure significantly influences by the coalition of shareholders particularly decisions about leverage (...) financing in addition to the firms' specific characteristics and institutional arrangements. Our empirical evidence shows that controlling shareholders with a lower fraction of equity are more concerned about limited holding thus prefer leverage over equity financing to inflate their equity stake to protect them from the potential takeovers and mergers. We report that capital structure decisions in Singapore are linked with the trade-off between the controlling shareholders' target of mitigating firm risk and their non-dilution entrenchment needs. Further, we found an inverted U-shaped association between control ownership and leverage financing. In terms of moderating effect of family-controlled ownership, our findings exhibit that leverage financing is less pronounced for family firms in Singapore due to the under-diversified investment portfolio. (shrink)

:The emergence of personalized healthcare, or precision medicine, represents a paradigm shift in the medical field, focusing on tailoring medical treatments and interventions based on individual patient data. Cloud computing, when combined with Big Data analytics and Artificial Intelligence (AI), has the potential to revolutionize personalized healthcare by enabling the storage, processing, and analysis of vast amounts of patient data from multiple sources. This paper explores the role of cloud computing in advancing personalized healthcare, with a focus on its ability (...) to support Big Data and AI technologies for precision medicine. By facilitating real-time access to patient data, enhancing data interoperability, and enabling predictive analytics, cloud-based platforms provide healthcare providers with the tools necessary to deliver more accurate, effective, and individualized care. Additionally, this paper discusses how cloud computing enables collaboration among healthcare professionals, researchers, and patients, further enhancing the capabilities of personalized medicine. Through case studies and examples, we demonstrate how cloud infrastructure has already started transforming healthcare practices and delivering more personalized, data-driven solutions. (shrink)

Purpose – Although there have been several studies on corporate justice and employee ethical behavior, little is known about the conditions in which this link develops. The purpose of this study is to investigate the direct effect of organizational justice and moral attentiveness toward employee ethical behavior. Importantly, this study also considers the moderating role of moral attentiveness on the links between organizational justice and employee ethical behavior. -/- Design/methodology/approach – The data was collected from 350 employees who were assessed (...) directly to supervisors in 12 manufacturing companies placed in Malaysia, operated full-time, and had regular interaction with their direct supervisors. In particular, using two-wave survey data obtained from 270 employees working in the manufacturing industry in Malaysia. -/- Findings – Results showed that organizational justice and moral attentiveness positively impact the employee ethical behavior as predicted. New to the literature, findings disclose that moral attentiveness strengthens this relationship. Importantly, the positive impact of organizational justice is sharply positive under high than low moral attentive employees and ceases to be significant among low morally attentive personnel. -/- Research limitations/implications – This research focused on the notion of ethics and how important it is for society. The principles, norms and ideals that guide an individual’s behavior are referred to as ethics. Because the authors need to be treated with dignity as human beings, ethical behavior is essential in society. -/- Practical implications – The findings of this study send a clear signal to managers that “failing to ensure that their employees perceive organizational justice” may undermine every effort made by them to improve their organizations’ ethical quality. Importantly, the findings emphasize the role of moral attentiveness in improving the ethical behavior of employees both directly and by strengthening the effectiveness of organizational justice to impact such a behavior positively. So, given the advantages of moral attentiveness in terms of improving employee ethical conduct, businesses should make every effort to hire and choose people who meet this requirement because it is not easy to spot this personality trait. Human resource managers may assess candidates’ moral attentiveness using a range of methods such as group debate, an in-basket exercise, organized interviews and business games that concentrate on specific ethical concerns. -/- Social implications – This research focused on the notion of ethics and how important it is for society. The principles, norms and ideals that guide an individual’s behavior are referred to as ethics. Because the authors need to be treated with dignity as human beings, ethical behavior is essential in society. (shrink)

A book chapter exploring the potential consquences and ethical ramifications of using coercive measures within community mental healthcare. We argue that, althogh the move towards 'care in the community' may have had liberalising motivations, the subsequent reduction in inpatient or other supported residential provision, means that there has been an increasing move towards coercive measures outside of formal inpatient detention. We consider measures such as Community Treatment Orders, inducements, and other forms of leverage, explaining the underlying concepts, aims, and exploring (...) adverse consequences and ethical difficulties. (shrink)

Humans leverage material forms for unique cognitive purposes: We recruit and incorporate them into our cognitive system, exploit them to accumulate and distribute cognitive effort, and use them to recreate phenotypic change in new individuals and generations. These purposes are exemplified by writing, a relatively recent tool that has become highly adept at eliciting specific psychological and behavioral responses in its users, capability it achieved by changing in ways that facilitated, accumulated, and distributed incremental behavioral and psychological change between individuals (...) and generations. Writing is described here as a self-organizing system whose design features reflect points of maximal usefulness that emerged under sustained collective use of the tool. Such self-organization may hold insights applicable to human cognitive evolution and tool use more generally. Accordingly, this article examines the emergence of the ability to leverage material forms for cognitive purposes, using the tool-using behaviors and lithic technologies of ancestral species and contemporary non-human primates as proxies for matters like collective use, generational sustainment, and the non-teleological emergence of design features. (shrink)

Behavioral intervention techniques leveraging reactive responses have gained popularity as tools for promoting ethical behavior. Choice architects, for example, design and present default opt-out options to nudge individuals into accepting preselected choices deemed beneficial to both the decision-maker and society. Such interventions can also employ mild financial incentives or affective triggers including joy, fear, empathy, social pressure, and reputational rewards. We argue, however, that ethical competence is achieved via reflection, and that heavy reliance on reactive behavioral interventions can undermine (...) the development of ethical competence over the long term. Specifically, drawbacks may occur through motivational displacement, dependency, moral crowding out, loss of personal autonomy, and reactance. We introduce complementary cognitive boosting techniques designed to stimulate reflective cognition, as a more promising long-term strategy for instilling ethical behavior. One such approach is the Moral Self-Awareness (MSA) motivational construct, which incrementally leads agents to increasing levels of ethical reflection. We explain why ethical boosting approaches present more edifying and durable alternatives to reactive behavioral interventions and offer suggestions for social and organizational policy. (shrink)

Clinicians and health researchers frequently encounter opportunities to rescue people. Rescue cases can generate a moral duty to aid those in peril. As such, bioethicists have leveraged a duty to rescue for a variety of purposes. Yet, despite its broad application, the duty to rescue is under-analyzed. In this paper, we assess the state of theorizing about the duty to rescue. There are large gaps in bioethicists’ understanding of the force, scope, and justification of the two most cited duties to (...) rescue—the individual duty of easy rescue and the institutional rule of rescue. We argue that the duty of easy rescue faces unresolved challenges regarding its force and scope, and the rule of rescue is indefensible. If the duty to rescue is to help solve ethical problems, these theoretical gaps must be addressed. We identify two further conceptions of the duty to rescue that have received less attention—an institutional duty of easy rescue and the professional duty to rescue. Both provide guidance in addressing force and scope concerns and, thereby, traction in answering the outstanding problems with the duty to rescue. We conclude by proposing and propose research priorities for developing accounts of duties to rescue in bioethics. (shrink)

In artificial intelligence, recent research has demonstrated the remarkable potential of Deep Convolutional Neural Networks (DCNNs), which seem to exceed state-of-the-art performance in new domains weekly, especially on the sorts of very difficult perceptual discrimination tasks that skeptics thought would remain beyond the reach of artificial intelligence. However, it has proven difficult to explain why DCNNs perform so well. In philosophy of mind, empiricists have long suggested that complex cognition is based on information derived from sensory experience, often appealing to (...) a faculty of abstraction. Rationalists have frequently complained, however, that empiricists never adequately explained how this faculty of abstraction actually works. In this paper, I tie these two questions together, to the mutual benefit of both disciplines. I argue that the architectural features that distinguish DCNNs from earlier neural networks allow them to implement a form of hierarchical processing that I call “transformational abstraction”. Transformational abstraction iteratively converts sensory-based representations of category exemplars into new formats that are increasingly tolerant to “nuisance variation” in input. Reflecting upon the way that DCNNs leverage a combination of linear and non-linear processing to efficiently accomplish this feat allows us to understand how the brain is capable of bi-directional travel between exemplars and abstractions, addressing longstanding problems in empiricist philosophy of mind. I end by considering the prospects for future research on DCNNs, arguing that rather than simply implementing 80s connectionism with more brute-force computation, transformational abstraction counts as a qualitatively distinct form of processing ripe with philosophical and psychological significance, because it is significantly better suited to depict the generic mechanism responsible for this important kind of psychological processing in the brain. (shrink)

Humeanism about laws of nature — the view that the laws reduce to the Humean mosaic — is a popular view, but currently existing versions face powerful objections. The non-supervenience objection, the non-fundamentality objection and the explanatory circularity objection have all been thought to cause problems for the Humean. However, these objections share a guiding thought — they are all based on the idea that there is a certain kind of divergence between the practice of science and the metaphysical picture (...) suggested by Humeanism. -/- I suggest that the Humean can respond to these objections not by rejecting this divergence, but by arguing that is appropriate. In particular the Humean can, in the spirit of Loewer (2012), distinguish between scientific and metaphysical explanation — this is motivated by differing aims of explanation in science and metaphysics. And they can further leverage this into distinctions between scientific and metaphysical fundamentality and scientific and metaphysical possibility. We can use these distinctions to respond to the objections that the Humean faces. (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)

The following leverages a queer(ed) somaesthetic lens to analyze the protagonist of "Poor Things" (2023), Bella Baxter, as a figure of deviance. Bella's body, as a queer(ed) space, serves as a vehicle for learning through the sensual erotic, a source of social discourse on personhood, morality, and autonomy, and a space where others project hegemonic norms of beauty, gender, and sexuality. The article further examines queerness through her identity, actions, gender, and sexuality and challenges conventional norms, making her body a (...) site for exploration and expression of deviation. Finally, the article examines the role of power, policing, and violence as a by-product of deviance, drawing parallels between Bella's experiences and those of queer people and other marginalized groups. By exploring Bella's character, the paper illuminates "Poor Things" (2023) as a compelling commentary on queer(ed) and embodied discussions of identity, autonomy, and personhood. (shrink)