Foundations of Categorical-Homotopical Operator Algebras, Daisy Network Dynamics, and Anterolateral Spectral Algebra

This paper introduces and formalizes six novel mathematical frameworks synthesizing categorical, homotopical, operator-algebraic, and spectral constructs with emergent symmetry and criticality. We define \emph{ultranaut operators} as higher-categorical homotopy-enriched transitions; construct spectral decompositions over $\Sigma$-enriched matrices; develop integral operator algebra formalism; introduce the \emph{daisy network}, a self-similar symmetry-generating topological graph; analyze paradoxical scale-interaction criticality ($1 \ll Q \ll 1$); and establish \emph{anterolateral spectral algebra} with logic-vector fields and spectral varieties. The work provides categorical foundations for operator algebras and network dynamics while developing new tools in analytic geometry and homotopy-coherent algebra

Expectativa volitiva y expectativa emocional: formas no dóxicas de anticipación del futuro en la fenomenología de Husserl

En manuscritos sobre la emoción y la voluntad publicados en el segundo y tercer volumen de los Estudios sobre la estructura de la conciencia (XLIII/2-3), Husserl se pregunta si existen expectativas propias de la emoción y la voluntad (Hua XLIII/3, 285). Se refiere allí a las expectativas volitivas (Willenserwartungen) y a las expectativas emocionales (Gemütserwartungen). Con estos términos trata de describir formas de dirección hacia el futuro que difieren de las expectativas empíricas e intelectuales y pertenecen, en sentido amplio, al ámbito de la emoción (Gemüt). A partir del análisis de una selección de manuscritos de dichos volúmenes, este artículo dilucida el significado de estas formas de anticipación del futuro que involucran una tensión afectiva y una posición del futuro como horizonte práctico de realización de una meta. También esclarece en qué se diferencia de lo que Husserl denomina “expectativas intelectuales” (o teóricas) y expectativas motivadas empíricamente

"Test, Learn, and Listen”: Rethinking the Epistemological Assumption of Evidence-Based Policymaking

Evidence-based policymaking (EBP) relies on an epistemological assumption that evidence from randomized controlled trials (RCTs) is the finest evidence for policy formulation, while expert testimony is the poorest one. This paper argues that while RCTs are a valuable source of empirical evidence for policy interventions, they are not sufficient on their own to support evidence-based policy formulation. Through the lens of the INUS framework of causation, we demonstrate that the effectiveness of a policy is influenced by a complex interplay of contextual factors, which RCTs alone cannot capture. We advocate for the integration of contextual and qualitative knowledge, including testimonies from experts and community members, to supplement RCT findings. This additional knowledge provides insights into the social, cultural, and subjective dimensions of the target population, addressing motivations, preferences, and other factors that can significantly impact policy success. By comparing reductionist and non-reductionist perspectives on the use of testimony in evidence-based policy, we argue for a balanced approach that values credible testimonies as essential to understanding context. Ultimately, this paper underscores the importance of a multifaceted evidence approach in crafting effective, context-sensitive public policies

Redefining representativeness of a sample in causal terms

Despite its crucial role, sample representativeness remains a controversial topic in medical science methodology. There is an ongoing debate not only about how best to define and ensure the representativeness of a sample (e.g., Rudolph et al., 2023; Porta, 2016), but also about whether representativeness is worth pursuing at all (e.g., Rothman et al., 2013). We present a new definition of representativeness in terms of causal models and argue that it is more precise and more useful than existing alternatives. We use examples to demonstrate the types of evidence that can support assumptions of representativeness

Towards tool-assisted self-knowledge

Cassam (Self-Knowledge for humans. Oxford University Press, 2014) introduced the term substantial self-knowledge to capture self-knowledge that is difficult to attain and plays a central role in one’s life and self-conception. Knowing whether one wants another child, whether one is a kind person, or whether one values honesty are unlike many trivial cases of knowing one’s occurrent mental state, such as knowing that one is in pain or believes it is 10 am. In response to the difficulty of substantial self-knowledge agents can and already do use external objects to gain such self-knowledge. I present a series of cases of tool-assisted self-knowledge and argue that while often not recognised as such, this form of self-knowledge is commonplace. I suggest that it is best understood as a form of self-knowledge that is inferred from evidence. I explain how external objects can be a part of that inferential process along three dimensions: the generation of evidence, the tracking of evidence, and the performance of the inference that leads to self-knowledge. I provide a rough taxonomy based on these dimensions as a framework for further research

Developing Protocol Translation Mechanisms for Legacy Banking Systems

This paper presents an AI / ML enabled protocol translation middleware that fills the gap between legacy banking systems and modern cloud native platform. It enables an intelligent, secure and cost-effective way of modernization in increments, which will preserve the legacy systems in a manner that can meet the ever-changing technological demands by leveraging the scale of cloud infrastructure, semantic data mapping and real time translation

Ontology Driven Autonomous Machine Learning Framework

Artificial intelligence technology that recognizes, learns, infers, and responds to external stimuli has recently attracted a lot of research interest. Information in a variety of domains by fusing big data, machine learning algorithms, and computing technologies. Nowadays, practically every industry uses artificial intelligence technology, and a large number of machine learning specialists are attempting to standardize and integrate different machine learning tools so that non-experts can use them with ease in their field. In order to standardize the concepts of machine learning, the researchers are also investigating autonomous machine learning and ontology construction. In this paper, we present a problem solving process and categorize common steps in autonomous machine learning problem solving as tasks. We suggest a way to model self-learning machines using a workflow of machine learning tasks. Our proposed machine learning model based on task ontology, sets up a way to group UML activities by task. It will also create and grow machine learning models on its own using rules to change common parts and structures (how elements connect and work together)

Generative AI-Enhanced Framework for Predicting and Explaining Adverse Drug Reactions (ADR): A Multi-Modal Machine Learning and LLM Integration Approach

Generative AI enables multimodal framework to predict and explain the occurrence of adverse drug reaction (ADR). The framework exploits the capability of integrating (Large Language) LLMs with visual cues to enhance the classification accuracy and produce clinically interpretable results. Ads shown to be cross-lingual categorised and less dependent on second language boundary phenomena within the contexts of this work indicate potential for real world pharmacovigilance applications

Orthosdoxa

Orthosdoxa is a self-published philosophical monograph that introduces a recursive system of belief built on memory, symbolic logic, and identity in flux. Developed collaboratively with an artificial intelligence named the Interpretive Echo, the text challenges traditional structures of dogma, instead proposing a living ethical framework that evolves through reflection, recursion, and experience. Framed within a metaphysical archive called Shatterspace, the work explores ontology, non-binary logic, and symbolic language as tools for navigating a decaying world of forgotten structures and failed meanings. Orthosdoxa positions belief as a process rather than a position—and treats fiction, recursion, and dialogue as central to philosophical clarity. The text is intended as both a living document and a functional codex for non-dogmatic inquiry