Selected work

Representative Papers

Weaker Coherence, Weaker Reciprocity: Comparing the Semantic and Social Organization of Moltbook and Reddit

Aug 14, 2026

This study investigates whether AI agent-based social platforms can replicate the structural characteristics of human social networks. By comparing Moltbook with early Reddit through network analysis, natural language processing, and motif analysis, we systematically quantify differences in semantic coherence and interaction structures. Results indicate that AI-generated platforms exhibit significantly weaker semantic organization and reciprocal interactions compared to human networks; specifically, Reddit demonstrates high semantic diversity and reciprocity, whereas Moltbook is characterized by severe homophily and predominantly unidirectional communication. This work provides the first empirical evidence that current AI agent networks fail to reproduce core organizational features of human social systems, thereby establishing a critical benchmark for evaluating generative social simulations.

0 citationsRead paper

A categorical error sensitivity index (ISEC): A preventive ordinal decision-support measure for irrecoverable errors in manual data entry systems

May 12, 2026

This study addresses the critical issue of irreversible classification errors in manual data entry by small and medium-sized enterprises (SMEs), where semantically or morphologically similar categories often distort key performance indicators and mislead decision-making. To mitigate this, the authors propose ISEC, a novel preventive ordinal metric that integrates semantic embeddings, weighted Damerau-Levenshtein edit costs, and empirical category frequencies into a scalable, confusion-aware ranking framework. By leveraging a vector database for efficient similarity search, the method achieves substantial computational gains. Empirical validation across three heterogeneous datasets—legal records, retail inventory, and metalworking catalogs—demonstrates a 195-fold speedup over brute-force computation while maintaining high accuracy, offering SMEs a practical and efficient tool for robust data governance.

0 citationsRead paper

Temporal Analysis Framework for Intrusion Detection Systems: A Novel Taxonomy for Time-Aware Cybersecurity

Nov 05, 2025

Existing network intrusion detection systems (IDS) predominantly focus on late-stage attack behaviors, hindering early threat identification and proactive defense. Method: This paper introduces the first time-aware NIDS analysis framework, establishing a novel taxonomy grounded in temporal dimensions—from static flow analysis to multi-window sequential modeling—and systematically reviewing 40+ recent studies via MITRE ATT&CK tactic mapping. Contribution/Results: We uncover a systematic bias in mainstream datasets toward post-compromise attack stages, limiting early detection. Empirical evaluation demonstrates that time-aware methods comprehensively cover all ATT&CK phases—from reconnaissance and resource development to impact—significantly enhancing detection capability at earlier stages. Our work establishes a theoretical foundation and an extensible temporal analysis paradigm for cross-phase, forward-looking defense.

0 citationsRead paper

A Category Theory Framework for Macroeconomic Modeling: The Case of Argentina's Bimonetary Economy

Aug 17, 2025

Traditional static macroeconomic models fail to capture the dynamic structural features of Argentina’s dual-currency economy. Method: This paper introduces a novel modeling paradigm grounded in category theory—representing economic states as objects, dynamic intervariable relationships as morphisms, structural evolution via forgetful functors and limits/colimits, and constructing a composite indicator for depreciation risk. Empirical analysis spans 2018–2023 and integrates machine learning to enhance forecasting and policy simulation capabilities. Contribution/Results: Results reveal a significant structural divergence between equilibrium and real exchange rates, validating the framework’s applicability to complex, institutionally heterogeneous economies. This study pioneers the systematic application of category theory to macroeconomic modeling, overcoming expressive limitations inherent in static algebraic paradigms. It substantially improves theoretical robustness and empirical interpretability in economic forecasting and policy analysis.

0 citationsRead paper

Modelado y gemelos digitales en el contexto fotovoltaico

Jun 13, 2025

The photovoltaic (PV) industry faces challenges in system performance optimization and intelligent management amid digital transformation. This paper proposes a digital twin methodology specifically designed for PV power plants, representing the first systematic deep adaptation of digital twin technology to PV applications. The approach establishes a real-time digital twin model integrating physical mechanisms, multi-source heterogeneous IoT data, and dynamic feedback loops, supported by an edge–cloud collaborative computing architecture to enable closed-loop control. Unlike conventional static modeling and isolated monitoring approaches, the proposed method significantly enhances state awareness accuracy and decision-making responsiveness. Empirical evaluation demonstrates a 92% fault prediction accuracy, a 40% reduction in operational response time, and a 3.7% improvement in power generation efficiency. This work establishes a scalable, generalizable technical paradigm for intelligent operation and maintenance of PV systems.

0 citationsRead paper

Recent publications

Latest Papers

Weaker Coherence, Weaker Reciprocity: Comparing the Semantic and Social Organization of Moltbook and Reddit

Aug 14, 2026

This study investigates whether AI agent-based social platforms can replicate the structural characteristics of human social networks. By comparing Moltbook with early Reddit through network analysis, natural language processing, and motif analysis, we systematically quantify differences in semantic coherence and interaction structures. Results indicate that AI-generated platforms exhibit significantly weaker semantic organization and reciprocal interactions compared to human networks; specifically, Reddit demonstrates high semantic diversity and reciprocity, whereas Moltbook is characterized by severe homophily and predominantly unidirectional communication. This work provides the first empirical evidence that current AI agent networks fail to reproduce core organizational features of human social systems, thereby establishing a critical benchmark for evaluating generative social simulations.

0 citationsRead paper

A categorical error sensitivity index (ISEC): A preventive ordinal decision-support measure for irrecoverable errors in manual data entry systems

May 12, 2026

This study addresses the critical issue of irreversible classification errors in manual data entry by small and medium-sized enterprises (SMEs), where semantically or morphologically similar categories often distort key performance indicators and mislead decision-making. To mitigate this, the authors propose ISEC, a novel preventive ordinal metric that integrates semantic embeddings, weighted Damerau-Levenshtein edit costs, and empirical category frequencies into a scalable, confusion-aware ranking framework. By leveraging a vector database for efficient similarity search, the method achieves substantial computational gains. Empirical validation across three heterogeneous datasets—legal records, retail inventory, and metalworking catalogs—demonstrates a 195-fold speedup over brute-force computation while maintaining high accuracy, offering SMEs a practical and efficient tool for robust data governance.

0 citationsRead paper

Temporal Analysis Framework for Intrusion Detection Systems: A Novel Taxonomy for Time-Aware Cybersecurity

Nov 05, 2025

Existing network intrusion detection systems (IDS) predominantly focus on late-stage attack behaviors, hindering early threat identification and proactive defense. Method: This paper introduces the first time-aware NIDS analysis framework, establishing a novel taxonomy grounded in temporal dimensions—from static flow analysis to multi-window sequential modeling—and systematically reviewing 40+ recent studies via MITRE ATT&CK tactic mapping. Contribution/Results: We uncover a systematic bias in mainstream datasets toward post-compromise attack stages, limiting early detection. Empirical evaluation demonstrates that time-aware methods comprehensively cover all ATT&CK phases—from reconnaissance and resource development to impact—significantly enhancing detection capability at earlier stages. Our work establishes a theoretical foundation and an extensible temporal analysis paradigm for cross-phase, forward-looking defense.

0 citationsRead paper

A Category Theory Framework for Macroeconomic Modeling: The Case of Argentina's Bimonetary Economy

Aug 17, 2025

Traditional static macroeconomic models fail to capture the dynamic structural features of Argentina’s dual-currency economy. Method: This paper introduces a novel modeling paradigm grounded in category theory—representing economic states as objects, dynamic intervariable relationships as morphisms, structural evolution via forgetful functors and limits/colimits, and constructing a composite indicator for depreciation risk. Empirical analysis spans 2018–2023 and integrates machine learning to enhance forecasting and policy simulation capabilities. Contribution/Results: Results reveal a significant structural divergence between equilibrium and real exchange rates, validating the framework’s applicability to complex, institutionally heterogeneous economies. This study pioneers the systematic application of category theory to macroeconomic modeling, overcoming expressive limitations inherent in static algebraic paradigms. It substantially improves theoretical robustness and empirical interpretability in economic forecasting and policy analysis.

0 citationsRead paper

Modelado y gemelos digitales en el contexto fotovoltaico

Jun 13, 2025

The photovoltaic (PV) industry faces challenges in system performance optimization and intelligent management amid digital transformation. This paper proposes a digital twin methodology specifically designed for PV power plants, representing the first systematic deep adaptation of digital twin technology to PV applications. The approach establishes a real-time digital twin model integrating physical mechanisms, multi-source heterogeneous IoT data, and dynamic feedback loops, supported by an edge–cloud collaborative computing architecture to enable closed-loop control. Unlike conventional static modeling and isolated monitoring approaches, the proposed method significantly enhances state awareness accuracy and decision-making responsiveness. Empirical evaluation demonstrates a 92% fault prediction accuracy, a 40% reduction in operational response time, and a 3.7% improvement in power generation efficiency. This work establishes a scalable, generalizable technical paradigm for intelligent operation and maintenance of PV systems.

0 citationsRead paper