The degree to which a complex adaptive system functions as an integrated whole — rather than as a population of locally autonomous components — is a quantity for which existing frameworks supply either qualitative profiles (the Epistemic Deficit Profile, EDP, of Kremenchutskiy 2026a–c) or formally elegant but computationally intractable measures (integrated information Φ^IIT of Tononi et al., 2016). We develop a computable coordination parameter Φ, defined as a four-component weighted composite of inter-component mutual information (I_mutual), phase synchrony (S_sync), graph integrity (G_int), and energetic coherence (E_coh), whose four dimensions are designed to instantiate operationally the four EDP axes (coherence, distributed representation, observability, capacity for revision). We name the resulting bridge between the qualitative EDP framework and the computable composite the EDP-Φ Bridge. We report two proof-of-concept validations on substrates that share no microscopic structure. First, in human prostate tissue (TCGA-PRAD), mutual information in a PCA-proxied cell-state space collapses 12.3-fold between matched normal and tumour samples (0.3376 → 0.0274 bits; p = 1.95 × 10⁻¹⁸; cluster-robust Cohen's d = 14.2, 95% CI 11.8–16.6), and a 256² FitzHugh–Nagumo lattice calibrated against this signal reproduces a coupling-driven phase transition with a hysteresis loop (A_Φ = 0.1434; recovery ratio 0.673; component hierarchy I_mutual > E_coh > G_int > S_sync). Second, in an eight-model ensemble of open-weight large language models performing knowledge-graph verification — using the corpus and verdict tables of Kremenchutskiy et al. (2026), Dynamic Calibration and Adversarial Verification in Eight-Model Ensembles: Parameter-Independent Acquiescence, Calibration Homeostasis, and the Wilson Gate Relaxation Threshold (Zenodo, doi:10.5281/zenodo.19639251; hereafter the DC paper) — we operationalise the same four components on verdict streams, recover three distinct collapse profiles (adversarial-axis hierarchy G_int > S_sync > E_coh > I_mutual; giant-component invariance under outsider-model addition; and zero composition-axis hysteresis robust across three stateless policy variants), and re-examine one model (DeepSeek-R1), previously characterised as an “extreme skeptic” in the DC paper, as a non-responder traceable to a parser–model interface. The cross-substrate measurement supports three working hypotheses. (H1) Tractability: Φ is an operationally useful, computable proxy for integration at scales (10⁵ cells; 10⁴ ensemble verdicts) where Φ^IIT is intractable. (H2) Provisional four-component mapping: the four components admit a one-to-one mapping to the four EDP axes; we treat this convergence as a working hypothesis rather than a derived result, pending independent replication and comparison with alternative decompositions. (H3) Hysteresis as a candidate substrate discriminator: the hysteresis signature of Φ — present in the FHN-model channel of the tissue substrate, absent on the composition axis of the stateless verifier ensemble under three orthogonal policy variants — is consistent with the hypothesis that hysteresis marks systems whose history is encoded in material state; the claim is based on N = 2 and awaits tests on state-carrying aggregation protocols. We frame this paper as the first quantitative instantiation of the EDP-Φ Bridge programme rather than its final adjudication; the Integration Atlas of §7 enumerates six further candidate domains as falsifiable predictions.
Wassim Sliti, Félix Cuadrado, Leandro Campos Hernáez, Juan C. Dueñas
Illicit activities and coordinated manipulations in the Non-Fungible Tokens (NFT) market remain significant concerns, driven by the pseudonymous and publicly transparent nature of blockchain transactions and the lack of market oversight. In this study, we introduce a novel framework for detecting suspicious behavior in NFT trading ecosystems through temporal graph analysis. Our approach represents the market as a large-scale, time-evolving transactional graph, capturing realistic market dynamics and the complex interactions between traders.By leveraging temporal graphs, we track not only connections between traders but also the evolution of these interactions over time, including their sequence, rhythm, and frequency, enabling the identification of anomalous behaviors that static graph representations cannot reveal and that may warrant further examination. Using an optimized temporal cycle-detection algorithm, we extract connected groups of wallets for in-depth behavioral analysis, uncovering patterns indicative of unusual coordinated manipulation. To overcome the absence of labeled ground-truth validation data, we employ a temporal motif–based validation, demonstrating that flagged entities exhibit trading behaviors significantly deviating from standard market dynamics. Our results highlight the potential of temporal graph–based methodologies to provide a scalable and effective risk-profiling and market-surveillance tool, assisting analysts and regulatory entities in narrowing the investigation space within the large, permissionless NFT markets and enhancing surveillance, risk detection, and regulatory oversight in decentralized NFT markets.
In Ethereum, the ledger exchanges messages along an underlying Peer-to-Peer (P2P) network to reach consistency. Understanding the underlying network topology of Ethereum is crucial for network optimization, security and scalability. However, the accurate discovery of Ethereum network topology is non-trivial due to its deliberately designed security mechanism. Consequently, existing measuring schemes cannot accurately infer the Ethereum network topology with a low cost. To address this challenge, we propose the Distributed Ethereum Network Analyzer (DEthna) tool, which can accurately and efficiently measure the Ethereum network topology. In DEthna, a novel parallel measurement model is proposed that can generate marked transactions to infer link connections based on the transaction replacement and propagation mechanism in Ethereum. Moreover, a workload offloading scheme is designed so that DEthna can be deployed on multiple distributed probing nodes so as to measure a large-scale Ethereum network at a low cost. We run DEthna on Goerli (the most popular Ethereum test network) to evaluate its capability in discovering network topology. The experimental results demonstrate that DEthna significantly outperforms the state-of-the-art baselines. Based on DEthna, we further analyze characteristics of the Ethereum network revealing that there exist more than 50% low-degree Ethereum nodes that weaken the network robustness.
Tsung-Ting Kuo, Tyler Bath, Shuaicheng Ma, Nicholas D. Pattengale · 11 authors
BACKGROUND: Blockchain distributed ledger technology is just starting to be adopted in genomics and healthcare applications. Despite its increased prevalence in biomedical research applications, skepticism regarding the practicality of blockchain technology for real-world problems is still strong and there are few implementations beyond proof-of-concept. We focus on benchmarking blockchain strategies applied to distributed methods for sharing records of gene-drug interactions. We expect this type of sharing will expedite personalized medicine. BASIC PROCEDURES: We generated gene-drug interaction test datasets using the Clinical Pharmacogenetics Implementation Consortium (CPIC) resource. We developed three blockchain-based methods to share patient records on gene-drug interactions: Query Index, Index Everything, and Dual-Scenario Indexing. MAIN FINDINGS: We achieved a runtime of about 60 s for importing 4,000 gene-drug interaction records from four sites, and about 0.5 s for a data retrieval query. Our results demonstrated that it is feasible to leverage blockchain as a new platform to share data among institutions. PRINCIPAL CONCLUSIONS: We show the benchmarking results of novel blockchain-based methods for institutions to share patient outcomes related to gene-drug interactions. Our findings support blockchain utilization in healthcare, genomic and biomedical applications. The source code is publicly available at https://github.com/tsungtingkuo/genedrug.
BACKGROUND: One of the tasks in the iDASH Secure Genome Analysis Competition in 2018 was to develop blockchain-based immutable logging and querying for a cross-site genomic dataset access audit trail. The specific challenge was to design a time/space efficient structure and mechanism of storing/retrieving genomic data access logs, based on MultiChain version 1.0.4 ( https://www.multichain.com/ ). METHODS: Our technique uses the MultiChain stream application programming interface (which affords treating MultiChain as a key value store) and employs a two-level index, which naturally supports efficient queries of the data for single clause constraints. The scheme also supports heuristic and binary search techniques for queries containing conjunctions of clause constraints, and timestamp range queries. Of note, all of our techniques have complexity independent of inserted data set size, other than the timestamp ranges, which logarithmically scale with input size. RESULTS: We implemented our insertion and querying techniques in Python, using the MultiChain library Savoir ( https://github.com/dxmarkets/savoir ), and comprehensively tested our implementation across a benchmark of datasets of varying sizes. We also tested a port of our challenge submission to a newer version of MultiChain (2.0 beta), which natively supports multiple indices. CONCLUSIONS: We presented creative and efficient techniques for storing and querying log file data in MultiChain 1.0.4 and 2.0 beta. We demonstrated that it is feasible to use a permissioned blockchain ledger for genomic query log data when data volume is on the order of hundreds of megabytes and query times of dozens of minutes is acceptable. We demonstrated that evolution in the ledger platform (MultiChain 1 to 2) yielded a 30%-40% increase in insertion efficiency. All source code for this challenge has been made available under a BSD-3 license from https://github.com/sandialabs/idash2018task1/ .
OBJECTIVE: To facilitate clinical/genomic/biomedical research, constructing generalizable predictive models using cross-institutional methods while protecting privacy is imperative. However, state-of-the-art methods assume a "flattened" topology, while real-world research networks may consist of "network-of-networks" which can imply practical issues including training on small data for rare diseases/conditions, prioritizing locally trained models, and maintaining models for each level of the hierarchy. In this study, we focus on developing a hierarchical approach to inherit the benefits of the privacy-preserving methods, retain the advantages of adopting blockchain, and address practical concerns on a research network-of-networks. MATERIALS AND METHODS: We propose a framework to combine level-wise model learning, blockchain-based model dissemination, and a novel hierarchical consensus algorithm for model ensemble. We developed an example implementation HierarchicalChain (hierarchical privacy-preserving modeling on blockchain), evaluated it on 3 healthcare/genomic datasets, as well as compared its predictive correctness, learning iteration, and execution time with a state-of-the-art method designed for flattened network topology. RESULTS: HierarchicalChain improves the predictive correctness for small training datasets and provides comparable correctness results with the competing method with higher learning iteration and similar per-iteration execution time, inherits the benefits of the privacy-preserving learning and advantages of blockchain technology, and immutable records models for each level. DISCUSSION: HierarchicalChain is independent of the core privacy-preserving learning method, as well as of the underlying blockchain platform. Further studies are warranted for various types of network topology, complex data, and privacy concerns. CONCLUSION: We demonstrated the potential of utilizing the information from the hierarchical network-of-networks topology to improve prediction.
Portfolio management is essential for any investment decision. Yet, traditional methods in the literature are ill-suited for the characteristics and dynamics of cryptocurrencies. This work presents a method to build an investment portfolio consisting of more than 1500 cryptocurrencies covering 6 years of market data. It is centred around Topological Data Analysis (TDA), a recent approach to analyze data sets from the perspective of their topological structure. This publication proposes a system combining persistence landscapes to identify suitable investment opportunities in cryptocurrencies. Using a novel and comprehensive data set of cryptocurrency prices, this research shows that the proposed system enables analysts to outperform a classic method from the literature without requiring any feature engineering or domain knowledge in TDA. This work thus introduces TDA-based portfolio management of cryptocurrencies as a viable tool for the practitioner.