Andre Septianus Nababan, Raymon Simamora, Cristina Situmorang, Lotar Mateus Sinaga
Pertumbuhan jumlah pengguna dan volume lalu lintas data menuntut jaringan komputer yang mampu menyediakan koneksi stabil, cepat, dan terkontrol. Penelitian ini bertujuan menganalisis kualitas layanan jaringan pada server Debian 12 melalui penerapan algoritma Hierarchical Token Bucket sebagai metode manajemen bandwidth. Penelitian menggunakan metode eksperimen dengan membandingkan kondisi jaringan sebelum dan sesudah penerapan HTB pada topologi bintang yang melibatkan satu server Debian, perangkat MikroTik sebagai switch atau bridge, serta dua client pengujian. Konfigurasi HTB membagi bandwidth sebesar 10 Mbps untuk Client 1 dan 5 Mbps untuk Client 2. Pengukuran dilakukan terhadap parameter Quality of Service yang meliputi throughput, delay, jitter, dan packet loss menggunakan iperf3 dan ping. Hasil penelitian menunjukkan bahwa sebelum penerapan HTB, throughput kedua client relatif sama, yaitu 94,7 Mbps pada Client 1 dan 94,8 Mbps pada Client 2. Setelah HTB diterapkan, throughput menjadi 8,81 Mbps dan 4,71 Mbps, sehingga pembatasan bandwidth berjalan sesuai kebijakan. Nilai delay rata-rata tetap rendah, yaitu 3 ms pada Client 1 dan 5 ms pada Client 2, sedangkan packet loss tetap 0% pada kedua client. Nilai jitter sesudah HTB sebesar 0,292 ms dan 5,862 ms, yang masih termasuk kategori sangat baik berdasarkan standar TIPHON. Temuan ini menunjukkan bahwa HTB efektif mengatur distribusi bandwidth secara adil, terstruktur, dan efisien tanpa menurunkan stabilitas layanan jaringan secara signifikan.
The Time Token Arends learning model can be used as an alternative learning strategy to improve students’ learning outcomes, particularly in the Fiqh subject. This study aimed to determine the effect of implementing the Time Token Arends learning model on the learning outcomes of Grade XI students at MAS 09 Sidomulyo in the Fiqh subject. The study employed a quantitative approach using a quasi-experimental method with a non-equivalent control group design. The results of the analysis showed a significant difference between the learning outcomes of students taught using the Time Token Arends model and those taught using the conventional model. This finding indicates that the Time Token Arends learning model has a positive effect on students’ learning outcomes.
SUMMARY Autonomous bioluminescence systems enable continuous light emission in engineered organisms by genetically encoding both luciferase enzymes and their substrate biosynthetic pathways, offering a powerful platform for non-invasive and long-term monitoring of biological processes. However, bioluminescence output is highly sensitive to substrate availability and host metabolic state, often leading to signal instability under energy-limited conditions. Here, we report an alternative luciferin biosynthetic strategy for the bacterial Lux bioluminescence system in which the adenosine triphosphate (ATP)-dependent LuxEC complex is replaced by α-dioxygenase (αDOX), an enzyme that directly converts fatty acids into fatty aldehydes without consuming ATP. Using machine learning–guided directed evolution, we engineered αDOX variants that markedly enhanced bioluminescence intensity when coupled with bacterial luciferase. The resulting ATP-free Lux bioluminescence system enabled single-cell–level bioluminescence imaging and maintained stable light emission under diverse antibiotic treatments, demonstrating enhanced robustness against metabolic perturbations. SIGNIFICANCE Autonomous bioluminescence imaging has become an attractive method for long-term observation of biological phenomena without the need for exogenous substrate addition. However, the light output of existing autonomous bioluminescence systems, including bacterial and fungal pathways, remains ATP-dependent and often declines when cellular metabolism is perturbed, limiting their reliability for quantitative analysis. Here, we report the development of an ATP-independent substrate biosynthesis pathway for the bacterial luciferase system using αDOX. To improve system performance, we applied machine learning– guided directed evolution, which significantly enhanced signal intensity and enabled single-cell bioluminescence imaging. Furthermore, the αDOX-based bacterial luciferase system maintained stable luminescence under antibiotic treatments, in contrast to the conventional ATP-dependent bacterial luciferase system. In summary, our findings establish a robust ATP-independent autonomous bioluminescence imaging platform that enables monitoring of cellular events under metabolic perturbations.
Contract Role: Software Developer PKP is seeking a mid-career or senior software developer to join the software development team in coding the future of PKP's software applications, particularly Open Journal Systems (OJS), Open Monograph Press (OMP), and Open Preprint Systems (OPS). These applications are written in PHP and VueJS. There will be many opportunities for related work such as
This paper examines whether information stress affects trading frictions and liquidity resilience in cryptocurrency markets. Using public information proxies and OHLCV-based friction indicators, the analysis applies local projections to trace the response of trading conditions. The results show that information stress mainly affects trading frictions in the short run. Spread-based friction reacts immediately and recovers quickly, while the Amihud-based proxy adjusts more gradually. The cumulative effect is strongest in the early horizons and weaker over longer horizons. Overall, information stress creates temporary trading pressure rather than persistent deterioration in market resilience.
Cryptographic software forms a critical foundation of modern computing systems, but the security guarantees of cryptographic protocols do not automatically extend to their implementations. Errors in arithmetic operations, validation logic, data conversion, constraint generation, or component integration can cause deployed software to deviate from the intended protocol while still producing plausible outputs. Such risks are difficult to detect in compiled binaries and become even more challenging in modern cryptographic systems such as zero-knowledge proofs, where implementations combine finite-field arithmetic, constraint systems, witness generation, proving procedures, verification logic, and serialization formats.Securing cryptographic implementations requires analysis techniques that can reason about both low-level program behavior and high-level cryptographic intent. To address this need, cryptographic function identification in binaries is first examined. It categorizes existing detection techniques, develops a unified benchmarking framework, and evaluates current tools through reproduction and replication studies across different compilers, optimization levels, obfuscation strategies, and algorithm variants. The second part introduces an automated security analysis framework for zkSNARK implementations that combines constraint checking with fuzzing-based testing to detect and locate cryptographic logic errors. This approach helps determine whether an implemented zkSNARK system correctly enforces the intended computation and security design. The third part develops a grey-box differential fuzzing approach for zero-knowledge proof binary applications. It uses structured input generation, coverage monitoring, control-dependency-aware taint tracking, and error localization to guide testing toward security-relevant code and expose inconsistencies in circuit construction, witness conversion, proof generation, and verification logic.Together, these contributions connect binary analysis, automated checking, and protocol-aware fuzzing to improve the practical security of cryptographic software. They provide methods for identifying implementation-level weaknesses that may remain hidden during ordinary testing, especially when programs produce valid-looking outputs despite incorrect cryptographic behavior. By combining systematic evaluation, zkSNARK-specific analysis, and binary-level testing, the resulting methodologies advance the development of more reliable techniques for analyzing, testing, and securing real-world cryptographic systems.
The development of madrasahs and Islamic boarding schools in Indonesia has increasingly required policy approaches capable of balancing educational modernization with the preservation of Islamic traditions. However, limited studies have examined this development from the perspective of Charles E. Lindblom's incrementalism theory. This study aimed to analyze how incremental policy characterizes the contemporary development of madrasahs and Islamic boarding schools and to identify the factors influencing the incremental policy process within these Islamic educational institutions. The research employed a qualitative library research design using official government policy documents, scholarly books, peer-reviewed journal articles, and other relevant academic publications as primary data sources. Data were analyzed through qualitative content analysis involving data reduction, thematic categorization, interpretation, and source triangulation. The findings reveal that the development of madrasahs and Islamic boarding schools consistently follows an incremental policy trajectory characterized by gradual improvements in educational regulations, curriculum and learning systems, teacher professional development, institutional governance, and educational financing. The study also demonstrates that institutional leadership, organizational capacity, government support, educational decentralization, socio-cultural values, political commitment, bureaucratic coordination, digital transformation, and globalization collectively influence the continuity of incremental policy development. These findings confirm the continued relevance of Lindblom's incrementalism theory in explaining policy change within Islamic education and suggest that gradual policy adaptation provides an effective mechanism for maintaining institutional stability while encouraging sustainable educational innovation. The study contributes to both public policy and Islamic education literature by offering a comprehensive conceptual understanding of incremental policy development in contemporary Islamic educational institutions.
As of 2024, 730 million people worldwide lacked electricity access, roughly eight in ten of them in sub-Saharan Africa. Closing this gap requires engineering approaches suited to the technical, financial, and institutional constraints of low-resource settings, not conventional grid extension alone. This paper reviews four engineering pathways expanding renewable energy access in developing countries — decentralized mini-grids, IoT-enabled pay-as-you-go (PAYG) solar financing, frugal engineering, and AI-assisted smart-grid digitalization — using case evidence from Kenya, India, and East Africa's PAYG sector.
Oscar M. Bedoya, Jeferson Arango‐López, Jorge Hochstetter
The management of intellectual property (IP) agreements in universities continues to rely on static legal documents that are signed, archived, and consulted when necessary, but whose content is rarely formalized to facilitate their operation and verification. Consequently, obligations, permissions, restrictions, deadlines, scopes, and exceptions often remain scattered across clauses drafted in natural language, annexes, emails, and different document versions, which hinders their monitoring and makes compliance review dependent on intensive legal and administrative work. In response to this limitation, this article proposes an ontology to formalize non-disclosure agreements (NDAs) at the University of Caldas, Colombia, understood as a specific case within the broader management of IP agreements. The proposal adopts a modular Semantic Web architecture composed of a reusable ontological core and a specialized profile for NDAs. Its construction followed the METHONTOLOGY methodology, and its specification was supported by Competency Questions (CQs), which were subsequently translated into SHACL constraints and SPARQL queries. In addition, a SKOS vocabulary is incorporated to normalize synonyms and terminological variants typical of legal drafting in Spanish, together with a lightweight weak supervision layer based on regular expressions, SKOS, and structural signals to support clause labeling and the batch generation of RDF instances. Thus, the proposal enables querying, traceability, and verification over NDA content, while offering a formal basis for progressing toward automatable controls and their eventual articulation with smart contracts.
This country profile demonstrates how France plays a leading role in the FCT domain, ranking amongst the top beneficiaries of Horizon Europe funding, with approximately 11.4% of the total allocated budget. French stakeholders are highly engaged in cross-sector projects, frequently assuming coordination responsibilities or leading key work packages. A distinctive feature of France’s participation is the role of the Police Nationale and Gendarmerie Nationale, two key actors in many EU-funded security projects. Their involvement ensures a strong alignment between research activities and real-world law enforcement needs, particularly in areas such as Internal Security, Crisis Management, and the Protection of Public Spaces. Furthermore, a broad ecosystem of public research institutions, governmental bodies, and industrial partners contributes to the national effort. France’s research and innovation community is actively involved across a wide range of FCT priorities, including Artificial Intelligence, Cybersecurity, Border Management, and Digital Transformation. Beyond technological development, French actors also play a significant role in shaping regulatory frameworks and promoting the exchange of best practices, particularly in law enforcement and judicial cooperation. Overall, France shows strong participation in Horizon Europe, with representatives in over half of FCT-funded projects, particularly in activities related to the dark web and cryptocurrencies, the trafficking of humans and goods, and strong support for training and exercises. We’re collecting feedback on this report through the EU Survey Platform, if you’d like to share your thoughts please click on the link below. https://ec.europa.eu/eusurvey/runner/enact-report-feedback
The rapid digitalization of financial services has transformed the global financial ecosystem, enabling faster transactions, enhanced customer experiences, and greater financial inclusion. However, this digital transformation has simultaneously increased the complexity, scale, and sophistication of financial fraud. Traditional rule-based fraud detection systems often struggle to identify evolving fraud patterns, resulting in delayed responses, increased false positives, and substantial financial losses. Artificial Intelligence (AI)-powered real-time fraud monitoring systems have emerged as a transformative solution capable of detecting suspicious activities instantly through advanced data analytics, machine learning, deep learning, natural language processing, and behavioral intelligence. These systems continuously analyze vast volumes of transactional and non-transactional data, enabling financial institutions to identify anomalies, predict fraudulent behavior, and automate risk management processes with unprecedented accuracy and speed. This literature review examines the evolution, applications, technological foundations, benefits, challenges, and future directions of AI-powered real-time fraud monitoring systems in modern financial services. The review highlights how AI enhances fraud detection capabilities across banking, payment systems, insurance, digital wallets, cryptocurrencies, and investment platforms while discussing critical concerns related to privacy, algorithmic bias, explainability, cybersecurity, and regulatory compliance. The findings demonstrate that AI-driven fraud monitoring represents a fundamental component of modern financial security infrastructure and will continue to shape the future of fraud prevention in increasingly digital financial environments.
This paper empirically compares four architectures for financial time-series forecasting: LSTM, CNN, the original Regularized Self Attention Regression (RSAR) model, and a multiobjective optimized RSAR variant, denoted MO-RSAR, obtained using the Non dominated Sorting Genetic Algorithm II (NSGA-II). The models are evaluated on six datasets covering Forex, equity index and cryptocurrency markets, for short and long horizons. All models share a common preprocessing pipeline and evaluation framework and are assessed using standard error metrics, with emphasis on Mean Absolute Percentage Error (MAPE). MORSAR yields the lowest average prediction error across all datasets and provides significant gains for longer, more volatile horizons, while simpler architectures remain competitive for short-term forecasts. The key methodological contribution is the first empirical integration of the RSAR architecture with NSGA-IIbased multi-objective hyperparameter optimization for financial time-series forecasting. The proposed framework treats RSAR configuration as a bi-objective search over accuracy and generalization (via the train-validation gap), and evaluates the resulting model under a unified protocol across heterogeneous markets and horizons.
The rapid increase in cryptocurrency adoption among Generation Z in Indonesia has raised concerns regarding investment decision-making in highly volatile digital asset markets. This study examines the influence of herding behavior, social media exposure, and fear of missing out (FOMO) on cryptocurrency investment decisions, with financial literacy as a moderating variable. A quantitative approach was employed using survey data from 200 Generation Z cryptocurrency investors in Pontianak City. The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4. The results show that herding behavior and social media significantly influence investment decisions. Fear of missing out also affects investor decision-making. Financial literacy moderates the relationship between herding behavior and investment decisions as well as between social media and investment decisions, but does not moderate the relationship between FOMO and investment decisions. These findings indicate that cryptocurrency investment decisions among Generation Z are influenced by social interactions and emotional biases.
Diana Bonilla Guzmán, Sofía de las Nieves García Gámez, Rubén Mora-Ruano, Alvaro-Antonio Salas-Suárez
This study aims to identify the extent to which a country's level of governance implicitly determines and encourages the use of cryptocurrencies, and the main elements associated with the use of alternative currencies to traditional ones. The methodology used is a descriptive analysis of the variables, an econometric analysis through an ANOVA, and the application of a truncated regression model, which aims to bring the research closer to the possible correlation between governance indicators and the rate of adoption of cryptocurrencies. The study concludes that countries with low levels of governance are directly related to the greater adoption of cryptocurrencies. To the best of our knowledge, this study is the first to analyse the relationship between cryptocurrency adoption and institutional governance by comparing two regions with different levels of development. The research is limited by the existence of other factors that influence the analytical framework of cryptocurrency adoption, but the availability of data has allowed the present study to focus on governance aspects. Now, despite the fact that the governance indicators present a global analysis in terms of their measurement, the relevant aspects of each country are not specified. The adoption of cryptocurrencies in some countries may not be strongly related to governance aspects but rather to the friendly regulations that have been implemented.
Traditional distributed systems theory has long encoded hard forks as a signof consensus rupture and governance failure. This paper proposes an alternativeanalytical framework: in the practice of decentralized governance, a hard fork isnot a system malfunction but a structural mechanism through which incommensurable cognitive architectures achieve legitimate evolution via the separation ofconceptual space when a dispute touches upon the fundamental commitments ofthe protocol. The paper first redefines a fork as a jump of the authority to modify rules across governance levels—a soft fork adjusts parameters within existingconstraints, while a hard fork alters the boundaries of the constraints themselves,constituting a “dimensionality lift” operation in governance space. Second, it distinguishes three normative types of forks—consensual, controversial, and cognitivelyincommensurable—and argues that only the third type reaches the governancelimits of soft forks. Using the 2015–2017 Bitcoin block size war and the 2016 TheDAOincident as core cases, the paper reveals the internal dynamics through whicha controversial fork evolves from a parameter dispute into framework incommensurability, and how an extreme semantic crisis forces a community to confrontthe tension between code rules and substantive justice. Based on this analysis, thepaper proposes three normative criteria for fork legitimacy—feedback anchoring integrity, cross-verification operability, and conceptual-space appropriateness—andargues that forks, as an “exit-separation” mechanism, possess a meta-governancefunction in decentralized governance analogous to the right of exit in traditionalpolitical theory.
This study examines the short-run effects of U.S. monetary policy shocks on cryptocurrency returns and asks whether digital assets respond to conventional macroeconomic transmission mechanisms. Focusing on the post-2020 period, it evaluates the magnitude, direction, and persistence of Federal Reserve rate shocks across Bitcoin, Ethereum, Solana, Ripple, and TRON. The analysis applies an SVAR-X framework to daily data for January 2020-December 2025. Cryptocurrency log returns are treated as endogenous variables, while the U.S. Dollar Index and VIX are included as exogenous controls; federal funds rate changes are modelled as strictly exogenous policy shocks. Impulse-response results show positive and significant contemporaneous responses for Bitcoin, Ethereum, Solana, and TRON, but no significant reaction for XRP. These effects dissipate within days, indicating modest, short-lived, and heterogeneous monetary-policy transmission rather than persistent effects on cryptocurrency return dynamics over time.
This study proposes a hybrid forecasting framework that integrates sentiment analysis with deep learning to predict Bitcoin’s hourly and daily closing prices. Hourly BTC/USD market data spanning June 2021 to November 2025 were combined with approximately 326,000 Bitcoin-related news headlines published over the same period. Sentiment scores in the range of [-1, +1] were generated for each headline using FinBERT, a transformer-based language model trained on financial texts, and were subsequently integrated with technical indicators such as trading volume, MACD, and RSI. The resulting combined feature set was modeled using an LSTM network to capture temporal dependencies. Empirical results demonstrate that sentiment-enhanced hybrid models consistently outperform models based solely on technical indicators across RMSE, MAE, MAPE, and R² metrics. The hourly hybrid model achieved the best performance, with an RMSE of 1,009 USD and an R² of 99.23%. Furthermore, a 30-day out-of-sample real-time evaluation yielded an RMSE of 941 USD. The consistency between in-sample and out-of-sample results indicates that the proposed framework maintains stable predictive performance over time.
The Luminous Framework · A Field Theory of Living Networks · Chapter One — The Opening A story about supply chains, honest feelings, and a guy in Providence who got out of his own way. This chapter argues that supply chains built civilization — not armies, not philosophers — and that the discipline has been reasoning with the wrong topology. It moves in six parts. The Origins. The Silk Road as a living information network moving prices, availability, quality signals and political risk in both directions across four thousand miles; the Hanseatic League as a stateless multinational running shared Kontors, standardised weights, collective trade agreements and its own naval defence on quill pens and candles; the British East India Company as an organisational achievement of staggering power and hollow intent, kept explicitly on the moral ledger rather than in a footnote; Whitney's interchangeable parts and Ford's River Rouge as the industrial platform; and Toyota, where Ohno and Shingo replaced forecast-driven push with kanban pull-based replenishment. The Feeling. The dissonance between civilisational grandeur and the 7 AM call about a delayed container is treated as accurate signal rather than fatigue: climate volatility rewriting lead times, geopolitical fragmentation dismantling decades-old trade relationships, and labour no longer absorbing the costs of systems optimised to extract maximum efficiency from minimum investment in people. The Story and the Mathematics. A composite case — a Tier 2 force majeure at 11:47 PM with an eleven-day buffer and an eighteen-month requalification timeline — is used to derive the chapter's central claim: resilience is not a function of internal redundancy but of the effective connectivity of the larger network a firm participates in, weighted by the quality of the relationships that make that connectivity real. A chain of n nodes has n−1 connections and zero redundancy; a network has a fundamentally different failure topology. Ashby's law of requisite variety explains why no central planner can regulate a real system: variety must meet variety locally. The Return to Toyota. Kanban is recast not as an inventory technique but as a distributed control system — requisite variety implemented in cardboard and plastic bins. Little's Law (L = λW) is presented as the binding constraint it actually is: safety stock, larger warehouses and better forecasting appear nowhere in the equation, so flow time is the only lever. The kanban card count N = ⌈(D × L × (1+α)) / C⌉ imposes a hard ceiling on work-in-process; the bullwhip relation of Lee, Padmanabhan and Whang quantifies how forecast error amplifies upstream in push topologies. The chapter closes on the variable the equations never contain. The Toyota Production System has been installed in hospitals, in software teams, and in warehouses run to grind people down to their throughput; the cards do not care and Little's Law does not care. Capability is neutral. Intent is not. The math tells you how much you can move. It will never tell you what deserves to move. Note on figures: the case figures are illustrative composites built to be arithmetically consistent with the sourced literature, not field measurements, and are identified as such in the chapter's own notes.
Reviewing the evolution of classical physics and modern economics, all macroeconomic symbols and economic tools invented by humankind in the past (such as fiat currency and GDP) are essentially merely limited fittings to objective reality and "symbolic hallucinations." The previous work, The Entropy Standard: The Biosphere Fluid, Time Ledgers, and the Physical Limits of Civilization, starts from the first principles of thermodynamics and information theory, redefining human society as a "colloidal fluid" dissipative structure on the Earth's surface. It points out that traditional economics, having long been confined to lagging and subjective statistical reports, has driven modern society into terminal crises known as "Mechanization Blockade Disease" and "Complexity Parasitism". To address this, this proposal constructs the "Entropy Standard" economic physics tracking framework. In its underlying logic, this framework resolutely rejects the creation of unchanging, metaphysical absolute physical prototypes or "axioms." This is because social systems differ from the rigid, lifeless matter studied in classical physics; human civilization is a highly non-equilibrium, viscoelastic, and time-evolving complex fluid system. No static axiom or closed formula can lock down the truth once and for all; instead, they would simply devolve into dogma. Therefore, this framework instead embraces high-frequency, large-scale physical-level sampling protocols—sinking the sampling base directly to distributed physical edges (such as smart grid total loads, API call timestamps, and supply chain logistics). By continuously expanding the spatial sampling scope and increasing the temporal sampling frequency, it captures the system's true internal resistance and waste heat loss in real time, thereby endowing the dashboard with self-correcting and adaptive capabilities. Regarding the specific measurement methodology, this framework references the developmental trajectory of classical physics measurement history from "local crudeness" to "fundamental constants and absolute benchmarks," achieving hard-core tracking through two core measurement objects and quantitative mapping equations: Physical Stripping of the Social Total Time Ledger: Utilizing high-frequency timestamps and information-energy interfaces, the waking time of carbon-based individuals is objectively sliced to distinguish between effective working time (which genuinely extracts negative entropy from the physical world) and complexity overhead (which is swallowed by red tape). Circuitry Mapping of Negentropic Energy and System Internal Resistance: Introducing macro Ohm's law and the Joule heat formula, this approach discards illusory monetary prices and directly anchors to physical Joules and the basal metabolic energy level baseline. Through the dynamic tracking of waste heat loss and the establishment of a critical thermal breakdown criterion, it transforms metaphysical economic crises into physical phase-transition processes that can be warned in advance using mathematical integration.
Hassan Cessi Ibrahim, Damilare Timothy Ogunjobi, Philip Mensah
The networks that run operational technology (OT) substations, water treatment plants, oil and gas pipelines, and manufacturing lines are moving from a centralized control to a federated, multi-stakeholder architecture coordinated by permissioned distributed ledgers. Protection and control loops in the electrical grid and other critical infrastructure have protection-relay tripping times, IEC 61850 GOOSE message classes, and SCADA/PMU polling cycles that impose multi-millisecond to sub-second deadlines on protection and control operations, while Byzantine fault-tolerant (BFT) consensus protocols like PBFT, Tendermint, HotStuff, and HoneyBadgerBFT were designed for settlement workloads that can tolerate hundreds of milliseconds to seconds of latency. In this paper, we survey four representative BFT families, discuss their structural latency and scalability constraints for OT deployment, and introduce a hybrid consensus algorithm called IsoBFT (Isochronous Byzantine Fault Tolerance), which combines an optimistic single-round-trip fast path with a PBFT-style fallback mechanism based on a network-stability monitor, and elects a small rotating committee using a verifiable random function (VRF). A formal system model, safety/liveness/termination proof, and security analysis for eight attack classes are provided, with a proposition quantifying the degradation of the practical availability of the safety guarantee when the global Byzantine fraction is approaching one-third. Using realistic Modbus/DNP3/IEC 61850 OT traffic, the discrete-event simulation of the design IsoBFT managed to execute realistic workloads with median consensus latency ranging from 4.90ms at n = 10-50 to 9.17-11.26ms at n = 100 and n = 500, remaining competitive with or better than PBFT and Tendermint across this range. Committee-bounded communication overhead stayed essentially flat with respect to the number of validators from n = 10 to n = 50, but newly completed runs at n = 100 and n = 500 (n = 200 still outstanding) show overhead growing faster than the quadratic scaling of PBFT and Tendermint over that range, together with a heavy P95/P99 latency tail not present at smaller scale; this discrepancy with the theoretical scale-independence result is reported and discussed rather than resolved. IsoBFT could reduce the median latency by approximately 81% and 56% under up to 33% Byzantine faults compared to HotStuff and HoneyBadgerBFT, respectively, at n = 10-50, while maintaining the safety of the system; a Byzantine-resilience sweep at n = 100 shows a narrower advantage over PBFT/Tendermint than at smaller scale.
The democratisation of digital content creation tools has transformed media production, enabling individuals to move from being only consumers to active creators. Yet, content marketplaces and AI ecosystems remain highly centralised, limiting transparency, control, and fair compensation. Generative AI (GenAI) systems, trained on massive web-scraped datasets, exacerbate these issues by reusing creative work without consent, attribution, or reward, raising legal and ethical concerns. This thesis explores how decentralisation can redistribute power in the creative economy by giving creators agency over the use of their media in GenAI. First, we introduce a decentralised registry through which creators can assert opt-in/out preferences for AI training. Content is embedded with provenance metadata and registered with robust fingerprints, enabling provenance tracing even after editing or manipulation. This establishes machine-readable, traceable consent specification as the foundation for downstream attribution and reward. Building on this, we propose methods for training data provenance, attribution, and compensation in GenAI training. The Content ARCs (Authenticity, Rights, Compensation) framework defines a scalable protocol for managing rights and creator compensation. We instantiate this in a decentralised system that traces generative outputs back to the most influential training assets and executes royalty payments to contributors. Several practitioner-facing demonstrators developed in collaboration with GLAM (galleries, libraries, archives, and museums) professionals further illustrate how distributed ledgers could reshape licensing and reward in the creative economy. Further, GenAI models are prone to memorising training data and reproducing it at generation time, a phenomenon that is particularly problematic for copyrighted creative works, where such regurgitation undermines both creator rights and data privacy. To address this challenge, we present a decentralised federated learning protocol for diffusion models that reduces training data memorisation using a novel sample-based metric integrated into the protocol to detect and discourage memorisation. Complementing this, we develop a framework for end-to-end cryptographically verifiable AI pipelines using zero-knowledge proofs to enable trustless, privacy-preserving audits. Finally, we explore privacy-preserving natural language search across decentralised content repositories using encrypted queries for similarity search at scale. In this way, decentralisation supports discovery and access to creative content, completing a holistic body of work for a fairer, more transparent GenAI ecosystem and creative economy.
本文在作者已发表的“消费黑洞”理论基础上,完成从批判到建构的理论跨越。文章指出,传统政治经济学的根本局限在于将“价值”视为一种可被生产、占有与分配的实体性存在。本文提出一个截然不同的起点:分配的本质并非物质财富的权属分割,而是主体贡献的本体论承认。 基于对笛卡尔“我思故我在”、黑格尔—马克思“我劳动故我在”的存在论谱系溯源,本文拓展劳动实践范畴,建构“贡献存在论”(Contribution Ontology)*3(WD-2026-B003),提出“我贡献故我在”的本体论命题。本文确证:人的社会性存在通过劳动、消费、关系、文明四维贡献结构得以显现。这是马克思实践存在论在数字时代的延伸与深化。 依托贡献存在论,本文揭示资本主义的本质矛盾是制度化的“存在论暴力”(Ontological Violence)*4(WD-2026-B004)——资本通过三重褫夺否定消费、关系、文明维度的人类贡献。在此基础上,本文界定“消费无产阶级”(Consumption Proletariat)*5(WD-2026-B005)范畴:同一批劳动者在生产中是劳动无产阶级(被剥夺剩余价值),在消费中是消费无产阶级(被褫夺消费贡献价值)。这是劳动无产阶级的第二重属性,揭示当代资本主义“生产端剥削+消费端褫夺”的双重剥夺结构。 本文论证按消费贡献分配的历史必然性,建构“贡献流动理论”(Contribution Flow Theory)*6(WD-2026-B006)与“消费贡献值六重质变”(Sixfold Qualitative Transformation)*7(WD-2026-B007)核心架构。六重质变遵循“贡献值只升维不归零”的根本原则,实现消费贡献从隐匿到全球流通再到文明守护的全维度价值升维。第四重质变升华生成“公信值”(Public Trust Equity Value,PTV)——包含“社保值”(Personal Social Security Value,SSV)和“社权值”(Personal Governance Rights Value,GRV),标志着贡献从经济领域升华为公共治理领域。第五重质变升华生成“共信币”(Global Trust Coin,GTC)——归国家所有,全球流通,反制资本霸权。第六重质变升华生成“圣火币”(Eternal Fire Coin,EFC)——国家消耗共信币于全人类最高事业时燃烧升华,锚定国际治理话语权。 战略层面,本文提出“利益虹吸效应”(Interest Siphon Effect)*8(WD-2026-B008)理论,论证通过市场化理性选择实现消费者觉醒、资源集聚、主权转移的四阶段和平升维路径。本文确立的按消费贡献分配制度框架,是实现从资本主权到消费者主权(进而指向贡献者主权)文明和平升维的战略方案。 本文的最高文明论断是:共信主义(WD-2026-999)不是与暴力文明、资本文明、劳动文明并列的第四种特殊文明,而是人类文明的完成形态——一个终于承认一切贡献的普遍文明。它是共产主义在数字时代的制度化展开,是人类分配制度演进中从局部到全域的必然升维。共信主义不是资本的敌人,而是资本的归宿——它将资本从压迫和异化的根源,转化为服务人类共同福祉的贡献形态。按贡献分配是对“按资分配”和“按劳分配”进行历史扬弃后的完成形态。 关键词:按贡献分配(WD-2026-000);按消费贡献分配(WD-2026-B001);消费者主权(WD-2026-B002);贡献存在论(WD-2026-B003);存在论暴力(WD-2026-B004);消费无产阶级(WD-2026-B005);贡献流动理论(WD-2026-B006);六重质变(WD-2026-B007);贡献者主权(WD-2026-B009);共信主义(WD-2026-999) This paper,building upon the author‘s previously published theory of the Consumption Black Hole,completes the transition from critique to construction in political economy.It argues that the fundamental limitation of traditional political economy lies in treating“value”as a substantive entity that can be produced,possessed,and distributed.The paper proposes a radically different starting point:the essence of distribution is not the division of material wealth,but the ontological recognition of subjective contribution. Based on a critical examination of the ontological genealogy from Descartes’“I think,therefore I am”to Hegel-Marx‘s“I labor,therefore I am,”this paper extends the category of labor practice to the broader domain of contributive existence.It demonstrates that human social existence manifests through multiple dimensions——labor,consumption,relationality,and civilization——which together constitute the four-dimensional ontological structure of human contribution. Drawing upon this framework,the paper deconstructs the deep operational logic of capitalism:the essential contradiction of capitalism is not superficial distributional inequality,but institutionalized Ontological Violence——the systematic deprivation of contributions in the dimensions of consumption,relationality,and civilization.On this basis,the paper defines the category of the Consumption Proletariat as the second attribute of the proletariat,revealing the complete structural mechanism of dual deprivation in contemporary capitalism. Integrating the materialist premises of digital productive forces——big data,blockchain,and artificial intelligence——this paper demonstrates the historical inevitability of Distribution According to Consumption Contribution.It constructs a Sixfold Qualitative Transformation framework:welfare-based→savings-based→investment-based→public governance(Public Trust Equity Value,embracing Social Security Value and Governance Rights Value)→international(Global Trust Coin)→civilizational(Eternal Fire Coin)——achieving full-dimensional value return from contribution visibility to global circulation to civilizational guardianship.At the strategic level,this paper proposes the Interest Siphon Effect theory,demonstrating that consumer resources constitute the structural core node of capital circulation.It outlines a four-stage peaceful evolutionary path of human civilizational upgrading and capital reclamation from consumer awakening to sovereignty transformation,ultimately pointing toward Contribution Sovereignty. The supreme civilizational thesis of this paper is:Convivialism is not the fourth special civilization alongside the civilizations of violence,capital,and labor,but the completed form of human civilization——a universal civilization that finally recognizes all contributions.It is the institutional unfolding of communism in the digital age and the inevitable upgrading from partial to universal recognition in the evolution of distribution systems.Convivialism is not the enemy of capital,but its ultimate destination——transforming capital from a source of oppression and alienation into a contribution form that serves human common well-being. Keywords:Distribution According to Contribution(DAC,WD-2026-000);Distribution According to Consumption Contribution(DACC,WD-2026-B001);Consumer Sovereignty(WD-2026-B002);Contribution Ontology(WD-2026-B003);Ontological Violence(OV,WD-2026-B004);Consumption Proletariat(CP,WD-2026-B005);Contribution Flow Theory(CFT,WD-2026-B006);Sixfold Qualitative Transformation(SQT,WD-2026-B007);Contribution Sovereignty(WD-2026-B009);Convivialism(WD-2026-999)