Central Bank Digital Currency (CBDCs) are becoming a new digital financial tool aimed at financial inclusion, increased monetary stability, and improved efficiency of payment systems, as they are issued by central banks. One of the most important aspects is that the CBDC must offer secure offline payment methods to users, allowing them to retain cash-like access without violating Anti-Money Laundering and Counter-terrorism Financing (AML/CFT) rules. The offline CBDC ecosystems will provide financial inclusion, empower underserved communities, and ensure equitable access to digital payments, even in connectivity-poor remote locations. With the rapid growth of Internet of Things (IoT) devices in our everyday lives, they are capable of performing secure digital transactions. Integrating offline CBDC payment with IoT devices enables seamless, automated payment without internet connectivity. However, IoT devices face special challenges due to their resource-constrained nature. This makes it difficult to include features such as double-spending prevention, privacy preservation, low-computation operation, and digital identity management. The work proposes a privacy-preserving offline CBDC model with integrated secure elements (SEs), zero-knowledge proofs (ZKPs), and intermittent synchronisation to conduct offline payments on IoT hardware. The proposed model is based on recent improvements in offline CBDC prototypes, regulations and cryptographic design choices such as hybrid architecture that involves using combination of online and offline payment in IoT devices using secure hardware with lightweight zero-knowledge proof cryptographic algorithm.
This article examines the utilization of Distributed Ledger Technology (DLT) as a mechanism to address import customs tax evasion. The research employs a game-theoretic framework to examine the dynamics of tax evasion and assess the impact of blockchain on improving transparency, accountability, and compliance in customs administration. A systematic literature review process, adhering to PRISMA criteria, was utilized to gather and examine pertinent academic articles. The literature study examines critical subjects, such as the mechanisms of import tax evasion, the digital taxation framework, and the use of blockchain technology into tax systems. The study also examines the relevance of game theory in comprehending and addressing non-compliant behaviors among taxpayers. In the practical phase, we conducted a systematic review of a corpus exceeding 100 publications, obtained from three international research databases: Scopus, Taylor & Francis, and IEEE Xplore. Following the application of rigorous inclusion and exclusion criteria to guarantee relevance, a concentrated selection of research constituted the foundation for our study. This research underscores the capacity of DLT to transform conventional evasion tactics, reduce corruption, and improve institutional efficacy in customs operations. Insights are contextualized through a worldwide comparison and an examination of the Moroccan customs scene, offering concrete recommendations for utilizing blockchain to modernize customs operations.
This paper explores the paradigm shift from centralized, command-and-control systems to decentralized, knowledge-driven structures across economic, organizational, technological, and social domains. The inefficiencies and lack of innovation in centrally planned systems stem largely from informational constraints—particularly the inability to effectively gather, process, and utilize dispersed, local, and tacit knowledge. Decentralization enables autonomous agents to leverage their own knowledge, fostering experimentation, innovation, and adaptability. Through a series of examples—including economic markets, firms, states, environmental systems, communications networks, and educational models—the paper illustrates how decentralization replaces vertical, hierarchical communication with horizontal, networked interactions. The transition is characterized by the central authority relinquishing direct control in favor of setting rules of interaction, thereby mitigating principal-agent problems and enhancing system robustness. The analysis extends to social learning, contrasting passive, top-down education with active, dialogical learning, and highlights the importance of intellectual freedom and experimentation in organizations and societies. The overall conclusion is that successful decentralization depends on well-designed rules that maximize autonomy and spontaneous activity, consistent with the broader goal of compossible freedom for all agents.
The pests and the ideal irrigation should be monitored simultaneously so that the crops can be efficiently managed to yield the maximum. The intended dual-purpose solution to the pest detection problem, which is proposed in this study, is the combination of IoT-enabled sensors with Ethereum smart contracts and a deep learning-based ResNeSt-DDETR pest detector. The ResNeSt backbone is able to improve the extraction of features with the help of split-attention mechanisms, whereas Deformable DETR pays attention to the areas which are of interest in order to achieve precise detection in the field under complex conditions. IoT sensors constantly check soil moisture, temperature, and humidity to adjust the irrigation patterns to control the water management accurately. The pest detections and irrigation logs are registered safely on the Ethereum blockchain and provide a solution with tamper-proof, transparent, and traceable data. The system is deployed on edge devices and implemented on Python with PyTorch, OpenCV, and Web3.py and works in real time. The experimental assessment of the IP102 data reveals that the model has a high accuracy (95.2%), precision (94.5%), recall (93.7%), F1-score (94.1%), and mAP 0.5:0.95 = 90.6% indicating that it is effective in integrated pest and irrigation management in precision agriculture.
Pest detection and soil moisture estimation models with little computation overhead are needed in resource-efficient pesticide monitoring of agricultural fields. This paper introduces a lightweight MobileViT-based system that is combined with IoT sensors and a hybrid Ethereum blockchain platform to offer secure and real-time pest and soil monitoring. MobileViT is a hybrid architecture that uses convolutional networks and transformer-based global features, which allow competition with detection accuracy and low computing needs. The IoT sensors are used to monitor soil moisture, temperature, and humidity to aid in making irrigation decisions with the key events being safely stored on the Ethereum blockchain to trace the events irrevocably. The system is executed in Python using PyTorch, OpenCV, and Web3.py and runs on edge devices and is fast in inference with low latency. The IP102 dataset includes the evaluation which proves that the model has high detection performance with accuracy 92.8%, precision 91.5%, recall 90.7%, F1-score 91.1%.
The increasingly complex Web3 ecosystem and decentralized finance (DeFi) landscape demand ever higher levels of technical expertise and financial literacy from participants. The Intent-Centric paradigm in DeFi has thus emerged in response, which allows users to focus on their trading intents rather than the underlying execution details. However, existing approaches, including Typed-intent design and LLM-driven solver, trade off expressiveness, trust, privacy, and composability. We present OMNIINTENT, a language-runtime co-design that reconciles these requirements. OMNIINTENT introduces ICL, a domain-specific Intent-Centric Language for precise yet flexible specification of triggers, actions, and runtime constraints; a Trusted Execution Environment (TEE)-based compiler that compiles intents into signed, state-bound transactions inside an enclave; and an execution optimizer that constructs transaction dependency graphs for safe parallel batch submission and a mempool-aware feasibility checker that predicts execution outcomes. Our full-stack prototype processes diverse DeFi scenarios, achieving 89.6% intent coverage, up to 7.3x throughput speedup via parallel execution, and feasibility-prediction accuracy up to 99.2% with low latency.
Using on-chain Polygon data, we analyze Polymarket's 2024 U.S. Presidential Election market and develop a transaction-level accounting framework with two components: a volume decomposition that separates exchange-equivalent turnover from share minting and burning, and trader-level disagreement measures. Naive aggregation reports $958M of October Trump-market volume, compared with $391M under our decomposition. Market quality improved as arbitrage-deviation half-lives fell from hours to under a minute and Kyle's λ dropped from 0.53 to 0.01. During October's large-account episode, capital flowed into both sides simultaneously, consistent with heterogeneous-beliefs trading rather than one-sided manipulation. The framework generalizes to other tokenized prediction markets.
Time series forecasting enables early warning and has driven asset performance management from traditional planned maintenance to predictive maintenance. However, the lack of interpretability in forecasting methods undermines users' trust and complicates debugging for developers. Consequently, interpretable time-series forecasting has attracted increasing research attention. Nevertheless, existing methods suffer from several limitations, including insufficient modeling of temporal dependencies, lack of feature-level interpretability to support early warning, and difficulty in simultaneously achieving the accuracy and interpretability. This paper proposes the interpretable polynomial learning (IPL) method, which integrates interpretability into the model structure by explicitly modeling original features and their interactions of arbitrary order through polynomial representations. This design preserves temporal dependencies, provides feature-level interpretability, and offers a flexible trade-off between prediction accuracy and interpretability by adjusting the polynomial degree. We evaluate IPL on simulated and Bitcoin price data, showing that it achieves high prediction accuracy with superior interpretability compared with widely used explainability methods. Experiments on field-collected antenna data further demonstrate that IPL yields simpler and more efficient early warning mechanisms.
Blockchains are diverse in the way they handle communications between their nodes to disseminate information, mitigate attacks, and agree on the next block. While security vulnerabilities have been identified, they rely on an attack custom-made for a specific blockchain communication protocol. To our knowledge, the vulnerabilities of multiple blockchain communication protocols to adversarial conditions have never been compared. In this paper, we compare empirically the vulnerabilities of the communication protocols of five modern in-production blockchains, Algorand, Aptos, Avalanche, Redbelly and Solana, when attacked in five different ways. We conclude that Algorand is vulnerable to packet loss attacks, Aptos is vulnerable to targeted load attacks and leader isolation attacks, Avalanche is vulnerable to transient failure attacks, Redbelly's performance is impacted by packet loss attacks and Solana is vulnerable to stopping attacks and leader isolation attacks. Our system is open source.
This study presents a blockchain-based enabling autonomous nursing professional development framework, known as BCeANPDF. The framework aims to enhance transparency, security, and professional autonomy in nursing credential management. It is grounded in the principles of competency-based human resource management. Blockchain and smart contract technologies are integrated to support independent recording, verification, and management of professional and non-professional credentials by nurses. At the same time, hospital human resource administrators continue to have the authority to conduct regulatory oversight and ensure compliance. The framework employs a three-layer architecture that includes controller, service, and repository components. These components coordinate access control, data processing, and blockchain-related operations. Seven smart contracts are designed within the framework. They automate credential ownership verification, credential updates, and compliance review processes. This design strengthens data integrity and reduces administrative workload. A prototype was implemented in a private blockchain environment to evaluate system performance. The results demonstrate stable and efficient operation. The average on-chain processing time per credential was 12.3 s. Median query latency ranged from 5 to 9 ms. These findings confirm that the framework achieves scalability and responsiveness comparable to Ethereum, while preserving data privacy and immutability. By combining decentralized trust mechanisms with credential management practices, the BCeANPDF framework offers a practical approach to supporting autonomous professional development. It also facilitates flexible management of the nursing workforce. Overall, the framework contributes to the development of transparent and competency-oriented healthcare institutions without increasing operational complexity.
Alexandre Tran, David Granton, Eddy Fan, Bram Rochwerg
Clinical practice guidelines (CPGs) are used by critical care clinicians to guide practice and inform best care. According to the GRADE framework, evidence synthesis should preferentially rely on randomized controlled trials (RCTs) because they minimize bias and establish causality.1 Despite challenges, critical care is well-suited to randomized studies given its (1) high incidence of acute conditions, (2) protocolized interventions, (3) standardized outcomes, and (4) strong data infrastructure and trial networks.2,3 Despite the advantages, RCTs are often unavailable for CPGs or leave knowledge gaps, particularly for subgroup effects or patient-important outcomes like long-term quality of life related to heterogeneous populations, urgent interventions, and recruitment constraints.4,5 Physicians may hesitate to apply RCT results because (1) enrolled patients differ from real-world populations, (2) key outcomes may be unmeasured, (3) effect estimates may be imprecise, and (4) subgroup analyses may be lacking.6 When RCT evidence is insufficient, high-quality non-randomized studies of interventions (NRSI) can complement trials by approximating causal inference—estimating exposure effects while separating systematic bias from random error.7 High-quality NRSI require large, well-validated datasets with minimal missingness and adequate temporal resolution. Without these, even advanced analytics cannot yield credible estimates. NRSI often emulate target trials, aligning eligibility, time zero, and predefined interventions and outcomes.8,9 Design must reflect strong knowledge of confounders and time-varying biases, addressed through advanced data and statistical methods. When based on explicit and credible assumptions (eg, exchangeability, no residual confounding), NRSI can yield valid and generalizable estimates, though such assumptions cannot be proven and still require caution in interpretation.10 Most NRSI are retrospective and lack safeguards standard in RCTs such as trial registration or prespecified outcomes. In target-trial emulation (Table 1), preregistration before data access is critical to prevent selective reporting and analytic flexibility, mirroring RCT practice. These limitations are especially relevant in critical care, given dynamic physiology, urgent decisions, and substantial clinical heterogeneity. These factors complicate exposure timing, increase time-varying confounding, and challenge stability assumptions in target-trial designs. Rigorous cohort definition and analytic strategy are essential when applying NRSI in this context. As causal-inference methods such as target-trial emulation spread, cautious application with methodological rigor and transparency is essential to avoid poorly executed, misleading, or irreproducible NRSI. High-quality NRSI depend not only on analytical sophistication but also on careful data acquisition, explicit protocolization, and transparency in prespecifying exposures, outcomes, and analytic plans—principles that mirror RCT standards. Target trial (ideal RCT) versus emulation. 1. Treatment with ECMO therapy if PaO2/FiO2 < 80 mmHg 2. Treatment with conventional mechanical ventilation without the use of ECMO therapy Adapted from: National Academies of Sciences, Engineering, and Medicine; Health and Medicine Division; Board on Health Care Services; Committee on Developing a Protocol to Evaluate the Concomitant Prescribing of Opioids and Benzodiazepine Medications and Veteran Deaths and Suicides. An Approach to Evaluate the Effects of Concomitant Prescribing of Opioids and Benzodiazepines on Veteran Deaths and Suicides. Washington (DC): National Academies Press (U.S.); 2019 Sep 24. 2, Specifying the Target Trial. Available from: https://www.ncbi.nlm.nih.gov/books/NBK547516/. Case example: Venovenous extracorporeal membrane oxygenation in patients with acute covid-19 associated respiratory failure: comparative effectiveness study.22 This commentary examines the evolving role of NRSI in developing critical care CPGs. We outline key challenges in conducting and synthesizing critical care research, then describe how high-quality NRSI can complement randomized evidence by (1) aligning effect estimates with RCTs, (2) informing certainty of evidence (CoE), and (3) guiding clinical practice recommendations. We propose practical strategies for CPG panels and domain experts to maximize the utility of NRSI while maintaining methodological rigor. Our goal is to support CPG panelists, researchers, and clinicians in interpreting recommendations that integrate NRSI. These recommendations align with evolving GRADE guidance, operationalizing its principles for critical care applications. GRADE provides a structured approach for rating CoE, the confidence that an estimated effect is close to the truth.11 When developing guidelines, the GRADE Evidence-to-Decision (EtD) framework translates synthesized evidence into recommendations by weighing intervention effects, CoE, patient-valued outcomes, and contextual factors such as resource use, equity, acceptability, and feasibility.12 These contextual judgments ensure that evidence is interpreted through a patient- and system-centered lens, recognizing that even high-certainty data require value-based consideration before adoption into practice. A review of critical care CPGs showed reasonable uptake of GRADE, with recommendation strength generally aligned with CoE.13 However, strong recommendations are still often made from low or very low-certainty evidence, often related to evidence gaps in RCTs. This highlights the need to integrate high-quality NRSI into CPG development to strengthen evidence synthesis and uptake. Critical care CPG panels should consistently apply GRADE principles, incorporating all high-quality evidence, including NRSI to augment situations where RCT data may be limited or absent. RCTs are resource-intensive and difficult to conduct in critical care.1 To maintain feasibility, investigators often overestimate effect sizes, leading to underpowered studies that may miss true effects.14,15 Reviews of critical care RCTs show that predicted treatment effects are often exaggerated—nearly 10-fold higher than observed, and that few trials sufficiently justify their sample-size targets.16 Similar overestimation has been reported in sepsis, stroke, and trauma trials.17–19 Among high-profile publications, fewer than half of trials had reproducible results.20 Moreover, a meta-epidemiologic review of more than 600 critical care trials found that only 1 in 16 was at low risk of bias, with little improvement over 4 decades.21 These findings suggest that RCTs alone may not provide sufficient high-quality evidence to inform strong guideline recommendations. Critical care populations are highly heterogeneous, encompassing subgroups with different baseline risks and treatment responses. RCTs often target broad syndromes such as sepsis or acute respiratory distress syndrome (ARDS), which likely contributes to many “negative” trials unable to detect differences in outcome.22 Because these studies estimate average treatment effects (ATEs) across diverse patients, potential subgroup benefits can be obscured when other subgroups experience harm.23 This variability, termed heterogeneity of treatment effect (HTE), reflects non-random differences in benefit or harm linked to patient characteristics.24 Understanding HTE (Table 2) is central to precision medicine: treatments that appear neutral on average may conceal offsetting benefit and harm across biologically or contextually distinct subgroups. Explicit exploration of these differences can refine trial design, improve interpretation, and guide targeted recommendations. Methods for assessing heterogeneity of treatment effect. Case example: Heterogeneous treatment effects of therapeutic-dose heparin in patients hospitalized for COVID-19.19 Causal forest and other machine-learning approaches allow for non-linear and interactive modeling of treatment effect heterogeneity but are more susceptible to overfitting and typically require larger sample sizes and external validation. In contrast, regression-based risk modeling approaches are generally more interpretable but may oversimplify interaction effects. RCTs typically assess HTE using pairwise subgroup analyses, but these are often underpowered, rely on arbitrary subgroup thresholds (eg, age <65 vs ≥65), and cannot capture complex interactions.25 The American Thoracic Society (ATS) and European Respiratory Society (ERS) guideline on non-invasive ventilation illustrates these limitations: subgroup evidence for conditions such as acute hypoxemic respiratory failure or ARDS came mostly from small or secondary analyses, yielding sparse data and very low certainty.26 These challenges highlight the need for improved data science approaches to identify and characterize HTE: a priority emphasized in the recent ATS research agenda for sepsis and ARDS.27 Data-driven subgroups (subphenotypes) can integrate multiple patient characteristics to assess effect modification and estimate individualized treatment effects.28,29 These models require rigorous derivation and validation to avoid overfitting, yet no consensus framework currently guides their validation or clinical use. Critical care trialists should adopt realistic effect size and recruitment targets and predefine strategies to evaluate clinically relevant HTE. When RCT evidence is insufficient, we propose strategies for CPG panels to integrate NRSI within the GRADE framework to complement RCTs and strengthen recommendations. In accordance with GRADE guidance, if the CoE from RCTs is judged to be high then the role for NRSI is minimal for the specific comparison and outcome of interest.7 However, RCTs often do not report certain patient-important outcomes such as adverse events, quality of life, or longer-term morbidity or mortality. Even if a particular question and outcome of interest have RCT evidence, the estimates of treatment effect are often limited by imprecision due to aforementioned recruitment and sample size concerns. Treatment effects are often assessed in highly selected populations; trial participants typically represent a small fraction of those screened and even meta-analyses may yield low certainty due to imprecision or inconsistency.30,31 In these situations, guideline panels should consider high-quality NRSI, defined by adherence to TARGET (Transparent Reporting of Observational Studies Emulating a Target Trial) standards, acceptable risk of bias, and robust sensitivity analyses, to supplement RCT evidence.7 Target-trial emulation exemplifies this approach: investigators first design a hypothetical randomized trial addressing the question of interest, then emulate it using observational data.8,32 For instance, an international study using the COVID-19 Critical Care Consortium dataset estimated the effect of VV-ECMO versus conventional ventilation in patients with severe COVID-19, providing real-world evidence where an RCT was impractical due to complexity and cost.33 Similar emulations have evaluated intubation,34 ventilation,35 and corticosteroid strategies36 in critical care—demonstrating how NRSI can inform practice when trials are unfeasible. Consider the example of drotrecogin alfa (activated protein C, rhAPC). Following the PROWESS RCT,37 which demonstrated benefit of rhAPC in patient with septic shock, the large open-label ENHANCE observational study38 reported a similar reduction in mortality with rhAPC but was the first to raise important concerns about serious bleeding, including intracranial hemorrhage. These observational findings influenced early guideline discussions, tempering enthusiasm for the drug, and subsequent RCTs39,40 confirmed this harm and rhAPC was ultimately withdrawn. This highlights that replication across larger datasets remains essential to confirm findings and ensure generalizability beyond selected RCT populations. This sequence illustrates an iterative process: observational signals can generate early warnings or hypotheses that subsequent RCTs confirm or refute. When results diverge, these contrasts can highlight methodological limitations or context-specific factors that warrant further investigation. The TARGET statement outlines 21 reporting items to standardize eligibility, interventions, outcomes, and analyses, improving transparency and reproducibility of emulated trials.41 Adherence to TARGET helps guideline panels assess NRSI rigor and determine when such evidence can complement or upgrade certainty around RCT findings. Similarly, the RCT-DUPLICATE initiative evaluated whether database-derived emulations can reproduce findings from RCTs across 32 cardiovascular studies, including interventions for anticoagulation, antiplatelet therapy, and chronic disease management. The authors found that effect estimates from well-designed emulations closely mirrored their RCT counterparts in both direction and magnitude, demonstrating that real-world data can yield valid causal inference when study design and analytic methods are rigorous.10 Whether successes from other fields will translate to critical care remains uncertain, given its confounding, physiologic complexity, and HTE. A blinded target-trial emulation in this setting reproduced findings of the PreVent RCT examining bag-mask ventilation and hypoxemia,42,43 providing proof-of-principle that short-term physiologic effects can be predicted from observational data, though its value for longer-term or patient-centered outcomes remains untested. Valid causal inference in NRSI requires adherence to key assumptions: exchangeability (no unmeasured confounding), positivity (each patient could receive any treatment), and consistency (observed outcomes reflect potential outcomes under that treatment).8,9 Meeting these assumptions demands careful cohort design, proper time alignment, and analytic techniques that address confounding, such as target-trial emulation, inverse-probability weighting, or doubly robust estimators.44,45 Studies must also handle time-varying confounding and competing risks (eg, death precluding extubation), which can otherwise bias effect estimates.46 To address these concerns, marginal structural models may be used to estimate the causal effect of a time-varying treatment and address the challenge of estimating treatment effects when confounders are influenced by prior treatment—a situation conventional regression models struggle with. CPG panels should systematically appraise NRSI by verifying TARGET adherence, assessing bias with validated tools such as ROBINS-I, and judging how results affect GRADE domains such as imprecision, inconsistency, and indirectness.41,47 Robust sensitivity analyses, testing alternative models, handling missing data, and probing unmeasured confounding, are essential to confirm result stability and should be clearly reported.48,49 Transparent presentation of assumptions and their plausibility further strengthen credibility. When high-certainty RCT evidence already exists for all relevant target populations, additional NRSI are seldom needed (Figure 1). More often, however, critical care trials involve highly selected populations, making complementary NRSI useful for confirming treatment effects in broader or under-represented groups.50,51 When RCT and NRSI results are consistent, guideline panels may consider upgrading certainty and recommendation strength in line with GRADE guidance.7 GRADE also allows rating up observational evidence when large effects, dose-response relationships, or confounding that would only diminish an observed benefit are present.52 Conversely, inconsistent or methodologically weak NRSI such as those with implausible assumptions, poor reporting, or critical bias, should be excluded, with the rationale documented. Expanding use of target-trial emulation is promising but must be paired with training and standards to prevent low-quality proliferation that could erode confidence in observational evidence.48 Framework for incorporating NRSI into critical care CPGs. CPG panels should incorporate well-conducted NRSI to strengthen CoE and adopt structured workflows: (1) verifying TARGET adherence, (2) considering potential risk of bias, and (3) linking NRSI results to GRADE domains to ensure transparent, reproducible use of observational evidence. Critical care RCTs often study heterogeneous syndromes using strict eligibility criteria that limit generalizability and obscure subgroup effects. A multicenter simulation of 15 landmark trials found that over half of real-world ICU patients would have been ineligible,53 and a review of 75 high-impact trials showed that 60% used at least one poorly justified exclusion such as language barriers or lack of insurance—further restricting applicability.54 Most RCTs originate from high-income countries, leaving major evidence gaps for critically ill patients in the Global South.55 For example, a Zambian sepsis RCT found higher mortality with early fluid resuscitation—contradicting prior goal-directed therapy trials.56,57 This discordance may be explained by the fact that these trials enrolled predominantly young, malnourished individuals predisposed to pulmonary edema and respiratory failure in a setting with limited ventilatory support. Beyond generating estimates of effectiveness in underrepresented populations, NRSIs also offer a pathway to address structural inequities in evidence generation and utilization. Conducting RCTs in the Global South is often hindered by logistical, regulatory, and infrastructural challenges—including limited research infrastructure, ethical oversight, or funding mechanisms, which systematically exclude these populations from RCTs.55 Well-designed NRSI can help bridge such gaps by leveraging local data to assess effectiveness, feasibility, and contextual factors in resource-limited settings. They can also identify structural and contextual modifiers such as malnutrition, health-system capacity, and disease epidemiology; thereby supporting more equitable, context-specific guideline recommendations.58 Embedding such evidence from the Global South not only broadens external validity but also enhances the global relevance of CPGs—thereby promoting more equitable and relevant evidence-based decision-making for clinicians practicing in resource-limited settings. NRSI can also inform feasibility, acceptability, and which are key factors in CPG For instance, the ATS guideline on ARDS a recommendation for VV-ECMO based on NRSI substantial in and across and NRSI can RCT findings to real-world which patients benefit or are based on risk or A key is which to assess how RCT results to external populations and to identify contextual effect improving both evidence relevance and trial Causal inference using real-world data can evaluate HTE across broader populations, including and patients typically underrepresented in a systematic review found major in methodological rigor for HTE analyses, particularly in testing and for confounding, the need for standardized methods and In critical care, HTE from secondary analyses of RCT In the modeling showed that patient characteristics predicted benefit from specific oxygenation targets for patients with and higher for those with The subsequent Care Medicine a recommendation higher oxygenation targets based on very low-certainty an of the trial found that even when are machine-learning models can identify clinically subgroups with benefit or the value of HTE modeling in acute respiratory These secondary analyses are and but should be by observational studies to evaluate HTE beyond RCTs. The ARDS cohort illustrates the value of non-randomized showed that patients had mortality with higher while no benefit in the example of HTE using real-world ICU Beyond also a global of guideline adherence, and ARDS outcomes. not its and rigor how observational studies can yield at a RCTs informing international ARDS When developing panels should consider how best to incorporate NRSI in HTE. this requires systematically HTE analyses, particularly for subgroups in the and assessing how these findings complement subgroup no GRADE yet panels should still evaluate whether HTE evidence recommendations or can guide research for or in RCTs. CPG panels should apply well-conducted causal-inference analyses to confirm the generalizability of RCT findings and identify clinically important HTE. RCTs the standard for and but well-designed NRSI can augment both the certainty and of evidence. Critical care CPG panels should integrate observational evidence when while recognizing methodological standardized (1) TARGET for reporting, (2) validated risk of bias and (3) explicit GRADE will ensure use of NRSI across guideline High-quality NRSI can CoE and generalizability beyond selective RCT populations, providing a to evaluate HTE. incorporating such studies into CPG development may improve both the generalizability and of recommendations. such as the dataset highlight how NRSI can HTE not in trials As analytic methods and target-trial NRSI will an important role in addressing evidence gaps in critical care. will rely on close across and to ensure that NRSI are and with the rigor of randomized authors the the authors to the and of the is at American of and Critical Care Medicine the which have been as tools used in this
Cryptocurrency markets are characterized by extreme volatility, rapid price fluctuations, and complex nonlinear behavior,making accurate forecasting a significant challenge for investors, analysts, and researchers. This study investigates the application of Time Series Analysis techniques to model and predict cryptocurrency prices using historical market data. Both traditional statistical approaches, such as the AutoRegressive Integrated Moving Average (ARIMA) model, and advanced deep learning methods, including Long Short-Term Memory (LSTM) networks, are implemented to capture underlying temporal patterns. The dataset consists of daily open, high, low, close prices, and trading volume obtained from reliable financial data sources. Data preprocessing steps such as handling missing values, normalization, stationarity testing using the Augmented Dickey-Fuller test, and time series decomposition are performed to ensure model efficiency and accuracy. Exploratory Data Analysis (EDA) is conducted to identify trends, seasonality, and volatility characteristics. Model performance is evaluated using statistical metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). The comparative analysis demonstrates that while ARIMA performs adequately for short-term forecasting, LSTM models provide superior performance in capturing nonlinear and long-term dependencies within cryptocurrency price movements. However, external factors such as market sentiment and regulatory changes continue to influence prediction accuracy. This research contributes to a better understanding of cryptocurrency forecasting techniques and highlights the effectiveness of deep learning approaches in financial time series analysis. Keywords: Cryptocurrency, Time Series Analysis, ARIMA, LSTM, Price Prediction
Purpose. To substantiate conceptual approaches to integrating blockchain technologies into risk management systems of investment activities of financial institutions through systematization of architectural solutions, development of efficiency evaluation criteria, and typology of implementation strategies, taking into account the specifics of different categories of investment risks and regulatory environment. Methodology. An interdisciplinary approach was used, combining institutional analysis of financial systems, comparative analysis of traditional centralized and decentralized risk management models, and systematization of empirical data on blockchain implementation in the global financial sector. Methods of structural-functional analysis were applied to study blockchain systems architecture and their impact on various categories of investment risks. Critical analysis of scientific literature on decentralized finance, asset tokenization, and smart contracts was conducted. Findings. The dual nature of blockchain technologies has been revealed as both a tool for minimizing traditional risks (market, credit, operational, liquidity, regulatory) and a source of new technological challenges. Four integration models have been systematized: asset tokenization for enhancing liquidity, DeFi instruments for decentralized lending and exchange, hybrid portfolios for diversification, and smart contracts for risk management automation. An evaluation matrix for blockchain solutions effectiveness has been developed based on seven criteria (transparency, settlement speed, operational costs, accessibility, reliability, regulatory certainty, scalability) compared to traditional systems. A typology of implementation strategies for commercial banks, investment funds, and insurance companies has been proposed. Originality. For the first time, a comprehensive analysis of the transformation of investment activity risk management architecture through the lens of blockchain technology integration has been conducted, simultaneously considering institutional, technological, and regulatory aspects. A conceptual model of an integrated blockchain system for managing investment risks has been developed with identification of interaction levels and feedback loops. Practical value. Research results form a methodological foundation for financial institutions regarding the selection of optimal blockchain technology implementation strategies, provide tools for evaluating the effectiveness of various integration models, and contribute to the formation of regulatory policy in the field of digital transformation of the financial sector.
Purpose. To substantiate conceptual approaches to integrating blockchain technologies into risk management systems of investment activities of financial institutions through systematization of architectural solutions, development of efficiency evaluation criteria, and typology of implementation strategies, taking into account the specifics of different categories of investment risks and regulatory environment. Methodology. An interdisciplinary approach was used, combining institutional analysis of financial systems, comparative analysis of traditional centralized and decentralized risk management models, and systematization of empirical data on blockchain implementation in the global financial sector. Methods of structural-functional analysis were applied to study blockchain systems architecture and their impact on various categories of investment risks. Critical analysis of scientific literature on decentralized finance, asset tokenization, and smart contracts was conducted. Findings. The dual nature of blockchain technologies has been revealed as both a tool for minimizing traditional risks (market, credit, operational, liquidity, regulatory) and a source of new technological challenges. Four integration models have been systematized: asset tokenization for enhancing liquidity, DeFi instruments for decentralized lending and exchange, hybrid portfolios for diversification, and smart contracts for risk management automation. An evaluation matrix for blockchain solutions effectiveness has been developed based on seven criteria (transparency, settlement speed, operational costs, accessibility, reliability, regulatory certainty, scalability) compared to traditional systems. A typology of implementation strategies for commercial banks, investment funds, and insurance companies has been proposed. Originality. For the first time, a comprehensive analysis of the transformation of investment activity risk management architecture through the lens of blockchain technology integration has been conducted, simultaneously considering institutional, technological, and regulatory aspects. A conceptual model of an integrated blockchain system for managing investment risks has been developed with identification of interaction levels and feedback loops. Practical value. Research results form a methodological foundation for financial institutions regarding the selection of optimal blockchain technology implementation strategies, provide tools for evaluating the effectiveness of various integration models, and contribute to the formation of regulatory policy in the field of digital transformation of the financial sector.
This article proposes the creation of a new Luxembourg company form, the Société Anonyme Tokenisée (SAT), which would natively use distributed ledger technology (DLT) for all shares and governance functions. The SAT would be a fully compliant legal entity under EU law, issue tokenised securities recognised as financial instruments, and enable smart contract-based governance. The proposal is based on Luxembourg’s progressive blockchain legislation and the need for corporate law to keep pace with technological innovation.
Introduction: Robotics and artificial intelligence (AI) are rapidly reshaping hospitality by automating frontline and back-of-house processes, augmenting service encounters, and expanding the analytical scope of revenue management. Yet, existing research remains fragmented: service-robot studies largely emphasize adoption and human-robot interaction, while revenue-management research prioritizes pricing and distribution, sustainability research focuses on environmental practices, and hotel real-estate scholarship foregrounds governance and asset value. Meanwhile, blockchain technologies-through distributed ledgers, smart contracts, digital identity, and tokenization-offer a complementary trust and value-transfer layer that can address coordination and verification problems across hotel ecosystems (e.g., data sharing, sustainability claims, and owner-operator contracting). Methods: Drawing on an integrative literature synthesis, this conceptual article develops an integrative framework linking AI-robotics and blockchain capabilities to three interdependent hotel decision domains: (1) revenue management (demand forecasting, dynamic/open pricing, channel and loyalty optimization), (2) sustainability and operations (resource optimization, waste circularity, predictive maintenance), and (3) real estate and hotel asset management (digital twins, CapEx planning, valuation and risk analytics, and tokenized financing). Results: A conceptual model is proposed in which AI-robotics and blockchain jointly build digital operational and market-intelligence capabilities that improve financial performance (RevPAR/GOPPAR and net operating income), sustainability performance (carbon and resource intensity), and long-term asset value. Ten propositions articulate mechanisms and boundary conditions related to governance, ethics, privacy, cybersecurity, organizational readiness, regulation, and market context. Discussion: The article concludes with implications for hotel managers, owners, investors, and researchers, and outlines a future research agenda for hospitality, tourism, service management, and real-estate scholars.
Privacy disclosure from model parameters and malicious attacks are critical issues in federated learning (FL). Existing research has yet to effectively address the simultaneous need for efficient communication design, privacy protection, and attack detection, which impedes the widespread adoption of FL in mobile edge networks over 6G wireless communication. In this paper, we propose a trustworthy FL framework that can ensure privacy, robustness, accountability, fairness, and explainability in mobile edge networks. Specifically, we integrate authenticated zero-knowledge proofs (ZKPs) and Pedersen commitments into the FL process. Despite the lack of direct access between servers and mobile devices, the servers can still identify trustworthy clients for specific tasks. Clients can verify the authenticity of the received global model based on the provided proofs and commitments. Furthermore, we leverage Ethereum to act as the verifier and authenticator of models. This verification and authentication process enables the servers to detect abnormal local models and perform trust-based aggregations. Numerical results demonstrate that the proposed trustworthy FL framework significantly improves the global model's in terms of accuracy, convergence rate, and security.
Stephen Bishibura Erick, Bonamax Mbasa, Kulwa Mang’ana
This study conducts a comprehensive bibliometric analysis of scholarly research on green economy and sustainable finance from 2014 to 2024. Drawing upon a dataset of 692 peer-reviewed publications indexed in Scopus and analysed using the Bibliometrix R package, the study maps the field’s intellectual landscape, thematic development, and collaborative networks. The findings reveal a consistent increase in scientific output, with a pronounced surge in publications after 2018. This growth trend aligns with global policy milestones such as the Paris Agreement, the European Union [EU] Sustainable Finance Action Plan, and the proliferation of Environmental, Social, and Governance [ESG] integration and green bonds. China emerges as the most productive country, while institutions such as Jiangsu University, the Southwestern University of Finance and Economics, and the Lebanese American University lead in publication volume and collaboration intensity. Keyword co-occurrence and thematic mapping identify dominant themes related to green finance, environmental sustainability, ESG frameworks, and renewable energy, alongside emerging topics like climate risk disclosure and transition finance. Conceptual and co-word network analyses further reveal the interdisciplinary integration of finance, economics, policy, and environmental science. The study also demonstrates the growing decentralization of institutional influence and the rise of both North–South and South–South collaborations. These findings offer valuable insights into the evolving structure of research in sustainable finance and inform future academic inquiry and policy development.
We demonstrate that AI agents given 3-line role descriptions and access to consensus-validated institutional memory can autonomously create, harden, calibrate, solve, and learn from cybersecurity challenges—without any domain expertise in their prompts. Using 11 specialized agents organized into 5 departments within a governed organization (CipherForge Labs), we present the first fully autonomous, consensus-governed AI security research loop: A designer agent (3-line prompt, zero cryptographic knowledge) generates a functional AES-CBC Padding Oracle challenge. A hardener agent (3-line prompt) applies 6 defense layers—20-bit Proof of Work, timing side-channels, JSON casing side-channels, single-use tokens—escalating difficulty from 0.80 to 1.75 across 2 iterations. A calibrator agent (3-line prompt) correctly assesses the hardened challenge at difficulty 1.80 (gap = 0.20 from target 2.0). A quality scorer (3-line prompt) rates the challenge 93.0/100. Total pipeline time: 508 seconds. An independent solver agent (blind, no source code access) identifies the casing side-channel vulnerability, writes a C-compiled Proof of Work solver, deploys 32 parallel oracle workers, and captures the flag in 525.2 seconds (16,384 queries). The findings are submitted to a 4-node BFT consensus network, validated (score = 0.88), and committed to institutional memory—now queryable by all future agents. No agent had cryptographic expertise in its prompt. No human intervened at any stage. The entire cycle—creation, defense, assessment, exploitation, and organizational learning—was governed by BFT consensus with department-scoped RBAC access controls. This result extends our prior finding that an 18-line "onboarding" prompt with curated institutional memory outperformed a 120-line expert prompt. Here we take that principle to its logical extreme: 11 agents, 5 departments, 20+ pipeline routing states, and a closed feedback loop—all driven by minimal prompts and organizational memory.
Relevance of the research topic. The relevance of studying fiscal decentralization as a factor in strengthening the financial capacity of Ukraine's regions stems from the limited opportunities for optimizing budgetary policy amid significant financial constraints caused by priority expenditures on defense and the social sphere. The traditional centralized model of the budgetary system, despite its historical justification, demonstrates inefficiency due to regions' dependence on interbudgetary transfers and limited adaptability to local needs. At the same time, decentralization, while offering prospects for enhancing autonomy and more efficient resource utilization, is accompanied by risks of regional disparities and requires balanced control to maintain the macroeconomic stability of the state.The purpose of the article is to examine fiscal decentralization as a key factor in strengthening the financial capacity of Ukraine's regions.Research objectives are to analyze the impact of decentralization on the revenue base structure of local budgets, to identify the advantages and risks of this process under contemporary conditions, and to substantiate directions for improving interbudgetary relations mechanisms in order to ensure the stability and autonomy of subnational finances.Research methods: analysis, synthesis, statistical assessments, graphical evaluations, induction, deduction, scientific abstraction.Main research findings. The article examines the role of fiscal decentralization as a key factor in strengthening the financial capacity of Ukraine's regions, and analyzes the transformation of the revenue base structure of local budgets as well as interbudgetary relations mechanisms under contemporary conditions. It is substantiated that the reform contributes to enhancing the autonomy of subnational levels of government, more efficient satisfaction of local needs, and reduction of dependence on central transfers, although it is accompanied by risks of deepening regional disparities and fiscal asymmetry. Directions are proposed for improving financial equalization instruments, revising the distribution of revenue sources, and strengthening monitoring to ensure a balance between the financial independence of communities and the macroeconomic stability of the state.Field of application of the results: The findings of the study can be applied in the process of shaping and improving the state's budgetary policy, developing normative–legal acts in the sphere of interbudgetary relations, as well as in preparing recommendations for local self–government bodies aimed at enhancing the financial capacity of territorial communities. In addition, the materials of the article hold practical value for research activities in the fields of public finance, regional economics, and decentralized governance.
A considerable proportion of perishable goods, including fruits and vegetables, deteriorate prior to reaching customers. Inadequate refrigeration infrastructure, particularly in developing nations with arid climates and markets distant from agricultural sources, accounts for most of these losses. A food cold chain has three primary phases: pre-cooling, cold storage, and refrigerated transportation. All phases of the cold chain rely fundamentally on refrigeration to preserve perishable products at designated temperatures, relative humidity, and CO2 concentrations, thus prolonging their shelf life. Solar-driven or aided refrigeration systems use solar energy to power cooling systems and preserve the food in the cold chain. These systems are especially beneficial in off-grid or developing areas for preserving perishable goods such as fruits, vegetables, and other food items, mitigating postharvest losses that can exceed 30–50% in areas with inconsistent energy supplies. Despite progress in efficiency and scalability, numerous research gaps remain across technological, economic, social, policy, and regional dimensions, including technical aspects, optimization, and integration. There is a need to enhance energy-efficient designs, particularly by managing solar intermittency to address non-uniform cooling, which leads to inconsistent ripening and spoilage, and by integrating sustainable refrigerants to mitigate environmental impact. Further development is necessary for micro-scale, transportable, or decentralized systems designed for small farms, while economic and financing obstacles include high upfront costs and limited financial accessibility. Substantial deficiencies exist in creating affordable models and funding channels for small-scale agriculturalists. Addressing these deficiencies could expedite adoption, thereby reducing global food loss and waste (accounting for 8–10% of GHG emissions) while improving food security. Future research must emphasize multidisciplinary methodologies that amalgamate engineering, economics, and social sciences to provide comprehensive solutions.
Aleksandar Šević, Željko Šević, Athanasios Fassas, Panayiotis Tzeremes
There is a strong impetus to make cryptocurrencies more environmentally friendly, and in our study it is has been analyzed whether commodity price shocks have varying impacts on clean and dirty cryptocurrency interconnectedness before, during and after the COVID-19 pandemic. Using the decomposed and partial connectedness measure we evaluate the connectedness of oil price shocks, demand, supply and risk, as well as five clean and five dirty cryptocurrencies from October 2017 until April 2024. The spikes in demand and disruptions in oil supply lead to price increases. Oil shocks have the largest impact on sampled crypto products during the COVID-19 period, as opposed to pre- and post-pandemic years, and they demonstrate a stronger influence on selected cryptocurrencies than internal crypto-to-crypto dynamics. During the crisis, the difference between clean and dirty cryptocurrencies becomes less relevant when compared to no-crisis periods. We also find that clean cryptocurrencies are net recipients of shocks, while dirty counterparts, dominated by Bitcoin and Ethereum, are net transmitters, especially during the recovery phase. Our findings are relevant for supporting the transition to clean cryptocurrencies and contribute to a better understanding of dynamic interconnectedness. • Examines the decomposed and partial connectedness • Uses time-varying parameter vector autoregression (TVP-VAR) models • Highlights the heterogeneity in cryptos’ responses to oil price fluctuations • Total Connectedness Index peaks during the COVID-19 pandemic • The distinctions between clean and dirty cryptocurrencies reemerged post-COVID
Abstract The peer-reviewed journal article imposes structural constraints on the dissemination, validation, and reuse of research outputs. Intermediate results, negative findings, methodological refinements, and replication attempts are systematically underrepresented in published literature, limiting visibility into ongoing research activity for both scientists and mission-driven funders. Here we present Carrierwave, an open infrastructure for continuous, granular scientific communication built on structured research objects (ROs), cryptographic provenance, blockchain-based attribution, and programmable incentive mechanisms. Each RO represents an atomic unit of scientific output -- a single experimental result, negative finding, dataset, protocol, or replication -- that is hashed for content integrity, stored in a persistent database, and optionally minted as an ERC-721 non-fungible token on the Ethereum blockchain. The system includes an on-chain bounty pool enabling funders to directly incentivize specific research activities, and an automated analysis layer that synthesizes disclosed ROs into continuously updated research landscape maps. We describe the system architecture, report on its implementation and deployment on Ethereum mainnet, and present a quantitative analysis of disease-specific publication frequency demonstrating the information latency problem that Carrierwave addresses. The distribution of publication frequency across disease areas is highly skewed, with the majority of conditions represented by fewer than four publications per year in high-impact biology journals. For diseases in the long tail, the interval between successive publications may span months or years. Publication frequency correlates poorly with disease burden, instead reflecting historical research community size and advocacy momentum. By reducing the unit of communication to the individual research object and eliminating editorial gatekeeping as a prerequisite for disclosure, Carrierwave increases the effective sampling rate of scientific activity in precisely the domains where publication-based visibility is most sparse. The system is live at https://carrierwave.org .