Earlier studies used classical time series models to forecast the nonlinear connectedness of conventional crypto-assets with CO2 emissions. For the first time, this study aims to provide a data-driven Nonlinear System Identification technique to study the nonlinear connectedness of crypto-assets with CO2 emissions. Using daily data from January 2, 2019, to March 31, 2023, we investigate the nonlinear connectedness among conventional crypto-assets, sustainable crypto-assets, and CO2 emissions based on our proposed model, Multiple Inputs Single Output (MISO) Nonlinear Autoregressive with Exogenous Inputs (NARX). Intriguingly, the forecasting accuracy of the proposed model improves with the inclusion of exogenous input variables (conventional and sustainable crypto-assets). Overall, our results reveal that conventional crypto-assets exhibit slightly stronger connectedness with CO2 emissions compared to sustainable crypto-assets. These findings suggest that, to some extent, sustainable crypto-assets provide a solution to the environmental issues related to CO2 emissions. However, further improvements in sustainable crypto-assets through technological advances are required to develop more energy-efficient decentralised finance consensus algorithms, with the aim of reshaping the cryptocurrency ecosystem into an environmentally sustainable market.
Cryptocurrency is a digital asset secured by cryptography that has become a popular medium of exchange and investment known for its anonymous transactions, unregulated markets, and volatile prices. Given the popular subculture of traders it has created, and its implications for financial markets and monetary policy, scholars have recently begun to examine the political, psychological, and social characteristics of cryptocurrency investors. A review of the existing literature suggests that cryptocurrency owners may possess higher-than-average levels of nonnormative psychological traits and exhibit a range of non-mainstream political identities. However, this extant literature typically employs small nonrepresentative samples of respondents and examines only a small number of independent variables in each given study. This presents the opportunity for both further testing of previous findings as well as broader exploratory analyses including more expansive descriptive investigations of cryptocurrency owners. To that end, we polled 2,001 American adults in 2022 to examine the associations between cryptocurrency ownership and individual level political, psychological, and social characteristics. Analyses revealed that 30% of our sample have owned some form of cryptocurrency and that these individuals exhibit a diversity of political allegiances and identities. We also found that crypto ownership was associated with belief in conspiracy theories, "dark" personality characteristics (e.g., the "Dark Tetrad" of narcissism, Machiavellianism, psychopathy, and sadism), and more frequent use of alternative and fringe social media platforms. When examining a more comprehensive multivariate model, the variables that most strongly predict cryptocurrency ownership are being male, relying on alternative/fringe social media as one's primary news source, argumentativeness, and an aversion to authoritarianism. These findings highlight numerous avenues for future research into the people who buy and trade cryptocurrencies and speak to broader global trends in anti-establishment attitudes and nonnormative behaviors.
Cryptococcus neoformans, a high-priority pathogen (WHO, 2022) and ubiquitous fungus, is responsible for hundreds of thousands of meningoencephalitis cases annually, with a high fatality rate. Its distribution is uneven: it primarily affects immunocompromised individuals (especially HIV-positive patients). Our study aims to explore the Cryptococcus' brain tropism in immunosuppressed patients, its gender preference and the possible interactions with other opportunistic neurotropic microorganisms, such as Mycobacterium tuberculosis (MTB) and the brain microbiota, with a particular focus on Toxoplasma gondii (T. gondii). We conducted a retrospective descriptive analysis of all cases diagnosed with central nervous system cryptococcosis (Crypto-CNS) in HIV-positive patients admitted over 10 years (2010-2019) in a tertiary Romanian hospital. We examined their demographic, clinical, immunobiological, and imaging data, as well as their medical history, comorbidities, and coinfections. Forty-two cases were admitted, with a male predominance (3.6:1) and a mean age of 33.3 years; 24% were diagnosed concomitantly with HIV infection and Crypto-CNS. All patients were severely immunosuppressed, with CD4 counts <200 cells/mm3 (median = 20.5 [1-163], mean = 31.6). Recent/concomitant tuberculosis was found in 10 (27.7%). T. gondii-seropositive patients developed Crypto-CNS at a lower immunological state than seronegative ones (27.1 CD4 cells/mm3 vs. 46.7 cells/mm3, means). Of 25 cases with available brain imagery, 28% had high intracranial pressure. Twelve patients (28.5%) died during the hospitalization within 26.3 days (mean, SD = 21.4); 1-year mortality increased to 50%. In-hospital mortality was associated with lower CD4 counts, increased intracranial pressure, and T. gondii-seropositivity. Crypto-CNS in HIV-positive patients mainly affects men and may be promoted by concomitant or recent tuberculosis. T. gondii may confer some protection even at low immune levels but increases mortality when immunity is critically low.
In today's digital world, cryptocurrencies like Bitcoin can secure transactions without banks. However, the rise of quantum computing poses significant threats to their security, as traditional cryptographic methods may be easily compromised. In addition, the existing algorithms face difficulties like slow transaction speeds, interoperability issues between different cryptocurrencies, and privacy concerns. Hence, Quantum Crypto Guard for Secure Transactions (QCG-ST), a novel blockchain framework, is introduced, offering enhanced security and efficiency for cryptocurrency transactions. The QCG-ST employs lattice-based cryptography to provide robust protection against quantum threats and incorporates a new consensus mechanism to increase the transaction speed and reduce energy consumption. The QCG-ST system uses lattice-based encryption that is based on the Ring Learning With Errors (Ring-LWE) issue to protect itself from quantum assaults. It uses sharding, a Proof-of-Stake (PoS) consensus method, and a threshold signature scheme (TSS) to make the system more scalable and use less energy. Zero-knowledge proofs (ZKPs) are used to check transactions without giving out private information. We offer a cross-chain atomic swap protocol that uses hashed time-lock contracts to make sure that it works on all platforms. Blockchain transaction data utilized in testing originated from the Bitcoin Historical Dataset available on Kaggle, and quantum resistance has been assessed using the Qiskit Aer simulator. It evaluated the framework's performance to that of traditional methods like Payment Channel-Lightning Network (PC-LN), Variational Quantum Eigensolver (VQE), and Cross-Chain Transaction with Hyperledger (CCT-H). Results show that QCG-ST does far better than traditional systems in terms of transaction success rate (up to 98.5%), speed, energy efficiency, latency, and throughput, especially when tested in a quantum-simulated environment. This study completes in an essential vacuum in blockchain technology by suggesting a strong, quantum-resistant, privacy-protecting architecture that can handle the problems that could arise up in decentralized digital banking in the future.
Cryptococcus neoformans and Cryptococcus gattii are World Health Organization critical and medium priority pathogens, respectively. These mainly impact people with human immunodeficiency virus residing in low- and middle-income countries, but other patient groups and settings are also affected. The high global morbidity and mortality and the limitations of current treatments provided an impetus for the development of a target product profile (TPP) for new anti-cryptococcal agents. Key attributes of the TPP include improved safety, superior (or at least comparable) activity to current treatments against all syndromes across the full disease spectrum (cryptococcal meningitis, cryptococcal pneumonia, etc.), relevance for C. neoformans and C. gattii, suitability for all age groups, oral and intravenous formulations, an acceptable treatment regimen, minimal/manageable drug-drug interactions, thermostability, and a barrier to resistance at least as high as current options. The aim of this TPP, along with the suggested discovery and development paths, is to assist all stakeholders in the development of novel cryptococcal disease treatments.
The rapid expansion of artificial intelligence (AI)-enabled systems and cryptocurrency mining poses significant challenges to climate sustainability due to energy-intensive operations relying on fossil-powered grids. This work investigates the strategic coupling of AI data centers and cryptocurrency mining through shared energy infrastructure including colocated renewable power installations, battery energy storage, green hydrogen infrastructure, and carbon offsetting measures to achieve cost-effective and climate-neutral operations. Employing a novel energy systems modeling framework, it explores synergistic AI-crypto operations with a detailed scenario design along with an optimization modeling framework to assess the decarbonization potential and economic implications, enabling a transformative shift in the digital landscape. The results indicate that synergizing the AI-crypto operations while achieving net-zero targets can avoid up to 0.7 Gt CO2-equiv through 2030. Moreover, reaching these targets with synergistic strategies globally requires up to 90.7 GW of solar power and 119.3 GW of wind power capacity. The findings advocate for robust policy measures that facilitate the strategic expansion of synergistic AI-crypto operations including carbon credit schemes tailored for the digital sector, incentives for energy efficiency improvements, and international collaborations to bridge economic disparities. Future research should focus on refining strategic interventions across different geopolitical contexts to enhance global applicability.
Cryptocurrencies have emerged miraculously all over the globe due to their legitimacy, transparency, immutability, and the traceability that blockchain technology provides. However, the benefits it provides are dwarfed by how unpredictable and extremely price-volatile the cryptocurrencies are. That makes it really tough for investors to find their profitable opportunities in such volatile markets. Social media sources, like Twitter and Reddit, have evolved as crucial tools of sentiment estimation above the explosively volatile price movements of decentralized currencies. Here we introduce an attention-based hybrid CNN-LSTM model optimized for social media sentiment analysis to use them towards investment decisions in a broad portfolio of cryptocurrencies. The existing Convolutional Neural Network (CNN) effectively extracts the essential features, and Long Short-Term Memory (LSTM) has the potential to capture the long dependencies between phrases. Although these models can process massive textual data, they limit treating all the features equally important. Therefore, the proposed model induces the attention mechanism into hybrid CNN-LSTM for emphasizing more or fewer weights on different words according to their contributions and optimizes the parameters of employed neural networks using grid search. In our pipeline, the attention-augmented CNN-LSTM first transforms each tweet/review into a 512-dimensional task-specific embedding; a calibrated radial-basis SVM then serves as the final decision layer, refining the margin for classes that the neural network alone tends to blur. This sequential ('deep-features-plus-SVM') architecture boosts F1 by 3.2 pp over a pure Softmax head while adding only 0.4 ms of inference time. Extensive experiments conducted on cryptocurrency-related tweets and Reddit reviews reveal the outperformance of the proposed model over existing Deep Neural Networks (DNNs) and state-of-the-art models. Trained on 9.9 k crypto-tweets and 33 k Reddit comments, AEH attains 98.7% accuracy, 0.987 F1, and κ = 0.94, outperforming strong baselines (pure LSTM + 8.3 pp; pure CNN + 19.3 pp) and the widely-used VADER toolkit (+ 11.8 pp). On the forecasting side, a complementary GRU regressor trained on eight-year price series yielded MAE = 0.0315, MAPE = 5.95%, and MSE = 0.0022 for Bitcoin, beating an ARIMA benchmark at p < 0.001. The primary objective of the proposed hybrid model attributed to processing huge social sentiments with an attention mechanism to break the dilemma of cryptocurrency investors.
This study delves into the impact of reversals and investor attention on cryptocurrency returns before and during the COVID-19 pandemic. We employ the Two Stages Least Squares to analyze a sample of the top 20 cryptocurrencies from January 2016 to April 2021. Our results reveal that investor attention positively influences bitcoin returns in both periods, with a more pronounced effect during the pandemic. Conversely, reversals demonstrate a positive correlation with cryptocurrency returns before the outbreak but a negative relationship during the pandemic. Our robustness test further indicates that investor attention positively affects the returns of small and medium-cap cryptocurrencies, while reversals only exhibit positive consequences for small-cap cryptocurrencies. Additionally, our findings highlight stablecoins as a safe haven during the epidemic. The results suggest that investor attention has little influence on the returns of stablecoins, indicating that these coins are primarily resistant to market sentiment due to their inherent stability. The negative impact of the pandemic on the crypto market demonstrates a downward trend through each wave. Despite aligning with attention-induced price pressure and behavioral finance hypotheses, our results do not support efficient market theory or the notion of heterogeneity among investors. This research provides valuable insights for investors and policymakers in devising effective strategies for the cryptocurrency market.
The decentralized and pseudonymous nature of cryptocurrency has facilitated its extensive use in illicit activities, including money laundering, tax evasion, and ransomware. Limiting such activities requires a well-established forensic framework. However, a dedicated methodology for examining cryptocurrency wallets remains underdeveloped. This study presents a systematic forensic analysis of Electrum wallets installed on virtual machines running Windows 10, outlining the wallet taxonomy and meticulously listing all artifacts. This study primarily focuses on memory forensics, with most of the analysis devoted to memory-based artifacts extracted from five distinct memory dump scenarios. Artifacts extraction were performed using Volatility 3 plugins, in conjunction with Python-based analysis scripts, within a Kali Linux environment. Following the memory-based analysis, a limited disk examination was conducted after wallet inactivity or system shutdown to assess whether any residual Electrum artifacts persisted beyond memory. The research examines the artifacts retrievable from wallet files, both before and after backup, and compares these results with those obtained from other methods reported in the literature. The experimental outcomes demonstrate the impact of this research on the successful extraction of private keys, wallet addresses, extended public keys, wallet files, and transaction IDs. The extracted Electrum addresses and private keys provided access to critical wallet details, and unspent Bitcoin were successfully recovered using these keys, confirming the feasibility of forensic cryptocurrency recovery and revealing data of high evidentiary value to the digital forensic community.
Cryptococcus causes life-threatening opportunistic infections in immunocompromised hosts. Data on the most severe cases requiring ICU admission remain limited. We conducted a retrospective, multicenter study of patients admitted to 30 French ICUs for severe cryptococcosis between 2000 and 2022. Among 151 patients included, 56.9% were patients with HIV. Cases in patients without HIV became increasingly prevalent over time (51.3% in 2012-2022 vs. 32.4% before 2012); 82.5% were receiving immunosuppressive therapy. Central nervous system infection was predominant (91.1%), followed by lung infection (39.7%). Fungemia occurred in 59.8% patients, and 75.2% had disseminated infection. Neurological failure was the leading organ impairment at admission (75.5%) followed by respiratory failure (47.7%), acute kiney injury (41.7%) and shock (24.5%). The median SOFA score was 4 [2-7]. Invasive mechanical ventilation, vasopressors and renal replacement therapy were required in 54.9%, 34.4% and 18.5% of patients, respectively. At day 90, 94% of patients requiring mechanical ventilation and vasopressors were deceased, compared to 38.7% with invasive ventilation alone and 17.2% without any organ support (p<0.001). Overall, 90-day mortality reached 49.6%. SOFA score (HR 1.04 [1.02-1.06]), admission between 2000 and 2012 (HR 2.30 [1.36-3.89]), disseminated infection (HR 2.32 [1.15-4.67]) and initiation of antifungal therapy before ICU admission (HR 0.38 [0.22-0.63]) were independently associated with 90-day mortality, whereas HIV serostatus was not (HR 0.93 [0.47-1.84]). Severe cryptococcosis requiring ICU admission affects an increasing number of patients without HIV and is associated with high, though declining, mortality. Early diagnosis and treatment are mandatory to improve prognosis.
Cryptococcal infection remains a leading cause of mortality among HIV-1-positive individuals, particularly in regions with limited access to antiretroviral therapy and diagnostics. This study aimed to assess Cryptococcal Antigen (CrAg) seroprevalence and its immune-virological correlates among ART-naïve and ART-experienced HIV-1 positive individuals. This prospective cross-sectional study was conducted from May 2023 to August 2024 at Edo State University Teaching Hospital, Nigeria. Blood samples were analyzed for CD4 + T-cell counts using a Partec™ CyFlow analyzer, HIV-1 viral load using the COBAS® AmpliPrep/COBAS® TaqMan® Test, and CrAg detection with the Immy Latex-Crypto Antigen Lateral Flow Assay. Among 229 HIV-1 positive individuals, 72.5% were aged 15-20 years, and 69% were female. Most (68.6%) were ART-experienced, while 31.4% were ART-naïve. Severe immunosuppression (CD4 + < 200 cells/mm³) was present in 64.6%, and 71.2% had viral loads > 1,000 copies/mL. Cryptococcal infection (CI) prevalence was 10.04%. No significant link was found between CI and age or gender, but ART-naïve status, low CD4 + counts, and high viral loads were significantly associated with CI. ART-naïve individuals had higher viral loads (median 4.95 vs. 4.19 log10 copies/mL, p = 0.00). A stronger inverse correlation between CD4 + counts and viral load was observed in ART-experienced patients (r = -0.535). These findings emphasize the necessity for routine Cryptococcal screening, particularly in ART-naïve and severely immunocompromised individuals, to facilitate timely interventions and improve clinical outcomes.
Molecular information coding (MIC) involves biomolecules to encrypt and transmit messages, remains in its early stages of development. This work presents a versatile molecular integration framework and a proof-of-concept multi-level security system that combines Morse code, ASCII code, and Beale's cipher through molecular logic computing, using a molecular dye-oligonucleotide platform (single-stranded DNA, duplex DNA, stem-loop, and G-quadruplex (G-4) structures). This study demonstrates the integration of nanotechnology with crypto-steganographic methods to visualize and decipher codes, embedding elementary logic operations into molecular signal transduction. Additionally, a graphical user interface (GUI) is developed for classifying elementary logic gates using a decision tree algorithm, providing researchers with an accessible tool for rapid prediction. The Morse code-mediated strategy enables static key generation using dots, dashes, and intervals, and dynamic key generation through a polyalphabetic cipher framework. In parallel, ASCII-based logic gate operations facilitate multi-key decryption of decimal values to recover hidden information. Furthermore, a multilayered hybrid cryptographic technique combining Beale's cipher with Morse code implemented via a pangramic codebook, establishes an exceptionally resistant system against brute-force attacks. These methods provide insights into the evolution of communication and highlight the importance of encryption without relying on highly complex materials or sophisticated instruments.
The rise of cryptocurrency trading has sparked global interest and raised concerns about its potential links to problematic gambling behaviours. This study examined the prevalence of problematic gambling amongst cryptocurrency traders and identified psychological predictors, focusing on gambling motivations and cognitive distortions. Cross-sectional survey study using YouGov Opinion Polling's sample-matching methodology. A sample of 700 cryptocurrency traders was drawn from a larger behavioural addiction project (N = 4363). Participants completed the Problem Gambling Severity Index (PGSI), Gambling Motives Questionnaire-Financial (GMQ-F), and Gambling Related Cognitions Scale (GRCS). Analyses included chi-square tests, one-way ANOVAs with Tukey's post-hoc tests, and multinomial logistic regression. Problematic gambling was identified in 33.7 % of traders, with 33.9 % classified as at-risk gambling and 32.4 % as non-problematic gambling. Enhancement motivation (OR = 1.60, 95 % CI [1.10, 2.34]) and interpretative bias (OR = 1.38, 95 % CI [1.06, 1.81]) positively predicted at-risk gambling, whereas social motivation showed protective effects (OR = 0.61, 95 % CI [0.41, 0.91]). Coping motivation strongly predicted problematic gambling (OR = 4.47, 95 % CI [2.28, 8.78]), as did inability to stop gambling (OR = 3.18, 95 % CI [2.22, 4.54]). Age was negatively associated with problematic gambling (OR = 0.94, 95 % CI [0.91, 0.97]). Findings reveal high rates of problematic gambling amongst cryptocurrency traders, with distinct motivational and cognitive predictors at different risk levels. Results suggest the need for targeted educational programmes and intervention strategies tailored to address specific risk factors.
Introduction: Cryptococcal meningitis is a major cause of death in HIV/AIDS patients due to the existence of Cryptococcus neoformans in the central nervous system. Our objective was to evaluate the prevalence of Cryptococcus antigenuria in a population of HIV-infected patients in Libreville, Gabon. Patients and Methods: This study was conducted from April to October 2021 at the Infectious Diseases ward of the Centre Hospitalier Universitaire de Libreville. Hospitalized patients with HIV were included. The detection of cryptococcal antigen (CrAg) in urine was performed using the Pastorex Crypto Plus Kit. Results: Out of the 255 PLHIV, 142 benefited from the CrAg detection. The prevalence of urine CrAg was 24.6% (n = 35). The majority of CrAg+ patients (82.8%; n = 29) were under 55 years old. Almost three-quarters of them (n = 25; 71.4%) had CD4 counts < 200, and 80.0% (n = 28) were at WHO clinical stages III and IV. All patients with neck stiffness at admission had a CrAg positive test. Conclusion: This study showed a non-negligible prevalence of Cryptococcal urinary antigen in HIV-infected patients with neurological symptoms. These data underline the importance of CrAg screening in routine care for better management of PLHIV.
<b>Indroduction:</b> Cryptoglandular perianal fistula represents a prevalent benign anorectal condition, primarily addressed through surgical interventions, occasionally posing considerable therapeutic challenges. The associated decline in patient quality of life underscores the significance of effective management. However, the lack of a fully understood pathogenesis complicates the treatment approach. Recent research has proposed the involvement of adipose fat tissue in the inflammatory response and pathogenesis of cryptoglandular anal fistula.<b>Aim:</b> The study aims to characterize the role of adipose fat tissue in the pathogenesis of cryptoglandular anal fistula, with a specific focus on understanding the potential involvement of proinflammatory cytokines in the development of chronic inflammation.<b>Materials and methods:</b> This study involved the characterization of serum levels of inflammatory cytokines and adipose tissue hormones. A total of 35 samples from both simple and complex cryptoglandular perianal fistula cases were collected during surgical procedures.<b>Results:</b> Serum levels of leptin, resistin, IL-1β, and IL-8 were significantly elevated in patients operated on due to complex cryptoglandular perianal fistula when compared to patients with simple fistula. Adiponectin was significantly lowered in samples from complex perianal fistula in comparison to simple fistula.<b>Conclusions:</b> Complex perianal cryptoglandular fistula has a reduced level of anti-inflammatory adipokines i.e. adiponectin, and an increased level of proinflammatory resistin, leptin, IL-1β, and IL-8.
Background/Objectives: This study aimed to identify and analyze the risk factors associated with Cryptosporidium infection in hospitalized patients in western Romania. Methods: A total of 312 patients, aged between 2 months and 90 years and residing in both urban and rural communities, were included. Stool samples were collected and analyzed using the CerTest Crypto qualitative chromatographic test and the modified Ziehl-Neelsen staining method (Henricksen & Pohlenz). Risk factors were assessed through a questionnaire completed by patients or by the parents of pediatric patients. Results: The overall prevalence of Cryptosporidium infection was 5.77%. Among the evaluated risk factors, only the area of residence showed a statistically significant association (p < 0.05), with a higher prevalence in urban areas (9.2%) compared to rural areas (3.6%). Other factors-including age, gender, contact with animals, pet ownership, handwashing after animal contact, type of housing, fruit washing habits, use of potable water, use of public transportation, international travel, and visits to playgrounds or swimming pools-were not significantly associated with infection. Conclusions: These findings suggest that urban residency may be a significant factor in Cryptosporidium transmission and may inform future research and the development of targeted public health strategies.
Blockchain-enabled products (e.g., cryptocurrencies and fan tokens) have rapidly expanded across professional sport, but the research landscape remains dispersed across finance, marketing, information systems, and sport management. This study conducted a thematic review of Web of Science Core Collection records supplemented by snowball searching, yielding 30 English-language peer-reviewed studies published between 2019 and 2025. Based on the included titles, we mapped how the literature has developed and what it collectively implies for sport organizations, platforms, and consumers. Five recurring strands were identified: (1) fan tokens and sport cryptoassets as financial assets, emphasizing volatility, spillovers, and sensitivity to sport- and crypto-market events; (2) adoption, identity, and engagement research explaining why supporters buy/hold tokens, participate in voting, and engage in advocacy; (3) computational and platform-data approaches (e.g., sentiment/discourse analyses and poll/voting participation patterns) to quantify online engagement and market narratives; (4) blockchain applications and governance, including stakeholder-oriented discussions and ethical critiques regarding value creation, transparency, and power asymmetries; and (5) gambling-like risks and addiction-related correlates, highlighting the convergence of trading, betting-like dynamics, and potentially harmful consumption. Limitations include dependence on WoS-indexed English-language publications, topic and context concentration (especially European football and major platforms), and heterogeneity in study designs and outcomes that precludes comprehensive data synthesis. Future research should broaden contexts beyond dominant sports/regions and use stronger longitudinal or quasi-experimental designs to test mechanisms and harms.
In the domain of digital data exchange, ensuring information security is the supreme demand for data storage and transmission. Interlinking cryptographic techniques with steganographic principles can enhance data confidentiality. However, there have been no reports thus far to develop molecular platforms for hybrid crypto-steganography systems. Using our synthesized nanoclusters (BSA-Au/Ag NCs) through bovine serum albumin (BSA) as a versatile scaffold, we fabricated a molecular platform for concatenated logic circuits and molecular keypad lock. Then, we integrate terrestrial direction information transmission through molecular navigation, employing a double block cipher by combining stego key and shifting cipher key techniques to develop a hybrid crypto-steganography system, aimed at enhancing security paradigms. Furthermore, we prioritize information protection by developing an enhanced distress call protocol using a polyalphabetic cipher to activate covert communication capabilities, thereby safeguarding data against potential infiltrators.
The pursuit of the Sustainable Development Goals (SDGs) requires considerable new green crypto investments. To attract the flow of this investment, it is necessary to develop and apply robotic artificial intelligence (AI) as it has the potential to encourage the adoption of environmental innovation and increase individuals' environmental awareness. Our research employs the DCC-GARCH Copula Model to examine time-varying spillovers and prove interlinkages between the development of AI and green cryptocurrencies in the period from January 1, 2018, to September 8, 2023. Comparing the optimum hedge ratios with the optimal portfolio weights, we demonstrate that the optimal hedge strategy for BOTZ is the most successful one. However, the success of hedging depends on the portfolio's risk profile. Based on our analysis of the cumulative profit profile of different approaches, we continue to believe that the best portfolio weighting strategy is the one that produces positive returns in the middle of 2020 and the first part of 2022 and 2023. This demonstrates that the most profitable diversification approach is not always the most successful one. Our results have important policy implications for investors and governments.
Blockchain technology, once limited to niche technological communities, has seen widespread global adoption in recent years, with the potential to reshape financial and social systems. Launched in July 2015, the Ethereum blockchain introduced programmable Smart Contracts. This innovation enabled the creation of user-defined crypto-assets adhering to the ERC-20 standard, supporting a wide range of decentralized applications beyond simple value transfer. We present a large-scale, temporally annotated dataset of ERC-20 token transactions recorded on the Ethereum blockchain. Spanning from November 2015 to December 2024, the dataset encapsulates the trading activity of 216,336,529 users trading 1,138,136 unique tokens, offering a detailed view of crypto-market activity over time. Uniquely, it enables the analysis of a financial ecosystem from its inception, providing rare insights into its structural evolution, participant dynamics, and emergent behaviors. As the largest publicly available resource of its kind, it supports research in blockchain analytics, market dynamics and temporal network analysis. The full dataset and accompanying code are released for public use.