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Cryptocurrencies are digital assets that differ from traditional currencies in their unique characteristics. An increasing number of individuals are showing interest in cryptocurrency trading and spending long hours engaged in this activity. Current studies point out that some behaviors of cryptocurrency investors may be associated with problematic mental health outcomes. The present study aims to investigate problematic cryptocurrency trading behaviors and to examine these behaviors in terms of gambling disorder and psychological risk factors. Two hundred four volunteered participants whose ages ranged between 18 and 65 years (mean = 31.62, SD = 6.95) were recruited. For data collection, the Problematic Cryptocurrency Trading Scale, Problem Gambling Severity Index, Eysenck Personality Questionnaire Revised - Abbreviated Form, UPPS Impulsive Behavior Scale, and Positive and Negative Affect Schedule were administered. Findings demonstrated that participants exhibiting symptoms of problematic cryptocurrency trading had higher levels of problem gambling severity, negative emotions, extraversion, and urgency, along with lower levels of premeditation and perseverance. Additionally, the linear regression with backward elimination revealed that problem gambling severity and extraversion are associated with problematic cryptocurrency trading. These findings suggest that greater engagement in cryptocurrency speculation may be associated with increased gambling behaviors and that both activities could be linked to underlying personality traits, impulsivity, and negative emotions.

With the rise of cryptocurrency, using cryptocurrency investments to conduct fraud has become a common criminal tactic. Drawing on survey data from 287 victims in China, this study explored the determinants and mechanisms of investment intention among victims of cryptocurrency investment scams. Based on the TPB and using SEM, we identified three main findings: (1) Investment attitude and perceived behavioral control have a significant positive impact on victims' intention to invest. (2) Risk-seeking personality traits, laws and regulations, investment education, and fraud cases exposure not only directly affect this intention but also influence it indirectly through investment attitude and perceived behavioral control. (3) Subjective norms have a limited impact on investment intention. These conclusions suggest a challenge to the traditional TPB. The decentralized nature of cryptocurrency may make victims rely more on personal judgment than social influence. This finding expands the applicability of the TPB. It also provides a basis for developing targeted fraud prevention systems.

There is a continuum between gambling and investing behaviors, with speculative investment instruments positioned in the middle. Cryptocurrencies, being significantly more volatile than traditional investment tools, have increasingly been linked to gambling disorder (GD). This study aims to examine the relationship between cryptocurrency trading behavior and GD, high-risk substance use, high-risk alcohol use, and tobacco dependence among healthcare professionals in T&#xfc;rkiye. A total of 192 healthcare professionals were assessed using the Problematic Cryptocurrency Trading Scale (PCTS), Gambling Disorder Screening Test (GDST), and the Addiction Profile Index Risk Screening Form (APIRS) (Alcohol and Drug Scales). Categorical data comparisons between two independent groups were conducted using Chi-square or Fisher's Exact tests. Spearman correlation coefficients were used to examine relationships between PCTS scores and APIRS/GDST scores. Additionally, linear regression models assessed the predictive relationships between PCTS scores and APIRS/GDST scores. Among the participants, 25.5% reported engaging in cryptocurrency trading, 41.7% had tobacco dependence, 15.1% reported high-risk alcohol use, 5.7% had high-risk substance use, and 8.9% met the criteria for GD. Cryptocurrency traders demonstrated higher rates of substance use (p&#xa0;=&#xa0;0.033), tobacco dependence (p&#xa0;<&#xa0;0.001), and GD (p&#xa0;=&#xa0;0.043). Additionally, the severity of problematic cryptocurrency trading behavior was positively correlated with the severity of substance use (r&#xa0;=&#xa0;0.172, p&#xa0;=&#xa0;0.017) and GD (r&#xa0;=&#xa0;0.455, p&#xa0;<&#xa0;0.001). The findings indicate a significant relationship between cryptocurrency trading behavior and addiction. Further research with clinical interviews and larger sample sizes is required to validate these findings. The high rates of alcohol, substance, tobacco, and gambling addictions observed among healthcare professionals underscore the need for targeted preventive measures and interventions in this population.

This study employs a smooth transition vector autoregressive model combined with a network topology approach to capture the nonlinear impact of political uncertainty on multi-scale systemic risk spillovers in cryptocurrency market. To reveal the risk characteristics at different time scales, we use wavelet packet decomposition to decompose the sequence into short-term, medium-term, and long-term components; We also use forecast error variance decomposition to quantify risk spillovers, in order to study the direction and intensity of risk spillovers between different cryptocurrencies. In terms of the responses of cryptocurrency systemic risks to political uncertainty shocks, we find significant asymmetries. The mid- and long-term risk components indicate that most cryptocurrencies exhibit a stronger response during periods of high political uncertainty than during low periods. Moreover, shocks during high political uncertainty periods enlarge the cross-cryptocurrency risk spillovers. Finally, Bitcoin, Ethereum and Monero are stable risk transmitters, while Peercoin and Namecoin are more vulnerable risk receivers. This paper provides some insights into the differential impact of political uncertainty on the stability and interconnectivity of various cryptocurrencies.

While deep learning models have demonstrated superior performance in cryptocurrency forecasting, their deployment is often hindered by a lack of interpretability and trustworthiness. To bridge this gap, this paper proposes the Cryptocurrency Counterfactual Explanation (CryptoForecastCF) model. Recognizing the inherent volatility and complex non-linear dynamics of cryptocurrency markets, we argue that understanding the sensitivity of model outputs to slight variations in historical conditions is fundamental to robust risk management. CryptoForecastCF employs a gradient-based optimization strategy to generate meaningful counterfactual explanations. Specifically, it identifies minimal modifications, defined as the optimal perturbations to historical market features such as price constrained by &#x2113;1 or &#x2113;2 norms, that are sufficient to steer the model's future predictions into user-specified target intervals. This approach not only elucidates the key driving factors and decision boundaries of opaque models but also equips traders and risk managers with actionable insights, enabling them to identify the specific market shifts required to navigate high-stakes scenarios and mitigate unfavorable predictive outcomes.

With the widespread adoption of cryptocurrencies, the ability to conduct continuous offline payments has increasingly become a critical technological requirement. In network-constrained scenarios, current dual-offline payment technologies are useful for single transactions. However, their limitations in continuous payment scenarios have become increasingly evident, making them unable to meet real-world application needs. This has prompted the industry to demand more urgent innovations in research on continuous offline payment capabilities. To address these challenges, this paper proposes a continuous dual-offline payment system capable of supporting multiple continuous payments. The system integrates elliptic curve cryptography (ECC) and zero-knowledge proof (ZKP) technology to generate secure asset credentials, ensuring both immutability and privacy credentials throughout the offline payment lifecycle. A dynamic credential decomposition mechanism enables the splitting of input credentials into change credentials and receipt credentials, facilitating uninterrupted dual-offline payments between hardware wallets. Additionally, it incorporates a batch verification scheme based on smart contracts, utilizing zero-balance verification and chained hash tracing to ensure payment uniqueness and prevent double-spending attacks, thereby guaranteeing the verifiability and validity of payment settlements. Experimental evaluations demonstrate that the proposed system reduces gas consumption per payment and improves execution efficiency during batch processing, combining high security with strong performance. This research provides a feasible solution for the application of digital currencies in offline scenarios, carrying significant theoretical value and practical significance for driving technological innovation and application expansion in the cryptocurrency field. In addition to cryptocurrency payments, the proposed system is also applicable to IoT and sensor network environments. Many IoT devices operate in disconnected or network-limited areas and require secure micro-transactions. Our dual-offline payment mechanism supports such scenarios, as the main cryptographic operations are lightweight enough for typical IoT hardware. This further extends the practical value of our system beyond traditional cryptocurrency payments.

Cryptocurrency markets are characterized by high volatility and behavioral biases. Although several studies have examined psychological, social, and technological factors separately, few have comprehensively integrated them within a unified framework based on behavioral finance. This study addresses this gap by examining the interplay among digital financial literacy, impulsivity, social media financial influencers, fintech self-efficacy, and attitude toward investment in shaping investment decisions in Vietnam's cryptocurrency market. Data were collected from 505 individual investors, and partial least squares structural equation modeling was employed to test the proposed framework. The results indicate that digital financial literacy fosters positive attitudes toward investment and directly supports informed investment decision-making. However, contrary to conventional expectations, digital financial literacy is positively associated with impulsivity-related investment behavior, suggesting that higher perceived digital competence may foster overconfidence and an illusion of control in highly volatile cryptocurrency markets. Social media financial influencers significantly shape investors' attitudes and also exert a direct influence on investment decisions, highlighting both attitudinal and behavioral pathways. Furthermore, fintech self-efficacy moderates the relationship between attitudes and investment decisions by reducing investors' reliance on attitudinal cues when making investment choices. The findings highlight the importance of distinguishing rapid informed decision-making from affect-driven impulsivity and emphasize the role of overconfidence and perceived digital literacy in shaping investor behavior. Practically, the results call for targeted digital financial education and regulatory oversight of online financial content to promote informed and sustainable investment practices.

Our research investigates the predictive performance and robustness of machine learning classification models and technical indicators for algorithmic trading in the volatile cryptocurrency market. The main aim is to identify reliable approaches for informed decision-making and profitable strategy development. With the increasing global adoption of cryptocurrency, robust trading models are essential for navigating its unique challenges and seizing investment opportunities. This study contributes to the field by offering a novel comparison of models, including logistic regression, random forest, and gradient boosting, under different data configurations and resampling techniques to address class imbalance. Historical data from cryptocurrency exchanges and data aggregators is collected, preprocessed, and used to train and evaluate these models. The impact of class imbalance, resampling techniques, and hyperparameter tuning on model performance is investigated. By analyzing historical cryptocurrency data, the methodology emphasizes hyperparameter tuning and backtesting, ensuring realistic model assessment. Results highlight the importance of addressing class imbalance and identify consistently outperforming models such as random forest, XGBoost, and gradient boosting. Our findings demonstrate that these models outperform others, indicating promising avenues for future research, particularly in sentiment analysis, reinforcement learning, and deep learning. This study provides valuable guidance for navigating the complex landscape of algorithmic trading in cryptocurrencies. By leveraging the findings and recommendations presented, practitioners can develop more robust and profitable trading strategies tailored to the unique characteristics of this emerging 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.

Predicting cryptocurrency price movements using social media sentiment remains challenging due to the noisy, heterogeneous, and rapidly evolving nature of online signals. While prior studies commonly combine sentiment analysis with deep learning models, less attention has been given to how sentiment signals are constructed, aggregated, and aligned with price dynamics. This study investigates the impact of sentiment representation and price change labeling on short-term Bitcoin price movement classification. Over 1.1&#xa0;million Bitcoin-related tweets spanning April to August 2021 are analyzed using a RoBERTa-based sentiment model, incorporating both sentiment probabilities and user-level activity metrics. These features are consolidated via Principal Component Analysis (PCA) and aggregated over time using a decay-weighted scheme to emphasize recent information. Price movements are categorized into discrete regimes using a data-driven K-means clustering approach, with controlled Gaussian noise applied to improve boundary robustness. Multiple predictive models, including a Gated Recurrent Unit (GRU), Temporal Convolutional Network (TCN), LightGBM, and multinomial logistic regression, are evaluated. Although the GRU achieves the highest overall performance, an extensive ablation study demonstrates that the primary performance gains arise from the proposed sentiment construction and labeling framework rather than the forecasting architecture alone. Removing PCA-based aggregation, adaptive clustering, or noise injection leads to substantial degradation, particularly for extreme price movement classes. The findings highlight the importance of sentiment feature design and class definition in cryptocurrency prediction and provide empirical guidance for constructing robust sentiment- driven financial models.

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&#xa0;=&#xa0;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&#xa0;% of traders, with 33.9&#xa0;% classified as at-risk gambling and 32.4&#xa0;% as non-problematic gambling. Enhancement motivation (OR&#xa0;=&#xa0;1.60, 95&#xa0;% CI [1.10, 2.34]) and interpretative bias (OR&#xa0;=&#xa0;1.38, 95&#xa0;% CI [1.06, 1.81]) positively predicted at-risk gambling, whereas social motivation showed protective effects (OR&#xa0;=&#xa0;0.61, 95&#xa0;% CI [0.41, 0.91]). Coping motivation strongly predicted problematic gambling (OR&#xa0;=&#xa0;4.47, 95&#xa0;% CI [2.28, 8.78]), as did inability to stop gambling (OR&#xa0;=&#xa0;3.18, 95&#xa0;% CI [2.22, 4.54]). Age was negatively associated with problematic gambling (OR&#xa0;=&#xa0;0.94, 95&#xa0;% 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.

BACKGROUND Cryptocurrencies trade continuously on highly volatile markets and can elicit emotionally driven, gambling-like behaviors. Physicians experience high occupational stress and burnout, potentially predisposing them to risky financial activities. We examined whether hopelessness and perceived financial well-being are associated with problematic cryptocurrency trading among physicians. MATERIAL AND METHODS In a cross-sectional online survey, 300 licensed physicians from Diyarbak&#x131;r, Turkey, completed the Beck Hopelessness Scale (BHS; score range, 0-20), Financial Well-Being Scale (FWBS; 0-100), and Problematic Cryptocurrency Trading Scale (PCTS; 16-80). Group differences were evaluated with t tests and chi-square tests, and multivariable linear regression models estimated PCTS predictors. RESULTS Participants' mean age was 39.8&#xb1;7.2 years; 70% were male; mean practice duration was 14.1&#xb1;6.9 years. Male physicians had higher PCTS scores than female physicians (33.0&#xb1;6.8 vs 29.8&#xb1;5.9; P=0.03); BHS and FWBS scores did not differ by sex. In regression models, older age (&#xdf;=0.32, P=0.04) and male sex (&#xdf;=1.45, P=0.02) predicted higher PCTS scores. Hopelessness was positively associated with PCTS (&#xdf;=0.80, P=0.001), whereas financial well-being showed a trend toward significance (&#xdf;=-0.03, P=0.067). The demographics-only model explained approximately 8% of PCTS variance; the psychosocial model R&#xb2;=0.35 (P<0.001). CONCLUSIONS Among physicians, male sex, older age, and higher hopelessness are independently associated with problematic cryptocurrency trading, while perceived financial well-being is not clearly protective. Targeted institutional interventions (financial literacy and stress-management programs) may mitigate compulsive trading and support physician well-being.

Cryptocurrencies, as a disruptive innovation in the financial sector, are reshaping traditional investment behaviors. Despite their growing adoption, many individuals remain hesitant to invest, influenced by both psychological and technological factors. This study integrates behavioral finance principles with the Unified Theory of Acceptance and Use of Technology (UTAUT), incorporating perceived security, perceived enjoyment, technology competency, and personality traits to examine their effects on cryptocurrency investment attitudes and intentions. Data from U.S. investors highlight how these factors shape investment decisions, with a particular focus on the moderating role of personality traits in the relationship between attitude and investment intention. Findings reveal that performance expectancy, perceived enjoyment, and perceived security significantly influence attitudes toward cryptocurrency investment and drive investment intention. Interestingly, openness negatively moderates this relationship, suggesting that individuals with high openness may be less likely to act on positive investment attitudes. However, other predictors including facilitating conditions, extraversion, agreeableness, conscientiousness, and neuroticism did not demonstrate any significant influence on investment attitudes or intentions. These insights contribute to the behavioral finance literature by highlighting how psychological and technological factors influence investment behavior. Practical implications extend to businesses, policymakers, and cryptocurrency platforms seeking to better understand and engage potential investors. Future research should explore additional behavioral and technological factors across diverse investor demographics and geographic contexts to further advance knowledge in this evolving field.

Traditional mean-variance portfolio optimization proves inadequate for cryptocurrency markets, where extreme volatility, fat-tailed return distributions, and unstable correlation structures undermine the validity of variance as a comprehensive risk measure. To address these limitations, this paper proposes a unified entropy-based portfolio optimization framework grounded in the Maximum Entropy Principle (MaxEnt). Within this setting, Shannon entropy, Tsallis entropy, and Weighted Shannon Entropy (WSE) are formally derived as particular specifications of a common constrained optimization problem solved via the method of Lagrange multipliers, ensuring analytical coherence and mathematical transparency. Moreover, the proposed MaxEnt formulation provides an information-theoretic interpretation of portfolio diversification as an inference problem under uncertainty, where optimal allocations correspond to the least informative distributions consistent with prescribed moment constraints. In this perspective, entropy acts as a structural regularizer that governs the geometry of diversification rather than as a direct proxy for risk. This interpretation strengthens the conceptual link between entropy, uncertainty quantification, and decision-making in complex financial systems, offering a robust and distribution-free alternative to classical variance-based portfolio optimization. The proposed framework is empirically illustrated using a portfolio composed of major cryptocurrencies-Bitcoin (BTC), Ethereum (ETH), Solana (SOL), and Binance Coin (BNB)-based on weekly return data. The results reveal systematic differences in the diversification behavior induced by each entropy measure: Shannon entropy favors near-uniform allocations, Tsallis entropy imposes stronger penalties on concentration and enhances robustness to tail risk, while WSE enables the incorporation of asset-specific informational weights reflecting heterogeneous market characteristics. From a theoretical perspective, the paper contributes a coherent MaxEnt formulation that unifies several entropy measures within a single information-theoretic optimization framework, clarifying the role of entropy as a structural regularizer of diversification. From an applied standpoint, the results indicate that entropy-based criteria yield stable and interpretable allocations across turbulent market regimes, offering a flexible alternative to classical risk-based portfolio construction. The framework naturally extends to dynamic multi-period settings and alternative entropy formulations, providing a foundation for future research on robust portfolio optimization under uncertainty.

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 &#x3ba;&#x2009;=&#x2009;0.94, outperforming strong baselines (pure LSTM&#x2009;+&#x2009;8.3 pp; pure CNN&#x2009;+&#x2009;19.3 pp) and the widely-used VADER toolkit (+&#x2009;11.8 pp). On the forecasting side, a complementary GRU regressor trained on eight-year price series yielded MAE&#x2009;=&#x2009;0.0315, MAPE&#x2009;=&#x2009;5.95%, and MSE&#x2009;=&#x2009;0.0022 for Bitcoin, beating an ARIMA benchmark at p&#x2009;<&#x2009;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 paper investigates the temporal evolution of cryptocurrency time series using information measures such as complexity, entropy, and Fisher information. The main objective is to differentiate between various levels of randomness and chaos. The methodology was applied to 176 daily closing price time series of different cryptocurrencies, from October 2015 to October 2024, with more than 30 days of data and not completely null. Complexity-entropy causality plane (CECP) analysis reveals that daily cryptocurrency series with lengths of two years or less exhibit chaotic behavior, while those longer than two years display stochastic behavior. Most longer series resemble colored noise, with the parameter k varying between 0 and 2. Additionally, Natural Language Processing (NLP) analysis identified the most relevant terms in each white paper, facilitating a clustering method that resulted in four distinct clusters. However, no significant characteristics were found across these clusters in terms of the dynamics of the time series. This finding challenges the assumption that project narratives dictate market behavior. For this reason, investment recommendations should prioritize real-time informational metrics over whitepaper content.

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.

Based on the cryptocurrency market dynamics, this study presents a general methodology for analyzing evolving correlation structures in complex systems using the q-dependent detrended cross-correlation coefficient &#x3c1;(q,s). By extending traditional metrics, this approach captures correlations at varying fluctuation amplitudes and timescales. The method employs q-dependent minimum spanning trees (qMSTs) to visualize evolving network structures. Using minute-by-minute exchange rate data for 140 cryptocurrencies on Binance (January 2021-October 2024), a rolling window analysis reveals significant shifts in qMSTs, notably around April 2022 during the Terra/Luna crash. Initially centralized around Bitcoin (BTC), the network later decentralized, with Ethereum and others gaining prominence. Spectral analysis confirms BTC's declining dominance and increased diversification among assets. A key finding is that medium-scale fluctuations exhibit stronger correlations than large-scale ones, with qMSTs based on the latter being more decentralized. Properly exploiting such facts may offer the possibility of a more flexible optimal portfolio construction. Distance metrics highlight that major disruptions amplify correlation differences, leading to fully decentralized structures during crashes. These results demonstrate qMSTs' effectiveness in uncovering fluctuation-dependent correlations, with potential applications beyond finance, including biology, social and other complex systems.