Bitcoin treasury companies have taken stock markets by storm amassing billions of dollars worth of tokens in hundreds of entities. The paper discusses, how leverage - whether created through corporate debt or investors using stock as loan collateral - fuels this trend. The extension of the binary-choice Kelly criterion to incorporate uncertainty in the form of the Kullback-Leibler divergence or more generally Bregman divergence is also briefly discussed.
Abstract This article presents an innovative contract framework to improve Quality-of-Service (QoS) within the CoreDAO blockchain, focusing on the 9NFTMania token. The research is driven by the need for reliable, decentralized processes that improve data security and transaction efficacy inside CoreDAO. The proposed Solidity-based smart contract incorporates easy reflection, liquidity provision, fee processing, and secure token transfers. The development process included criteria definition, research, contract design, platform selection, coding, rigorous testing, and real-time maintenance to ensure functionality and security. The enhancements to the ERC20 token standard improved liquidity, token exchange, and ownership management. A Dividend Token contract introduced governance mechanisms, fee structures, liquidity availability, and tax settings. Comparative analysis demonstrates the framework’s superior accuracy, precision, recall, and F1-score performance compared to conventional mechanisms. Quantitative metrics highlight significant improvements in data security, transaction efficiency, and blockchain scalability, particularly in healthcare applications. By using PoS systems, the suggested structure essentially improves CoreDAO’s quality of service—especially for the 9NFT Mania token. It adds to a safe, scalable, and effective blockchain ecosystem by surpassing the transaction efficiency and scalability of conventional Proof-of-Work methods.
Illegal marketplaces have increasingly shifted to concealed parts of the internet, including the deep and dark web, as well as platforms such as Telegram, Reddit, and Pastebin. These channels enable the anonymous trade of illicit goods including drugs, weapons, and stolen credentials. Detecting and categorizing such content remains challenging due to limited labeled data, the evolving nature of illicit language, and the structural heterogeneity of online sources. This paper presents a hierarchical classification framework that combines fine-tuned language models with a semi-supervised ensemble learning strategy to detect and classify illicit marketplace content across diverse platforms. We extract semantic representations using ModernBERT, a transformer model for long documents, finetuned on domain-specific data from deep and dark web pages, Telegram channels, Subreddits, and Pastebin pastes to capture specialized jargon and ambiguous linguistic patterns. In addition, we incorporate manually engineered features such as document structure, embedded patterns including Bitcoin addresses, emails, and IPs, and metadata, which complement language model embeddings. The classification pipeline operates in two stages. The first stage uses a semi-supervised ensemble of XGBoost, Random Forest, and SVM with entropy-based weighted voting to detect sales-related documents. The second stage further classifies these into drug, weapon, or credential sales. Experiments on three datasets, including our multi-source corpus, DUTA, and CoDA, show that our model outperforms several baselines, including BERT, ModernBERT, DarkBERT, ALBERT, Longformer, and BigBird. The model achieves an accuracy of 0.96489, an F1-score of 0.93467, and a TMCC of 0.95388, demonstrating strong generalization, robustness under limited supervision, and effectiveness in real-world illicit content detection.
The rapid evolution of digital currency trading, fueled by the integration of blockchain technology, has led to both innovation and the emergence of smart Ponzi schemes. A smart Ponzi scheme is a fraudulent investment operation in smart contract that uses funds from new investors to pay returns to earlier investors. Traditional Ponzi scheme detection methods based on deep learning typically rely on fully supervised models, which require large amounts of labeled data. However, such data is often scarce, hindering effective model training. To address this challenge, we propose a novel contrastive learning framework, CASPER (Contrastive Approach for Smart Ponzi detectER with more negative samples), designed to enhance smart Ponzi scheme detection in blockchain transactions. By leveraging contrastive learning techniques, CASPER can learn more effective representations of smart contract source code using unlabeled datasets, significantly reducing both operational costs and system complexity. We evaluate CASPER on the XBlock dataset, where it outperforms the baseline by 2.3% in F1 score when trained with 100% labeled data. More impressively, with only 25% labeled data, CASPER achieves an F1 score nearly 20% higher than the baseline under identical experimental conditions. These results highlight CASPER's potential for effective and cost-efficient detection of smart Ponzi schemes, paving the way for scalable fraud detection solutions in the future.
Sohel Rana, Rizal Mohd Nor, Mohammad Enayet Hossain, Md Amiruzzaman
The increasing adoption of cryptocurrency has underscored the critical need for robust security measures to protect digital assets stored in cryptocurrency wallets. Traditional security approaches have often proven inadequate in addressing the rapidly evolving threats in the digital landscape. In response, cloud-based security solutions have emerged as a promising method to enhance wallet protection, leveraging scalability, flexibility, and advanced security features. This study investigates the security challenges faced by cryptocurrency wallets and explores the potential of cloud-based solutions, focusing on multi-factor authentication, encryption protocols, real-time monitoring, and secure backup and recovery. The research assesses the effectiveness of these solutions in mitigating risks such as unauthorized access, data breaches, and digital asset theft. Findings reveal that cloud-based security solutions significantly improve protection by offering scalable, adaptable frameworks. However, challenges remain, including privacy concerns, regulatory compliance, and the cost of implementation. The research introduces a cost-efficient approach that integrates cloud-based technologies to optimize the total cost of ownership while maintaining robust security. This study also discusses the regulatory and privacy implications of cloud security in cryptocurrency ecosystems. In conclusion, this research provides novel insights into the integration of cloud-based security solutions, offering a comprehensive framework for safeguarding digital assets in cryptocurrency wallets. It contributes to the growing body of knowledge on the feasibility and impact of cloud technologies in enhancing the security of cryptocurrency systems.
M. Zulfiqar, Muhammad Babar Rasheed, Daniel Rodríguez, María D. R‐Moreno
Contemporary power grid systems increasingly rely on sophisticated energy trading mechanisms to optimize resource allocation and operational performance. While prior studies have examined the coordination roles of energy intermediaries and utility operators, particularly through distributed ledger technologies that ensure data provenance and transaction verifiability in decentralized energy marketplaces, significant security vulnerabilities persist. Notably, fraudulent practices by energy suppliers characterized by payment collection without corresponding energy delivery pose substantial risks to market integrity and participant confidence. This research presents the Blockchain-based Energy Trading with Multi-Factor Trust Framework (BC-ET-MF), a novel architecture that addresses critical security deficiencies through advanced cryptographic protocols and consensus mechanisms. The framework utilizes anonymous credential systems to safeguard participant privacy while implementing time-locked commitment schemes that ensure transaction fairness and verifiability. The architecture incorporates granular access control mechanisms for secure service orchestration and establishes a consortium blockchain infrastructure among energy intermediaries to facilitate distributed transaction validation and immutable record-keeping. To mitigate computational overhead associated with conventional consensus algorithms, we introduce a Proof-of-Verifiability protocol that dynamically calibrates to real-time energy production and consumption patterns. This adaptive mechanism reduces system resource requirements while maintaining security guarantees. Experimental evaluation demonstrates that BC-ET-MF achieves substantial performance improvements: energy consumption reduction of 43.0 %, peak-to-average ratio optimization from 8.27 to 3.21 and 5.88 under 25 % and 50 % demand reduction scenarios respectively, and establishment of 92.5 % participant trust levels. The framework additionally yields 37.6 % transaction latency reduction while preserving user anonymity and enabling comprehensive audit capabilities, thus establishing a secure, efficient, and trustworthy energy trading ecosystem.
Climate-vulnerable megacities like Dhaka, Bangladesh, face escalating challenges in managing mounting volumes of municipal solid waste (MSW), exacerbated by rapid urbanization, climate shocks, and inadequate resource recovery systems. This research proposes an advanced AI-driven Smart Waste-to-Energy (AI-CIR-WtE) framework designed to transform linear waste systems into adaptive, circular, and climate-resilient urban infrastructure. Integrating artificial intelligence, life cycle modeling, digital twins, and blockchain, the framework offers a comprehensive pathway to optimize waste valorization, emissions reduction, and sustainable energy generation in resource-constrained settings. The proposed system leverages Long Short-Term Memory (LSTM) networks for forecasting waste generation by ward and season, coupled with Non-Dominated Sorting Genetic Algorithm II (NSGA-II) for multi-objective optimization of waste routing, energy efficiency, and environmental impact. An AI-LCA engine, developed using OpenLCA and TensorFlow, dynamically quantifies GHG emissions, carbon offsets, and energy returns under multiple WtE configurations. Simulations are embedded within a 3D digital twin of Dhaka, constructed in Unity/Unreal Engine, enabling real-time modeling of disaster impacts (e.g., monsoon flooding, urban heatwaves) on infrastructure and service delivery. To ensure transparency and verifiability in carbon credit mechanisms, a blockchain-enabled MRV (Monitoring, Reporting, and Verification) layer tracks waste origin, conversion outputs, and emission reductions across the value chain. The framework incorporates climate equity through a gender and social inclusion lens, offering AI-based training modules and digital participation platforms for women, youth, and informal waste workers. Results show a projected 27–35% increase in circular material recovery, up to 41% reduction in lifecycle emissions, and 18% rise in decentralized energy yields under optimized conditions. The AI-CIR-WtE model demonstrates strong alignment with UN SDGs, Verra’s Verified Carbon Standard, and investment criteria from the Green Climate Fund (GCF) and World Bank climate finance facilities. By converging data-driven optimization, immersive simulation, and climate-just governance, this research offers a scalable blueprint for circular economy transition in megacities under climate threat. The framework is replicable in other Global South contexts and serves as a digital, equitable infrastructure roadmap toward net-zero urban futures.
The financial services industry has experienced a fundamental transformation through the strategic adoption of distributed systems architecture, fundamentally altering how institutions design, deploy, and scale their product offerings. Traditional banking infrastructure, characterized by monolithic architectures and centralized processing systems, increasingly struggles to meet contemporary demands for real-time processing, continuous availability, and seamless scalability. Distributed systems address these challenges through horizontal scaling capabilities, enabling institutions to accommodate exponential growth in transaction volumes without proportional infrastructure cost increases. The implementation of distributed computing has enabled comprehensive portfolios of digital-first financial products, including mobile banking platforms, real-time transaction processing systems, AI-driven financial advisory services, intelligent customer support solutions, and advanced fraud detection mechanisms. These systems demonstrate superior resilience through redundancy and fault isolation, achieving exceptional availability levels through multi-region deployment strategies. Future developments in distributed financial systems encompass blockchain integration, decentralized finance protocols, advanced artificial intelligence capabilities, and edge computing with IoT integration. However, implementation presents complex technical challenges, including data consistency maintenance, security considerations, regulatory compliance across multiple jurisdictions, operational complexity, and performance optimization requirements that institutions must carefully navigate to realize distributed computing benefits effectively.
Abstract This research examines the dynamics of the non-fungible tokens (NFT) market by utilizing key financial metrics such as Bitcoin prices, the Crypto Fear-Greed Index, and DeFi indicators. It analyzes NFT-USD values, the Crypto Fear-Greed Index, total value locked in DeFi, and Bitcoin interactions between February 2021–July 2023. Employing ordinary least squares regression, quantile regression, Johansen cointegration, and VECM Granger analysis, the study uncovers complex relationships shaping the NFT market. The findings reveal a positive correlation between Bitcoin prices and NFT values, a negative relationship between total value locked in DeFi and NFT values, and an inverse connection between the Crypto Fear-Greed Index and NFT values. Additionally, cointegration exists among the variables, and causality analysis indicates that Bitcoin influences total value locked, while shifts in the Crypto Fear-Greed Index reflect market sentiment changes. These insights contribute to a deeper understanding of behavioral finance by illustrating how psychological factors, such as investor sentiment and the bandwagon effect, interact with digital asset markets. From a practical perspective, the results emphasize the importance of recognizing these interdependencies for policymakers and market participants striving to foster innovation in the rapidly evolving NFT ecosystem. By aligning with the transformative potential of Blockchain and DeFi, this study provides strategic insights for optimizing resource allocation, enhancing market efficiency, and shaping regulatory frameworks within innovative financial landscapes.
This study aims to analyze and compare the performance of three major cryptocurrencies—Bitcoin, Ethereum, and Solana—during the 2021–2024 period, based on return, risk, and risk-adjusted performance indicators (Sharpe Ratio). The research applies a comparative quantitative method using secondary data from CoinMarketCap. The analysis includes descriptive statistics, annual return calculations, standard deviation, Value at Risk (VaR), Expected Shortfall (ES), and Sharpe Ratio. ANOVA was used to test differences in return, while Kruskal-Wallis and Mann-Whitney U tests were employed for risk and performance due to non-normal data distributions. The results show no significant differences in average return among the three assets. However, there are significant differences in risk levels, with Solana being the most volatile, followed by Ethereum and Bitcoin. In terms of Sharpe Ratio, no significant difference in performance was found. These findings indicate that while there are absolute differences in return and risk, the three assets provide a balanced level of return when adjusted for risk. Hence, diversification among these assets may serve as a relevant strategy for investors depending on their risk profiles.
Niveen O. Jaffal, Mohammed Alkhanafseh, David Mohaisen
Large Language Models (LLMs) are transforming cybersecurity by enabling intelligent, adaptive, and automated approaches to threat detection, vulnerability assessment, and incident response. With their advanced language understanding and contextual reasoning, LLMs surpass traditional methods in tackling challenges across domains such as IoT, blockchain, and hardware security. This survey provides a comprehensive overview of LLM applications in cybersecurity, focusing on two core areas: (1) the integration of LLMs into key cybersecurity domains, and (2) the vulnerabilities of LLMs themselves, along with mitigation strategies. By synthesizing recent advancements and identifying key limitations, this work offers practical insights and strategic recommendations for leveraging LLMs to build secure, scalable, and future-ready cyber defense systems.
Hoang Viet Anh Le, Quoc Duy Nam Nguyen, Tadashi Nakano, Thi Hong Tran
The Blockchain-based Decentralized Identity Management System (BDIMS) is an innovative framework designed for digital identity management, utilizing the unique attributes of blockchain technology. The BDIMS categorizes entities into three distinct groups: identity providers, service providers, and end-users. The system’s efficiency in identifying and extracting information from identification cards is enhanced by the integration of artificial intelligence (AI) algorithms. These algorithms decompose the extracted fields into smaller units, facilitating optical character recognition (OCR) and user authentication processes. By employing Merkle Trees, the BDIMS ensures secure authentication with service providers without the need to disclose any personal information. This advanced system empowers users to maintain control over their private information, ensuring its protection with maximum effectiveness and security. Experimental results confirm that the BDIMS effectively mitigates identity fraud while maintaining the confidentiality and integrity of sensitive data.
This interim report provides a comprehensive overview of stakeholder perspectives on the opportunities and risks associated with the development and administration of fintech, distributed ledger technology and artificial intelligence in Aruba. The Government of Aruba aims to diversify its economy away from its reliance on tourism by developing its information and communications technology (ICT) sector. The report is based on consultations with stakeholders from the public and private sectors, civil society and academia, conducted during an on-site mission in February 2025. Key findings highlight the potential for Aruba to become a base for export-oriented ICT services, leveraging its strong infrastructure and well-trained labour force. However, challenges have also been identified, including regulatory and institutional constraints, limited job opportunities and the risk of brain drain. The report emphasizes the need for a supportive regulatory environment and targeted initiatives to foster innovation and competition in the ICT sector, ultimately contributing to Aruba's economic diversification and resilience.
In educational psychology, emphasizing the situational context is clearly ‘du jour’, becoming arguably most apparent in the renaming of Eccles' Expectancy Value Model to ‘Situated Expectancy-value Model’ (SEVT), outlined in several papers she coauthored (Eccles & Wigfield, 2020, 2024; Gladstone et al., 2022). According to Eccles and Wigfield (2024), the programmatic shift was necessary to reflect the expansion of the theory since its beginnings as a framework to explain gender differences in learning motivation and educational choices of students, to a now full-fledged socio-cognitive developmental theory. As such, the model is explicit about the recursive nature of the underlying processes and acknowledges the idiosyncratic circumstances of each behavioural moment, be it students' decision what classes to take or a teacher's decision about the feedback they give each student. While this makes a lot of sense conceptually, the new framing of the model comes with two challenges. One is of epistemological nature, related to the fact that the emphasis of the ‘situatedness’ weakens the generalizability of empirical finding to other, even very similar contexts. The second challenge lies in the translation of the expanded model to adequate empirical research strategies that reflect the new model complexity or, put more simply: How do we overcome the limitations of questionnaires as the most commonly used tool to collect data in this line of research? It feels inadequate now to pack the ‘situatedness’ in the item stem, for example, ‘When doing your math homework…’ or ‘In general, I love being a science teacher’. This might logically make the response somewhat context-specific, but situation-specific enough in the sense of SEVT. Overcoming this limitation is the common theme throughout the six papers which, each in its unique way, are pushing towards a more convincing empirical approach to illustrate and understand the relevance of the situational context and to identify aspects of it that allow us to carefully generalize findings to a similar class of situations. The latter is important as ‘situatedness’ in the SEVT model is not meant to be merely a new label for otherwise unexplained variance in an analysis that uses stable teacher and student characteristics as predictors. Instead, it suggests characterizing the context in order to integrate relevant features into a predictive model. For example, Stark, Camburn & Kaler (this volume) demonstrate that teacher motivation varies across different but typical work activities. But instead of the ‘classic approach’ to rely on item construction for a cross-sectional study (‘When I teach in the classroom…’, ‘When I interact with colleagues…’, ‘When I grade papers…’, etc.), they use the ‘day reconstruction method’ (DRM) to get not only a more valid measurement of the motivational state of teachers in a given context but also a precise account of how often teachers encounter those qualitatively different, but nevertheless typical, professional situations. It is obvious that a teacher's motivational state during actual teaching is not predictive of their long-term experience of burnout, for example, if this context represents only a small fraction of the professional contexts a teacher navigates on a daily basis. They are able to demonstrate that roughly two thirds of the variance in teacher motivation lies between periods, that is, distinct situations throughout a workday, negligible variance between days (controlling for periods) and roughly a third of the variance resides (stably) between teachers. Wang, Thompson-Lee and Klassen (this volume) combine the emphasis on ‘situatedness’ with the advances made in classroom simulations as a tool for teacher training, which is steadily moving towards the use of virtual reality as a standard tool (see Huang et al., 2023). Wang et al. demonstrate that even in the reduced complexity setting of a simulation in an online training setting, the success in adequately reacting in a set of 15 situations a teacher typically encounters on a daily basis has a consistent impact on student teachers self-efficacy beliefs and their assessment of how good they see themselves aligned with the affordances of the job. Unintentionally, the exposure to the scenarios tends to have a somewhat sobering effect since self-efficacy and career intentions trended down on average. However, one could argue that this is reflective of a more accurate self-assessment of the students regarding their readiness to be a teacher. They can take this either as a call to intensify their learning efforts or as a critical appraisal of their decision to become a teacher. As long as the 15 scenarios authentically reflect the professional life of a teacher, this study implicitly reflects the situational variability of the profession, and one is invited to speculate how this might impact a teacher's motivation in the long run. Similar to Stark, Camburn and Kaler (this volume), Bross, Frenzel and Nett (this volume) consider ‘day’ the key temporal unit of observation for a longitudinal study on teacher motivation or, in this case, the emotion regulation of teachers. Emotion regulation is strongly related to teacher motivation as successful regulation of negative emotions an important predictor of maintained teacher motivation is (Wang et al., 2023). The interesting twist in their study is the use of latent profile analysis that allows them, in addition to identifying coping patterns for two emotions in different situational settings, to reveal flexibility/consistency of teachers' emotion regulation across situations as a trait-like characteristic. Even if the authors do not discuss this explicitly, their approach introduces an interesting expansion of the SEVT model: While it is true that situations matter for the response of teachers, only some teachers actually vary in their response to negative emotions while the majority of teachers show very similar emotion regulation patterns. This could be understood as a situation by person interaction: Only 17.4% of the teacher sample used different combinations of flexibility across situations. The approach also reveals that the remaining three patterns consist of teachers who differ in their coping profiles but not across situations. This opens the door for further investigation beyond the emotion regulation research because it is conceivable that similar ‘meta patterns’, that is, stability of different patterns across situations for some teachers but not for others, exist for other motivational constructs as well. Moving to the papers that focus on the instructional process, we again see the need to resort to more complex statistical tools if ‘situatedness’ is of particular interest. Oschwald, Moeller, Kracke, Viljaranta and Dietrich (this volume) present probably the most fine-grained analysis of ‘situatedness’ in the context of motivational research to date, analysing the ‘micro-cycles’ of instructional quality on college students motivation in 9-min intervals (combining three ratings of 3 min). The basic idea was to illustrate that change/variation in the instructional clarity (detail, variation, consistency) has an immediate/short-term lag effect on student motivation. While the authors are very circumspect in considering methodological and conceptual shortcomings of their Null findings, I am more inclined to take them at face value: Motivational dispositions of students, as conceptualized in the SEVT context, are more inert than the study design implies. If this is true, it is good news for future research in the sense that it is not necessary to choose such a high-resolution (and hence expensive) research design. Most likely, a low-clarity teaching style simply does not dampen college students motivation immediately and maybe not even from 1 day to the next. However, if a teacher consistently over days and weeks teaches with low clarity, students become gradually frustrated, start to question their own competence, etc. The idea that zooming out the time-frame somewhat is corroborated by the Rubach and von Keyserlink paper (this volume) which used 5 weeks within the semester as the elapsed time to investigate longitudinal trends. The consistency of the student assessment of the quality of the instruction dominated observation specificity when the course was held constant. However, at a given time point, students rated different courses differently, suggesting that their assessment reflected substantial differences in their perception of the different courses. Also important is their finding that roughly 30% of variance is a stable difference between students who adds substantial noise to any statistical analysis that aims at identifying causal impact over time. Accordingly, Rubach and von Keyserlink acknowledge that their study is limited as it is a single source study, that is, students rated the instructional quality as well as their interest and expectations. But that consistency of instructional quality throughout the semester is a limiting factor to demonstrate ‘situatedness’ of student motivation comes from other research contexts as well, for example, the research on the often replicated ‘thin-slice-effect’ (Ambady & Rosenthal, 1993): Student evaluations at the end of the semester can be extremely well predicted by the assessment of the first 10 min of the first lecture of the semester. While this is often taken as proof of the importance of the first impression, our own (experimental) research suggests that this high correlation is mainly due to the consistency of teacher behaviour throughout the semester (Samudra et al., 2016). The first impression is a good indicator of the teaching quality for the teacher's behaviour/quality of the rest of the semester. A final course evaluation may well be more or less an accurate average of the experience throughout the semester and therefore a valid measure of instructional quality. With the caveat that student assessment and student motivation are different constructs, this observation would suggest for the Oschwald et al. study that the authors would find more robust effects if the time unit was not 9-min intervals, but daily or weekly aggregates of instructional quality. For both, the Oschwald et al. as well as the Rubach and von Keyserlink study, the measurement of instructional quality becomes a critical issue when we want to avoid artefacts of common-source bias or too short-cycled causal models. Göllner, Lazarides and Stark (this volume) make a foray into new territory by exploring the validity of large language models (LLMs) to assess teaching quality which, in the future, could eliminate the human factor in coding entirely. If a holistic semantic analysis could be able to capture relevant aspects of teaching quality reliably, human coding through expert or student assessment would become obsolete. Quality could even be assessed in real time as the teaching is still happening or shortly thereafter, opening the opportunity to use it as immediate feedback in teacher training. In a more rudimentary fashion, we used the same idea for specific teacher training purposes a decade ago. A voice-recording device (LENA) that distinguished teachers' and students' speaking turns identified in-class discourse segments the teachers were learning to use more frequently in their mathematics classes. Teachers received feedback within 24 h, and for some (not all), it was helpful for improving their teaching (Wang et al., 2014). Göllner et al.'s cutting-edge exploratory study shows that LLMs have potential in this regard, but we have still ways to go. The semantic representations are ‘sensitive enough’ to reflect variation between segments, lessons and teacher. They also were associated with human-coded quality assessment, but a ballpark 20% of shared variance is not even close to the level where the human–AI interrater reliability could reach the level of human–human reliability after efficient coder training. However, they used a zero-shot GPT model which mean that no additional information was provided to guide the semantic analysis, and the PCA-based dimensionality reduction is indicative of the exploratory nature of the approach with its inherent difficulty to interpret the dimensions and questions of replicability. However, the prompted transcript analysis is a first step towards a use of LLMs that is closer aligned with theoretical concepts and hence a promising step to the next level. After all, the LLM can identify the strength of instructional dialogue best when it can use samples of human-identified examples of dialogue that represent the quality dimension in question (multi-shot GPT). There is no doubt that LLMs will in the near future take over a lot of (if not all) coding tasks of texts and video footage. But what and how the AI codes material will always depend on theoretical considerations about student–teacher and student–student interactions and how they facilitate academic learning. The tool does not come with a guiding theory and Göllner et al.'s contribution makes that clear. In her reflections on the situative approach to research in educational psychology, Nolen (2024) points out that the situative view leads to an emphasis on understanding the processes that underlie change. This, in turn, leads to a reflection on what kind of change is to be analysed and what kind of change is considered desirable. Academic learning in educational psychology is, for the most part, conceptualized as a cumulative process, as relatively stable gains over an observed time period, adding to the prior knowledge level. Weeks or months as temporal units of analysis seem appropriate as standard in the learning context of curriculum-based schooling, unless the learning of smaller units is the focus, like learning the content of one particular mathematics lesson. In contrast, the underlying idea in the Stark, Camburn and Kaler contribution on teacher motivation is that high teacher motivation is desirable and a potential goal for interventions. Or it stimulates a teacher's self-directed action by minimizing exposure to situations that are demotivating or to change the quality of the social interactions to avoid the demotivating impact. It is not a cumulative, but rather a protective change model. At least implicitly, the self-efficacy belief of student teachers in Wang, Thompson-Lee and Klassen similarly is a variable one would wish to be and remain high, based on the normative assumption that high self-efficacy beliefs are a characteristic of a good teacher. But different from academic learning, there is a logical ceiling for self-efficacy beliefs. Therefore, it is not a cumulative change model, but an optimization model. The environment should lead teacher to—and keep them at—a ‘5 out of 5’ level of self-efficacy. While those two papers have similar underlying change models, the theory of emotion regulation in Bross, Frenzel and Nett is based on a qualitatively different conceptualization of change: homeostasis. For a teacher, anger is arguably a dysfunctional state and it is desirable to quickly and effectively regulate it down to an emotional set point if the situational trigger cannot be avoided. It is apparent that the logical temporal unit of analysis in this context is probably minutes, if the goal is to investigate the process as such. This, of course, is not the intention of the authors as their focus lies on the coping patterns of teachers across situations encountered throughout the day. The assumption is, in fact, cumulative in the sense that exposure to a lot of anger-inducing situations paired with a suboptimal coping pattern will wear teachers down in the long run and reduce their professional motivation. The intention of the Oschwald et al. study was to demonstrate that instructional clarity has an immediate positive impact on college students learning motivation—again not as a cumulative model but with the normative goal to reach and maintain a high level of learning motivation. At least implicitly, the assumption is that a somewhat consistent lack of clarity over a longer period of time, that is, not 9 min but several weeks of low-clarity instruction, will wear a student's learning motivation down. Even if the short-term lag effect could not be shown, the long-term effect might still—and it is likely to—exist. The measurement used in the Rubach and von Keyserlink study is Likert-scale based, which means that it comes with a maximal value despite the fact that theoretically, at least, interest is logically unlimited and could therefore follow a cumulative model. If the quality of the instruction is extremely high every week I am in class, my interest might continuously grow until the end of the term. I might reach the scale's ceiling, but that would be an artefact of the measurement scale. Why are these considerations important? They identify the epistemological challenge of an overly situation-focused perspective. While it might be relevant in some research contexts to understand features of the situation and not treat it as error variance (Nolen, 2024), we will still need to transcend the insights gained from these analyses to a more general level in order to be of educational relevance. At least for the run-of-the-mill K-12 schooling context, it would be difficult to drop the traditional positivistic rationale when we consider the practical relevance of our research: Once causal mechanisms are identified as tentative truths, they are of practical relevance only if they show long-term impact on academic learning and psychosocial development across a fairly broad class of situational contexts. The more specific the context is defined in the research, the more limited the practical implications. For example, it might be of psychological interest to demonstrate that a student's academic self-concept dips down after 20 instances of unclear instruction. But if the teacher simply was underprepared on that day and otherwise presented the material clearly and accessibly throughout the semester, treating this as a random ‘error’ is probably justified. When long-term development is the main focus of our research (here motivation), the minute-to-minute fluctuations in the clarity of instructions are unlikely to be important. The reason is that, in the back of our heads, we have a model of how motivation affects learning. A student who is—more or less—stably interested in the content of the class will be more likely to work happily on assignments, etc. in the evening and on weekends. As a general rule (non-situative), research has shown that unclear instruction has a negative impact on self-concept and interest in the long run, and we assume that this is true for a broad array of situations, student characteristics, grade levels, etc. This is why it is reasonable that teacher training works with student teachers on instructional clarity as a skill set. If well, across a of situations and contexts a teacher will Even if we that every situation is different and mechanisms are ‘situatedness’ cannot mean that educational psychology, as an of the long-term developmental that are the of educational It means to be more to of the learning environment that are necessary for the impact of on learning means to acknowledge the of but this becomes a only if the goal is to identify situational characteristics that allow The Stark, Camburn and Kaler paper a for this idea because their the teachers themselves to identify situations and their over time and how they in those contexts. While every with other teachers might be different from the they as a features that with other situations in the daily professional for example, actual instruction in the on the situations we on a daily basis to be a good point to ‘situatedness’ into a research that does not of the focus of our process of learning in
This article explores changes and trends in volatility pricing of Bitcoin (BTC), and analyzes its historical performance, key drivers, market dynamics and future prospects. By studying macroeconomic and technical indicators, we can fully understand Bitcoin's market behavior patterns and volatility patterns, and explore the impact of regulatory policies, market sentiment and institutional intervention on Bitcoin price.
Qazi Muhammad Osama, Usman Ali, Tahira Ali, Danish Ali
The rapid rate of technological development necessitates a comprehensive and morally sound framework to guarantee acceptable integration between commercial innovation, mechanical systems, and artificial intelligence (AI). This review article presents a unified approach to integrated technology management by examining the intersection of future business models, mechanical sustainability, and AI ethics. The study emphasizes the significance of matching innovation with long-term environmental and societal objectives by analyzing the ethical ramifications of AI deployment—such as algorithmic bias, transparency, and accountability—as well as developments in sustainable mechanical engineering, such as eco-efficient machinery, lifecycle optimization, and circular manufacturing. Additionally, it explores revolutionary business models that are changing value generation and operational efficiency in the contemporary technology ecosystem, including platform economies, digital twins, decentralized finance (DeFi), and Industry 5.0 frameworks. Cross-sectoral synergies that support scalability, equality, and resilience are given particular attention. In order to balance the ethical use of AI with sustainable engineering methods and progressive entrepreneurship, the assessment also emphasizes the crucial roles that legislative frameworks, data governance, and stakeholder collaboration play. In the end, this integrated viewpoint offers practical advice to academics, politicians, and business executives working to create technology systems that are nimble, sustainable, and responsible in a time of complexity and ongoing innovation.
Xin Liu, Xinyuan Guo, Dan Luo, Liang Li · 9 authors
Federated learning promotes the development of cross-domain intelligent applications under the premise of protecting data privacy, but there are still problems of sensitive parameter information leakage of multi-party data temporal alignment and resource scheduling process, and traditional symmetric encryption schemes suffer from low efficiency and poor security. To this end, in this paper, based on the modified NTRU-type multi-key fully homomorphic encryption scheme, an asymmetric algorithm, a secure computation scheme of multi-party least common multiple and greatest common divisor without full set under the semi-honest model is proposed. Participants strictly follow the established process. Nevertheless, considering that malicious participants may engage in poisoning attacks such as tampering with or uploading incorrect data to disrupt the protocol process and cause incorrect results, a scheme against malicious spoofing is further proposed, which resists malicious spoofing behaviors and not all malicious attacks, to verify the correctness of input parameters or data through hash functions and zero-knowledge proof, ensuring it can run safely and stably. Experimental results show that our semi-honest model scheme improves the efficiency by 39.5% and 45.6% compared to similar schemes under different parameter conditions, and it is able to efficiently process small and medium-sized data in real time under high bandwidth; although there is an average time increase of 1.39 s, the anti-malicious spoofing scheme takes into account both security and efficiency, achieving the design expectations.
Tanusree Sharma, A.K.M. Atiqur Rahman, Silvia Sandhi, Yang Wang · 6 authors
Cryptocurrency practices worldwide are seen as innovative, yet they navigate a fragmented regulatory landscape across different countries. Many national authorities aim to balance promoting innovation, safeguarding consumers, and managing potential threats. In particular, it is unclear how people deal with cryptocurrencies in the countries where trading or mining is prohibited. This insight is crucial in conveying the risk reduction strategies. To address this, we conducted semi-structured interviews with 28 cryptocurrency traders and miners from Bangladesh, where the environment is hostile towards cryptocurrencies. Our research revealed that the participants use unique strategies to mitigate risks around cryptocurrencies. Our findings indicate a prevalent uncertainty at both personal and organizational levels concerning the interpretation of laws, a situation worsened by the actions of the major financial service providers who indirectly facilitate cryptocurrency transactions. We further connect our findings to the broader issues in HCI regarding folk models, informal market and legality, and education and awareness.
Abstract Background: Kenya’s public health sector is facing a crisis of poor quality of healthcare, as evidenced by an acute shortage of healthcare workers, frequent industrial unrest, broken-down healthcare facilities, and erratic supply of essential commodities. In a bid to enhance the quality of healthcare through the availability of modern medical equipment and technologies, the government, since 2015, rolled out asset leasing financing for national referral healthcare facilities. Methods: We conducted a study with the objective of exploring stakeholders’ perspectives on the effectiveness of asset lease financing in enhancing the quality of tertiary healthcare in Kenya. The study used qualitative data, utilizing a case study design and an interpretivism approach. A total of 32 stakeholders participated. These include 7 Ministry of Health policymakers, 7 National Treasury policymakers, 10 tertiary hospitals managers, and 8 social health investors’ financial experts. We used semi-structured interviews to collect qualitative data, which was transcribed and analyzed using a thematic approach. Results: The results showed that though Asset Leasing Financing mechanism has addressed structural inequities by redistributing high-end medical infrastructure across tertiary hospitals geographies and contributed to improved timeliness and reach of care, stakeholders felt that it has not fully achieved its transformative potential in Kenya’s healthcare due to a convergence of governance weaknesses, implementation inefficiencies, and institutional misalignments. Conclusions: We recommend the need for a contextualized, accountable, policy-backed but possibly an asset leasing model decentralized to the tertiary hospitals governance. This could enhance the entire delivery of the financing model to improve tertiary healthcare quality. We also demonstrated the theoretical contribution and local policy implication of the study.
To prevent vulnerabilities and ensure app security, smart contract vulnerability detection identifies flaws in blockchain code. To overcome the limitations of traditional detection methods, this study introduces a novel approach that combines Explainable Artificial Intelligence (XAI) with Deep Learning (DL) to detect vulnerabilities in smart contracts. The proposed intellectual engine operates in multiple stages. First, a smart contract is created, and the user provides a value during the runtime phase. XAI and DL then analyze the opcodes in high-value contracts to detect potentially risky processes. If violations such as security protocol failures, insufficient funds, or account restrictions are found, the engine halts the transaction and generates an error report. If the contract passes this vulnerability assessment, it continues executing without interruption. This ensures flagged transactions remain functional while being assessed. Our proposed Hybrid Boot Branch and Bound Long Short-Term Memory (HB 3 LSTM) approach achieves outstanding performance, with an accuracy of 99.68%, precision of 99.43%, recall of 99.54%, and an F1-score of 99.40%, which surpasses the performance of existing methods.
Yao Ma, Wen Yu Kon, J. O. Chu, Kevin Han Yong Loh · 6 authors
Identity verification is the process of confirming an individual's claimed identity, which is essential in sectors like finance, healthcare, and online services to ensure security and prevent fraud. However, current password/PIN-based identity solutions are susceptible to phishing or skimming attacks, where malicious intermediaries attempt to steal credentials using fake identification portals. Alikhani et al. [Nature, 2021] began exploring identity verification through graph coloring-based relativistic zero-knowledge proofs (RZKPs), a key cryptographic primitive that enables a prover to demonstrate knowledge of secret credentials to a verifier without disclosing any information about the secret. Our work advances this field and addresses unresolved issues: From an engineering perspective, we relax further the relativistic constraints from 60m to 30m, and significantly enhance the stability and scalability of the experimental demonstration of the 2-prover graph coloring-based RZKP protocol for near-term use cases. At the same time, for long-term security against entangled malicious provers, we propose a modified protocol with comparable computation and communication costs, we establish an upper bound on the soundness parameter for this modified protocol. On the other hand, we extend the two-prover, two-verifier setup to a three-prover configuration, demonstrating the security of such relativistic protocols against entangled malicious provers.
Ovaj rad istražuje teorijske temelje i praktičnu primjenu dokaza nultog znanja u blockchain sustavima, s fokusom na Polygon zkEVM blockchain. Analiziraju se zk-SNARK i zk-STARK sustavi dokazivanja te njihova implementacija u ZK-rollup rješenjima za poboljšanje skalabilnosti blockchain mreža. Teorijska analiza pokazuje kako dokazi nultog znanja omogućavaju verifikaciju transakcija bez otkrivanja osjetljivih podataka, čime se adresiraju izazovi privatnosti i skalabilnosti. Praktični dio uključuje implementaciju decentralizirane aplikacije za glasovanje na Polygon zkEVM Cardona Testnet mreži, demonstrirajući primjenu tehnologije u realnoj situaciji. Analiza transakcijskih podataka potvrđuje značajne uštede goriva kroz batch procesiranje transakcija u odnosu na direktno izvršavanje na Ethereum glavnom lancu. Rad identificira ključne prednosti i ograničenja trenutnih implementacija te predlaže smjerove za buduća istraživanja u području post-kvantne kriptografije i hardverske akceleracije.
The rapid integration of blockchain, cryptocurrency, and Web3 technologies into digital banks and fintech operations has created an integrated environment blending traditional financial systems with decentralised elements. This paper introduces the CryptoNeo Threat Modelling Framework (CNTMF), a proposed framework designed to address the risks in these ecosystems, such as oracle manipulation and cross-chain exploits. CNTMF represents a proposed extension of established methodologies like STRIDE, OWASP Top 10, NIST frameworks, LINDDUN, and PASTA, while incorporating tailored components including Hybrid Layer Analysis, the CRYPTOQ mnemonic for cryptocurrency-specific risks, and an AI-Augmented Feedback Loop. Drawing on real-world data from 2025 incidents, CNTMF supports data-driven mitigation to reduce losses, which totalled approximately $2.47 billion in the first half of 2025 across 344 security events (CertiK via GlobeNewswire, 2025; Infosecurity Magazine, 2025). Its phases guide asset mapping, risk profiling, prioritisation, mitigation, and iterative feedback. This supports security against evolving risks like state-sponsored attacks.