Blockchain Papers

Mathew Sebastian

Cryptography has played a pivotal role in securing communication across human history. From ancient techniques such as hieroglyphic substitutions and Caesar's cipher to contemporary cryptographic systems like RSA and Elliptic Curve Cryptography, the field has continuously adapted to evolving technological paradigms. This article provides a comprehensive review of the historical development of cryptography, highlighting key milestones from ancient Egypt and Mesopotamia, through the mechanical encryption devices of World War II, to the theoretical foundations established by Claude Shannon. It examines the revolutionary introduction of public-key cryptography and follows developments into the digital era, where blockchain technology and privacy innovations like Zero-Knowledge Proofs have expanded cryptographic applications beyond traditional security roles. The article also explores emerging challenges and innovations, particularly those involving artificial intelligence and quantum computing, considering the implications of quantum threats and the ongoing global efforts to develop quantum-resistant encryption standards.

N. Nasurudeen Ahamed, Tanweer Alam, Mohamed Benaida

Blockchain technology is often regarded as a highly advanced and pioneering breakthrough in modern times. Blockchain technology is a distributed ledger that uses encryption to prevent security breaches and securely stores data across many systems. This facilitates collaborative transactions by providing a solitary, dependable reference point, revealing the purported trust intermediaries. This study aims to investigate the core principles of blockchain technology and assess its potential to support sustainability across various sectors. It seeks to examine how blockchain technology enhances reliability, effectiveness, and transparency in industries such as supply chain management and the energy sector. This study addresses these concerns by assessing the valuable applications, advantages, and drawbacks of blockchain in promoting sustainable industrial practices. Bitcoin and other cryptocurrencies rely on hashing as the foundation of their blockchain technology. Blockchain is a digital ledger that documents and tracks financial transactions. Blockchain technology has become prevalent across several sectors, encompassing artificial intelligence, machine learning, and the Internet of Things. Therefore, once the blockchain is prepared for dissemination, the data cannot be modified by anyone. This implies that it is immutable. Hyperledger offers a neutral platform for facilitating collaborative operations among organisations that frequently engage in competitive activities. Hyperledger is specifically designed to provide explicit support for blockchains as a means of business agreements. Authorisation is a prerequisite for a framework, ensuring that only those with proper authorisation can join the organisation. The ability of the manager to impose limitations on user access to the blockchain enhances security measures. Moreover, instead of being universally accessible through online platforms, trades are maintained secretly, limiting access to only essential participants. Using distributed code bases and open-source record upgrades facilitates enhanced efficiency in corporate activities. The fast expansion of blockchain technology has led to its widespread adoption across several industries worldwide. Illustrations encompass various domains, including logistics, copyright, finance, medicine, and supply chain management. Furthermore, we offer an introductory overview of blockchain technology, encompassing topics such as different types of blockchains and their utilisation across many sectors.

Iuon‐Chang Lin, Jyun-Yan Ruan, Ching-Chun Chang, Chin‐Chen Chang · 7 authors

In response to the Cybersecurity Law, organizations face numerous management and technical requirements. Detection techniques such as vulnerability scanning and penetration testing are employed to identify risks. Addressing these vulnerabilities demands substantial manpower, time, and financial resources. Security concerns also arise during digital file transmission and remediation efforts. This study proposes a security detection platform with step-by-step implementation guidelines, enabling resource-limited units to replicate the setup and address security gaps. It compares detection results between open-source and commercial tools, highlighting key differences and offering remediation strategies. Numerous digital files (e.g., test reports) are generated during testing. To ensure secure storage and sharing, the system integrates IOTA’s distributed ledger and IPFS, generating HASH values and uploading files on-chain to preserve integrity and authenticity. The objective is to deliver a scalable, cost-effective security detection framework that enhances system resilience while minimizing resource consumption.

Aryan A Ayare, Vaishnavi A Jadhav, Mustafa K Banatwala, Shashank V Changlere · 6 authors

Blockchain is a decentralized and distributed ledger technology that ensures data security, transparency, and immutability, making it a promising solution for academic record management. Currently, academic records are managed through centralized databases controlled by educational institutions, relying on manual processes, institutional servers, and third-party services. These systems are prone to inefficiencies, data breaches, and authentication challenges, often requiring time-consuming verification processes vulnerable to fraud. Blockchain technology addresses these limitations by offering a decentralized, tamper-proof framework that enhances security, accessibility, and trust in academic credential verification. This study reviews various blockchain platforms, consensus mechanisms, and scalability solutions, with a focus on Hyperledger Fabric and Ethereum, assessing their applicability in educational contexts. Furthermore, off-chain storage techniques like InterPlanetary File System, consensus algorithms, and access control mechanisms are analyzed to optimize the efficient and secure management of sensitive academic data. By integrating blockchain technology, educational institutions can modernize record-keeping, streamline verification processes, and enhance trust in academic credentials, ultimately creating a more secure and transparent academic record management system. Statistical analysis further highlights blockchain's growing adoption in education, demonstrating its effectiveness in reducing fraud, improving accessibility, and ensuring data integrity.

Essa Al-Mansouri

This paper investigates Bitcoin’s resilience against the U.S. dollar—widely recognized as the global reserve currency—by applying a multi-method wavelet analysis framework to daily price data of Bitcoin, the USD strength index (DXY), the euro, and other assets ranging from August 2015 to June 2024. Quantitative measures—particularly the Frobenius norm of wavelet coherence and an exponential decay phase-weighting scheme—reveal that Bitcoin’s out-of-phase relationship with the dollar is lower and more sporadic than that of mainstream assets, indicating it is not tightly governed by dollar fluctuations. Even after controlling for the euro’s dominant influence in the DXY, BTC continues to show weaker coupling than mainstream assets—reinforcing the idea that it may serve as a partial hedge against dollar-driven volatility. These results support the hypothesis that Bitcoin may serve as a resilient store of value and hedge against dollar-driven market volatility, placing Bitcoin within the broader debate on global monetary frameworks. As global monetary conditions evolve, the resilience of Bitcoin (BTC) relative to the world’s leading reserve currency—the U.S. dollar—has significant implications for both investors and policymakers.

Senior Data Engineer - Lead, Citibank, USA, Pradeep Rao Vennamaneni

The financial services industry is transforming batch processing to real-time, AI-driven architectures. This article looks at how the frameworks Apache Kafka and Apache Spark are used as bases for building scalable and low-latency, fault-tolerant data pipelines, meeting the special requirements of the financial sector. These real-time applications include high-frequency trading, fraud detection, compliance monitoring, and customer engagement. They are made possible through these open-source platforms that publicly ingest, process, and make decisions. Integrating cloud-native infrastructure—using Kubernetes, service mesh, and container orchestration—ensures elasticity, security, and regulatory alignment. Large language models (LLMs) are now being entrenched into micro services for decision support, regulatory reporting automation, and the automation of client interactions. The article also contains detailed architectural guidance on how to integrate Kafka and Spark, tips for improving Kafka Spark performance, and best practices around observability and DevSecOps. Real-time stream processing combined with AI-driven analysis serves as a real-world use case for trade surveillance. The future impact of emerging trends such as edge-native computing, federated learning, and decentralized finance is also examined. Strategic recommendations to CTOs and architects for developing secure, AI-native, and future-proof financial systems are presented to close.

Weimin Wang, Martin Enilov, Petar Stankov

This study examines whether cryptocurrency markets offer more resilient safe haven properties than gold for stock markets in the BRICS economies from 28th April 2013 to 27th September 2024. Unlike traditional studies that primarily focus on Bitcoin or top-market cap cryptocurrencies , we introduce a novel Crypto index that includes 9468 active and defunct cryptocurrencies, providing a comprehensive view of daily market fluctuations across all listed crypto assets. We also investigate the impact of the Russia-Ukraine military conflict on the safe haven status of these assets. Using a time-varying robust Granger causality framework, we analyse the dynamic relationships between potential safe haven assets and BRICS stocks. Additionally, we explore the network structure of gold, cryptocurrencies, and BRICS stocks across different quantiles . Our results show limited evidence of time-invariant causality, but strong evidence of time-varying causality, suggesting that neither gold nor cryptocurrencies act as safe havens for BRICS stocks over the entire sample period. We find increased market interconnectedness during extreme conditions, with gold and cryptocurrencies initially acting as net receivers of shocks, but gold shifting to a net transmitter during the conflict, indicating stronger safe haven properties for gold. Portfolios favour gold over crypto, and small-cap cryptocurrencies are cheaper but less efficient hedges compared to large-cap cryptos, with Bitcoin emerging as the optimal investment for returns. These findings offer valuable insights for investors and policymakers, particularly for optimizing portfolio management and supporting financial stability during market turbulence.

Yvonne O’Connor, Katie O’ Reilly

Headlines globally have highlighted the role of Non-Fungible Tokens (NFTs) within the metaverse in radically reshaping future retail in the digital world. Yet, NFTs are not fulfilling its disruptive potential in virtual marketplaces. Researchers must understand the strategic concerns of non-sellers of NFTs before theorising about long-term implementation, diffusion, and adoption. An exploratory case study of non-selling content creators is employed in this study. The findings of this study reveal that resistance to NFT sales is influenced by several factors including external influences, initial conditions, social responsibility beliefs and individual values. Whilst a lot of research has examined the positive associations with NFT (e.g. motivations to buy) this paper focuses on resistance from a seller’s perspective. It further provides a foundation for understanding resistance from individuals who are self-employed and thus sheds new insights for this domain. • Developed a conceptual model focusing on resistance to NFTs. • Focuses on self-employed individuals who could use NFTs to sell digital assets. • Moves beyond the perspective of NFT buyers. • Identifies social, psychological, and behavioural factors behind NFT resistance. • Provides propositions for further research.

Authors unavailable

THE IMPACT OF ARTIFICIAL INTELLIGENCE ON CLINICAL PRACTICE IN DIABETES MANAGEMENT Muzammil Mohammed Shadan Institute of Medical Sciences, Hyderabad, Telangana, India Background: Artificial Intelligence (AI) is revolutionizing diabetes management by enhancing early diagnosis, optimizing treatment plans, and enabling real-time monitoring. AI-driven tools improve clinical decision-making, reduce complications, and enhance patient adherence, ultimately transforming diabetes care. Aims and Objective: This research aims to investigate the impact of Artificial Intelligence (AI) on diabetes clinical practice, particularly its role in facilitating early diagnosis, tailoring treatment, enabling real-time monitoring, and enhancing decision-making processes. The study intends to evaluate the statistical significance of AI-based interventions and their effectiveness in improving patient outcomes, alongside a comprehensive examination of modifications in clinical protocols and the quality of patient care. Methodology: A retrospective analysis was conducted using clinical data from multiple healthcare facilities that have adopted AI technologies in diabetes management. The review encompassed AI-enhanced glucose monitoring systems, predictive models for disease progression, and individualized treatment strategies. Data were gathered from clinical trials, patient records, and AI-supported decision-making tools over a five-year timeframe. Statistical evaluations were performed using multivariate regression to assess the impact of AI on lowering HbA1c levels, enhancing treatment adherence, and mitigating diabetesrelated complications. Results were analysed by comparing AI-assisted care with traditional treatment approaches. Results: The analysis revealed a statistically significant enhancement in patient outcomes associated with the integration of AI in diabetes management. Patients utilizing AI-based monitoring systems experienced an average HbA1c level reduction of 1.2%, with a p-value of less than 0.01, indicating statistical significance. AI-driven predictive models demonstrated an 85% accuracy rate in anticipating diabetes progression, facilitating timely interventions, and decreasing the incidence of severe complications, such as diabetic retinopathy, by 40%. Furthermore, AI personalized treatment algorithms resulted in a 25% increase in medication adherence, supported by an odds ratio of 2.5 (95% CI: 1.9–3.1), suggesting that patients engaged in AI-supported interventions were significantly more likely to adhere to prescribed treatment plans compared to those receiving traditional care. AI tools have significantly improved clinical decision-making processes. The integration of real-time data from AI-assisted glucose monitoring systems has enhanced clinician responsiveness to both hypo- and hyperglycaemic incidents, leading to a 30% reduction in emergency hospitalizations. Additionally, the implementation of AI-enabled decision support systems has allowed clinicians to identify optimal treatment plans more swiftly, decreasing the average time required to achieve glycaemic control by 20%. This not only accelerates patient recovery but also alleviates the strain on healthcare resources. The statistical analysis of these outcomes demonstrates a high level of accuracy in predicting and enhancing clinical results. Conclusion: This study’s findings indicate that AI has profoundly transformed diabetes clinical practice, resulting in significant improvements in patient outcomes, adherence to treatment, and overall healthcare efficiency. AI-driven tools provide a tailored approach to diabetes management, enabling early diagnosis, predictive modelling of disease progression, and continuous monitoring. These advancements not only improve the accuracy of clinical decision-making but also help mitigate long-term complications related to diabetes. As AI technology continues to advance, its influence on chronic disease management is expected to grow, offering even greater advantages for both patients and healthcare providers. Keywords: Artificial intelligence in diabetes, clinical decision support, predictive analytics INTEGRATING REST STRATEGIES AND AI-DRIVEN TOOLS TO ENHANCE DIABETOLOGIST PRODUCTIVITY AND WELLNESS Harsh Atul Hirani, Alok Modi1, Dr. Bharat Saboo2 Life Care Centre for Diabetes, Hyderabad, Telangana, 1Dr. Alok Modi’s Diabetes Center and Kevalya Hospital, Thane, Maharashtra, 2Prayas Diabetes Center, Indore, Madhya Pradesh, India Background: Diabetologists face significant challenges, including managing extensive clinical workloads, adapting to rapid advancements in diabetes care, and addressing the emotional toll of chronic disease management. Burnout among diabetologists negatively impacts their personal well-being and the quality of care delivered to patients. There is a pressing need to integrate wellness strategies with innovative technological solutions to support diabetologists in sustaining high performance and improving patient outcomes. Aim and Objective: To assess the effectiveness of combining diversified rest strategies with AI-driven tools in enhancing the productivity, wellness, and patient care outcomes of diabetologists. Methodology: This study reviewed wellness practices and their applicability to diabetologists, focusing on seven types of rest: physical, mental, emotional, spiritual, social, sensory, and creative. Tailored interventions were developed for each rest type, incorporating AI-driven tools such as Large Language Models (LLMs), wearable devices, and mindfulness applications. Key outcomes were analyzed in terms of their impact on burnout reduction, productivity improvement, and patient satisfaction. Methods and specific strategies for each type of rest are detailed in Figure 1.Figure 1: Methods of rest, which outlines various strategies for achieving different types of rest: physical, mental, emotional, spiritual, social, sensory, and creative, along with supportive technologies and practices for eachResults: The integration of rest strategies and AI tools produced notable benefits. Wearable devices optimized physical activity and sleep patterns, improving focus and reducing fatigue. LLMs alleviated cognitive overload, supporting decision-making and administrative efficiency. Emotional resilience improved through AI-based journaling and coaching, while mindfulness apps enhanced mental clarity and reduced stress. Networking platforms facilitated meaningful social connections, and brainstorming tools fostered innovation, aiding creative rest. These interventions collectively enhanced work-life balance, reduced burnout, and improved patient care outcomes. Conclusion: Integrating rest strategies with AI-driven tools offers a transformative approach to addressing burnout and enhancing productivity among diabetologists. By fostering sustainable well-being practices, this framework ensures improved physician wellness and better diabetes care delivery. Keywords: Artificial intelligence, burnout prevention, diabetologist wellness, rest strategies, technological solutions, work-life balance NOVEL MARKERS FOR EARLY ONSET OF DIABETES: EVALUATION OF GLYCAEMIC VARIABILITY IN TYPE- 2 DIABETES MELLITUS USING CONTINUOUS GLUCOSE MONITORING SYSTEM: A PILOT STUDY Ravi Kumar, Santosh Kumar Singh Department of Internal Medicine, Armed Forces Medical College, Pune, Maharashtra, India Background: Continuous glucose monitoring (CGM) systems allow us to perform real-time monitoring of blood glucose levels. It can be used for assessment of the Glycemic Variability (GV) in subjects who are at high risk for the development of Diabetes, which can be taken as an early marker of the onset of diabetes. Aimand Objective: To detect glycaemic variability as an early marker for derangement in blood sugar using the CGM system in medium-high-risk subjects for diabetes. To evaluate GV in individuals with medium-high risk groups for type-2 diabetes mellitus and to find the association of GV with beta-cell dysfunction and Insulin resistance. Methodology: The study was an analytic cross-sectional study, done in a tertiary care hospital in western Maharashtra. Since this was a pilot study a sample size of 60 was taken. All patients reporting to OPD who are in medium or high-risk groups for Diabetes as per the IDRS were interviewed, their anthropometric parameters, and basic laboratory parameters were taken and the CGM system (Abbot freestyle Libre) (> 3 days -14 days) were attached to access the parameters of GV. The GV parameters were calculated using the standard software, and HOMA-IR & HOMA-B were calculated using the standard formulas. The data tabulated in the Excel sheets were evaluated using the statistical methods and software (SPSS) to access the prevalence of GV using its parameters i.e. early dysglycemia in mediumhigh-risk individuals for diabetes. Results: The various parameters GV like mean, standard deviation, LI, J index, HBGI, CONGA, and MAGE, were found to be high in the study population and with a statistically significant correlation between the various parameters of glycemic variability [Scatter plot diagram of the various parameters of GV is as attached as Figure 1].Figure 1: Scatter plot diagram of the various parameters of GVConclusions: The present study has helped us in the early detection of diabetes in patients having moderate to high risk for diabetes and will adequate preventive and therapeutic interventions will be advised to the individuals for management and prevention of complications. Need for more similar studies in various patient subsets of healthy individuals, and patients with chronic diabetes. Keywords: CGM, glycaemic variability, type-2 diabetes mellitus RETROSPECTIVE STUDY CORRELATING SELF MONITORING BLOOD GLUCOSE (SMBG) VALUES WITH HBA1C Abhisekh Raha Lumding Divisional Railway Hospital, Indian Railway Health Services, Lumding, Assam, India Background: Self‐monitoring of blood glucose (SMBG) plays an important role in the management of Type I Diabetes. SMBG has many proven benefits in Type 1 diabetics such as, minimizing glucose variability, helping to predict severe hypoglycemia and aiding the achievement of hemoglobin A1c (HbA1c) targets. Aim and Objective: Correlating HbA1c with multiple SMBG values in persons with Type 1 DM. Methodology: Retrospective analysis of 10 persons’ SMBG values with Type I DM above the age of 18,who were measuring their pre‐meal and bedtime sugars regularly (more than 20 times per week) was done. SMBG values between 70 to 180mg/dl were considered Points In Range (PIR). The mean HbA1c values of these persons were observed and correlated with SMBG data. Results: The SMBG values were more than 70% in range in 2 out of 10 persons. The mean HbA1c of these persons was 6.9%. 4 persons with 65% to 70% PIR had mean HbA1c of 7.2%, 2 persons with 60 to 65% PIR had mean HbA1c of 7.5%, 1 person with 55 to 60% PIR had mean HbA1c of 8.1% and 1 persons with 45 to 50% PIR had mean HbA1c of 8.4%. Conclusions: PIR correlates well with HbA1c in persons with Type I DM who measure their blood glucose more than 3 times in a day. Keywords: HbA1C, point in range, SMBG USAGE OF TECHNOLOGY IN GLYCEMIC CONTROL FOR RURAL PEOPLE WITH DIABETES Abhisekh Raha Lumding Divisional Railway Hospital, Indian Railway Health Services, Lumding, Assam, India Background: In a technological triad model, a connection is established between doctor and two voluntary workers to monitor remotely the patient’s glycemic levels and educate the patients online via conference video calls following up and daily monitoring the regular treatment and diet intake. Aim and Objective: Here, we studied the role of teamwork in achieving glycemic control in rural patients using a cost-effective technological triad model. Methodology: 65 people with diabetes were selected based on inclusion criteria, out of which only 30 agreed to participate in the study. Inclusion criteria: Men and women aged between 30 to 60 years with type 2 diabetes mellitus (T2DM), having an uncontrolled fasting blood sugar (FBS), post prandial blood sugar (PPBS) and HbA1c between 7.1 - 10.5 %. Exclusion criteria: Type 1 diabetes mellitus (T1DM), pre-existing renal, hepatic, or cardiac disease, HbA1c > 10.5 %. Tie up was made with the two voluntary worker to monitor remotely the patient’s glycemic levels and educate the patients online via conference video call following up and daily monitoring the regular treatment and diet intake. A technological triad was established two voluntary workers and the treating doctor. Results: 30 patients were randomly divided into two groups, i.e., group A and B which comprised of 15 patients each. Parameters such as FBS, PPBS, HbA1c and lipid profile, complete blood count, complete urinary analysis, Liver function test, Serum Creatinine, BMI were collected on day 1 and follow up data which includes measurement of FBS, PPBS, HbA1c and lipid profile were collected at 3 months and at 6 months. Data obtained was measured with SPSS version 17 software. A 10 were on daily remotely with the help of worker for diet regular of a in 6 months the B were on day 1 and these patients of B were the of diet and these groups were on in the for There was statistically significant in the mean FBS, PPBS, HbA1c and lipid profile at the of the study. the follow up 3 months was observed that the mean FBS, and were significantly in the group A 6 months was observed that mean FBS, PPBS, and was significantly in group A There was statistically significant in the mean levels between Conclusion: technological has a of to is a that the of and the not only for the patient but also for the care A with a physician not achieve glycemic control as in group B compared to a approach and monitoring that the adherence to medication and diet as demonstrated in group Keywords: Health rural technology triad FOR AND USING AI Hospital, India Background: and age related in patients with diabetes, for multiple can in early The intelligence (AI) on the on can for and The of AI for in diabetes care Aim and Objective: To for and using the AI in patients with diabetes. Methodology: This was a study of patients with diabetes at a tertiary care hospital in India using the were analyzed by the for each Results: In this study were was present in in and or in Patients for at 1 or 2 was and disease was in Conclusion: in patients had and 1 in 2 for at 1 in diabetes care can significantly improve early detection and management of multiple This study the need for to while treating patients with diabetes. The AI offers an to this approach and Keywords: comprehensive diabetic retinopathy, AI OF CONTINUOUS GLUCOSE MONITORING (CGM) IN GLYCEMIC CONTROL OF DIABETES IN A STUDY IN THE OF Department of Medicine, Hospital, Hospital, India Background: Continuous glucose monitoring (CGM) systems have as a for managing diabetes, real-time glucose data and that better glycemic This study the effectiveness of CGM systems in improving glycemic control among patients with diabetes in care Aim and Objective: the effectiveness of CGM in improving glycemic control among diabetes patients in a care the impact of CGM on diabetes management and patient adherence to treatment plans in the of the role of CGM in reducing complications associated with diabetes, such as glycemic control and quality of To evaluate the of care to CGM into clinical and its on their Methodology: A was conducted patients with type 1 and type 2 diabetes care were randomly to a CGM group or a control group using traditional of blood glucose HbA1c levels, of and outcomes on quality of were conducted at and months. Results: Patients in the CGM group a significant reduction in HbA1c levels compared to the SMBG group reduction of The CGM group also experienced per compared to the SMBG group per Additionally, outcomes and improved quality of among CGM Conclusion: The study demonstrates that CGM systems significantly improve glycemic control and reduce in care CGM also greater with their diabetes management. These findings that CGM into care practices can enhance diabetes management and improve patient outcomes, a for implementation in care Keywords: Continuous glucose monitoring, diabetes management, glycemic patient care OF IN DIABETES IN 2 Diabetes India Background: The of solutions to reduce and to and personal as well as has in years The treatment and care of patients with diabetes and its and regular monitoring and by solutions to standard in and provide access to diabetes care. Aim and Objective: The of this study was to the and glycemic in patients who up as their treatment in diabetes care. Methodology: A retrospective analysis was done of the patients in Diabetes at diabetes care A and follow up was done for months along with through from to Patients were in person 6 months as per Parameters like in of patients their follow up and adherence and were Results: It was observed that out of reduction of A1c was in of patients Conclusion: to patient care through the glycemic control and overall well of the patients. Keywords: type 2 diabetics INTEGRATING TOOLS IN DIABETES THE IN CLINICAL Health India Background: Type Diabetes is a chronic in which personalized disease management and adherence to The Diabetes for the of Diabetes the of patient’s approach for management. a personalized interventions with Aim and Objective: To evaluate the impact of the in managing clinical outcomes for individuals with Methodology: This study data from 20 patients in personalized diet and plans, glucose and regular cognitive The in in Range Range Range fasting blood glucose and blood glucose was evaluated as outcomes and in lipid and as outcomes. Results: The mean age of patients in the study was with a of the mean HbA1c significantly from to with and of and of patients were in the which to 85% by the of the study. Patients who improved their a mean HbA1c reduction of Additionally, from to while at compared to Additionally, a reduction in and reduction in BMI was Conclusion: The significant impact on clinical outcomes, along with high patient adherence, as a therapeutic that and the standard of care for management. Keywords: Diabetes care, AND CLINICAL IMPACT OF THE ON AND GLYCEMIC CONTROL IN Health India Background: Type 2 Diabetes a significant and healthcare a focus on both glycemic control and management. like Continuous (CGM) with platforms increase patient by real-time glucose The Glycemic in Diabetes this approach by personalized patient care. Aim and Objective: To evaluate the effectiveness of the in facilitating improvements in quality of and Health Methodology: This study 20 patients with who were by the physician to be the were personalized plans, and cognitive with CGM The outcomes measured were improvements in evaluated through reduction in the of an increase in and parameters such as outcomes evaluated through in and hospital Results: This study demonstrated with 85% of patients reducing and an average reduction of These were in better glycemic with HbA1c reduction observed in of is by in management and sleep quality by and Insulin were reduced in of and the between was for 25% of patients who an HbA1c of more than Conclusion: The in improving glycemic and in offers a to enhance outcomes and patient care. Keywords: Diabetes, glycemic A RETROSPECTIVE STUDY OF OF IN AND USING TOOLS Hospital, India Background: is of the complications of Diabetes and a leading of and as disease which leading to and in of DM glycemic with associated are the is on and as and and based on of and disease for early detection using as AI is revolutionizing management this study to incidence in and identify healthcare and Aim and Objective: for early detection using as AI is revolutionizing management this study to incidence in and identify healthcare and Methodology: A Retrospective study was conducted with data of patients from and from All were this study through examination with the help of AI software. such as of diabetes were taken into and and were calculated between and Results: and were both out of and were both out of Patients with also had significantly disease and HbA1c values and a prevalence of such as Conclusion: This a of in population compared to to glycemic of diabetes and of The findings the need in managing complications AI tools and predictive analytics for early detection risk and By AI a role in healthcare Keywords: Artificial intelligence, diabetic retinopathy, AI AND - A TO DIABETES MANAGEMENT of for India Background: Diabetes and have levels with people with diabetes in and indicating this will to by of with many individuals of their The is with healthcare in and to by healthcare systems focus on care, a need for innovative preventive strategies. and AI-driven transformative tools for personalized interventions, enabling real-time monitoring and predictive for diabetes prevention and management. This study a that these technologies while incorporating a for are not Aim and Objective: this study is to focus on & the in this innovative Methodology: A of with diabetes or evaluated the of a Health & This to AI-driven for personalized and technology for real-time and levels were Results: of that adherence to through AI 85% in AI-driven to blood sugar levels and improve 70% found the of for the impact of on be to to their were not demonstrated a of healthy by and personalized Conclusion: this of we are to a of this approach on this and This approach has the to care for by combining with studies its through and its be on and for more and This approach has the to care for by combining with studies its through and its be on and for more Keywords: AI coaching, intelligence, CGM, diabetes, analysis, predictive care AI and Madhya Pradesh, India Background: The the effectiveness of personalized interventions in improving intelligence (AI) platforms like were used in for but their in their in care. To these we developed algorithms and for AI to which are to improve Aim and Objective: The of this study is to investigate the effectiveness of of AI in improving compared to by a Methodology: with type-2 diabetes from diabetes years and glycaemic from to were in the study. group comprised subjects age years and control group 15 subjects age was Serum and were compared at the and of study A the of control group for group was by a a software to and these to Results: and control group statistically significant in study parameters but group performed better as compared to control 1: parameters with intelligence parameters with by Intelligence with in improving in care Keywords: Artificial intelligence, diabetes, personalized AI-DRIVEN EVALUATION OF FOR DIABETES of for India Background: a role in managing diabetes by improving glycemic and the variability in and of their Artificial Intelligence evaluations an innovative approach to and This study AI algorithms to evaluate the of different in diabetes management, offering into glycemic outcomes and Aim and Objective: The of this study is to the effectiveness of various including the and in improving glycemic control and through AI-driven Methodology: AI-driven and of trials, and studies between and was The analysis measured the impact of on fasting glucose and outcomes. and were calculated using Results: HbA1c reduction of (95% CI: to and improved reduction of (95% CI: to with significant and HbA1c reduction of (95% CI: to with benefits. HbA1c reduction of (95% CI: to with reduction of and improvements in glycemic control and with better adherence Conclusion: AI-driven that the and provide the glycemic The analysis the of personalized interventions in diabetes management. research platforms and data to and This study the transformative role of AI in studies are required to AI-driven and their applicability technology with continuous glucose monitoring systems enhance personalized strategies for optimal outcomes. Keywords: AI diabetes management, patterns, glycemic personalized

Sovi Dwi Febrian Silva

RAIMUNDO is an innovative decentralized application (DApp) designed for the legal sector, leveraging Ethereum's sustainable blockchain technology (now based on Proof of Stake) to certify documents. By using a dual-hash system, it enables attorneys to produce tamper-proof "blockchain evidence," eliminating the need for state intermediaries. This empowers legal professionals, especially in regions with authoritarian regimes or corruption, to independently certify documents. However, judicial acceptance of blockchain evidence varies. Common law systems increasingly recognize it as valid, while civil law jurisdictions, with formal and state-centric traditions, often prioritize public certification over private digital methods. Factors such as blockchain's anonymity and the strict public certification duties of European notaries contribute to this divide. Although technically compatible with notarial roles in civil law, the integration of blockchain into regulatory frameworks remains uncertain, highlighting the need for ongoing evaluation of its evidentiary value compared to traditional public documents.

Syahrul Ma’arif, Fiqih Maufiquddin, Benny Dhevyantoa, Krisdiana Krisdiana · 5 authors

This research explores the developments and challenges in decentralized finance (DeFi) since 2015 and the increasing use of blockchain technology. DeFi provides access to financial services without traditional intermediaries, improving the economic system's efficiency through automated and transparent smart contracts. The bibliometric analysis shows a significant growth in DeFi-related publications, with 1,909 articles identified between 2015 and 2025. The research also highlights the importance of collaboration between authors and stakeholders to build a more secure and sustainable financial ecosystem. Analysis results using VOSviewer identified 173 keywords in 18 clusters, focusing on "digital twin" and "artificial intelligence." This research recommends further exploration into DeFi adoption, blockchain technology innovation, and the application of smart contracts to support the development of an inclusive, efficient, and innovative DeFi ecosystem in the future. In addition, this research aims to bridge existing research gaps and provide deeper insights into the potential of DeFi in the global financial system.

М. А. Абрамова, С. В. Криворучко, Oleg V. Lunyakov, Алим Борисович Фиапшев

The sphere of decentralized finance is the subject of widespread debate as the ways of providing services in the financial market. Using distributed registry technologies, smart contacts and a decentralized format of cooperation, it is capable, to a certain extent, of replacing traditional financial intermediaries in some product segments of the financial market. The authors set the task of identifying possible markers of liquidity flow into the sphere of decentralized finance, as well as assessing the scale and dynamics of its development compared with segments of the financial sector of the economy. The purpose of the study is to form a system of comparable indicators, based on which national regulators will be able to objectively assess the scale and dynamics of development of the DeFi sector. To achieve the goal, the article conducted a quantitative analysis of the relationship between changes in the money supply and the total value locked of crypto assets in the DeFi sector; a comparative analysis of various segments of the DeFi sphere and the financial sector of the economy was carried out. As the main methods, the authors used methods of regression analysis, systemic and logical methods, induction and deduction, methods of economic statistics, which made it possible to identify tendencies in the development of the sphere of decentralized finance against the background of indicators of development of the financial sector of the economy. The source data consisted of statistical databases on key indicators of the development of the financial sector of the economy at the international level, as well as databases on services provided by participants of decentralized finance. As a result of the study, the impact of changes in money supply on total value locked in DeFi is evaluated, as well as tendencies and scale of development of the sphere of decentralized finance in comparable indicators of the financial sector of the economy are identified. It is concluded that the scale of the current development of decentralized finance is not significant. However, according to a number of comparable indicators, this sphere already represents a certain parity with the financial sector of the economy. First of all, this applies to the trading turnover of decentralized exchanges and the volume of trading in crypto derivatives. The results of the study can be used by national regulators when assessing the scale of development of the sphere of decentralized finance under certain monetary and financial conditions.

Mohamed Salah Bouafif, Mohammad Hamdaqa, Edward Zulkoski

Mutation testing is a widely recognized technique for assessing and enhancing the effectiveness of software test suites by introducing deliberate code mutations. However, its application often results in overly large test suites, as developers generate numerous tests to kill specific mutants, increasing computational overhead. This paper introduces PRIMG (Prioritization and Refinement Integrated Mutation-driven Generation), a novel framework for incremental and adaptive test case generation for Solidity smart contracts. PRIMG integrates two core components: a mutation prioritization module, which employs a machine learning model trained on mutant subsumption graphs to predict the usefulness of surviving mutants, and a test case generation module, which utilizes Large Language Models (LLMs) to generate and iteratively refine test cases to achieve syntactic and behavioral correctness. We evaluated PRIMG on real-world Solidity projects from Code4Arena to assess its effectiveness in improving mutation scores and generating high-quality test cases. The experimental results demonstrate that PRIMG significantly reduces test suite size while maintaining high mutation coverage. The prioritization module consistently outperformed random mutant selection, enabling the generation of high-impact tests with reduced computational effort. Furthermore, the refining process enhanced the correctness and utility of LLM-generated tests, addressing their inherent limitations in handling edge cases and complex program logic.

Junjie Hu, Sisi Duan

Nakamoto consensus are the most widely adopted decentralized consensus mechanism in cryptocurrency systems. Since it was proposed in 2008, many studies have focused on analyzing its security. Most of them focus on maximizing the profit of the adversary. Examples include the selfish mining attack [FC '14] and the recent riskless uncle maker (RUM) attack [CCS '23]. In this work, we introduce the Staircase-Unrestricted Uncle Maker (SUUM), the first block withholding attack targeting the timestamp-based Nakamoto-style blockchain. Through block withholding, timestamp manipulation, and difficulty risk control, SUUM adversaries are capable of launching persistent attacks with zero cost and minimal difficulty risk characteristics, indefinitely exploiting rewards from honest participants. This creates a self-reinforcing cycle that threatens the security of blockchains. We conduct a comprehensive and systematic evaluation of SUUM, including the attack conditions, its impact on blockchains, and the difficulty risks. Finally, we further discuss four feasible mitigation measures against SUUM.

Alex Nezlobin, Martin Tassy

Although modern blockchains almost universally produce blocks at fixed intervals, existing models still lack an analytical formula for the loss-versus-rebalancing (LVR) incurred by Automated Market Makers (AMMs) liquidity providers in this setting. Leveraging tools from random walk theory, we derive the following closed-form approximation for the per block per unit of liquidity expected LVR under constant block time: \[ \overline{\mathrm{ARB}}= \frac{\,σ_b^{2}} {\,2+\sqrt{2π}\,γ/(|ζ(1/2)|\,σ_b)\,}+O\!\bigl(e^{-\mathrm{const}\tfracγ{σ_b}}\bigr)\;\approx\; \frac{σ_b^{2}}{\,2 + 1.7164\,γ/σ_b}, \] where $σ_b$ is the intra-block asset volatility, $γ$ the AMM spread and $ζ$ the Riemann Zeta function. Our large Monte Carlo simulations show that this formula is in fact quasi-exact across practical parameter ranges. Extending our analysis to arbitrary block-time distributions as well, we demonstrate both that--under every admissible inter-block law--the probability that a block carries an arbitrage trade converges to a universal limit, and that only constant block spacing attains the asymptotically minimal LVR. This shows that constant block intervals provide the best possible protection against arbitrage for liquidity providers.

Haoxiang Luo, Gang Sun, Yinqiu Liu, Dongcheng Zhao · 7 authors

Large Language Models (LLMs) have achieved remarkable success across a wide range of applications. However, individual LLMs often produce inconsistent, biased, or hallucinated outputs due to limitations in their training corpora and model architectures. Recently, collaborative frameworks such as the Multi-LLM Network (MultiLLMN) have been introduced, enabling multiple LLMs to interact and jointly respond to user queries. Nevertheless, MultiLLMN architectures raise critical concerns regarding the reliability and security of the generated content, particularly in open environments where malicious or compromised LLMs may be present. Moreover, reliance on centralized coordination undermines system efficiency and introduces single points of failure. In this paper, we propose a novel Trusted MultiLLMN framework, driven by a Weighted Byzantine Fault Tolerance (WBFT) blockchain consensus mechanism, to ensure the reliability, security, and efficiency of multi-LLM collaboration. In WBFT, voting weights are adaptively assigned to each LLM based on its response quality and trustworthiness, incentivizing reliable behavior, and reducing the impact of malicious nodes. Extensive simulations demonstrate that WBFT significantly improves both consensus security and efficiency compared to classical and modern consensus mechanisms, particularly under wireless network conditions. Furthermore, our evaluations reveal that Trusted MultiLLMN supported by WBFT can deliver higher-quality and more credible responses than both single LLMs and conventional MultiLLMNs, thereby providing a promising path toward building robust, decentralized AI collaboration networks.

Awid Vaziry, Sandro Rodriguez Garzon, Patrick Herbke, Carlo Segat · 5 authors

The intersection of blockchain (distributed ledger) and identity management lacks a comprehensive framework for classifying distributed-ledger-based identity solutions. This paper introduces a methodologically developed taxonomy derived from the analysis of 390 scientific papers and expert discussions. The resulting framework consists of 22 dimensions with 113 characteristics, organized into three groups: trust anchor implementations, identity architectures (identifiers and credentials), and ledger specifications. This taxonomy facilitates the systematic analysis, comparison, and design of distributed-ledger-based identity solutions, as demonstrated through its application to two distinct architectures. As the first methodology-driven taxonomy in this field, this work advances standardization and enhances understanding of distributed-ledger-based identity architectures. It provides researchers and practitioners with a structured framework for evaluating design decisions and implementation approaches.

Hongliang Zhang, Fenghua Xu, Zhongyuan Yu, Shanchen Pang · 6 authors

Federated learning is a distributed machine learning paradigm through centralized model aggregation. However, standard federated learning relies on a centralized server, making it vulnerable to server failures. While existing solutions utilize blockchain technology to implement Decentralized Federated Learning (DFL), the statistical heterogeneity of data distributions among clients severely degrades the performance of DFL. Driven by this issue, this paper proposes a decentralized federated prototype learning framework, named DFPL, which significantly improves the performance of DFL under heterogeneous data distributions. Specifically, DFPL introduces prototype learning into DFL to mitigate the impact of statistical heterogeneity and reduces the amount of parameters exchanged between clients. Additionally, blockchain is embedded into our framework, enabling the training and mining processes to be executed locally on each client. From a theoretical perspective, we analyze the convergence of DFPL by modeling the required computational resources during both training and mining. The experiment results highlight the superiority of DFPL in both model performance and communication efficiency across four benchmark datasets with heterogeneous data distributions.

Zhihong Deng, Chunming Tang, Taotao Li, Parhat Abla · 7 authors

Blockchain technology, introduced in 2008, has revolutionized data storage and transfer across sectors such as finance, healthcare, intelligent transportation, and the metaverse. However, the proliferation of blockchain systems has led to discrepancies in architectures, consensus mechanisms, and data standards, creating data and value silos that hinder the development of an integrated multi chain ecosystem. Blockchain interoperability (a.k.a cross chain interoperability) has thus emerged as a solution to enable seamless data and asset exchange across disparate blockchains. In this survey, we systematically analyze over 150 high impact sources from academic journals, digital libraries, and grey literature to provide an in depth examination of blockchain interoperability. By exploring the existing methods, technologies, and architectures, we offer a classification of interoperability approaches including Atomic Swaps, Sidechains, Light Clients, and so on, which represent the most comprehensive overview to date. Furthermore, we investigate the convergence of academic research with industry practices, underscoring the importance of collaborative efforts in advancing blockchain innovation. Finally, we identify key strategic insights, challenges, and future research trajectories in this field. Our findings aim to support researchers, policymakers, and industry leaders in understanding and harnessing the transformative potential of blockchain interoperability to address current challenges and drive forward a cohesive multi-chain ecosystem.

Siti Hernita Oktavia, Indira Shofia, Abdul Rauf, Abdul Halik

The objective of this research is to examine and analyze the performance comparison among Bitcoin cryptocurrency, stocks, and gold. This study employs a quantitative research approach utilizing a comparative method. The population consists of the monthly closing prices of Bitcoin, LQ45 stocks, and gold from 2018 to 2023, totaling 180 data points. A saturated sampling technique is applied in this research. The study utilizes time series data, relying on secondary data sources. Data analysis is conducted using Microsoft Excel, applying relevant formulas for each variable. The data is further processed using SPSS, specifically employing the ANOVA test. The findings reveal significant differences in returns and risks among Bitcoin, stocks, and gold. Additionally, the performance metrics—measured by the Sharpe, Treynor, and Jensen methods—also indicate notable differences among these three asset classes.

Florentin Şerban

This paper presents a robust multi-period portfolio optimization framework that integrates interval analysis, entropy-based diversification, and downside risk control. In contrast to classical models relying on precise probabilistic assumptions, our approach captures uncertainty through interval-valued parameters for asset returns, risk, and liquidity—particularly suitable for volatile markets such as cryptocurrencies. The model seeks to maximize terminal portfolio wealth over a finite investment horizon while ensuring compliance with return, risk, liquidity, and diversification constraints at each rebalancing stage. Risk is modeled using semi-absolute deviation, which better reflects investor sensitivity to downside outcomes than variance-based measures, and diversification is promoted through Shannon entropy to prevent excessive concentration. A nonlinear multi-objective formulation ensures computational tractability while preserving decision realism. To illustrate the practical applicability of the proposed framework, a simulated case study is conducted on four major cryptocurrencies—Bitcoin (BTC), Ethereum (ETH), Solana (SOL), and Binance Coin (BNB). The model evaluates three strategic profiles based on investor risk attitude: pessimistic (lower return bounds and upper risk bounds), optimistic (upper return bounds and lower risk bounds), and mixed (average values). The resulting final terminal wealth intervals are [1085.32, 1163.77] for the pessimistic strategy, [1123.89, 1245.16] for the mixed strategy, and [1167.42, 1323.55] for the optimistic strategy. These results demonstrate the model’s adaptability to different investor preferences and its empirical relevance in managing uncertainty under real-world volatility conditions.

Punam Bedi, Vinita Jindal, Ningyao Ningshen, Pushkar Gole

Smart contracts represent a predefined set of rules invoked when specific conditions are met within blockchain networks, eliminating the need for centralized authority to validate transactions. The absence of central authority can potentially expose smart contracts to fraudulent behavior. Moreover, implementation flaws in smart contracts can be exploited to cause unintended behavior, resulting in security or financial risks. Traditionally, the identification of vulnerabilities in smart contracts has relied on methods such as pattern matching, data flow analysis, and input testing. While these techniques are foundational, they are constrained by human limitations and may not comprehensively address the full spectrum of potential issues. This necessitates more advanced approaches to ensure robust security and reliability. Therefore, in the literature, numerous researchers have leveraged different Machine Learning (ML) and Deep Learning (DL) techniques to classify normal and malicious smart contracts. However, existing literature either grapples with class imbalance issues or relies on conventional methods. Moreover, existing research often falls short of locating the exact location of malicious code within the smart contracts. Therefore, to address these gaps, this paper proposes a novel model called the Dual-Branch Encoder Siamese Network (DBESN) for detecting malicious smart contracts. Furthermore, this model is extended to precisely identify the region of the vulnerable code fragment within the smart contract using the Local Interpretable Model-Agnostic Explanations (LIME) algorithm. Experimental results demonstrated a performance Accuracy of 98.62% and 99.30% F1-Score with an inference time of 0.296 seconds. Given the high performance coupled with the low inference time of the proposed DBESN model, it is suitable for deployment within blockchain networks to detect and identify malicious smart contracts effectively and efficiently.

Jiazhen Gan, Jianzhong Su, Kaixin Lin, Zibin Zheng

Smart contracts are Turing-complete programs that run on blockchain technology, capable of managing on-chain assets according to predefined logic, and become immutable once deployed on the blockchain. In recent years, the value of smart contracts on blockchains, notably Ethereum, has been on the rise. However, the hiding vulnerabilities made the substantial value of smart contracts a target of many hackers, leading to numerous attack incidents. Therefore, vulnerability detection in smart contracts before deployment is essential. Currently, many fuzzers for detecting smart contract vulnerabilities can only identify vulnerabilities based on the execution patterns of the underlying opcodes, overlooking the financial semantic properties of the contracts, which leads to many vulnerabilities being difficult to detect or resulting in a high rate of false positives. To this end, we focus on the financial characteristics of contracts, define contract vulnerability patterns starting from the high-level semantic properties of contracts, and combine fuzzers using evolutionary algorithms and symbolic constraint solving to detect vulnerabilities, culminating in the development of FinanceFuzz . Specifically, FinanceFuzz defines invariant and equivalence properties of finance that contracts should satisfy. Utilizing these properties, FinanceFuzz can generate transaction sequences for testing and identify vulnerable contracts that violate the properties. We conducted experiments on a dataset containing 437 smart contracts from the real world, the experimental results demonstrating that our tool outperforms other state-of-the-art tools in detecting vulnerabilities, achieving higher recall rate without false positive.

Zequn Xie, Wenlong Tian, Jianfeng Lu, Weijun Xiao · 5 authors

Detecting similar data is crucial for optimizing file storage and transmission in HTTP protocols and Content Delivery Networks.Traditional MinHash methods encounter significant efficiency challenges due to their reliance on K-shingle structures, resulting in high computational costs and storage requirements.Additionally, these methods expose privacy risks in cloud environments, where sensitive information can be inferred from MinHash signatures.To address both efficiency and security concerns, we propose Horse-MinHash, which integrates a fast, content-defined feature extraction scheme with a non-interactive zero-knowledge proof-based similarity estimation method.Our approach significantly enhances computational efficiency while ensuring robust privacy protection by preventing plaintext exposure.Experimental results demonstrate that Horse-MinHash achieves lower mean squared error in Jaccard similarity estimation and reduces time overhead for average block sizes of 16KB or more, outperforming state-of-the-art methods. CCS Concepts Security and privacy File system security; Management and querying of encrypted data.