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Current Issue
Latest Issue (Vol.:6,Issue:2, April-June 2026)
Title:
BLOCKCHAIN AND FINANCIAL REGULATION: A CRITICAL REVIEW OF LEGAL APPROACHES
Author:
Dr Mrityunjai Pandey* and Dr Dharmendra Kumar Dubey
Keyword:
Blockchain, Financial Regulation, Cryptocurrencies, DeFi, Legal Frameworks, Comparative Regulation
Page No:
82-91
DOI:
https://doi.org/10.5281/zenodo.19027627
Abstract:
Blockchain technology has rapidly transformed the architecture of modern financial systems by introducing decentralised, transparent, and cryptographically secure mechanisms for recording and validati
ng transactions. Initially conceptualised as the underlying technology for cryptocurrencies, blockchain has evolved into a foundational infrastructure supporting a wide range of financial innovations, including decentralised finance (DeFi), smart contracts, stablecoins, tokenised securities, and cross-border payment systems. These developments challenge traditional financial regulatory frameworks that are premised on centralised intermediaries, jurisdictional boundaries, and institutional accountability. As blockchain-based financial activities continue to expand globally, regulators face the complex task of balancing technological innovation with the imperatives of financial stability, consumer protection, market integrity, and regulatory compliance. The decentralised and borderless nature of blockchain technology creates significant legal and regulatory challenges. Unlike conventional financial systems, blockchain networks often operate without identifiable intermediaries, making it difficult to assign legal responsibility, enforce compliance, and apply existing licensing and supervisory mechanisms. Issues related to jurisdiction, governance, accountability, and dispute resolution are further complicated by the pseudonymous or anonymous nature of blockchain participants. Additionally, blockchain-based financial platforms raise concerns regarding anti-money laundering and counter-terrorism financing, data protection and privacy, cybersecurity risks, and systemic financial instability. High-profile failures of cryptocurrency exchanges, algorithmic stablecoins, and decentralised protocols have underscored the urgent need for effective regulatory oversight while simultaneously revealing the limitations of traditional legal approaches when applied to decentralised systems. The paper further explores the tension between innovation and regulation, emphasising the risks of both regulatory overreach and under-regulation. Excessively restrictive regulatory frameworks may stifle innovation, deter investment, and undermine the potential benefits of blockchain technology, including enhanced efficiency, financial inclusion, and transparency. Conversely, insufficient regulation exposes consumers and markets to fraud, manipulation, and systemic risk. The paper concludes by advocating for adaptive, technology-neutral, and risk-based regulatory frameworks that recognise the distinctive features of blockchain technology while upholding core principles of financial regulation. It emphasises the importance of international regulatory coordination, regulatory sandboxes, and flexible compliance mechanisms to ensure effective oversight without hindering innovation. By critically evaluating existing legal approaches, this study contributes to ongoing scholarly and policy debates on how law can evolve to govern blockchain-enabled financial systems in a manner that promotes innovation, safeguards fundamental rights, and preserves financial stability in an increasingly digital global economy. Keywords: Blockchain, Financial Regulation, Cryptocurrencies, DeFi, Legal Frameworks, Comparative Regulation
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Title:
AI-DRIVEN STRATEGIES FOR AUTOMATED STOCK TRADING: OPPORTUNITIES AND RISKS
Author:
Ngu Wah Aung, Aung Khant Paing, Yee Mon Aung, Hlaing Htake Khaung Tin*
Keyword:
Automated Stock Trading, Algorithmic Trading, Financial Forecasting, Risk Management, Market Volatility, Predictive Analytics.
Page No:
92-100
DOI:
https://doi.org/10.5281/zenodo.19386858
Abstract:
Artificial Intelligence (AI) is more transforming financial markets to enable sophistic forms of computerized stock trading. Although AI-based models have been identified to have enormous potential in
improving their forecast accuracy, trading efficiency and portfolio returns, they are also accompanied by high measures of risk bearing volatility, bias and market stability. In this paper, automated stock trading methods based on AI are considered with specific focus on opportunity and risk to implement. The different AI architectures such as deep learning models and hybrid models are bench-marked based on historical stock and exchange data, based on their predictive performance, risk-adjusted returns, and responsiveness to various market conditions. The findings offer a clear insight to policymakers, traders and researchers to consider the possibility of AI to use in the future of the trading exchange.
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Title:
COMPARATIVE STUDY OF AI-DRIVEN DECISION INTELLIGENCE AND TRADITIONAL IMAGE PROCESSING–BASED DECISION SYSTEMS
Author:
Thet Hnin Su, Mi Yu Par Mon, Hlaing Htake Khaung Tin*
Keyword:
Artificial Intelligence, Decision Intelligence, systematic evaluation, healthcare diagnostics, surveillance, comparison, traditional image.
Page No:
101-107
DOI:
https://doi.org/10.5281/zenodo.19394576
Abstract:
Traditional image processing systems that used rule-based algorithms and hand-defined features during a long time have been used to extract and reason on visual information. Though they generate comp
utationally efficient and transparent decision making with limited adaptability, these lack adaptability resulting in a performance constraint to the tasks in complex, dynamic environments. The more recent Artificial Intelligence (AI) and Decision Intelligence (DI) concepts proposed data-driven models that integrate machine learning, deep learning, and reasoning in context to improve the accuracy of decisions. In this paper, Decision Intelligence powered by AI is compared to traditional decision-making systems based on image processing regarding methodology, performance, scalability, and general applicability in different fields like healthcare diagnosis, surveillance, and industrial control. The paper points out the fact that the two methods present complementary aspects and present a hybrid model that inherits the interpretative abilities of conventional methods alongside the abilities to predict of AI-fueled DI to create a powerful real-world decision-making model.
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Title:
COMPARATIVE ANALYSIS OF VISITOR SATISFACTION AND ENVIRONMENTAL AWARENESS AT CHAUNG THA AND NGWE SAUNG BEACHES, MYANMAR
Author:
Aung Myint Myat, Win Pa Pa Myo, Khine Yin Mon Thant, Hlaing Htake Khaung Tin*
Keyword:
Visitor Satisfaction, Environmental Awareness, Coastal Tourism, Chaung Tha Beach, Ngwe Saung Beach.
Page No:
108-116
DOI:
https://doi.org/10.5281/zenodo.19404288
Abstract:
The current study analyzes the satisfaction level and environmental awareness of Chaung Tha and Ngwe Saung beaches, which are some of the attractive beaches to visit in the Ayeyarwaddy Region, Burma/
Myanmar. With tourism becoming more significant in the economy of the region, it is important to learn more about the tourist himself and his perspective regarding his experience and involvement in environmental issues, which serves as an important factor for sustainable development in the region. For assessing the satisfaction level regarding cleanliness, quality accommodation, safety, food, and experience, a structured survey study involving 200 respondents, equal numbers on the two beaches, is being used for the study. Predictors of environmental awareness include waste behavior of correct disposal, the reduction of plastics, one-way usage, involvement in current environmental initiatives in each location, and the views expressed regarding the ecological impacts of tourism. Significant differences between the two places were noted. Although the other beach resort of Ngwe Saung, known as the relatively more developed and exclusive beach resort, received higher ratings in terms of the quality of infrastructure and tourist comfort and satisfaction, the other beach resort of Chaung Tha, dominated by local tourists, revealed higher levels of awareness with respect to the environment due to efforts by the respective community-oriented approaches to waste management. These results provide implications of the trade-offs between the quality of the infrastructure that contributes to tourist satisfaction and behavior that indicates awareness of the environment. Nevertheless, the study outlines a template in terms of the sustainable tourism strategy for the respective coastlines in Myanmar.
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Title:
A COMPARATIVE EVALUATION OF DECISION INTELLIGENCE FRAMEWORKS IN IMAGE PROCESSING APPLICATIONS
Author:
Myat Su Mon, Khin Yadanar Hlaing, Khing Sa Pae Thein, Hlaing Htake Khaung Tin*
Keyword:
Decision Intelligence, frameworks, benchmarks, image datasets, evaluation.
Page No:
117-123
DOI:
https://doi.org/10.5281/zenodo.19405467
Abstract:
Decision intelligence (DI) is a methodology entailing data, models, and human expertise combined for assistance in making tough decisions. Its use in image processing over the recent past has gained m
uch traction, opening avenues for improving tasks such as classification, detection, and recognition. The paper compares some of the leading decision intelligence frameworks in image processing. It exposes how some frameworks combine machine learning with deep learning and rule-based decision-making to improve accuracy, efficiency, and interpretability. Comparison is made on benchmark image datasets in different domains for testing based on computational intensity, scalability, robustness, and decision interpretability. It emerged that even though accuracy is higher for computational intensity combined with DI frameworks based on deep learning, hybrid frameworks involving manual input by humans and automatic drawing of inferences are superior in balancing performance with interpretability. The comparison study shows shortcomings and advantages of each framework and where best it can implement in different image processing tasks while also providing support towards decision intelligence research in computer vision.
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Title:
COMPARATIVE ANALYSIS OF CONFORMATIONAL, ARCHITECTURAL AND THERMODYNAMIC SIGNALS IN UPSTREAM FLANKS OF MPING ELEMENTS INSERTIONS IN TWO SUBSP. OF CULTIVATED RICE
Author:
Virendra Kumar and Sanjeev Kumar Maurya
Keyword:
Transposable elements, Retrotransposon, Bio-computational approach, Structural, Physical.
Page No:
124-134
DOI:
https://doi.org/10.5281/zenodo.19511040
Abstract:
Transposable elements rewire gene regulation in eukaryotes. The proliferation and elimination of mobile genetic elements are well known to play a significant role in genome size variation within a sin
gle genus. The insertion of Transposable elements occurred millions of years ago, and active element copies are very low; therefore, it is exceedingly difficult to obtain enough data to test this hypothesis rigorously. Though a class II (Transposons) rice endogenous transposable element, mPING, is still active, its transposition is found to be highly significant under stress conditions, which would be key to answering this question. The bio-computational approach was used to analyse the dispersion of mPING in two cultivated rice subspecies, indica and japonica, which showed 14 and 52 insertions, respectively. The 50 base pairs 5’ strand upstream flanking sequences were analysed for structural and physical properties. The comparative analysis revealed a significant variation, which further indicated hidden physical/structural genomic sequence specificity.
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Title:
EXTRACTION AND PHYTOCHEMICAL ANALYSIS OF SECONDARY METABOLITES FROM ACHYRANTHES ASPERA LINN
Author:
Abhishek Sharma*and Omprakash Goshain
Keyword:
Achyranthes aspera; Apamarga; phytochemical screening; sequential extraction; column chromatography; GC-MS; alkaloids; ecdysteroids; saponin s’; flavonoids terpenoids medicinal plants natural products.
Page No:
135-142
DOI:
https://doi.org/10.5281/zenodo.19536155
Abstract:
Background: Apamarga (Achyranthes aspera Linn. (Family Amaranthaceae) is a common medicinal herb with ancient roots in Ayurvedic, Unani and Siddha systems of healing. It is used in tropical and subtr
opical regions for the treatment of inflammation, respiratory tract disease, gastrointestinal disorders, dermatologic conditions, kidney disease and as an antidote to snake envenomation. Despite this high ethnopharmacological recognition, a potentialwide comparative phytochemical survey of five different plant organs — roots, stems, leaves flowers and seeds using contemporary analytical methodology has not been carried out previously. Objective: To sequentially extract, isolate and characterize the secondary metabolite profiles of each major organ/part of this plant such as roots, bark, fruits and seed using polarity-gradient solvent extraction followed by qualitative/quantitative phytochemical screening (phytochemicals including alkaloids–the most dominant), chromatographic isolation in crude form and spectroscopic identification. Materials and methods: Root, stem, leaf, flower, and seed shade-dried plant materials were successively extracted using petroleum ether, chloroform, ethyl acetate, methanol and water. By using conventional methods such as maceration and Soxhlet extraction alongside green solvents, including ultrasound-assisted extraction (UAE) and microwave-assisted extraction (MAE). Standard qualitative phytochemical screening. Major constituents were isolated by silica gel column chromatography and their structures elucidated using UV, FTIR, 1H/13C NMR and mass spectrometry. Results: MAE gave the highest extract percentages for all studied plant parts (24.3–30.1% w/w for methanol), which were significantly higher than those obtained by maceration (17.1–23.0%). The column chromatography afforded fourteen characterized compounds which were identified as ß-sitosterol, chlorogenic acid, betaine, achyranthine, ecdysterone, ecdysone, lupeol, oleanolic acid, rutin and quercetin and caffeic acid and stigmasterol. Conclusions: A. aspera has chemically diverse and part-specific secondary metabolite profile that further validates its traditional uses inpharmacotherapy. Plant Parts: Leaves are best for phenolic and flavonoid extraction; roots and seeds are optimal for alkaloids, ecdysteroids, and saponins. However, MAE is advised to be the extraction technique of choice for high yield low solvent consumption. The isolated compounds and quantitative result sets lay the scientific groundwork for quality control of herbal products while providing insight for future bioassay-directed pharmacological studies.
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