European Journal of Science, Innovation and Technology

Cold Thermal Energy Storage Systems: A Viable Alternative for Energy Conservation in Israel

Boris M. Menin — 2025-05-16

This article offers a comprehensive analysis of Cold Thermal Energy Storage (CTES) systems as a realistic and cost-effective alternative to conventional renewable energy investments in the context of Israel’s energy strategy. The study begins by assessing the global progress of green technologies—such as solar, wind, and battery storage—and highlights their fundamental limitations, including intermittency, high disposal costs, and dependency on subsidies. Against this backdrop, the article evaluates the technical maturity and operational benefits of CTES systems, especially those utilizing liquid and pumpable ice. These systems enable demand-side energy shifting, reduce peak loads, and enhance grid resilience. Drawing on international data and case studies from Israel’s commercial and high-tech infrastructure, the paper proposes integrating CTES as a key component in achieving energy efficiency, climate adaptation, and national energy security.

How Obesity Increases the Risk of Developing Type 2 Diabetes and Some Cancers

Aditta Das — 2025-05-16

Obesity is becoming a global health problem, and its effects extend beyond weight gain. The condition has also been linked directly to an increased risk of type 2 diabetes and several cancers. Storage of body fat causes insulin resistance, which is one of the primary reasons for the development of type 2 diabetes. Further, obesity also generates chronic inflammation and disruption of metabolic pathways, both of which are involved in the pathogenesis of most cancers, such as breast cancer, colorectal cancer, and pancreatic cancer. It is essential to understand how obesity is connected with diabetes and cancer so that prevention can be best utilized. This paper describes how obesity heightens the risk of these diseases and highlights the significance of weight control as a preventive measure.

Pregnenolone as an Effective Prohormone for Multiple Therapeutic Uses

Manfred Doepp — 2025-05-21

If there is a deficit of endogenous steroid hormones, chemical derivatives can be used. However, an endogenous prohormone of the steroid hormones, namely pregnenolone, could also be applied. It was discovered and synthesized 90 years ago. Since then, many publications have demonstrated its positive effects, as it enables the endocrine glands to increase the production of corticoids and sex hormones. It is also effective as a neurosteroid against brain disorders. The widespread use of pregnenolone is therefore indicated.

Enterprise Revenue Analytics and Reporting in SAP S/4HANA Cloud

Ravi Kiran Puvvada — 2025-05-22

This scholarly article examines the evolution, architecture, implementation strategies, and business value of Enterprise Revenue Analytics & Reporting Cloud solutions for SAP S/4HANA environments. As organizations continue their digital transformation journeys, the integration of robust analytics capabilities with core ERP systems has become increasingly critical for driving data-informed decision making. This paper provides a detailed analysis of how cloud-based revenue analytics platforms enhance the capabilities of SAP S/4HANA, offering real-time insights, predictive capabilities, and actionable intelligence for finance and revenue management teams. Through examination of architectural frameworks, implementation methodologies, case studies, and future trends, this research contributes to the increasing number of knowledges on enterprise analytics in modern SAP environments.

Shareholders and Stakeholders in Higher Education Institutions in Nigeria: An Analytical Perspective

V. F. Peretomode — 2025-05-26

This paper analyses the concepts of shareholder and stakeholder often discussed in corporate organizations but have also found their way into the discourse of higher education governance in Nigeria. It x-rays the characteristics of these terms, considers their differences and highlights their similarities in the context of higher education institutions (HEIs). The point is explicitly made that while the word shareholder is associated with private-for-profit HEIs; both public and private HEIs have diverse stakeholders with different interests and conflicting interests. While both concepts are often used interchangeably as if they are the same, both should not be confused as there is a world of difference between them which has also been discussed in this paper. The paper distinguishes internal from external stakeholders and discusses in some details the significant roles each stakeholder group plays in the management of HEIs. It is believed that an understanding of their interests and roles is important to HEI managers to create value for each stakeholder group, manoeuvre through their differences and handle their diverse interests with utmost care and success. This ability and capacity will enable these managers to tap the strengths and resources of these diverse stakeholders to help revamp the curricula to produce graduates that are readily prepared for the labour market. Such a knowledge and understanding will also help such managers to meet their set goals, fulfil their vision, build up an enriched endowment, and in enhancing the development of infrastructure and the continued sustainability of quality higher education institutions in Nigeria.

Analyzing AI Applications in Oil and Gas Industry: Breakthrough Tools, Methods and Platforms

Dong Pham Van — 2025-05-29

The article explores the transformative impact of artificial intelligence (AI) on the oil and gas industry, highlighting how AI technologies are being leveraged to optimize operations, improve safety, and reduce costs. It examines a range of AI tools, methods, and platforms that are revolutionizing various stages of the oil and gas value chain, including exploration, drilling, production, and maintenance. Key applications discussed include predictive maintenance, reservoir modeling, automated drilling systems, and real-time data analytics. The paper also addresses the challenges of AI integration in legacy systems and emphasizes the need for industry-wide collaboration and regulatory support to fully realize AI's potential. Overall, the study underscores AI as a breakthrough enabler for digital transformation in the energy sector.

Designing an Integrated Enterprise Architecture for Unified Sales and Marketing Operations: Enhancing Customer Experience through Data-Driven Decision-Making

Saumya Dash — 2025-06-02

The rapid development of the digital economy requires businesses to deliver fully personalized customer care using streamlined operational methodologies. Misalignments between sales and marketing systems—when operating independently—create obstacles for achieving this goal. This leads to rigid data structures and strategic misalignment, which ultimately produce inconsistent approaches to customer management and employee engagement.

This article presents a comprehensive framework for designing an integrated enterprise architecture (EA) that unifies sales and marketing operations to enhance customer experience through data-driven decision-making.

The implementation of TOGAF-based EA standards, customer data platforms (CDPs), cloud- native applications, and AI analytics enables the convergence of siloed business units and aligns data networks with organizational goals. The paper highlights four essential architectural areas, focusing on data infrastructure, automation procedures, and multichannel client engagement.

Detailed implementation guidance, risk mitigation strategies, and evidence-backed insights are provided to support improvements in customer satisfaction, business velocity, and revenue growth.

Every customer interaction becomes strategically valuable as firms gain real-time visibility and operational agility by integrating systems architecture with actionable data. This guide is designed for enterprise architects, CIOs, and transformation leaders supporting digital transformation and sustained CX innovation.

Population Growth, Urbanization and Deforestation of the City of Kindu

Stanislas CHOMA NYEMBO — 2025-06-09

The findings on the evolution of the population of the city of Kindu during the study period on population growth, urbanization, and deforestation are alarming. The analysis of correlation reveals, on the one hand, a significant relationship between population growth and deforestation in the city, and on the other hand, a strong correlation between urban expansion and forest loss. This situation is largely due to the expansion of the city of Kindu, which has occurred through the densification of former colonial subdivisions and horizontal sprawl, including the creation of more than 50% of de facto (unplanned or informal) settlements.

Dynamics of the Deforestation of the City of Kindu between 1986 and 2020

Stanislas CHOMA NYEMBO — 2025-06-09

This study explores the factors underlying deforestation in the city of Kindu, Democratic Republic of Congo. It concludes that deforestation in Kindu is a serious problem, exacerbated by population growth and unsustainable agricultural practices.

Biochemical Synthesis and Characterization of Hybrid Groundnut Shell Powder and Coconut Husk Ash as Corrosion Inhibitor on Mild Steel in Acidic Environment

Benjamin Ufuoma Oreko — 2025-06-13

There is an increase in demand for environmentally friendly and cost-effective corrosion inhibitor materials, notably in the development of corrosion inhibitors produced from natural resources. However, the composition of natural inhibitors appropriate for effective control of corrosion has yet to be extensively studied. This work attempts to synthesize and characterize an environmentally friendly corrosion inhibitor from groundnut shell powder (GSP) and coconut husk ash (CHA) and examines the inhibitory potential of GSP-CHA on mild steel under 1M HCl environment, using SEM-EDX. It was observed that the CHA-GSP contains an appropriate amount of polyphenolic, carbon and active silica components present in its constituents. These active ingredients could make the GSP-CHA blend a good candidate for a corrosion inhibitive material.

Transformative Synergies of AI: Catalyzing Tipping Point Innovations in Telemedicine and Healthcare

Bongs Lainjo — 2025-06-16

Artificial Intelligence (AI) has enhanced telemedicine and healthcare, transitioning them to a new dimension of synergy that brings about efficient tipping-point innovations. This study examines the context and process through which AI has been systematically adopted in telemedicine with an understanding of innovation diffusion and key inflection points in the overall evolution of healthcare systems. Based on the innovation diffusion theory and the tipping point theory, the study further explores the adoption drivers, the system-level innovations that ensue, and the ethical and policy issues that arise from the same. Key insights revealed increased AI use in diagnostics, real-time monitoring, and decision support systems, emphasizing the need for policy cohesiveness across the care continuum, strong and reliable governance, and international strategies to address equity, ethical, and readiness issues related to technology. Based on these findings, recommendations can be made on how AI can be utilized to enhance the resilience and equity of healthcare systems for health facilities worldwide.

Leveraging Generative AI In Marketing Operations and Internal Management

Ravi Teja Surampudi — 2025-06-17

Applying Generative Artificial Intelligence (GenAI) to both marketing activities and internal work is reshaping today’s business strategies. GenAI is notable because it can independently produce content like text, images, videos, and audio which many businesses are now using AI for. With this technology, creativity, efficiency, and innovation can happen in important parts of an organization. The paper studies how strategic and operational value is increased by using GenAI in marketing functions and management systems. The study relies on theories and reviews recent academic papers, case studies from the industry, and ideas introduced by top technology businesses. GenAI is found to improve marketing methods by producing content on the spot, reaching out to audiences right away, analyzing consumer opinions, and creating highly personalized campaigns. GenAI helps the company by automating repeatable jobs, aiding workers, sharing data in real time, and aiding chief-level choices. At the same time, adopting GenAI does pose some problems. People still worry about data safety, unfair decisions from algorithms, rules imposed by authorities, employees adjusting to technology, and ethical issues that stand in the way of full adoption. The study ends with the proposal of a strategic plan to handle these problems and make the most of GenAI. The study creates practical and theoretical results that can guide business experts, researchers, and marketers in making the right decisions about using GenAI.

How Cloud Service Reliability is Improved after Shifting from Reactive to Proactive Incident Management

Saravanakumar Baskaran — 2025-06-18

Since organizations rely on the cloud more for important activities, keeping services running smoothly is now very important. Most legacy models are designed so teams address problems only when they happen. Often, this way of handling incidents causes businesses to lose their customers’ trust and pays higher costs for repair. Yet, improving how incidents are handled has changed the reaction of cloud services to problems. Such systems prevent small issues from developing into big challenges by using monitoring, noticing changes, and fixing them automatically. Using machine learning, designing events-driven architectures, and predictive analytics, organizations can intelligently decide, reduce human involvement, and handle situations more accurately and fast. The change from manual measures to automation greatly lowers the times needed to identify and solve issues, creating stronger cloud services.

Automated incident management systems are examined in this paper to see how they contribute to cloud service reliability by preparing for possible incidents in advance. It points out the challenges of manual incident management and shows that automation allows for continuous checking of the service, flexible resizing of resources, automatic problem-solving, and properly targeted alerts. It mainly uses success stories from AWS, Microsoft Azure, and Google Cloud Platform to show an increase in reliable system performance. Besides this, it also addresses issues such as incidents of false positives, challenges in working with mixed systems, and having ethics watch over AI-powered businesses. If organizations use proactive incident management, they can handle incidents before they happen, making the cloud environment stronger and more adaptable. The paper indicates that using automation is essential for ensuring cloud infrastructure can keep up with the digital world.

The Transformative Power of Love in Childhood Development: A Psychological and Neuroscientific Perspective

N. Hettige — 2025-06-23

Love is a fundamental force that shapes human development from conception through adulthood. This review aims to explore the transformative impact of maternal love on a child’s emotional, cognitive, and social growth across prenatal, infancy, childhood, and adolescent stages. Grounded in Attachment Theory and Self-Determination Theory, it focuses how love fosters secure attachment, emotional regulation, and intrinsic motivation, leading to lifelong psychological flourishing. Also, it employs a narrative review for the data collection and thematic analysis was used to identify key themes with qualitative reflections from adults on their childhood experiences of maternal love. Key findings suggest that maternal love during pregnancy reduces prenatal stress and enhances neural development. Secure attachment in infancy fosters emotional regulation and resilience, while nurturing love in childhood strengthens cognitive abilities, emotional intelligence, and social competence. During adolescence, consistent parental love contributes to identity formation, self-esteem, and well-being. Furthermore, long-term effects of early love include greater autonomy, purpose, and relationship satisfaction in adulthood. These findings underscore that maternal love is a catalyst for psychological safety, self-worth, and happiness, reinforcing its transformative role in human development. The findings contribute to positive psychology interventions by emphasizing the necessity of early emotional nurturance. Future research should explore cross-cultural perspectives and the impact of maternal love on stress resilience, leadership development, and overall well-being in adulthood.

From LLM to LLM-Driven Agent Building Autonomous AI Assistants for Everyday Users

Faruk Baran Öncel — 2025-07-01

AI development took an important step forward when Large Language Models (LLMs) were adapted to make autonomous AI assistants suitable for use in real life situations. Previously, LLMs exhibited powerful text generation, yet they had no ability to act on their own or use other tools without help. At the same time, today’s agents make use of LLMs, structured tools, logical thinking and learning to perform complicated duties requiring very little intervention from humans.

With the use of function calling, multimodal interfaces and long context abilities, the need for extensive agent orchestration from 2024–25 has greatly decreased. This new technology supports those without specialized IT expertise, giving them improved digital experiences, helping hands and increased productivity.

It is important to note that having tools, memory modules and understanding current settings are changes that bridge this gap. At the same time, there are continuing worries about whether data is reliable, if things are transparent and how well autonomous modes work safely. American lawmakers are relying more on modern materials like the EU AI Act, the White House Executive Order and the NIST AI Risk Management Framework when it comes to ethical issues.

With more developments in AI, it is becoming clear that more valuable progress will come from integrating robots and software into human cooperation than from simply improving the core models. As a result, interactions between people and AI start to feel easier, more interactive and more fun.

Cell Signaling Pathway Proteins that Can Be Targeted to Keep in Check Proliferation in Cancer Cells: Scientific Review

Otieno Rolex Opiyo — 2025-07-07

Cancer progression is primarily driven by uncontrolled cell proliferation, often resulting from dysregulated signaling and metabolic pathways. This review provides a comprehensive analysis of critical cell signaling pathway proteins that regulate cancer cell proliferation, highlighting potential therapeutic targets. It explores the role of proto-oncogenes, tumor suppressors, and key regulatory proteins such as p53, RB, cyclins, and CDKs in maintaining normal cell cycle progression and how their dysregulation promotes oncogenesis. The paper further delves into metabolic adaptations in cancer cells, including aerobic glycolysis and mitochondrial reprogramming, emphasizing enzymes like hexokinase, phosphofructokinase (PFK1), pyruvate kinase (PKM2), and lactate dehydrogenase (LDH) as central to the Warburg effect. Additionally, it discusses the significance of mitochondrial signaling in apoptosis, with a focus on the Bcl-2 family and the mitochondrial permeability transition pore (PTP) complex. The review integrates insights into protein import machinery (TOM and TIM complexes) and electron transport chain dynamics, underscoring their relevance to cancer cell survival and death. Emerging therapies such as targeted drugs, immunotherapies, and metabolic inhibitors are evaluated for their potential to selectively disrupt these pathways. The review advocates for a multi-targeted approach to cancer treatment that interferes with both proliferative signaling and metabolic support systems. This strategy may yield improved outcomes by halting tumor growth while preserving normal cellular function.

Religion and National Security in Nigeria: Exploring the Complexity of Religion and Security

Isaiah Grace Motunrayo — 2025-07-09

Religion and security issues have become a national concern for all in Nigeria. This is because the relationship between faith and security is ambivalent. Religion is not an independent force; religious adherents deploy it for their interests. This is vital to understand because it prevents the mistaken claim that religion is either inherently good and peaceful or bad and violent. The role of religion continues to pose as an ultimate concern because of the nature of the present-day society, which is characterised by unmitigated suffering, persistent violation of principles of justice and human rights, and the place of religion is questioned. Therefore, in light of the complex nature of religion, this paper examines the interplay between religion and security in Nigeria. This article discusses the concept of religion and national security and explains the role of religion in protecting its adherents. Using participant observations and oral interviews, this work concludes that religion, in and of itself, is neither inherently positive nor inherently negative, but rather depends on how adherents appropriate and deploy it. This paper ultimately recommends that religious adherents perceive religion as an instrument of peaceful coexistence and deploy it accordingly; furthermore, religious adherents should be prepared to adhere to and apply the fundamental ethical values of their religion, as outlined in this work. Additionally, the article recommends that as good citizens of Nigeria, every religious person should pray, participate in elections and electoral processes, and be obedient to civil authority.

Support Vector Machine-Based Model for the Classification of Effective Antibiotic Combination for Pediatrics: A Comparative Analysis of Pre-COVID-19 and COVID-19 Era

Anietie Ekong — 2025-07-10

The COVID-19 virus is suspected to have introduced a different layer of resistance to antibiotics for pediatric patients. Traditional methods for selecting antibiotics often yield suboptimal results due to their inability to adapt to evolving patterns of microbial culture and sensitivity. Therefore, to provide a more precise and individualized approach to selecting antibiotics and ascertaining their effectiveness before and after the onset of the pandemic, it is necessary to leverage the power of Artificial Intelligence. In this study, the Support Vector Machine (SVM), a machine learning algorithm, is proposed. The model trained on extensive datasets of microbial responses and different antibiotic combinations was designed and implemented. Findings revealed that the model outperforms conventional techniques with an accuracy of 83% and 89% respectively for the pre-COVID-19 and COVID-19 era in predicting effective antibiotic combinations. More importantly, the results showed that 75.6% of drugs in our dataset were effective before COVID-19 while 72.9 % were effective after the pandemic, indicating that more antibiotic drugs were effective before COVID-19 than afterward. Results also indicated an increase in resistance to antibiotics and antibiotics combination after the onset of COVID-19, substantiating the suspicion that COVID-19 has a significant negative impact on the effectiveness of antibiotic therapy in pediatric patients.

Differentiation of Monkeypox from Other Poxes Using Local Interpretable Model-Agnostic Explanation and Deep Learning Algorithm

Anietie Ekong — 2025-07-10

While still trying to recover from the harm brought by COVID-19, the pox virus, particularly MPox (Monkeypox), now poses a fresh threat of spreading to the entire world. Skin lesions and rashes caused by Mpox are similar to such caused by other pox diseases such as chickenpox and cowpox and these medical and visual resemblances of several pox diseases make it difficult for healthcare providers to establish an early diagnosis leading to inefficient control of the disease's transmission within a community. In image-based diagnostics, deep learning has shown enormous potential. However, there exist the challenges of complexity of the model and lack of understanding of the prediction parameters for making classification and inference. In this research, a machine learning approach for the classification of Mpox from other pox diseases is proposed. The dataset was created by compiling images from open-source and internet resources that place no limitations on usage. Three models were proposed and evaluated: the CNN model, ResNet50, and EfficientNetNB. Two of the models were pre-trained models while the CNN model was developed completely new. During testing, the CNN model was the best-performing model outperforming state-of-the-art pre-trained models. Our experimentation using the test dataset indicated that our Convolutional Neural Network (CNN) model can identify six classes of skin images with 99% accuracy, 98% precision score, 99% recall, and 98% F1 score. The AUC score of our CNN model was obtained as 99%. Finally, the use of Local Interpretable Model-Agnostic Explanations (LIME) was used to provide meaningful insight into how the CNN the best-performing model among the three arrived at a particular decision based on the extracted feature from the input data.

A Systematic Review on Ethical Leadership in Education

Jernie U. Capuyan — 2025-07-10

The emergence of ethical leadership caught the interest of scholars for its implications in educational contexts. The perceptions of teachers about ethical leadership differ according to educational levels, while principals are often perceived as lacking an understanding of ethical leadership despite practicing it in different ways at the institution. The purpose of this systematic review is to analyze and synthesize different studies from 2010 to 2024 on ethical leadership in education across various levels. During the screening process for the studies anchoring to research questions about the characteristics and impacts of ethical leaders in education, the PRISMA model with the set exclusion and inclusion criteria was used. After the extensive review and careful analysis, four (4) themes emerged as the characteristics of ethical leaders in education: ethical integrity, social responsibility, adherence to professional standards, and spirituality. Moreover, the impacts of ethical leadership were classified into three categories: impacts on the school organization, teachers, and students. In the organization, they are led toward the formation of a collaborative environment, ethical technology integration, a positive school climate, improvements in OCB, and the sustainability of humane and effective education. For teachers, they tend to develop morale, employee engagement, enhanced performance and effectiveness, and high retention and stability. Also, students develop morale, increase student learning outcomes, and foster resilience. Hence, the ethical leadership of school leaders significantly affects the whole organization. Finally, this review calls for future studies about how ethical leadership is applied in education and for investigating its impacts on students.

The Business Diaspora as a Factor in the Internationalization of the Colombian Economy

Blanca Elizabeth Bolaños Forero — 2025-07-11

The Colombian economy has internationalized due to various factors; one that has been consolidated over the last five decades has been the business diaspora. The rapid growth of migration, especially to countries with higher incomes than Colombia, such as the United States and Spain, has enabled entrepreneurial activity linked to sectors such as construction, tourism, food, ethnic commerce, apparel, and healthcare. This business sector has developed autonomously and could be a source of investment to further boost the country's economy.

Optimizing Machine Learning Workloads for Better Performance

Anudeep Katangoori — 2025-07-14

Because machine learning jobs are so big and complex now, maintaining high efficiency for training and inference is very hard. The requirement for more data increases training time, lags, and use of lots of computer resources, preventing modern ML systems from being used well and deployed at large. Because of these challenges, the work suggests an all-encompassing optimization method involving dynamic batch size, fused operators, and mixed precision to maximize throughput and reduce the time needed on different hardware. Since this method is applied to popular ML frameworks such as PyTorch and TensorFlow, it becomes broadly used. According to experimental results, ResNet-50 learned faster on ImageNet (its training was cut in half), and the BERT-base worked more efficiently on the SQuAD dataset (enhanced with 41%). Even so, there was less than 0.5% accuracy loss. Additionally, using an average of 62% more GPU doesn’t require a lot of extra memory. They prove that the framework works well in conserving resources and preserving how the model works. Some of the significant contributions of this work consist of a modular, cross-platform structure for optimization and a thorough look at how systems can be made scalable. It can make ML workloads more efficient and complete faster, so both researchers and industry can speed up their innovation and use of new technologies.

The Role of Circular Economy Models in Reducing Waste and Promoting Sustainable Growth in the United Kingdom

Barnabas S. Stanfast — 2025-07-21

The progressive environmental problems resulting from waste production and resource exhaustion require the development of new economic approaches. The circular economy (CE) model offers a viable approach that enables simultaneous economic development and environmental protection. This study examines how CE strategies could minimize waste and promote sustainable progress throughout the United Kingdom. The study shows that incorporating CE principles into policies could lead to significant environmental advantages by reducing landfill waste, resource extraction, and simultaneously create economic prospects through new market development and job creation. The study found key obstacles like regulatory fragmentation and technological limitations but noted that analysing consumer behaviour patterns and proposing strategic solutions for policymakers and industry leaders could enhance implementation of CE. This study concludes that successful integration of circular economy within the UK economy requires a unified effort from various stakeholders to build resilience and sustainability after the pandemic.

Exploring the Influence of Green Branding Strategies on Consumers’ Purchase Decisions

Cheng-Feng Cheng — 2025-07-28

The present study contributes to new insights into environmentally conscious consumer behavior by integrating both symmetrical and asymmetrical analytical frameworks. To begin, structural equation modeling (SEM) is employed to assess the direct relationships among green marketing, brand image, perceived risk, and purchase intention. In parallel, fuzzy-set qualitative comparative analysis (fsQCA) is used to uncover distinct combinations of conditions that can lead to elevated purchase intentions. Data were gathered through purposive sampling of individuals with prior experience purchasing sustainable or green-branded products. A suite of multivariate methods, including descriptive analysis, factor analysis, reliability testing, SEM, and fsQCA, was utilized to analyze the data. Findings from SEM demonstrate that green marketing exerts a significant positive effect on both perceived risk and purchase intention. Moreover, brand image and perceived risk are also found to positively influence purchase intention. The fsQCA results further identify two unique condition sets that effectively lead to high purchase intentions across varied consumer profiles.

Trigate: A Quantum Cryptocurrency Project

Jonathan Barlow Gee — 2025-07-24

Herein is proposed a three-staged progressive presentation toward the theoretical prism of creating any functional quantum cryptocurrency system. In the initial phase, a trinary quantum switch-gate will be proposed instead of the commonplace binary qubits as a means of encoding a quantum cryptocurrency. The switch-gate permits the construction of a tetra-helix blockchain that acts as a spherical radius for modeling encrypted data. The second phase deals with geometrically translating this tetra-helix structure on the surface of a Riemann sphere to represent trinary logic as spherical topologies. This projection allows encryption to be made more complex without increasing spatial volume, thereby perfectly suiting quantum coin systems that demand scalability. Phase 3 employs spinor dynamics so that quantum encrypted transaction data are stored as summable vectors within perfect magic number hypercubes, which act as multidimensional "wallets" for secure storage and management of varying denominations of quantum currency. This model provides a fresh landscape to incorporate a tetrahedral tessellation of the Bloch and Riemann spheres on entanglement-based spin quantification with an aim to encourage further investigation into encryption-rich, scalable, and physically realizable quantum financial architectures.