Abstract: Intracranial fusiform aneurysms represent a rare but clinically aggressive subtype of cerebrovascular disease, characterized by circumferential arterial dilation and a high risk of growth, ischemic complications, and rupture. Unlike saccular aneurysms, fusiform lesions lack well-established medical therapies to prevent progression or stabilize the aneurysm wall. Tumor necrosis factor–alpha (TNF-α) has emerged as a central mediator of aneurysm-associated inflammation and vascular remodeling, raising interest in TNF-α modulation as a potential therapeutic strategy. To systematically review and synthesize the available clinical and translational evidence evaluating TNF-α signaling and anti–TNF-α therapies in the context of intracranial fusiform aneurysms. A systematic literature search was conducted in PubMed/MEDLINE, Embase, and Google Scholar from database inception through February 2026 in accordance with PRISMA guidelines. Eligible studies included human, animal, and translational investigations examining TNF-α biology or anti–TNF-α interventions in relation to intracranial fusiform aneurysms, intracranial dolichoectasia, or vertebrobasilar dolichoectatic aneurysms. Study selection, deduplication, and screening were performed using Covidence systematic review software. Extracted outcomes included aneurysm growth, rupture, ischemic events, imaging characteristics, inflammatory signaling, and vascular remodeling. Given substantial heterogeneity in study design and outcome reporting, findings were synthesized narratively using structured evidence mapping. From 368 records identified, 14 studies met inclusion criteria following full-text review. Included studies encompassed preclinical models, translational mechanistic investigations, and limited clinical observational data. Across experimental models, TNF-α signaling was consistently associated with macrophage infiltration, matrix metalloproteinase activation, vascular smooth muscle cell phenotypic modulation, and aneurysm wall degeneration. TNF-α inhibition was associated with reduced aneurysm progression and rupture in preclinical settings, including when initiated after aneurysm formation. Clinical evidence remains limited but suggests a potential association between TNF-α modulation and aneurysm stability, although direct therapeutic data in intracranial fusiform aneurysm populations are sparse. Existing translational and preclinical evidence supports a contributory role for TNF-α–mediated inflammation in the progression of intracranial fusiform aneurysms and suggests that TNF-α inhibition may represent a promising disease-modifying strategy. However, clinical data remain insufficient to support routine therapeutic use. Prospective observational studies and early-phase clinical trials are needed to define the safety, timing, and efficacy of anti–TNF-α therapies in patients with intracranial fusiform aneurysms.

Abstract: In side-channel analysis, simple power analysis (SPA) is a widely used technique for recovering secret information by exploiting differences between operations in traces. However, in realistic measurement environments, SPA is often hindered by noise, temporal misalignment, and weak or transient leakage, which obscure secret-dependent features in single or very few power traces. In this paper, we provide a systematic analysis of moving-skewness-based trace preprocessing for enhancing asymmetric leakage characteristics relevant to SPA. The method computes local skewness within a moving window along the trace, transforming the original signal into a skewness trace that emphasizes distributional asymmetry while suppressing noise. Unlike conventional smoothing-based preprocessing techniques, the proposed approach preserves and can even amplify subtle leakage patterns and spike-like transient events that are often attenuated by low-pass filtering or moving-average methods. To further improve applicability under different leakage conditions, we introduce feature-driven window-selection strategies that align preprocessing parameters with various leakage characteristics. Both simulated datasets and real measurement traces collected from multiple cryptographic platforms are used to evaluate the effectiveness of the approach. Experimental results indicate that moving-skewness preprocessing improves leakage visibility and achieves higher SPA success rates compared to commonly used preprocessing methods.

Abstract: Polycystic ovary syndrome (PCOS) is a prevalent endocrine–metabolic disorder affecting 5.5–11.5% of women of reproductive age. While reduced adiponectin levels have been con-sistently demonstrated in adult women with PCOS, findings in adolescents remain less clearly defined. A systematic review and meta-analysis was conducted in accordance with PRISMA guidelines. PubMed, Embase, Scopus, and Google Scholar were searched from inception to October 31, 2025. Observational studies comparing adiponectin levels in post-pubertal adolescents with PCOS and controls were included. A random-effects model with REML estimator was applied. Study heterogeneity and publication bias were as-sessed. Eighteen studies comprising 1,590 participants (679 PCOS; 911 controls) were in-cluded. Adolescents with PCOS demonstrated significantly lower adiponectin levels com-pared to controls (mean difference [MD] −3.17 µg/mL; 95% CI −4.27 to −2.07; p = 0.001), I² = 94.6%. Egger’s (p = 0.81) and Begg’s (p = 0.16) tests indicated no evidence of publication bias. Adolescents with PCOS exhibit significantly reduced circulating adiponectin levels, suggesting that adipose tissue dysfunction and metabolic dysregulation are present early in the disease course. These findings support the role of adiponectin as a potential early biomarker of cardiometabolic risk in adolescent PCOS and underscore the importance of early metabolic screening and intervention.

Abstract: Objective: The study investigates the effects of modeling liquids (MLs) on the staining of composite resins, with a focus on unichromatic resins. Materials and methods: The research was carried out by subjecting samples of monochromatic resin (2mm height x 6mm internal diameter) to the immersion protocol in coffee solution (Nescafé Tradição Forte), with color evaluation after 21 days. Results: Statistics showed that the adhesive group presented greater color change when compared to the modeling group (p = .000). There was no statistically significant difference between water and experimental staining (= 0.104). Among the staining group factors, there was a difference for ∆E in the interactions mC-mE (p = 0.004), mC-aC (p < 0.001), mC-aE (p < 0.001), cE-aC (p = 0.015), cE-aE (p = 0.007), cC-aC (p = 0.033) and cC-aE (p = 0.017). Conclusion: These results indicate the need for further clinical studies on the applicability of modeling liquids to support decision-making in clinical practice.

Abstract: Both heat and photon energy are integral parts of scientific research. The study of the photon and the electron does not present up-to-date science in some phenomena. A misconception falls at the basic level. To eliminate the misconception, a discussion presents the electron dynamics in the silicon atom. The electron executes confined interstate dynamics for one forward or reverse cycle. As a result, the resulting shaped force-energy defines a unit photon. That unit photon has a shape similar to a Gaussian distribution with turned ends. A featured photon can interact with the side of a laterally orientated electron (of a semisolid or solid atom) to possibly convert into heat energy. When a featured photon interacts with the tip of a laterally oriented electron, that photon can convert into energy bits. The shapes of energy bits are similar to integral symbols. The reference point for the electron executing confined interstate dynamics is the center of a silicon atom. The north-south tips of the electrons align along the north-south poles. The energy shapes around the force tracing along the trajectory of electron dynamics. To execute confined interstate dynamics, forces of the two poles appear conservatively for turning the electron each time. The outer ring electron of the silicon atom reaches the ‘maximum limit point’ during the confined interstate dynamics. There is energy of one bit. In the remaining half cycle, that electron also generates energy of one bit. The electron dynamics of the silicon atom generate photons of a wave shape. Atoms of some other elements generate photons other than wave shapes. The execution of the electron dynamics is nearly at the speed of light. In addition to energy science, the study is useful in physical and chemical sciences.

Abstract: The Colorado River and its tributaries housed in the Colorado River Basin (CRB) are the primary source of water to the western United States and the Republic of Mexico. The river system is under intense stress due to prolonged drought and anthropogenic activities which have worsened its water quality. Total dissolved solids (TDS) and total suspended solids (TSS) are two water quality parameters (WQPs) that are crucial to the sustainability of the river system. These parameters are noted to have caused varied severity to the sustenance of the basin’s water. Monitoring of these WQPs has been conventionally conducted using field and laboratory analysis which are cost and labor-intensive. This study utilized a novel method to effectively develop machine learning (ML) models to estimate TDS and TSS concentrations in the CRB by utilizing the potential of optically sensitive multispectral Sentinel 2 A/B Multispectral Scanners (MSI) and Landsat 8 Operational Land Imager (OLI) remote sensing (RS) data retrieved from the Google Earth Engine (GEE) and in situ measurements collected from 2013-2022. Several standalone models such as linear regressions (LR), ridge regressions (Ridge), lasso regressions (Lasso), and k-nearest neighbor (KNN), and ensemble methods including the gradient boosting machines (GBM), random forest (RF), adaptive boosting (AdaBoost), eXtreme gradient boosting (XGBoost), and bagging were applied for the accurate estimation of TDS and TSS. Results found ensemble models like the XGBoost as the most optimal model estimating TDS using images from both Sentinel-2 MSI and Landsat 8 OLI with performance on the external validation dataset derived as 0.99, 26.52 mg/L, and 19.19 mg/L, respectively for R², RMSE, and MAE for Sentinel-2 images. The XGBoost yielded R², RMSE, and MAE of 0.97, 35.82 mg/L, and 27.90 mg/L, respectively. The AdaBoost was found to be best model for TSS estimations with values of 0.92, 29.48 mg/L, and 24.64 mg/L, respectively, for R², RMSE, and MAE for the Sentinel-2 image on the external validation dataset. The RF model was found to be the optimal model for TSS estimations with the Landsat 8 OLI with reported R², RMSE, and MAE of 0.90, 32.80 mg/L, and 22.91 mg/L, respectively on the external validation dataset. These findings show great potential of utilizing RS data to produce cost-efficient spatiotemporal changes on the WQPs which has an important implication for the continuous management of the limited water resources. Further study should consider the effect of land use land cover, runoff, and other climatic data to understand the complexity and dynamics of these parameters on TDS and TSS in the river.

Abstract: Bhimbor Deori (1903-1947) remains a pivotal yet insufficiently explored figure in the history of India's struggle for independence and the political evolution of Assam. A multifaceted individual-lawyer, tribal rights advocate, parliamentarian, and nationalist leader, Deori played a crucial role in mobilising the plain tribal communities of Assam and was instrumental in countering colonial and Muslim League efforts to incorporate the province into the proposed state of Pakistan. This review synthesises the available biographical, historical, and political information to construct a comprehensive profile of the Deori. It critically examines his early life and the discriminatory incident that catalysed his public career, his foundational role in institutionalising tribal politics through the Assam Backwards Plains Tribal League, his tenure as a Legislative Councillor and Minister, and his strategic collaboration with Gopinath Bordoloi. This article analyses a significant duality in his legacy: his simultaneous advocacy for Indigenous self-determination and his unwavering commitment to a unified Indian nation. It also interrogates the ideological tensions between his advocacy for tribal "homelands" and his Indian nationalism. Finally, this article identifies significant gaps in the existing scholarship, which relies heavily on commemorative sources, and proposes concrete avenues for future archival and critical research to fully integrate Jananeta Bhimbor Deori into the broader historiography of modern South Asia.

Abstract: This study aims to prove the Riemann Hypothesis and the Generalized Riemann Hypothesis by ex-tending the Riemann zeta function and Dirichlet L -functions to the elliptic complex domain, based ona newly constructed system of elliptic complex numbers Cλ(λ < 0) . The core challenge addressed is theinherent difficulty in resolving these conjectures within the traditional ”circular complex domain” frame-work (λ = −1); the author posits that a complete proof is unattainable strictly within this conventionalsetting.The primary innovation of this work lies in the formulation of the theory of elliptic complex numbers,specifically identifying the limiting case as λ → 0− as the key to the proof. Through rigorous deduction,a bijective correspondence between zeros across different complex planes is established. By employingproof by contradiction and leveraging the correspondence between Cλ (as λ → 0) and the circle complexplane C, the Riemann Hypothesis and the Generalized Riemann Hypothesis are ultimately proven. Thispaper is organized into three parts:(1) Construction and Geometric Properties: The first part details the construction of elliptic complexnumbers and their fundamental geometric properties, laying the necessary foundation for subsequentanalysis and the proof of the conjectures.(2) Analytic Extension: The second part introduces elliptic complex numbers into mathematical anal-ysis, deriving numerous results analogous to those in classical complex variable function theory.(3) Proof of Conjectures: The final part presents the formal proofs of the Riemann Hypothesis and theGeneralized Riemann Hypothesis.

Abstract: This ethnobotanical study documents medicinal plant diversity and traditional healing practices in Barguna District, a coastal region of Bangladesh. Twenty-seven traditional healers (kabiraj) were interviewed using semi-structured questionnaires during April-June 2025. A total of 68 medicinal plant species representing 34 botanical families were documented. Fabaceae emerged as the most represented family (10.3%), followed by Lamiaceae (8.8%). Trees constituted the dominant growth form (35.3%), with leaves being the most frequently utilized plant part (32.4%). The documented species treat twelve major ailment categories, with gastrointestinal disorders (22.8%) being most prevalent. Informant Consensus Factor (FIC) values ranged from 0.62 to 0.89, with gastrointestinal disorders showing highest consensus (FIC = 0.89), followed by respiratory ailments (FIC = 0.85) and diabetes (FIC = 0.82). Citation Frequency (Cf) analysis revealed Azadirachta indica (Cf = 0.89), Ocimum sanctum (Cf = 0.81), and Curcuma longa (Cf = 0.78) as culturally most significant species. Decoction (34.6%) and paste application (23.4%) were predominant preparation methods, with oral administration (61.2%) being most common. The demographic profile indicated that 81.5% of healers acquired knowledge through family inheritance, highlighting intergenerational transmission patterns. However, this traditional knowledge faces erosion threats from modernization, with 44.4% of practitioners lacking formal education and 18.5% aged above 60 years. The study reveals substantial ethnomedicinal diversity in coastal ecosystems, emphasizing the urgent need for conservation strategies, sustainable harvesting protocols, and systematic pharmacological validation to preserve indigenous knowledge while supporting rural healthcare and drug discovery initiatives.

Abstract: Quantum Mechanics is sufficiently capable of proving quantum gravity by itself without considering actual Einsteinian General Relativistic formalism. Due to the non-applicability of Einsteinian relativity in quantum gravity, in this article, we have described gravity as a correspondence between General (Quantum) Relativity and Quantum Field Theory (QFT) by introducing a (quantum) quadratic form and a (quantum) metric tensor along with dynamic time t. Here, we have developed a Kline-Gordon-like equation and a Dirac-like equation in QFT, which are themselves actually nothing but the quantum gravitational field equations (analogous to Einstein's field equation in General Relativity) for bosons and fermions, respectively. Furthermore, we have developed a Generalized Quantum Gravitational Field Theory, where QFT is conjugated with gravity and Dark Energy (for inconstant cosmological constant), so that it can unify Standard Model with gravity and Dark Energy in 'General Unified Theory' as SU(5)=SU(3)×(SU(2)⊕iSU(2)) through a Gravito-weak symmetry group. In addition, we have shown that unbounded operators, such as, i) the (quantum) relativistic mass and time, ii) the quantum scalar curvature and the proper time, iii) the (quantum) relativistic mass and its inversely stretched/shrank (3+1)D curvilinear quantum spacetime, all in pairs are satisfied their individual Uncertainty Principles, i.e., they cannot have definite and constant values at the same time. We have also proved that the present theory of Quantum Gravity is 'multiplicatively renormalizable'.

Abstract: Physics education traditionally presents the discipline as a collection of established recipes, omitting the personal, a priori reflection central to the historical development of theories. The rise of Generative AI has exposed the fragility of this recipe-based model, as AI can now execute algorithmic problem-solving effortlessly. This theoretical paper argues that this technological disruption is a catalyst for a fundamental reorientation: the physics classroom must be reconceived as a space for structured thinking, where theories are presented as subjective, creative resolutions to problems, a direct reflection of an inventor's mind. We synthesize historical and philosophical analyses with contemporary physics education research (PER) on student epistemologies, conceptual change, and metacognition to propose a pedagogical framework centered on three pillars: acknowledging subjectivity in theory-building, recognizing multiple equivalent formulations, and leveraging historical narrative. We further introduce AI-assisted classroom strategies designed to implement this shift, positioning AI as a Socratic partner and historical simulator. The paper concludes with a set of testable propositions to guide future empirical research. This work contributes a novel theoretical synthesis that integrates AI into a historically-grounded, reflective pedagogy, aiming to cultivate the \textit{inventor's mind} and prepare students not merely as consumers of knowledge but as creators of future possibilities.

Abstract: Hybrid use of Terraform for infrastructure andAnsible for configuration is common on Azure, but the two toolsare often joined only by ad hoc scripts and fragile handoffs in CIpipelines. Runbook Mesh proposes a small MCP based controlplane that treats Terraform and Ansible as one coordinatedchange unit rather than two independent stages. Azure DevOpstriggers an MCP server that drives a deployment state machine:it receives Terraform plans and apply results, derives a dynamicAnsible inventory from Terraform outputs, and orchestratesconfiguration playbooks with drain, cordon, and health checksfor VM scale sets, AKS nodes, and virtual machines. TheMCP enforces simple invariants on ordering, handoff safety,and rollback reachability, and packages each deployment intoa witness bundle containing plan digests, state and inventoryhashes, play outcomes, and Azure Resource Graph snapshots.The result is an Azure native pattern where infrastructure andconfiguration share a single timeline, a defined rollback path, anda tamper evident change ledger suited to regulated environments.

Abstract: Background: Hematopoietic stem cell transplantation (HSCT) is a widely utilized subtype of transplantation employed in various malignant and non-malignant diseases, particularly when conventional treatments or therapeutics prove ineffective. Despite the frequent occurrence of post-transplantation lymphoproliferative disease (PTLD) in patients undergoing HSCT, no comprehensive global prevalence rate has been established to date. Methodology: In this study, we selected 39 studies from 941 studies from three databases (i.e., PubMed, ScienceDirect, and Google Scholar) to identify the global prevalence rate of PTLD in HSCT patients. Results: The pooled prevalence was determined as 5.6% (95% CI: 5.0 to 6.3) and increased to 12.4% (95% CI: 10.2 to 14.7) after excluding outlier studies. The quality of the studies was also high. The prevalence of death cases among HSCT patients was determined as 0.6% (95% CI: 0.4 to 0.9). PTLD was most prevalent in allogenic HSCT (i.e., 5.6% (95% CI: 4.9 to 6.3)) and within the European region (i.e., 27.1% (95% CI: 21.4 to 32.8)). Among risk factors, HLA mismatch was reported in most of the studies. Conclusion: This study assessed and discussed the overall global prevalence of PTLD in HSCT patients, continent-based prevalence, and risk factors that can be helpful in finding the possible prevention mechanism of PTLD and implementing individualized treatment approaches based on the treatment availability during HSCT.

Abstract: This study develops a leakage-safe PCA–APT framework that constructs an idiosyncratic market-stress index from cross-sectional residual dispersion and evaluates its usefulness for anticipating equity drawdowns. Using daily adjusted prices for SPY and 11 U.S. sector ETFs from 2020–2025, we compute sector excess returns (sector minus SPY), estimate a low-dimensional common component via principal component analysis (PCA), and define residual stress as the cross-sectional root-mean-square magnitude of PCA reconstruction residuals. To prevent look-ahead bias, the PCA mapping is estimated using information available only through t−1, stress is computed out-of-sample at t, and stress regimes are identified using a rolling train-only quantile threshold that is shifted forward by one trading day. Drawdown-warning performance is assessed using drawdown-onset events and early-warning classification metrics (ROC-AUC, PR-AUC, and horizon-H precision/recall). Empirically, residual stress spikes cluster around drawdown onsets and provides predictive information, although a volatility-based benchmark remains stronger on average across discrimination metrics. Importantly, residual stress exhibits state-dependent complementarity with volatility: conditional on low volatility, high residual stress is associated with a materially higher probability of a drawdown onset within the next H=21 trading days (approximately 17% vs. 8%), and the joint high-stress/high-volatility regime identifies the highest-risk states (approximately 36% onset probability). Event-level overlap diagnostics further indicate that residual stress can flag a subset of drawdown onsets not captured by a volatility-threshold rule, while some onsets are not preceded by either signal. Economic relevance is examined under transaction costs through (i) a residual-ranked sector long–short portfolio and (ii) stress-managed SPY overlays that reduce exposure during detected regimes. In the baseline sample, a volatility-managed overlay improves drawdown control relative to buy-and-hold, whereas the residual-stress overlay does not reduce maximum drawdown and the residual-ranked long–short strategy is not robustly profitable after costs. Overall, the paper contributes a reproducible, leakage-safe evaluation pipeline linking cross-sectional residual dispersion to drawdown risk and clarifies when residual stress serves as a complementary market-structure risk indicator alongside standard volatility-based signals.

Abstract: The treatment of chronic lymphocytic leukemia (CLL) has significantly shifted from chemoimmunotherapy to targeted therapies like Bruton’s tyrosine kinase and BCL2 inhibitors. Despite these advancements, CLL remains an incurable disease characterized by immune dysregulation, therapeutic resistance, and cumulative toxicities. To overcome these challenges, novel immunotherapeutic strategies are emerging as fundamentally different approaches that target the immune-tumor interactions. These innovations include novel monoclonal antibodies, bispecific antibodies that redirect T-cell cytotoxicity, chimeric antigen receptor (CAR)-T cell therapies, and natural killer (NK) cell-based platforms. By actively engaging cellular cytotoxicity, these approaches show promise in high-risk and treatment-resistant scenarios where standard pathway inhibition is inadequate. Establishing the optimal use, toxicity management, and combination strategies of these cellularly engaged immunotherapies is now a critical priority in contemporary CLL research.

Abstract: The article explores the question how the new developments in the EU copyright law influence the Slovenian legislation. Presently, the Slovenian system of collective management of copyright and related rights is under scrutiny of European Commission, which recently opened infringement proceedings for failing to correctly apply the InfoSoc Directive and Collective Right Management Directive. The future Streamz decision of the Court of Justice of European Union, initiated by the Belgian Constitutional Court, could also significantly influence the Slovenian copyright rules, since the Slovenian legislator implemented the Digital Single Market Directive by similar means as Belgian legislator. One of the pressing issues in Slovenian copyright law, which was recently considered by the Higher Court of Ljubljana, is also the collection, management, and distribution of private copying levy as one of the permittable exceptions and limitations of exclusive authors rights under InfoSoc Directive. The thorough analysis of these pressing issues reveals complex intertwining of the EU and national law regarding collective management of exclusive author’s rights and of various remuneration rights. The article, focusing on legal-dogmatic approach and the analysis of legal sources using grammatical, purposeful, systematical and comparative legal methods, offers overview of Slovenia's system of copyright protection, draws attention to its possible incompatibilities with EU law, and provides possible legislative solutions.

Abstract: This paper examines the relationship between decentralisation, human resource development (HRD), and operational efficiency in water utilities managed by state and local governments across Madhya Pradesh, Chhattisgarh, and Jharkhand, India. Drawing on a random sample of 200 towns and villages, the study quantifies how decentralisation influences HRD investment and how both variables jointly shape service delivery outcomes. The findings challenge the widely held assumption — promoted by international donors and development institutions — that devolving authority to lower tiers of government inherently improves public service performance. Results indicate that decentralisation, as practised in India's drinking water sector, does not enhance operational efficiency. Instead, vocational training — the primary measure of HRD employed here — emerges as the principal driver of improved performance. Since decentralisation is negatively correlated with HRD investment, transferring authority without commensurate workforce development commitments risks compounding existing inefficiencies rather than resolving them.

Abstract: Artificial intelligence (AI) is significantly changing the landscape of banking and financial markets, improving predictive precision, operational productivity, and decision-making speed. However, underlying these improvements is an underexplored structural vulnerability: the risk of systemic AI convergence. As financial institutions become more dependent on similar foundational models, cloud infrastructure, data suppliers, and AI middleware platforms, diverse firms may begin to interpret and respond to market signals in similar ways. This paper presents the notion of model monoculture risk—a systemic fragility that arises when model similarities, market synchronisation, and infrastructure concentration align. Rather than focusing solely on traditional firm-level model risk management, this paper introduces the M³ Framework (Model–Market–Middleware) to clarify how shared AI architectures can transform localised optimisations into widespread amplification. The Model layer encapsulates the convergence of foundational architectures and training interdependencies; the Market layer reflects synchronised adjustments in portfolios, credit, and liquidity; and the Middleware layer emphasises the concentration of cloud services and vendors that quickens the spread. Collectively, these layers create multiplied exposure to correlated failures. To put this concept into practice, the paper proposes a qualitative Model Monoculture Risk Index (MMRI) to evaluate cross-layer alignment and the erosion of diversity within AI-driven financial systems. By redefining AI governance as a challenge of structural diversification rather than just a validation task, this contribution highlights cognitive diversity as an essential element of financial stability in the era of AI.

Abstract: The rapid proliferation of healthcare data from electronic health records (EHRs), medical imaging systems, laboratory devices, and IoT-enabled patient monitoring devices has created unprecedented challenges for healthcare data management. Traditional Extract, Transform, Load (ETL) tools have long been employed to collect, integrate, and load data into centralized repositories such as data warehouses and data lakes. However, conventional ETL processes are often limited by rigid rule-based transformations, inefficiencies in handling unstructured or semi-structured data, and lack of automation in data quality assurance. This study investigates the integration of Artificial Intelligence (AI) techniques into ETL pipelines to enhance healthcare data management. AI methods—including machine learning, deep learning, and natural language processing (NLP) are incorporated to automate anomaly detection, optimize transformation rules, and extract insights from unstructured clinical text. A conceptual framework is proposed for an AI-augmented ETL system that ingests heterogeneous healthcare data, applies intelligent transformations, and loads high-quality, enriched datasets into a secure data warehouse. The system architecture enables real-time and batch processing, anomaly detection, and adaptive learning to improve ETL efficiency over time. Evaluation metrics include data quality improvement, processing speed, anomaly detection accuracy, and scalability. The findings demonstrate that AI-enhanced ETL significantly reduces data errors, accelerates processing, and provides enriched datasets suitable for downstream analytics, predictive modeling, and decision-making in healthcare operations. By integrating AI into ETL workflows, healthcare organizations can achieve more reliable, timely, and actionable data management, supporting clinical decision-making, operational efficiency, and regulatory compliance. This study contributes to the literature on intelligent data engineering in healthcare, presenting a scalable framework for future research and practical implementation in complex healthcare IT ecosystems.

Abstract: In blockchain ecosystems, maintaining transparency and privacy has become an ethical dilemma. This is because, while certain specific information of the user is shared to ensure transparency of transactions across networks, such information could be detrimental to the user, as there is a possibility of it being tampered with. For instance, in the Catalyst voting process in Cardano, users can still see the amount of ADA tokens being held by other users, which can influence their voting options, especially when large ADA holders vote in support of certain ideas or proposals. To discourage such challenges as voter manipulation and vote buying, this study proposed the implementation of zero-knowledge proof (ZKP) in blockchain ecosystems to enhance the transparency of the catalyst voting process and enhance efficiency and speed of result release. Using survey questionnaire and a multivocal literature review, this study was able to proof that ZKP cannot only be applied in the catalyst voting process to enhance its transparency, but also addressed potential challenges to its applications such as scalability, encourage trust and fairness of the voting system, and improve voter participation due to its user-friendliness. Mathematical models emphasize scaled voting as optimal for balancing inclusion and plutocratic control.