AI-Powered Token Prediction and Automated Trading in Web3 Using On-chain Data and Decentralized Exchanges

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This article investigates the efficacy of implementing an AI-powered automated trading system on the blockchain using advanced machine learning algorithms and smart contract technology. The work addresses the challenges of cryptocurrency market volatility, the need for real-time decision making and the limitations of traditional trading approaches that often result in suboptimal returns and exposure to increased risk. This work develops a comprehensive trading platform that combines Long Short-Term Memory (LSTM) neural networks, Q-Learning reinforcement learning algorithms and blockchain-based smart contracts to create an autonomous, intelligent trading system. The methodology follows a multi-layered approach that integrates real-time market data collection from CoinGecko and Snowtrace APIs, advanced AI model training using TensorFlow.js, and smart contract deployment on the Avalanche C-Chain using Hardhat and OpenZeppelin libraries.  LSTM model is used for price prediction and Q-Learning agent is used for trading strategy optimization, while comprehensive risk management is implemented using Value at Risk (VaR) calculations, portfolio rebalancing algorithms and automated stop-loss mechanisms. The trading execution is facilitated through direct integration with Pangolin DEX smart contracts to ensure decentralized and trustless trade execution. The performance of the system is evaluated using a sophisticated backtesting engine with Monte Carlo simulations, comparing the AI-driven strategy against traditional buy-and-hold approaches. The performance metrics used were Sharpe ratio, maximum drawdown, win rate, and total return. The AI-powered token prediction system demonstrates a superior performance due to its ability to process complex, non-linear market patterns and adapt to changing market conditions through reinforcement learning, and execute trades with minimal latency through blockchain integration. The findings are expected to provide cryptocurrency traders and institutional investors with a robust and automated trading solution that leverages the benefits of both artificial intelligence and blockchain technology for improved investment outcomes and risk management.

A Stochastic Framework for Fully Distributed Control Systems and CPS: From Local State Transitions to Global Uncertainty Propagation

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The transition from hierarchical automation toward fully distributed Distributed Control Systems (DCS) and Cyber-Physical Systems (CPS) creates a new class of engineering problems in which local intelligence, networked coordination and physical dynamics must operate under uncertainty. In these systems, control is no longer concentrated in a single supervisory unit. Instead, sensors, controllers, actuators, edge devices and cyber agents cooperate through local decisions and partial information. This article develops a stochastic framework for examining fully distributed DCS/CPS by linking three levels of analysis: how local states shift through Markov transitions, how short‑term decisions accumulate over time through the Chapman–Kolmogorov relation, and how uncertainty spreads in continuous processes through the Fokker–Planck equation. All these aspects indicate that a distributed system is something much more than a mere configuration of communication; it is an adaptive, stochastic controller organism, where its global functioning arises from many small local decisions that modify probabilities and paths. The design should then consider how these local decisions affect one another over time, rather than simply interconnecting devices. The proposed framework is then analyzed from the perspectives of stability, resilience, communication delay, cyber-security, scalability, energy efficiency and digital-twin-based prediction. The result is a theoretical foundation suitable for smart factories, smart grids, intelligent buildings, autonomous transport systems and future smart urban infrastructures. The added interpretative value of the framework is that each equation is treated not only as a formal mathematical relation, but also as a design logic. Markov probabilities are interpreted as local operational tendencies, Chapman-Kolmogorov composition as the logic of accumulated distributed decisions, and Fokker-Planck dynamics as the evolution of confidence, risk and uncertainty in the whole cyber-physical network.

 

Migration, Education, and Labor Market Integration: Evidence from a Panel Data Analysis of Institutional Heterogeneity in Europe

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Purpose: This study examines the relationship between migration intensity, education-related integration mechanisms, and labor market outcomes of the foreign-born population in selected European countries over the period 2010–2024. It explores how migration flows and migrant education interact within different institutional contexts.

Methodology: The analysis focuses on Germany, France, Italy, Austria, Sweden,  and the United Kingdom and employs panel data techniques using two-way fixed and random effects models. Model selection is guided by the Hausman specification test, which strongly favors the fixed effects estimator, highlighting the role of country-specific institutional heterogeneity.

Findings: The results indicate that migration intensity alone is not significantly associated with improved migrant employment outcomes once unobserved heterogeneity is controlled for. In contrast, migrant tertiary education shows a positive relationship with employment performance. Public education expenditure, measured as a share of GDP, does not exhibit a robust direct effect, suggesting that aggregate spending levels are insufficient to drive integration outcomes.

Originality:  By providing recent longitudinal cross-country evidence, the study contributes to the applied econometrics literature on migration and labor markets. It highlights the importance of institutional context and educational attainment in shaping migrant labor market integration across Europe.

Behavior-Based Malaria Incidence Prediction Model in the Hanura Community Health Center Work Area, Pesawaran Regency, Lampung, Indonesia

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Malaria is an infectious disease caused by the Plasmodium parasite and transmitted through the bite of an infected female Anopheles mosquito. This disease remains a public health problem in the working area of ​​the Hanura Community Health Center, Pesawaran Regency, Lampung, Indonesia. Behavioral factors and home protection conditions are thought to play a role in increasing the risk of malaria, such as the use of mosquito nets, mosquito repellent, wire mesh, and activities outside the house at night. This study aims to analyze behavioral factors that influence the incidence of malaria in the working area of ​​the Hanura Community Health Center, Pesawaran Regency, Lampung, Indonesia. This study used an observational analytical design with a case-control approach conducted in November 2025–January 2026. The study sample consisted of 113 case groups and 113 control groups selected using proportional random sampling techniques. Data were obtained through questionnaires and analyzed using the Chi-Square test with a significance level of α = 0.05. The results showed that the use of mosquito nets had a significant relationship with the incidence of malaria (p-value = 0.016; OR = 2.00; 95% CI = 1.17–3.42), the use of mosquito repellent (p-value = 0.002; OR = 2.95; 95% CI = 1.53–5.70), and the use of wire netting (p-value = 0.008; OR = 2.12; 95% CI = 1.25–3.61). Meanwhile, activities outside the house at night did not have a significant relationship with the incidence of malaria (p-value = 0.893). Behavioral factors and physical protection of the house play an important role in the incidence of malaria. The use of mosquito nets, mosquito repellent, and wire netting has been shown to be associated with a reduced risk of malaria, so that sustainable prevention efforts are needed through improving healthy living behaviors and protecting the home environment.

Request-Aware Fuzzy Load Balancing for Heterogeneous Computing Systems

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In modern heterogeneous computing systems, efficient load balancing remains one of the key challenges affecting system performance, response time, and resource utilization. Traditional load balancing algorithms such as Round Robin and Least Connection generally distribute requests without considering the computational complexity and priority of incoming tasks, which may lead to resource imbalance and performance degradation under dynamic workloads. To address this limitation, this study proposes a Request-Aware Fuzzy Load Balancing (RA-FLB) model based on a Mamdani-type Fuzzy Inference System (FIS). The proposed approach evaluates both request characteristics, including URL structure, payload size, header information, and computational weight, together with the real-time state of virtual machines such as CPU utilization and workload level. Based on fuzzy inference rules, the system dynamically selects the most appropriate server for each incoming request. In addition, a dynamic feedback mechanism continuously updates server states after task execution, enabling adaptive and real-time decision-making. The proposed model was implemented and evaluated in the CloudSim Plus simulation environment. Experimental results demonstrate that the RA-FLB approach improves response time, throughput, and load distribution efficiency compared with conventional algorithms. The proposed method provides a scalable and adaptive solution for intelligent resource allocation in cloud and distributed computing environments.

The Effect of Effective Tax Rate, Moral Hazard, and Firm-Specific Determinants on Capital Structure

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This research aims to examine the effectiveness of Effective Tax Rate, Firm Size, Profitability, Non-Debt Tax Shield, and Moral Hazard toward Capital Structure in oil, gas, and coal subsector firms listed on the Indonesian Stock Exchange during the period 2016-2024. The study employs a quantitative research design using panel data regression analysis. The Fixed Effect Model is selected based on panel data model selection tests and analyzed using EViews 12. The empirical findings reveal that only Profitability has a statistically significant negative effect on Capital Structure. Meanwhile, Effective Tax Rate, Firm Size, Non-Debt Tax Shields, and Moral Hazard do not exhibit statistically significant effects. These results suggest that internal financing capacity plays a more dominant role than tax incentives, firm scale, alternative tax shields, or agency-related considerations in determining capital structure decisions within the observed subsector. These research findings suggest that the effect of internal financing capacity dominates the effect of tax and agency-related considerations in determining Capital Structure within the Indonesian energy subsector. This study is limited by its sector-specific focus and restricted observation period.

Prevalence and Factors Associated with Birth Asyphyxia among Neonates Admitted at Amana Regional Referral Hospital in Dar ES Salaam, Tanzania – October 2025

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Birth asphyxia is the failure to establish and sustain spontaneous breathing at birth, hence leading to decreased oxygen perfusion to various organs. Birth asphyxia is among the leading causes to neonatal mortality and morbidity in Tanzania, our study aimed to determine prevalence and associated factors of birth asphyxia among neonates at Amana Regional Referral Hospital. A cross-sectional study was conducted, enrolling neonates admitted at neonatal ward in ARRH. Data was collected through structured questionnaires given to mothers of neonates admitted, also antenatal cards and case files were used to obtain Apgar scores and additional information. Of all neonates admitted during study period, 303 neonates were included in the study where by, 10 newborns (3.3%) had birth asphyxia, and prolonged labor, hospital delivered neonates and age of the mother 20-34 were significant factors associated with birth asphyxia.

Birth asphyxia is still a public health concern in Tanzania and its aftereffects are irreversible so early and regular antenatal booking, proper management of labor and improvement of maternal and child health services can reduce the burden also, awareness of pregnancy demands can help mothers handle pregnancy with care hence avoiding risk factors and complicated labor and delivery.

Request-Aware Fuzzy Load Balancing for Human Action Recognition and Monitoring in Video Streams

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real-time human action recognition and behavior monitoring within video streams impose significant computational strains on backend server infrastructures. Traditional distributed system load balancers assign dynamic incoming media tasks based exclusively on infrastructure-side metrics like CPU utilization or memory bandwidth, completely omitting request-specific computational requirements. This mismatch results in suboptimal task allocation, frame drops, and execution latencies when multi-scale convolutional operations or dense optical flow models are triggered unpredictably. To resolve this bottleneck, this paper introduces a novel Request-Aware Fuzzy Load Balancing (RAFLB) framework. The proposed paradigm establishes an adaptive, two-phase scheduling ecosystem. First, high-throughput video streams are frame-decomposed and pre-processed using spatial-temporal filtering kernels and Lucas-Kanade optical flow equations to extract intrinsic stream metadata (resolution, frame rate, structural intensity). Second, a multi-input Mamdani Fuzzy Inference Engine computes real-time routing priorities by simultaneously processing the localized Request Weight (RQ) alongside Server Busy (SB) telemetry. Experimental simulations show that RAFLB drastically reduces structural frames latency by up to 34% and prevents cluster choke points compared to conventional round-robin and resource-only load balancers.

Financial Insecurity and Psychological Stress among Medical Students at Saint James School of Medicine: A Cross-Sectional Study

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Background: Financial insecurity is a growing concern among medical students and has been increasingly linked to adverse psychological outcomes. Students enrolled in international medical schools may be particularly vulnerable due to limited access to federal financial aid and increased reliance on private funding sources.

Objective: This study aimed to examine the relationship between financial insecurity and perceived psychological stress among medical students at Saint James School of Medicine (SJSM).

Methods: A cross-sectional survey was conducted among 84 SJSM medical students using an online questionnaire. Financial insecurity was measured using a custom financial insecurity scale, and psychological stress was assessed using the validated Perceived Stress Scale (PSS-10). Descriptive statistics, correlation analysis, and subgroup comparisons were performed.

Results: The mean PSS-10 score was 22.0 (SD = 7.98), indicating moderate to high stress levels. Overall, 82% of participants reported moderate or high perceived stress. A statistically significant positive correlation was found between financial insecurity scores and PSS-10 scores (r = 0.600, p < 0.001), demonstrating that greater financial insecurity was associated with higher psychological stress. Most students reported substantial financial strain, with 72.6% expressing concern about tuition affordability and 66.7% reporting that their educational debt felt overwhelming. Female students reported higher mean stress scores (23.8) compared to male students (18.2). Stress negatively affected academic functioning, with 65.5% reporting difficulty concentrating while studying, and more than one-third indicating that financial worries negatively impacted class attendance.

Conclusion: Financial insecurity was strongly associated with elevated perceived stress among SJSM medical students. These findings highlight the need for targeted institutional interventions, including expanded financial support systems, improved loan access, and integrated mental health resources to mitigate the academic and psychological impact of financial stress in international medical education settings.