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.

Job Satisfaction and its Effects on The Work Performance of Elementary Teachers

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Understanding job satisfaction is key to gaining insight into how teachers engage with their roles and contribute to the learning environment. This study assessed the level of job satisfaction and its effect on the work performance of elementary teachers in the Lanuza District. It also examined the relationship between teacher demographics and job satisfaction, identified which work performance domains had the most influence, and explored whether job satisfaction correlated with performance. Using a descriptive research design, data were collected through a validated researcher-made questionnaire from 93 elementary teachers (11 males and 82 females).

Results revealed that teachers were generally “Highly Satisfied” in areas such as responsibility, interpersonal relations, and the nature of the work itself. However, satisfaction was lower in domains related to salary and advancement opportunities. Work performance was consistently rated from “Very Satisfactory” to “Outstanding” across competency domains. Despite high satisfaction levels, the study found no significant relationship between job satisfaction and performance. Additionally, none of the five work performance domains significantly predicted performance outcomes. Most demographic factors were unrelated to satisfaction, although age showed a moderate association with satisfaction in areas like interpersonal relations, working conditions, and the work itself. Similarly, the number of trainings attended was moderately linked to satisfaction in professional growth and recognition.

The study concludes that while job satisfaction is generally high among teachers, it does not directly influence work performance. Further research is recommended to explore other factors that may mediate or moderate this relationship.

The Impact of Working Capital Management on Profitability: The Mediating Role of Liquidity in Pakistan’s Textile Sector

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This study investigates the impact of working capital management (WCM) on the profitability of textile firms listed on the Pakistan Stock Exchange (PSX), with a specific focus on the mediating role of firm liquidity. Pakistan’s textile sector, which contributes approximately 8.5% to GDP, accounts for 46% of total manufacturing output, and generates over 60% of national export earnings, operates under persistent macroeconomic pressures including energy shortages, currency depreciation, volatile cotton prices, and constrained access to short-term financing. The study employs a panel dataset comprising eight PSX-listed textile firms over the period FY2020-FY2024, yielding 40 firm-year observations; the dynamic GMM profitability model uses 30 observations because CCC and ITO data were unavailable for selected firm-years. The primary measures of working capital efficiency are the Cash Conversion Cycle (CCC) and Inventory Turnover (ITO), while firm profitability is measured by Return on Assets (ROA) and firm liquidity is captured through the Current Ratio. The empirical results show a positive and significant relationship between CCC and ROA (β = 0.068, p = 0.028), a strong positive relationship between ITO and ROA (β = 1.847, p = 0.015), and a dominant positive effect of liquidity on profitability (β = 28.640, p = 0.002). Firm liquidity partially mediates the relationship between working capital management and profitability, and panel cointegration tests confirm a stable long-run equilibrium among the study variables.

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.