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.

 

Nyepi from a Cultural Ecology Perspective: Ritual Silence, Inner Transformation, and Ecological Harmony

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This study aims to analyze the meaning of silence in the observance of Nyepi (Balinese Day of Silence) and its contribution to inner transformation and ecological harmony from a cultural ecology perspective. The research employs a qualitative approach using a library research method, focusing on the analysis of concepts, values, and cultural practices embedded in Nyepi. Data were collected from relevant academic literature, books, and scholarly journals, and analyzed through a descriptive-analytical method involving interpretation and conceptual reasoning.

The findings indicate that silence in Nyepi is not merely the absence of physical activity but functions as a reflective mechanism that promotes inner transformation through self-restraint and the development of ecological awareness. This collective practice also generates tangible environmental impacts, such as reduced emissions and decreased ecological pressure, thereby creating temporary ecological harmony. The novelty of this study lies in emphasizing Nyepi as a form of local wisdom that is not only symbolic but also operational in maintaining environmental balance. Therefore, Nyepi can be understood as an integrative model that connects spiritual, social, and ecological dimensions, offering relevant insights for addressing contemporary environmental sustainability challenges.

The Mediating Role of Self-Regulation in the Relationship Between Social Media Addiction and Loneliness

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The rapid expansion of digital technologies and social networks has significantly influenced students’ cognitive processes and social interactions. Although social networks facilitate communication, learning, and information sharing, excessive use may contribute to increased loneliness and diminished self-control. This study examined the mediating role of self-regulation in the relationship between social media addiction and loneliness among Afghan students. A quantitative, correlational design was employed, involving 181 randomly selected students from the Faculty of Special Education. Data were collected using the Shahin Social Media Addiction Questionnaire, the UCLA Loneliness Scale by Daniel Russell, and the Bofard Self-Regulation

Questionnaire. Analyses were conducted using IBM SPSS Statistics version 27 and the PROCESS Macro plugin. Descriptive statistics, Spearman’s correlation, regression, and mediation analysis (Model 4 of PROCESS with the bootstrap method) were utilized. Findings indicated that social media addiction was associated with higher levels of loneliness (β = 0.285, p < 0.001) and lower self-regulation (β = -0.314, p < 0.001). Additionally, self-regulation was negatively associated with loneliness (β = -0.278, p < 0.001). Mediation analysis revealed that self-regulation partially mediated the relationship between social media addiction and loneliness (Effect = 0.049, BootCI [0.018, 0.088]). These results suggest that interventions aimed at enhancing self-regulation skills may mitigate the adverse effects of excessive social media use and reduce loneliness among students.

Causal Factors of Verbal Bullying and Its Impact on Chronic Stress Among Upper Secondary School Students

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This study analyzes the causal factors of verbal bullying and its impact on chronic stress among upper secondary school students. The research was conducted using documentary research methods, synthesizing data from academic articles and reports from relevant organizations, including UNICEF, the World Health Organization (WHO), and the Department of Mental Health.The findings indicate that the causes of verbal bullying fall into three main areas: 1) Individual factors, such as deficits in emotional regulation and impulse control; 2) Social factors, including peer pressure and school cultural norms; and 3) Digital factors, stemming from inappropriate communication on social media platforms. Verbal bullying directly affects the mental well-being of victims, leading to chronic stress, anxiety, and diminished self-esteem, which can escalate into long-term depression. Therefore, addressing verbal bullying requires collaboration among schools, families, and online communities to foster a safe environment for students.

Assessment of Artificial Intelligence (AI) on Media Practitioners’ Creativity and Capacity Development

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Artificial intelligence (AI) is an innovative and modern technology that enables computers to perform tasks that typically require human-like intelligence. In January, 2025, empirical report showed that Nigeria surpasses global average with 70% AI adoption rate. The report said Nigeria’s online population is leading the global adoption of generative AI, with 70 percent of respondents reporting usage, far exceeding the global average of 48 percent. With this report, Nigerians dependence on AI is quite alarming and it is of believe that AI is gradually taken over human creativity and existence.

The study adopted survey method and in-depth Interview research methods with a focus on 120 respondents for the survey method and three (3) media practitioners for the in-depth-interview that cut across print, broadcast and online media in three selected states (Osun, Oyo and Lagos) in South-West, Nigeria. One editor from print media outfits, one editor from broadcast media and one respondent from online news outlets who were purposively selected based on their knowledge and experience in the field of journalism.

The study found out that AI has come to stay in modern world and it is very essential in different fields most especially field of journalism. Finding also indicated that when applied improperly or excessively, AI increases the efficiency and some of the skills but poses a threat to originality, depth, and standards.

The study therefore concludes that AI is a strong supporting system but not a substitute of human journalists. A balanced integration, ethical standards, and long-term capacity building can help the Nigerian media to embrace the benefits of AI and remain creative, accurate, and trusted by the people. The study recommends that media organizations ought to ensure that all their staff are trained on the ways of using AI intelligently, as an assistant and not a thinking substitute.

Educational Services for Hospitalized Children and Adolescents in Pediatric Oncology: A Cartography of Academic Production in Brazilian Federal Universities

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This study maps the academic production on Educational Services for Hospitalized Children and Adolescents related to pediatric oncology at Brazilian Federal Universities, aiming to understand how different fields of knowledge construct meanings about illness, schooling, and educational continuity for children and adolescents undergoing cancer treatment. The research adopts an exploratory and cartographic approach grounded in Antoine Culioli’s Theory of Predicative and Enunciative Operations (TOPE), analyzing lexical variations, metadata, institutional affiliations, and regional academic productions. Two analytical levels were established: Level II, focused on textual surface and metadata analysis, and Level I, centered on enunciative activity and networks of meaning. The results reveal that the naming of Educational Services for Hospitalized Children and Adolescents is not neutral but constitutes strategic enunciative operations that reflect distinct epistemic territories. Although Hospital Class remains the most stable descriptor nationwide, regional specificities demonstrate differentiated modes of institutionalization and conceptualization. The North region prioritizes continuity of schooling and preservation of pedagogical identity; the Northeast expands the field through ethics, aesthetics, and intersectoral dialogue; the Southeast consolidates institutional and professional dimensions; and the South emphasizes subjective, neurocognitive, and socio-emotional aspects. The study also identifies a progressive displacement from biomedical approaches toward relational and rights-based perspectives, in which the child ceases to be represented exclusively as a patient and becomes re-enunciated as a subject of learning, participation, and continuity. The findings reinforce Educational Services for Hospitalized Children and Adolescents as an ethical, pedagogical, and political practice essential to sustaining educational trajectories during illness.

Predictors of Behavior Problems in Preschool Children: The Role of Psychological Self-Regulation and Cognitive Executive Functions

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The aim of this transversal research was to examine the influence of predictors of psychological self-regulation (temperament – effortful control, positive and negative emotionality, and cognitive executive functions) on the prediction of criterion variables of internalized and externalized behavior problems in preschool-aged children. The pertinent sample included 170 parents (53% mothers and 47% fathers) and preschool children of both sexes aged 4–6.5 years from the preschool “Radosno detinjstvo” in Valjevo. The following measurement instruments were applied: Childhood Executive Functioning Inventory, The Early Childhood Behavior Questionnaire, Child Behavior Checklist, Questionnaire on the use of digital media by preschool children, executive function tasks for children (“Day–Night”), backward digit span, and verbal fluency. The obtained alpha reliability coefficients suggest that the used instruments, with reliable internal consistency, are valid for measuring the Serbian population. The results of the hierarchical linear regression model, with a relevant proportion of variance (29.28% and 28.52%), showed that externalized and internalized behavior problems are in a statistically significant positive correlation with factors of perceived executive functions – inhibition deficit and working memory deficit. Additionally, the temperament dimension of partial effortful control manifested as a relevant determinant contributing to the explanation of variability in the construct of behavior problems in preschool-aged children. The study discusses the theoretical and practical implications of these findings.