Thaksin University

Abstract Decentralized blockchain oracles are critical for bridging on-chain smart contracts with off-chain real-world data. However, existing reputation systems often rely on static cumulative profiling, leading to a phenomenon we define as Reputation Inertia. In this state, an oracle's accumulated historical honesty acts as a buffer that masks potential malicious behavior (Whitewashing attacks). It simultaneously fails to account for benign disagreement during market volatility (Flash Crash scenarios). To address this dilemma, this paper proposes a novel graph-based profiling mechanism utilizing an Adaptive Exponential Weighted Moving Average (AEWMA). Unlike typical static models, our approach introduces a Dynamic Trust Decay factor that regulates the weight of historical reputation based on real-time network volatility. This allows the system to act in a highly sensitive manner to deviations during stable market conditions in order to rapidly detect sleeper cells, whilst also dampening penalty mechanisms during high-volatility events to prevent false positives. We provide a complete Solidity smart contract implementation demonstrating the on-chain feasibility of the AEWMA mechanism with $O(1)$ storage per edge. We validated the proposed method through discrete-event simulations using historical cryptocurrency market data. Experimental results demonstrate that our dynamic approach reduces the Time-to-Detection (TtD) for whitewashing attacks by approximately \textbf{81\%} compared to static baseline (from 9.0 to 1.7 rounds). It also maintained a False Positive Rate (FPR) of near \textbf{0.4\%} even under extreme volatility conditions ($5\times$ standard deviation), compared to 7.1\% for the static approach. These findings suggest that volatility-aware profiling significantly enhances the security and incentive compatibility of decentralized oracle networks.

In multilingual professional environments, reciprocal language exchange initiatives can reduce communication barriers, foster collegial collaboration, and enhance intercultural competence. This study introduced the Social and Interactive Language Exchange (SAILE) model, a workplace-based approach that integrates WhatsApp-facilitated vocabulary sharing with informal face-to-face oral sessions. Introduced at an education institution in the United Arab Emirates (UAE), the program engaged 25 Arabic- and English-speaking educators over 12 weeks. Unlike prior tandem studies that primarily focus on students, this research situated language exchange within a professional context, addressing Arabic speakers’ need for English proficiency (particularly preparation for the International English Language Testing System (IELTS)) and English speakers’ desire to acquire functional Arabic for workplace integration. Guided by Rhizomatic Learning and Sociocultural Theory, the study employed a mixed-methods design combining pre- and post-tests, questionnaires, observation notes, and thematic analysis. The findings revealed that informal exchanges promote vocabulary acquisition, motivation, and intercultural rapport, though challenges such as dialectal variation, foundational literacy in Arabic, and time constraints persisted. Based on these findings, the study recommends that UAE educational institutions integrate reciprocal language exchange initiatives within staff development frameworks, provide targeted support for Arabic literacy and dialect awareness, and formally recognize participation through professional development pathways. The study also highlights the need for future research in the UAE context, particularly through longitudinal and large-scale investigations examining the sustainability and institutional impact of workplace-based language exchange programs. This research extends tandem learning literature into professional settings and offers context-sensitive guidance for educators, institutional leaders, and policymakers.

A field experiment was carried out to evaluate the effects of foliar-applied marine fish amino acids (MFA) on the growth, yield, yield components, and preventive antioxidant capacity of okra, as well as the interaction between okra varieties and MFA concentrations. The study used a split-plot design with four replications. Three okra varieties — RED FINGER, KN–OYV–02, and LUCKY FILE 473 — were assigned to main plots. Five concentrations were tested in the subplots (0.00, 1.50, 3.00, 4.50, and 6.00 ml/l). Yield per plant — a key indicator for growers — did not differ significantly among the three varieties but responded to MFA levels-plants treated with 3.00, 4.50, or 6.00 ml/l. MFA showed no significant differences among these higher concentrations; however, all produced higher yields than the untreated control and the 1.50 ml/l treatment. The greatest yield (1,271.49 g/plant) was obtained at 3.00 ml/l, followed by 4.50 ml/l (1,251.22 g/plant) and 6.00 ml/l (1,215.51 g/plant). Reducing sugar content did not vary significantly among the okra varieties but was influenced by MFA levels. LUCKY FILE 473 recorded the highest reducing sugar (~1.77 mg/ml). The concentrations 3.00, 4.50, and 6.00 ml/l yielded the highest reducing sugar levels (1.89, 1.84, and 1.77 mg/ml, respectively), with no significant difference among them, while the control plants had the lowest value (1.53 mg/ml). Among pigment traits, RED FINGER exhibited the lowest chlorophyll a, chlorophyll b, and total chlorophyll contents (2.96, 1.51, and 4.47 mg/g FW, respectively) but had the highest carotenoid concentration (0.50 µg/g FW).

Purpose of the study: The purpose of this study is to examine the effectiveness of a simple light sensor-based experiment in improving students’ experimental skills in physics learning, particularly in the topic of optics, among eleventh-grade vocational high school students. Methodology: This study used a quantitative experimental method with a one-group design. The tools included a simple light sensor based on an LDR, breadboard, resistors, LED, buzzer, and multimeter. Data were collected through observation sheets, product assessment, and student response questionnaires. Data analysis was conducted using IBM SPSS Statistics software. Main Findings: Students’ experimental skills reached a high level with a mean score of 81.61, significantly exceeding the Minimum Completeness Criteria score of 75 (p < 0.05). All students successfully completed the simple light sensor experiment. Skill indicators showed an overall average of 86.67. Student responses to the media and learning process were very positive, with mean percentages of 87.07% and 86.90%, while product evaluation by teachers and observers reached 100%. Novelty/Originality of this study: This study provides new empirical evidence on the effectiveness of low-cost, simple light sensor (light dependent resistor)-based experiments in real vocational classrooms, focusing on direct measurement of students’ science process skills. It advances existing knowledge by demonstrating that affordable, hands-on experimental media can significantly enhance practical skills and learning engagement in physics education contexts with limited laboratory resources.

The COVID-19 pandemic caused an unprecedented shock to the global tourism industry, including those in Japan and Thailand. In response, both the Japanese and Thai governments decided to launch large-scale domestic tourism stimulus campaigns – Go To Travel and We Travel Together, respectively – as mechanisms to support resilience. This article employs a documentary analysis of tourism agency data, government reports, academic articles, and news media. Adopting Norris et al.’s conceptual framework of resilience, the paper assesses tourism economic resilience provided by these campaigns based on three criteria: robustness, redundancy, and rapidity. In terms of robustness, both campaigns demonstrated strong financing and substantial returns for the overall tourism industry. Regarding redundancy, the campaigns were complementary and could be substituted by other similar programs such as Go To Eat and Let’s Go Halves. Lastly, the fact that both governments launched the campaigns only six months after confirming their first COVID-19 cases underscores their rapidity. Despite certain challenges, these campaigns contributed to the tourism sector’s resilience, as highlighted by their robustness, redundancy, and rapidity, and ultimately helped revitalize the tourism industry in both countries during the crisis.

Cyber-Medical Systems represent a transformative paradigm in modern healthcare by unifying artificial intelligence, digital twins, secure Internet of Medical Things, and cyberphysical infrastructures into a continuous and intelligent medical ecosystem. Unlike conventional digital health and telemedicine platforms, cyber-medical systems enable real-time physiological monitoring, predictive disease modeling, and personalized therapeutic control through tightly coupled data, computation, and clinical feedback loops. This review presents a comprehensive analysis of the core architectures, data pipelines, and learning frameworks that support AI-driven precision and preventive healthcare. We examine how multimodal patient data from wearables, imaging, genomics, and electronic health records can be integrated into digital patient twins that continuously adapt to evolving physiological states. The role of advanced machine learning, federated analytics, and secure cyber infrastructures in enabling early disease detection, treatment optimization, and risk mitigation is critically evaluated. In addition, we survey key datasets, benchmarking practices, and real-world case studies that demonstrate the clinical and economic potential of cybermedical systems. Finally, we identify open challenges related to data reliability, security, ethics, and regulatory compliance, and outline future research directions toward scalable, trustworthy, and globally accessible cyber-medical healthcare.

The LaNi(1−x)FexO3 (x = 0, 0.2, 0.4, 0.6, 0.8, and 1) perovskite powder was prepared using a conventional mixed-oxide method through high-energy ball milling and calcination at 800 °C. The powders were then sintered at 1000 °C for 2 hours with a heating/cooling rate of 5 °C min−1. The LaNi(1−x)FexO3 powders were analyzed using scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), thermogravimetric analysis (TGA), differential scanning calorimetry (DSC), X-ray diffraction (XRD), and cyclic voltammetry (CV). From the experimental results, it was found that the SEM micrograph of the LaNi0.6Fe0.4O3 powder shows that the calcined LaNi0.6Fe0.4O3 powder has a particle size ranging from approximately 30 to 400 nm. In comparison, the sintered powder at 1000 °C has an average particle size ranging from 50 to 600 nm. The LaNi0.6Fe0.4O3 powder exhibits a rhombohedral phase structure. The LaNi0.6Fe0.4O3 catalyst, when used in a 0.1 M local sorbitol solution and 0.1 M KOH, can be effectively utilized as a catalyst for electro-oxidation electrode materials in the oxygen reduction reaction.