Phuket Rajabhat University

<title>Abstract</title> The rapid growth of halal tourism has created demand for intelligent systems that can generate travel recommendations aligned with both logistical efficiency and Shariah principles. This study presents the design and implementation of the Halal Phuket system, an AI-driven platform developed to support Muslim-friendly tourism in Phuket Province, Thailand. The system is structured into three modules: (1) the Data Management Module, which ensures accurate and up-to-date information on mosques, halal restaurants, hotels, and attractions; (2) the Tourist Application Module, built with Flutter for cross-platform usability, which provides search, filtering, GPS-based recommendations, and real-time prayer-time notifications; and (3) the Route Planning Module, which integrates Genetic Algorithms (GA), fuzzy logic, and generative AI to compute optimized halal travel itineraries enriched with descriptive narratives. System testing confirmed the effectiveness of the approach. CRUD operations achieved a 100% success rate with negligible errors, and search and filtering accuracy exceeded 92% across multiple criteria. The GA optimization improved route fitness values by 18–25% compared with baseline itineraries, while consistently maintaining high prayer-time satisfaction scores (SMS ≥ 0.85). Real-time GPS features reliably detected nearby halal facilities within a 200-meter margin, and generative AI route descriptions received high ratings for readability (4.6/5) and cultural alignment (4.7/5) from subject matter experts (SMEs). The findings demonstrate that Halal Phuket successfully integrates AI optimization, cultural governance, and user-centric design to deliver practical and Shariah-compliant travel planning. This research contributes a novel framework for applying AI in halal tourism and introduces the prayer-time satisfaction metric as a unique measure of religious adherence.

Scanning spectrophotometers equipped with integrating spheres were once highly specialized spectrophotometric equipment but are now readily available commercially. They are particularly useful for objectively assessing the spectral reflectance of objects, hence their value for assessing gemstones and semiprecious stones, including pearls. We measured the reflectance properties of mabe (sometimes called cultivated mabe-pearls, half-pearls, blister-pearls, or shell cultured blisters) produced by the pearl oyster Pteria penguin (Röding, 1798) grown in Phuket, Thailand, prepared commercially as mabe buttons (mean 13.6 mm diameter). Reflectances were similar in white and pink mabe but not gray mabe in the violet/blue 400–550 nm range (violet to green) precluding the use of those wavelengths for diagnostic purposes. Minimal reflectance was found at about 550–600 nm (yellow to orange). Diagnostic color features are found above 600 nm in the conventional red visible range (625–700 nm), far red 700–750 nm and in the near infrared (NIR) 750–850 nm. Pink mabe reflect strongly (R% >70%) in the visible red (625–700 nm), far red (700–750 nm), and NIR (750–850 nm) with a conspicuous point of inflection at about 625 nm (orange to red). White mabe reflects more strongly in the red part of the spectrum but less strongly than the pink mabe. Some white mabe showed a weak point of inflection at ≈625 nm. Gray mabe have a very different spectrum to pink and white mabe: a conspicuous minimum at ≈570 nm (yellow) and low reflectance similar to that found in the blue part of the spectrum at visible red, far red, and NIR wavelengths. Objective spectrophotometric classification of mabe pearls from P. penguin is a viable proposition.

As destinations featured in films and television programs attract growing numbers of tourists, exploring the factors that sustain film tourists’ loyalty and advocacy has become increasingly important. This study explores the determinants of post-visit behaviors through the lens of cognitive appraisal theory (CAT), investigating how perceived authenticity, perceived value, and satisfaction shape revisit and word-of-mouth (WOM) intentions among 436 Chinese film tourists visiting Thailand. Data were analyzed using structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA) to capture both symmetrical and configurational effects. The SEM results reveal that perceived authenticity, perceived value, and satisfaction significantly enhance WOM intentions. The complementary fsQCA findings reveal multiple causal pathways leading to high revisit and WOM intentions. The study advances theoretical understanding by demonstrating the applicability of CAT to film tourism and showing how tourists’ cognitive evaluations and emotional appraisals jointly shape their post-visit behavioral intentions. The findings also offer practical guidance for developing authenticity-based strategies to foster loyalty and positive destination advocacy.

The paper investigates the oscillation, zero-convergence, and solutions of second-order neutral delay difference equations containing three nonlinear delayed terms with different growth rates. By using positivity and monotonicity conditions on an auxiliary function along with divergence-type conditions on the coefficient sequences of the neutral and delayed terms, the paper establishes new criteria that guarantee oscillation or convergence of all solutions. These novel findings extend and enhance several of the existing oscillation criteria established by the literature. Suggestions for further investigation are included with illustrative examples.

Rapid oil-palm expansion in Southeast Asia demands accurate, up-to-date information on plantation extent and stand age, yet single-sensor approaches often misclassify mixed tropical vegetation and cannot reconstruct planting histories that underlie yield forecasts and sustainability metrics. We integrated the full 1987–2024 Landsat surface-reflectance archive (38 years) with 2024 Sentinel-2 imagery to build a cloud-native monitoring pipeline in Google Earth Engine (GEE). A Random-Forest (RF) model, trained on stratified field samples, delineated plantation extent from six Sentinel-2 bands, five vegetation/surface indices, three Sentinel-1-derived back-scatter layers, and two terrain variables. Age was retrieved inside the verified extent mask by locating the most recent year in which smoothed NDVI dropped below its 20th percentile while the Bare-Soil Index (BSI) exceeded its 80th percentile, thereby flagging the latest planting or replanting events in the Landsat record. Model skill was assessed with a 30 % hold-out set using overall accuracy, κ, precision, and recall; age estimates were validated against 234 ground plots with MAE and RMSE. The multi-sensor classifier reached an overall accuracy of 90.5 % and Kappa = 0.81, with balanced error rates (oil palm: 88.6 % precision, 92.7 % recall; non-oil palm: 92.5 % precision, 88.4 % recall). Mapped plantations covered 561 km², or 60.9 % of the study district. Age retrieval achieved an RMSE of 4.0 yr, revealing distinct replanting events that correspond to known market cycles and providing spatially explicit age strata relevant to yield and carbon-stock modelling. Combining the long Landsat record with high-resolution Sentinel data markedly outperforms single-sensor methods for both extent mapping and age estimation. The workflow relies solely on free, globally available imagery and scalable cloud computing, making it immediately transferable to government and NGO monitoring programmes. By delivering accurate maps of plantation area and stand age, the approach fills a critical information gap for sustainable palm-oil certification, carbon accounting, and land-use policy across the humid tropics.

In this study, the Peak-to-Average Power Ratio (PAPR) and Bit Error Rate (BER) performance in Orthogonal Time Frequency Space (OTFS) modulation with Non-Orthogonal Multiple Access (NOMA) over Rayleigh fading channels were investigated. Simulations with 256-QAM and 512-QAM modulation schemes on 256 subcarriers evaluated several PAPR reduction techniques, including the Vandermonde matrix-based Partial Transmit Sequence with Companding (Vm-PTS+Companding), Vm-SLM+Companding, conventional PTS, Selective Mapping (SLM), Companding, and Clipping & Filtering (C&F). The proposed Vm-PTS+Companding achieves up to 9dB PAPR reduction for 256-QAM and 8.9dB for 512-QAM at a Complementary Cumulative Distribution Function (CCDF) of [Formula: see text], outperforming existing methods. Under a [Formula: see text] channel estimation error, the PAPR performance degrades, yet the proposed scheme maintains a 2.9-9.9dB gain over conventional approaches. BER analysis showed Signal-to-Noise Ratio (SNR) improvements of 3.5-13.7dB in ideal channels and up to 16dB under error conditions. Power Spectral Density (PSD) evaluation further confirmed the reduced spectral leakage, with the method achieving a level of [Formula: see text]dB. These results demonstrate that Vm-PTS+Companding effectively optimizes both the PAPR and BER in OTFS-NOMA systems, supporting high data rates in error-prone wireless environments.