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Assessing urban growth and pollution throughnightlight data: a case study in Thailand: Linking urban growth and CO concentrations via nightlights
(2026-04-30)
Kulworatit, Chaichana
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Kerdpramote, Phuvis
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Saetang, Saranya
This study explores the relationship between urban development and air pollution in Thailand by analyzing remote sensing nightlight data and carbon monoxide (CO) concentrations over six years (2019-2024). Using data from VIIRS Day/Night Band (DNB) satellite imagery, CO levels, electricity consumption, and lignite production, the study finds a significant positive correlation (Pearson coefficient = 0.586) between nightlight intensity and CO concentrations. This suggests that nightlight data can be an effective tool for monitoring urban-related pollution. Seasonal and regression analyses show that urban growth contributes to pollution, but this is influenced by seasonal patterns and energy consumption. Multiple regression models highlight nightlight intensity as the strongest predictor of CO levels, with energy factors adding significant explanatory power. Regional analysis identifies the Bangkok Metropolitan Region as having the highest nightlight intensity and CO levels (correlation = 0.598). Lag correlation analysis suggests that changes in CO and nightlight intensity are most strongly correlated at zero lag, with CO changes slightly leading in some areas. These findings have implications for urban planning, environmental policy, and public health in Southeast Asia.
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A HEURISTIC ENHANCING ARTIFICIAL IMMUNE SYSTEM FOR THREE-DIMENSIONAL LOADING CAPACITATED VEHICLE ROUTING PROBLEM
(2025-06-08)
Thapatsuwan, Peeraya
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Thapatsuwan, Warattapop
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Kulworatit, Chaichana
This study addresses the Three-Dimensional Loading Capacitated Vehicle Routing Problem (3L-CVRP), a highly complex NP-hard problem that combines vehicle routing with spatially constrained three-dimensional bin packing. To tackle this challenge, we propose an enhanced Artificial Immune System (En-AIS) that integrates a novel local search heuristic called “Bring-i-to-j,” designed to improve routing feasibility and loading efficiency. The En-AIS algorithm is further refined through rigorous parameter tuning using a full factorial design and ANOVA analysis. Comparative experiments were conducted against conventional AIS and the Firefly Algorithm (FA) across 27 benchmark instances. Results demonstrate that En-AIS consistently outperforms both baseline methods in terms of solution quality, achieving an average improvement of 15–20% while maintaining competitive computational times. These findings highlight the algorithm’s robustness and its practical potential for application in logistics and supply chain optimization tasks involving joint routing and loading decisions.
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Unknown Computerized-Attack Recognition via Open-Set Circumstance-Adaptive Learning Approach
(2025-01-01)
Petchhan, Jirayu
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Kulworatit, Chaichana
Cyber threats come in all forms and constantly evolve into newly unknown malicious attacks. The status quo is that current mobile and/or embedded devices are unaware of embedded data with newly unknown threats, making it difficult to predict new threat types. To access much higher mobile security, we have deployed open-set domain adaptation to understand existing information yet still be realizable and recognizable to the novel unseen class instances. Our demonstrations are validated against visual benchmarks, such as handwritten data, and applied to our datasets to verify the embedding of unfamiliar data on embedded devices. As a result, it can gather a lot of information for both known and unknown instances from both datasets. Besides, it raises the issue of what cyber threat data to use in the circumstance like embedding over system-level firmware updates.
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Analysis of Urban Heat Islands Using Nighttime Land Surface Temperature Data
(2025-01-01)
Kulworatit, Chaichana
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Petchhan, Jirayu
This study analyzes Urban Heat Island (UHI) in Thailand using Nighttime Land Surface Temperature (LST) data from the MODIS MYD11A2 dataset, covering 2002-2025, during the winter months (December-January). The research aims to assess heat accumulation in urban areas and identify factors such as heat-absorbing materials, energy consumption, and the lack of green spaces. It also addresses the misconception that water bodies reduce heat, showing that they can contribute to UHI when not managed properly. The data was processed using Google Earth Engine and QGIS to identify UHI zones and calculate Zonal Statistics at the provincial level.
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Cross-Domain Robust Liveness Detection: A Transfer Learning Approach for Combating Sophisticated Presentation Attacks in Mobile Authentication
(2025-01-01)
Kerdpramote, Phuvis
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Poomdeesittinon, Akeanant
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Jamsri, Rapeeploy
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Krueyos, Phanuwit
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Muangkan, Ronnakorn
As biometric authentication systems become ubiquitous in Southeast Asia's digital economy, sophisticated presentation attacks using deepfakes, high-resolution displays, and 3D masks pose critical security threats. This paper presents a comprehensive cross-domain liveness detection framework that addresses the generalization challenges plaguing current systems. Our approach leverages MobileNetV2-based transfer learning with a novel two-phase training strategy, achieving superior cross-domain performance while maintaining computational efficiency for mobile deployment. We introduce domain-aware augmentation techniques and evaluate our system across multiple benchmark datasets including NUAA and a locally-collected Thai demographic dataset. Experimental results demonstrate 84.35% accuracy on NUAA and 78.62% cross-domain accuracy, with significant improvements in Attack Presentation Classification Error Rate (APCER) reduction from 28.7% to 15.4% compared to baseline methods. The system successfully detects emerging attack vectors including deepfake videos and tablet-based spoofing attempts. We provide comprehensive analysis of deployment challenges in resource-constrained environments demonstrating practical applicability for Thailand's mobile banking and digital identity verification ecosystem.
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ENHANCEMENT OF ARTIFICIAL IMMUNE SYSTEMS FOR THE TRAVELING SALESMAN PROBLEM THROUGH HYBRIDIZATION WITH NEIGHBORHOOD IMPROVEMENT AND PARAMETER FINE-TUNING
(2024-01-01)
Thapatsuwan, Peeraya
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Thapatsuwan, Warattapop
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Kulworatit, Chaichana
This research investigates the enhancement of Artificial Immune Systems (AIS) for solving the Traveling Salesman Problem (TSP) through hybridization with Neighborhood Improvement (NI) and parameter fine-tuning. Two main experiments were conducted: Experiment A identified the optimal integration points for NI within AIS, revealing that position 2 (AIS+NIpos2) improved solution quality by an average of 27.78% compared to other positions. Experiment B benchmarked AIS performance with various enhancement techniques. Using symmetric and asymmetric TSP datasets, the results showed that integrating NI at strategic points and fine-tuning parameters boosted AIS performance by up to 46.27% in some cases. The hybrid and fine-tuned version of AIS (AIS-th) consistently provided the best solution quality, with up to a 50.36% improvement, though it required more computational time. These findings emphasize the importance of strategic combinations and fine-tuning for creating effective optimization algorithms.

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