Continuous monitoring of reservoir storage is essential for water resources management in tropical regions, where persistent cloud cover limits the use of optical satellite imagery. This study applies Sentinel-1 Synthetic Aperture Radar (SAR) data and Google Earth Engine (GEE) to support surface water monitoring and storage estimation for Mun Bon Dam, Nakhon Ratchasima Province, Thailand. Sentinel-1 Ground Range Detected (GRD) images acquired in Interferometric Wide Swath (IW) mode with VH polarization during 2020–2024 were processed through a reproducible GEE scripted workflow. Reservoir surface water areas were extracted using VH backscatter thresholding and calibrated against RID reference reservoir storage derived from water-level records and the reservoir capacity curve. Linear, second-order polynomial, and third-order polynomial models were evaluated. The second-order polynomial model was selected because it provided strong accuracy while maintaining physical plausibility within the observed operating range, with R² = 0.9424, RMSE = 9.36 Mm³, MAE = 7.58 Mm³, and MAPE = 10.36%. The results indicate that Sentinel-1 SAR and GEE can complement ground-based reservoir monitoring, although application to other reservoirs requires local calibration.
Small and Medium Enterprises (SMEs) in the manufacturing sector frequently experience persistent inefficiencies due to limited resources, manual operations, and inadequate process control. This study addresses these challenges by applying an integrated Lean Six Sigma (LSS) approach using the DMAIC framework in the production of Bracket SGS 57 at NIJU Ltd. Root cause analysis was conducted using Value Stream Analysis Tools (VALSAT), Failure Mode and Effects Analysis (FMEA), and time-motion studies. Key interventions included the implementation of preventive maintenance and the development of an ergonomic trolley based on Indonesian anthropometric data. The design utilized the 50th percentile for practical feasibility and was validated against the 5th–95th percentile range to ensure usability for approximately 90% of operators. Ergonomic risk was assessed using Rapid Upper Limb Assessment (RULA). The results demonstrate a 49% reduction in lead time (from 384.28 to 195.67 seconds), an increase in Process Cycle Efficiency (PCE) from 13% to 26%, and a reduction in defect rate from 12% to 0.5%. The RULA score improved from 7 to 3, while process capability reached Cp 1.33 and Cpk 1.15. These findings confirm the effectiveness of integrated interventions.
The China–Pakistan Economic Corridor (CPEC), a flagship component of China’s Belt and Road Initiative (BRI), aims to enhance regional connectivity and economic integration across South Asia, Central Asia, and the Middle East. This study examines CPEC’s prospects and challenges for Pakistan’s sustainable development, with emphasis on energy infrastructure, transport connectivity, industrial development, governance, and regional geopolitics. Using a qualitative policy-analysis approach supported by a structured narrative review of official reports, empirical studies, and recent project data, the study evaluates CPEC’s national and international role. The review shows that CPEC has expanded Pakistan’s energy, transport, logistics, and industrial-development capacity, but its long-term success depends on transparent governance, equitable provincial participation, security management, renewable-energy integration, and environmental safeguards. The study concludes that CPEC can support inclusive growth and regional stability if implemented through coordinated policy, institutional reform, and sustained international cooperation.
Urban ride-hailing behavior under systemic disruption is investigated by integrating traditional behavioral modeling with explainable machine learning techniques. The primary objective of this study is to identify key factors shaping ride-hailing preferences under both normal and disrupted conditions, and to assess how effectively different modeling approaches capture behavioral changes. The analysis is based on 381stated-preference survey responses collected in Chiang Mai, Thailand, during the pre-COVID and lockdown phases. Ordered logistic regression (OLR) models were developed to examine the influence of socio-demographic characteristics and perceptual factors on stated travel preferences, providing statistically grounded and interpretable insights. In parallel, Random Forest (RF) models were implemented to enhance predictive accuracy and capture complex, nonlinear interactions among variables. Explainable artificial intelligence (XAI) techniques were applied to interpret model outputs and to identify the most influential determinants of travel frequency and ride-hailing choice. The results indicate that monthly income, age, travel time, fare awareness, and service accessibility play critical roles in shaping ride-hailing decisions. Comparative analysis reveals strong consistency between high-performing OLR and RF models, reinforcing the robustness of the findings. The proposed framework offers a transparent and scalable approach for analyzing urban mobility behavior under volatile and uncertain conditions.
This work presents the development of a sensor network specifically designed for monitoring environmental conditions within rice storage warehouses. The primary goal is to provide early detection of rice spoilage, which typically arises from the accumulation of moisture or water leakage. The system employs low-cost thermistors to detect the heat generated by rice grain respiration, a strong indicator of high-moisture conditions. The developed hardware incorporates a microcontroller that connects to multiple thermistors via multiplexer circuits. Data are transmitted wirelessly using the NETPIE Internet of Things platform. The collected data are then processed using MATLAB software to generate daily average temperature difference maps. These maps visualize temperature changes over time and spatial coordinates in the rice pile, allowing accurate identification of high-risk spoilage areas. Field tests demonstrated the system’s ability to detect temperature variations in areas with simulated water exposure, closely reflecting real-world conditions. The proposed system is an effective tool for enhancing paddy rice storage efficiency and minimizing potential economic losses due to spoilage.
