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.
Rolled shapes using structural steel with a specified yield strength, Fy, of 65 ksi or higher are considered an alternative solution to built-up shapes using conventional steel grades. However, major evidence exists that current design rules (e.g., AISC 360, Eurocode 3) are likely overly conservative for these higher-strength steels with Fy ≥ 65 ksi—particularly at yield strengths of 80 ksi and above when employed as structural steel columns—due in part to assumptions regarding residual stresses. In order to understand the extent of these conservative predictions and explore if alternative design provisions could be provided to engineers utilizing specific shapes and high-strength materials grade, this study presents comprehensive nonlinear finite element analyses of rolled W-shape columns made from ASTM A992 (Fy = 50 ksi) and A913 Grade 80 (Fy = 80 ksi) steels. The models incorporate validated multiaxial residual stress distributions, geometric imperfections, and nonlinear material behavior, and are benchmarked against experimental data.Column flexural buckling curves are developed for a range of cross-sectional geometries and slenderness ratios and compared to predictions from AISC 360 and Eurocode 3 for both major and minor axis buckling. For A992 (Fy = 50 ksi), the AISC column curve overestimates the buckling strength for heavier sections and underestimates it for more slender or lightly built shapes, reflecting shape-dependent divergence. For A913 Grade 80 (Fy = 80 ksi), simulation results are generally close to AISC predictions, with a 5–14% increase in buckling strength observed, depending on the section geometry and slenderness. The findings generally support the use of AISC 360-22 but suggest refinement might be needed for heavier or high-strength rolled shapes in the inelastic buckling range. The results also highlight the potential need for shape- or grade-specific column curves. Future experimental validation will be essential to confirm these trends and guide potential updates to design standards.
Research on an innovative, rapid-assembly column splice is highlighted. The research is led by Dr. Jeffrey Berman and Dr. Dawn Lehman, professors in civil and environmental engineering at the University of Washington, and by Reid Zimmerman, Technical Director at KPFF in Portland, Oregon. Dr. Berman and Dr. Lehman share interests in seismic performance and design of steel structures, performance-based seismic design, and innovative structural systems. Both are recognized for their expertise in large-scale experimental testing, analytical investigations, and synthesis of experimental-analytical research to advance the state of the art and of the practice. Their honors include distinguished teaching and outstanding paper awards from multiple organizations, including the American Society of Civil Engineers (ASCE). Mr. Zimmerman is active in code development for ASCE 7 and ASCE 41, including helping to lead efforts in resilient seismic design and functional recovery. An AISC grant supports this column splice research. The SnaplocX connection is introduced, and highlights from work to date are presented.
Equations and a design table are developed to determine the available axial compressive strength of eccentrically loaded WT shapes with Fy = 50 ksi using both the LRFD and ASD methods. WTs considered are made from W-shapes ordinarily used as columns. Tabulated values account for the bending moment created in the member due to the load eccentricity, including second-order effects, and follow the provisions of Section H1.1 of the AISC Specification for Structural Steel Buildings (2022) for design of members subject to combined forces. Applicable limit states and cross-section classifications are considered in the development of the equations and the design table. Numerical example problems are presented.
Parametric numerical modeling was performed for three composite floor beam configurations (with different W-shape sections and one-way span lengths supported by shear connections) under exposure to one standard fire and three natural fire temperature-time histories. The parametric matrix included three levels of passive fire protection, four combinations of axial and rotational restraint at the beam ends, and three levels of applied flexural loading. A previously validated lumped mass heat transfer modeling approach was used to calculate steel temperatures for each flange and the web, and a one-dimensional finite element (FE) heat transfer modeling approach was used to calculate the temperature gradient through the structural thickness of the floor slab. A previously validated fiber-beam FE structural modeling approach was then used to model the flexural response of the one-way composite beam under fire. The results of parametric analysis showed that the loss of flexural resistance under any fire exposure can be conservatively predicted using a critical bottom flange temperature based on AISC 360-22, Table A-4.2.4, which is expressed as a function of the applied flexural utilization ratio, M/Mn. The bottom flange temperature at which flexural failure would occur was relatively consistent regardless of variations in beam end restraint as well as the level of applied fire protection. For composite beams that survived natural fire exposure through burnout, the bottom flange temperature always remained below the load-dependent critical value. In those cases, variations in beam end restraint significantly impacted the magnitude of residual tensile reaction forces that develop at the ends of the beam during cooling.
The purposes of this paper are to summarize the research on the torsional performance of square and rectangular hollow section members and compare the available experimental results to the applicable provisions in the AISC Specification (2022). A review of the research on the torsional strength of square and rectangular hollow section members revealed 49 experimental tests from 11 projects. A first-order reliability analysis was used to calculate appropriate resistance factors for the current design equations, revealing inconsistent reliability indices that are dependent on the predicted failure mode. Revisions are proposed for the provisions in AISC Specification Section H3.1 that result in a simpler design method with increased accuracy. Also, the accuracy of serviceability rotation calculations is evaluated using the available experimental data.
Errata to Vol. 62, No. 1 paper Generalized Elastic Lateral-Torsional Buckling of Steel Beams
Java is the most vulnerable region to hydrometeorological drought disasters in Indonesia. This study aims to identify recent climatological rainfall patterns and analyze meteorological drought-prone zones in Java. Spatial analysis was conducted using the Percent of Normal Precipitation Index (PNPI) based on high-resolution Global Precipitation Measurement (GPM) data for the period 1998-2024. A hybrid M-LSQM method, applied after M-QM, was introduced to improve satellite rainfall estimates. Results consistently enhanced the accuracy of GPM rainfall estimation, bringing it closer to in-situ observations across diverse topographic categories. The average correlation coefficient (r) reached 0.9498, the average RMSE was 22.869 mm/month, and the Mean Bias Error (MBE) was positive at 0.0184 mm/month. Overall GPM performance was represented by NSE (0.919), categorized as very good. Further analysis revealed that the combination of El Ni & ntilde;o and positive IOD events contributed to the intensification of extreme drought (PNPI <40%) in Java. Significant monsoon rainfall patterns were identified, with higher precipitation in western Java compared to the east. High-severity drought-prone zones were concentrated in central and eastern Java (108.5 degrees E-113 degrees E) during extreme drought periods. These findings highlight the urgent need for comprehensive planning and mitigation strategies to reduce the serious impacts of drought in Java.
This study investigates the lateral behavior of cold-formed steel (CFS) frames with different sheathing materials and coverage configurations under monotonic lateral loading. Fourteen one-bay two-story CFS frame specimens were tested using plywood and fiber cement board (FCB) panels installed at various sheathing heights of 0%, 50%, 75%, and 100% of the wall height. The experimental program evaluated the influence of sheathing type and sheathing height on failure modes, ultimate strength, stiffness, energy absorption, and ductility. The results show that plywood-sheathed frames primarily experienced ductile tearing and screw withdrawal, whereas FCB-sheathed frames failed through brittle fracture and rupture around screw connections. For the same sheathing configurations, the FCB-sheathed specimens exhibited higher strength and stiffness, compared to plywood. Increasing sheathing height significantly enhanced structural performance for both materials, with strength increasing by up to 1.9 times for plywood and up to 3.3 times for FCB compared to the unsheathed frame. Energy absorption also increased substantially with greater sheathing coverage. Despite having lower strength and stiffness, plywood-sheathed frames exhibited greater ductility due to their more flexible and less brittle behavior. Overall, the findings confirm that both the type of sheathing and the extent of sheathing coverage strongly influence the lateral resistance and deformation capacity of CFS frame systems.
Papillary thyroid carcinoma (PTC) is the most common type of thyroid cancer and has a high cure rate if detected in the early stage. Pathologists diagnose PTC by histology or cytology, paying special attention to tumour nuclear features. Despite being highly accurate in the hands of skilled observers, this approach is not entirely reliable, as some cases may lack classical features. The remarkable advances in artificial intelligence (AI) technology offer the possibility of assisting pathologists in the diagnosis of cancers such as PTC. In this study, convolutional neural networks (CNNs) were applied to classify histopathological images from 60 PTC cases, using normal adjacent thyroid tissue as control. The dataset was restructured at the patient level to prevent data leakage by ensuring that images from the same patient were assigned to a single subset. Five CNN architectures (VGG16, VGG19, ResNet50V2, DenseNet121, and EfficientNetB0) were evaluated using four architectural variations. Model selection was performed using validation data, followed by optimization of class-weight and threshold strategies. DenseNet121 achieved the best performance, with an average accuracy of 0.9883 and sensitivity of 0.9871. These results demonstrate that optimized CNN models can effectively identify PTC from histopathological images. Patient-level splitting provides a more realistic evaluation of model performance. The proposed approach shows strong potential as a decision-support tool, although further validation on multi-institutional datasets is required.
This study explores the synthesis and characterization of Ru catalysts supported on various TiO(2 )supports, with a focus on understanding the influence of TiO2 morphology on the catalytic performance in CO2 methanation. TiO2 supports were synthesized via a hydrothermal method with varying HCl concentrations, resulting in different crystallite sizes and morphologies. The introduction of Ru to these supports, followed by calcination, was shown to affect the crystallite size and surface characteristics, with significant variations in pore structure and surface area. Nitrogen adsorption-desorption isotherms revealed diverse porosity profiles, and H2-temperature programmed reduction (H2-TPR) experiments indicated variations in the reduction behavior and reducibility of the Ru catalysts, particularly highlighting the anomalous reducibility of the Ru/TiO2-4 sample. Catalytic activity tests for CO2 methanation were conducted over a temperature range of 200 to 400 degrees C. The results showed that the TiO2 morphology plays a crucial role in determining catalytic performance. Specifically, the Ru/TiO2-2.5 catalyst exhibited the highest CO(2 )conversion and CH4 selectivity among the hydrothermally synthesized TiO2 supports. The study found that change in TiO2 pore structure, particularly in the TiO2-4 support, led to a significant decrease in catalytic efficiency. Furthermore, the Ru/TiO2-2.5 and Ru/TiO(2-)r catalyst showed competitive performance relative to Ru/P25, a commonly reported optimal support in literature. However, the Ru/P25-cal catalyst displayed diminished activity, attributed to a substantial loss in surface area and pore volume due to high-temperature calcination.
Precise identification of Aquilaria species is essential for maintaining the authenticity and sustainability of agarwood, a high-value resinous wood used in perfumery, traditional medicine, and religious rituals. Conventional identification methods often fail due to overlapping morphological and chemical characteristics. This study introduces a dual machine learning approach combining Self-Organizing Maps (SOM) for unsupervised feature selection with Artificial Neural Networks (ANN) for supervised classification. Essential oil profiles from four Aquilaria species (A. beccariana, A. malaccensis, A. crassna, and A. subintegra) were analysed, revealing delta-guaiene, 10-epi-gamma-eudesmol, and gamma-eudesmol as significant discriminative markers. The ANN model achieved 100% classification accuracy, demonstrating the effectiveness of this hybrid approach. This approach provides a robust and scalable solution for species authentication, supporting quality assurance and sustainable resource management within the agarwood industry.
Sustainable Development Goals have emerged as a critical global agenda, including for micro, small, and medium enterprises (MSMEs). However, several MSMEs in Indonesia have struggled to implement these goals effectively. Kembang Mayang Batik Studio is one such enterprise, where a significant amount of waste is still being generated that could otherwise be avoided. To address this issue, a new method has been developed by integrating the Product-Service System and Sustainable Lean Production, which is named the Sus-PSS framework. The results of the study identified several types of waste, including defects, inventory, motion, and excess processing, as well as categorized activities into Value Added, Not Value Added, and Necessary Not Value Added. In addition, 11 sustainability indicators were identified, and based on the analysis, most have not met their targets, except for water consumption, with an overall sustainability index of 63.1%. Based on the results of Sustainable Value Stream Mapping, Sustainability Index, and Strengths-Weaknesses-Opportunities-Threats Analyses, twelve recommendations were formulated. After assessment, six of these recommendations were identified as immediately actionable. These efforts can help Kembang Mayang Batik effectively move toward sustainability by increasing value-added activities by 5%, eliminating waste, especially in the category of defects and unnecessary movements, and reducing non-value-added activities by 5%.
Most construction standards specify the asphalt-binder content for Permeable Friction Course (PFC) mix based on functional parameters, such as air voids (AV), and durability (Cantabro Loss CL). However, these standards do not account for parameters that assess the mixture's mechanical behavior. This study integrated the evaluation of mechanical and hydraulic performance, along with a multi-criteria analysis using the analytic hierarchy process (AHP), of a PFC mixture in the laboratory, testing asphalt contents of 4.5%, 5.0%, and 5.5%, modified with crumb rubber (CRM) (percentage of the total mixture mass). Hydraulic properties (AV and permeability coefficient), durability (CL at 25 degrees C and 60 degrees C), and mechanical characteristics were evaluated through indirect tensile strength (ITS), Resilient Modulus (RM), tensile strength ratio (TSR), and static creep test. An analysis of variance (ANOVA) was conducted to determine whether differences in results were significant. Additionally, AHP was employed to identify the optimal asphalt content based on laboratory test outcomes. The findings suggest that, following traditional construction standards in Colombia, the asphalt-binder content for the PFC mix should be 5.0%. Nevertheless, the multi criteria analysis based on the proposed methodology indicates that an asphalt-binder content of 5.5% provides better performance. Although this increase entails higher costs, it considerably improves the pavement's durability.
Machine learning adoption in domains such as healthcare, finance, autonomous driving, and law demands not only predictive accuracy but also transparency and trustworthiness. Conventional black box approaches, while powerful, often lack interpretability, limiting their acceptance in mission critical applications. The selected application domains-healthcare, autonomous driving, finance, and legal systems-are systematically chosen as representative high-stakes environments where decision errors can lead to severe consequences, including risks to human life, financial loss, and legal or ethical violations. The proposed LEAF-TML framework is specifically designed to address these contexts by ensuring transparency, accountability, and reliability under conditions where trust and interpretability are critical for decision-making. To address this challenge, we propose LEAF TML, a layered explainable AI framework for trustworthy machine learning. LEAF TML integrates governance, interpretable and hybrid modeling, multi-level explanation generation, human in the loop oversight, assurance monitoring, and adaptive feedback into a closed loop architecture. The framework was validated on four representative datasets: intensive care unit electronic health records, urban driving logs, financial transaction data, and legal case archives. Results demonstrated that LEAF TML consistently outperformed baselines including Logistic Regression, Decision Trees, Explainable Boosting Machines, Random Forests, and XGBoost. It achieved accuracy and fidelity scores of 0.930 and 0.940 in healthcare, 0.880 and 0.910 in driving, 0.920 and 0.930 in finance, and 0.880 and 0.920 in law. Additionally, trust perception increased by 22.84 percent, while explanation latency was minimized at 0.039 seconds. Statistical analyses confirmed the robustness, stability, and significance of improvements, establishing LEAF TML as a scalable, ethical, and transparent solution.
Thailand has experienced increasingly frequent landslides, posing significant risks to highway infrastructure. This study develops a corridor-scale landslide susceptibility mapping (LSM) framework for Thailand's highway network using a Random Forest (RF) model trained with conventional topographic, hydrological, and environmental conditioning factors. Model performance is evaluated using spatial cross-validation to mitigate spatial autocorrelation, yielding a mean ROC-AUC of 0.9626 +/- 0.0058, indicating strong predictive capability under geographically independent testing. Within the available Sentinel-1 coverage areas (Chiang Mai and Yala), persistent scatterer interferometric synthetic aperture radar (PS-InSAR) time-series deformation information is incorporated through feature-level integration to produce a deformation-informed dynamic landslide susceptibility map (LDSM). The deformation-informed model achieves a mean ROC-AUC of 0.9936 +/- 0.0108 under the same spatial validation setting, demonstrating improved discrimination performance within the InSAR footprints. Feature importance analysis indicates that terrain controls such as slope and roughness remain dominant predictors, while PS-InSAR deformation provides complementary time-dependent information that helps improve discrimination in actively deforming or marginally classified zones. Although dynamic susceptibility assessment is spatially restricted to areas with InSAR coverage, the results demonstrate that integrating time-series deformation indicators with machine-learning-based susceptibility modeling can refine hazard delineation along infrastructure corridors. The proposed footprint-aware framework provides a scalable approach for landslide risk assessment and infrastructure planning in regions where time-series InSAR data are available.
