This study proposes a systematic and practical framework for selecting autoregressive integrated moving average (ARIMA) and seasonal autoregressive integrated moving average (SARIMA) forecasting models based on multiple error evaluation metrics, including root mean square error (RMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE). In practical forecasting applications, different error metrics often produce conflicting model selection outcomes, resulting in uncertainty in decision-making. Accordingly, this study introduces a percentage-based consistency evaluation approach to identify the most reliable forecasting model across multiple datasets. This study proposes a systematic framework for ARIMA and SARIMA model selection that integrates multiple forecasting error metrics to address conflicting model rankings and improve the consistency of model-selection decisions. This framework is applied to Thailand’s export value data, consisting of 69 monthly observations across 12 datasets derived from major industrial product groups (HS-84, HS-85, and HS-87) and key trading partners, including China, Japan, and South Korea. Results indicate that ARIMA(1,2,1) achieves the highest level of consistency across error metrics, with 83.33% under RMSE and 75% under MAPE and MAE. In terms of seasonal models, SARIMA (2,2,2)(2,0,0)₁₂ demonstrates the best overall performance. The findings confirm that the choice of error metrics can significantly influence forecasting outcomes. The proposed consistency-based framework reduces model selection conflicts and enhances the reliability of decision-making. This approach provides practical guidance for forecasting applications in economic planning, industrial systems, and export management.
Tea waste is an abundant agro-industrial residue but its valorization into functional inorganic composites remains largely unexplored. In this study, the waste was torrefied at 230 °C and combined with commercial TiO2 by a solvent-assisted solid-state dispersion route followed by calcination at 500 °C, giving a Tea-Waste-Derived carbon/ TiO2 material denoted AT– TiO2. A total of 3 AT: TiO2 mass ratios (9:1, 8:2 and 7:3) were prepared with pure AT and pure TiO2 controls processed identically, and characterized by XRD and SEM–EDS. XRD results showed that single-phase rutile retained its structure in every composite, with no peak shift and secondary phase. The carbon content was obtained in EDS pattern, due to the effect of using ethanol as a composite solvent. Scherrer analysis of the (110) reflection gave apparent crystallite sizes of 61, 34, 61, and 49 nm for pure TiO2 as well as the 9:1, 8:2 and 7:3 composites. SEM image analysis of 60 objects per sample, fitted to a log-normal distribution, gave median agglomerate sizes of 11.99 ± 0.51, 2.96 ± 0.32, 9.91 ± 0.74, 5.97 ± 0.66 and 11.00 ± 0.81 µm respectively. The 8:2 composite showed both the smallest median and the narrowest distribution (geometric standard deviation 1.56), indicating the most uniform dispersion. This study can serve as the basis for the development of biomass–inorganic composite materials with the potential to be applied in the fields of environmentally friendly adsorption and photocatalysis.
Accurate quantity take-off is essential for construction cost estimation, procurement, and project control, particularly for reinforced-concrete structural works. This study evaluates the accuracy of BIM 5D-based quantity take-off generated by Tekla Structures and Cubicost for concrete and reinforcement works in a three-story building project in Surakarta, Indonesia. A quantitative comparative case-study design was applied using the same structural drawings, reinforcement details, manual calculation recapitulations, and consultant bill of quantities (BOQ). Manual calculation was treated as a controlled analytical reference rather than an error-free ground truth, while the consultant BOQ was used as a secondary practical benchmark. The analysis used absolute deviation, percentage deviation, mean absolute percentage error (MAPE), root mean square error (RMSE), mean bias, Bland-Altman limits of agreement, and R² as supporting exploratory indicators. Based on the corrected element-level quantities, Tekla Structures and Cubicost produced concrete deviations of 1.803 m³ (0.30%) and 3.341 m³ (0.55%), respectively. For reinforcement, Tekla Structures and Cubicost produced deviations of 9,879.109 kg (9.07%) and 1,379.923 kg (1.27%), respectively. Deviations relative to the consultant BOQ ranged from 11.83% to 12.36% in the project-documentation comparison. The findings indicate that concrete quantities were relatively stable because they were primarily geometry-based, whereas reinforcement quantities were more sensitive to modelling assumptions, detailing rules, and extraction settings. The contribution of this study is a case-specific BIM 5D validation framework that separates analytical accuracy from BOQ agreement and highlights the importance of work-item sensitivity in structural quantity estimation.
Disruptions in agri-food supply chains pose logistical and operational challenges for ASEAN member economies, whose agricultural sectors are simultaneously exposed to fluctuations in freight costs and climate-related production shocks. Prior time-series applications in shipping markets, agricultural commodity co-movement, and supply chain performance have generally examined these channels separately, and the joint transmission of these channels into ASEAN agri-food commodity logistics outcomes remains largely unexamined. This study applies a Vector Autoregression (VAR) time-series modeling framework to identify and quantify the principal drivers of disruption in ASEAN agri-food logistics networks, using 4,108 daily observations spanning January 2015 to March 2026. Seven variables are jointly modeled: Baltic Dry Index daily change (D_BDI), Brent crude oil log-return (R_Brent), Geopolitical Risk Index daily change (D_GPR), Oceanic Niño Index anomaly change (D_ONI), ASEAN aggregate agricultural production return (R_ASEAN_Production), and daily price changes of Robusta and Arabica coffee. Stationarity is verified by the Augmented Dickey-Fuller (ADF) tests. The lag order p = 2 is selected based on BIC and HQIC. GPR shocks exert statistically significant lagged effects on Arabica price (p = 0.007) and Robusta price (p = 0.021); ONI anomaly changes significantly propagate into Robusta prices (p = 0.021). A cross-domain pathway from ONI to GPR (p = 0.010) is consistent with climate disruption signals coinciding with subsequent geopolitical risk movements. BDI first-order persistence coefficient is 0.596 (p < 0.001). Forecast Error Variance Decomposition (FEVD) shows Robusta-to-Arabica cross-variance at 44.03% at horizon 10. Durbin-Watson statistics (1.958–2.061) confirm residual white noise across all equations. These results provide quantitative inputs for engineering early-warning systems for disruption and logistics resilience planning in ASEAN agri-food supply networks.
Holy basil (Ocimum tenuiflorum) is a perishable herb with a high moisture content. Traditional hot-air drying methods result in longer drying times, higher energy use, and reduced quality. This study investigates combined hot-air and microwave drying to improve drying efficiency, reduce energy consumption, and maintain product color. A drying method was developed to evaluate the effects of hot air temperature (60, 70, and 80 °C) and microwave power level (300, 400, and 500 W) on drying kinetics, moisture diffusivity, color retention, and energy consumption. A mathematical transport model was developed in COMSOL Multiphysics to simulate the drying process. The model showed good agreement with experimental data. The results showed that microwave-hot air drying substantially accelerated moisture removal. Higher microwave power and hot-air temperature reduced drying time and specific energy consumption while increasing the effective moisture diffusivity. Combined hot‑air and microwave drying resulted in a lower total color change. The highest effective moisture diffusion coefficient (2.79 × 10⁻¹¹ m²/s) and the lowest total specific energy consumption (529.91 MJ/kg water evaporated) were obtained at a hot-air temperature of 80 °C and a microwave power of 300 W.
