This study proposes a fuzzy-based adaptive temperature management system for hydroponic cultivation in tropical climates. Unlike conventional fixed-setpoint controllers, the proposed Mamdani fuzzy system utilizes pH and electrical conductivity (EC) as contextual inputs to dynamically adjust temperature control strategies. The underlying hypothesis is that maintaining lower root-zone temperatures (RZTs) during suboptimal pH/EC conditions may increase dissolved oxygen availability, partially compensating for nutrient stress. The Internet of Things (IoT)-enabled system employs Long Range wireless protocol (LoRa) communication for long-range, low-power data transmission, with fuzzy inference executed at the gateway for offline resilience. A five-month field validation (April–August 2024) in Ho Chi Minh City demonstrated effective temperature regulation, maintaining solution temperature within the 18–28 °C operational target range for 88.7% of the trial period, with zero exceedance of the 35 °C critical threshold. The system maintained pH at 5.72 ± 0.32 (86.4% time in optimal range) and EC at 1.87 ± 0.28 mS/cm (81.3% time in optimal range). Retrospective simulation comparing the proposed controller against On/Off, proportional-integral (PI) baselines, and temperature-only FLC baselines, demonstrated a 15–16% reduction in chiller runtime while maintaining equivalent thermal safety. Operational crop assessment across three cultivation cycles indicated commercially viable lettuce production. A dedicated system engineering analysis addresses architecture trade-offs, reliability, scalability, and cost-effectiveness for practical deployment in tropical commercial operations.

This study explores the macroeconomic impact of the Zero Over-Dimension Over-Load (ODOL) policy in Indonesia, especially its influence on logistics costs, inflation, and economic growth. The policy is not discussed here only as a matter of transport compliance, but also as a structural change in logistics governance that may affect the wider economy. A mixed-methods approach was used, based on primary survey data collected in 2025 from logistics stakeholders in DKI Jakarta and West Java. For the analysis, the Leontief Price Model was applied to estimate price transmission effects, while the dynamic Computable General Equilibrium (CGE) IndoTERM model was used to simulate cost shocks, investment adjustment, and fiscal reallocation. The findings show that the policy increases national logistics costs by 4.58% in the short term, which raises the logistics cost-to-GDP ratio to 14.94%. However, the longer-term results are more positive. The simulation suggests a 0.05% increase in GDP, equivalent to a net output gain of IDR 14.3 trillion. This result is associated with a 6.74% increase in fleet investment, estimated at IDR 42.4 trillion, as well as fiscal savings caused by lower infrastructure damage. These results suggest that stricter logistics regulation may bring broader economic benefits when the analysis goes beyond the immediate rise in transport costs. In practical terms, the policy should be supported by fiscal incentives for fleet modernization and by careful timing of enforcement, especially to limit inflationary pressure in food and construction-related sectors.

The rapid growth of global trade has heightened the importance of efficient container handling, environmentally responsible operations, and high-performing equipment selection in sustaining the competitiveness of modern supply chains. Container Freight Stations (CFS) serve as critical operational hubs where loading, unloading, inspection, and temporary storage activities are conducted, thereby requiring equipment capable of safely and efficiently handling heavy-tonnage cargo while aligning with green port transformation goals. Forklifts, which constitute one of the core equipment groups in CFS yards, differ significantly in terms of lifting capacity, power systems, maneuverability, hydraulic performance, ergonomics, and environmental impact, transforming forklift selection into a complex, multi-dimensional decision problem shaped by both technical and Environmental, Social, and Governance (ESG)-oriented considerations. Incorrect equipment choices may lead to operational downtime, energy inefficiency, equipment failures, and occupational safety risks, particularly in operations involving loads exceeding 25 tons. To address these challenges, this study proposes a hybrid decision-making framework that integrates expert-driven fuzzy assessments with sustainability-based evaluation using the FF-Hamacher-MEREC-ARLON methodology. In the first stage, expert weights and criterion importance values were calculated through the FF-MEREC approach, while alternative forklifts were ranked using the FF-ARLON method in the second stage. Two sensitivity analysis scenarios were applied: one by modifying the tradeoff ratio within ARLON and the other by sequentially removing each criterion. In both scenarios, the fourth alternative consistently emerged as the most suitable option. Furthermore, comparative analyses using eight established MCDM techniques; ALWAS, AROMAN, ARTASI, MABAC, MARCOS, RAM, SAW, and WASPAS; demonstrated complete agreement with the proposed model, confirming the fourth alternative as the top-ranked choice. The findings highlight the robustness, reliability, and sustainability alignment of the proposed framework for high-stakes heavy-duty equipment selection in port-based logistics operations.

The Load Haul Dumper (LHD) is essential machinery utilized for moving ore in the underground mining industry, in order to fulfil production targets. In this connection, the efficiency of the equipment should be maintained at an ideal standard, to be accomplished by reducing unexpected failure of components or subsystems in this intricate system. Downtime analysis helped identify faulty components and subsystems, which require the development of complementary maintenance plans to facilitate the replacement or fixing of parts. Proper practices of maintenance management improve the performance of the equipment. In this research, the efficiency of the LHD machine was assessed through reliability methods. Initially, the assumption of independent and identical distribution (IID) for the data sets was validated using trend and serial correlation analyses. The statistical tests indicated that the data sets adhered to the IID assumption. Therefore, a renewal process method was utilized for additional examination. The Kolmogorov-Smirnov (K-S) test was utilized to identify the most suitable distribution for the data sets. The theoretical probability distributions were estimated parametrically using the Maximum Likelihood Estimate (MLE) approach. The dependability of each separate subsystem was determined using the optimal fit distribution. Based on the reliability outcomes, preventive maintenance (PM) time plans were created to reach the targeted 90% reliability. Different maintenance strategies, in addition, were suggested to the maintenance team to extend the lifespan of the machine.

Inefficiencies in traditional spare parts management for aircraft maintenance—including excessive inventory costs, supply chain delays, and operational disruptions—have long hindered fleet readiness and increased maintenance expenditure. To address these challenges, an integrated, reliability-driven inventory optimization framework has been developed by leveraging predictive analytics, real-time sensor data, and emerging digital technologies. The proposed model is grounded in Reliability-Centered Maintenance (RCM) principles and enhanced by Artificial Intelligence (AI), the Internet of Things (IoT), and digital twin technologies. Through the deployment of advanced sensor networks, real-time performance data are continuously collected and analyzed to forecast component degradation and predict imminent failures. This enables the transition from time-based to condition-based maintenance scheduling. Predictive models, including Long Short-Term Memory (LSTM) neural networks and Random Forest classifiers, are employed to enhance the accuracy of failure prognostics and spare parts demand forecasting. The dynamic alignment of spare parts provisioning with actual equipment reliability has been shown to reduce overstocking and prevent critical shortages. A case study conducted within a commercial airline fleet demonstrated a 20% reduction in inventory-related costs and a 15% decrease in aircraft downtime. Furthermore, operational efficiency and safety were significantly improved by minimizing unscheduled maintenance events. The proposed framework not only supports predictive and prescriptive maintenance strategies but also establishes a replicable model for digital transformation in aviation logistics. By integrating real-time analytics with digital twin simulations, a data-centric paradigm is introduced for proactive maintenance decision-making. This advancement paves the way towards more sustainable, cost-effective, and resilient aviation operations, aligning with broader industry goals of environmental responsibility and performance optimization.