This study aims to develop a model for predicting daily sea wave heights in the Makassar Strait to support shipping safety in tropical waters. Observation data were obtained from the Makassar station of the Meteorology, Climatology, and Geophysical Agency (Badan Meteorologi, Klimatologi, dan Geofisika, BMKG) (January 2018–December 2023), covering wind speed, wind direction, sea surface temperature, and rainfall. Feature selection was performed using Frequent Pattern Growth (FP-Growth), which was chosen because it efficiently finds association patterns between variables with only two database scans, making it more economical than other techniques such as Recursive Feature Elimination or Principal Component Analysis. The selected features were used to build a Support Vector Regression (SVR) model optimised with the Fruit Fly Optimisation Algorithm (FOA). The evaluation was conducted with zonal validation in three sub-regions of the Makassar Strait (north, central, south) using a lead time of one day ahead. The results show that the SVR-FOA model produces an average root mean square error (RMSE) of 0.4938 m (95% confidence interval (CI): 0.472–0.516), mean absolute percentage error (MAPE) of 0.00208 (95% CI: 0.00195–0.00221), and a correlation of 0.935. SVR-FOA reduced the RMSE by 16.8% compared to the default SVR, while compared to the grid search SVR, there was a 6.7% reduction. The model’s performance is comparable to similar studies in the literature, although the RMSE is still higher than Long-Short Term Memory (LSTM) and XGBoost; however, SVR-FOA excels in stability between zones. In conclusion, SVR-FOA with FP-Growth feature selection effectively predicts daily sea wave height in the Makassar Strait. Further research is needed to test shorter time scale predictions, real-time data integration, and field validation with stakeholders.
This study aims to evaluate and rank regional agricultural technology competitiveness in East Java, Indonesia, using a structured multi-criteria decision-making approach. Specifically, it addresses four key objectives: (1) to apply the Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) method to assess and rank regional competitiveness across multiple technological dimensions; (2) to examine whether agricultural technology adoption levels differ significantly across regions using one-way Analysis of Variance (ANOVA); (3) to evaluate the sensitivity and robustness of the ranking results under alternative weighting scenarios through sensitivity analysis and rank correlation measures (Spearman’s ρ and Kendall’s τ); and (4) to derive policy-relevant and system-oriented implications for enhancing competitiveness and reducing regional disparities. The study employs a quantitative approach based on primary survey data collected from 210 farmers across seven regions in East Java. Four key dimensions are considered, namely environmental, irrigation, marketing, and production technologies. The PROMETHEE method is used to generate regional rankings, while ANOVA is applied to test for statistically significant differences in technology adoption. Robustness is further assessed through systematic weight variations and rank correlation analysis. The results reveal substantial regional disparities in relative technological competitiveness, with leading regions demonstrating more balanced, integrated adoption across multiple technological dimensions. ANOVA results confirm that differences in technology adoption across regions are statistically significant (p < 0.01), thereby providing complementary statistical evidence for inter-regional variation in the underlying technology adoption indicators used in the PROMETHEE analysis. The robustness analysis shows that the ranking results are highly stable across most weighting scenarios, with only minor variations observed when marketing-related criteria are emphasized. This study contributes methodologically by integrating multi-criteria decision-making with statistical validation and robustness testing in a unified framework. From a policy perspective, the findings highlight the importance of strengthening market access, improving technological integration, and implementing region-specific interventions to enhance agricultural competitiveness and reduce disparities.
This paper presents a reproducible workflow for three-dimensional modeling of a corridor-type building interior using terrestrial laser scanning (TLS) data. It also provides a quantitative evaluation of the workflow on a real object. In contrast to studies that focus mainly on automatic segmentation or scan-to-building information modeling (BIM), this study emphasizes the reproducible integration of a field protocol, registration graph control, and two-stage quality assurance (QA). The QA procedure combines internal registration statistics with independent metric verification. The field campaign included 82 Leica BLK360 scanner setups completed within one working day. Adjacent stations were acquired with controlled overlap, and the scanning network was locally reinforced in repetitive corridor geometry. The setup height ranged from 1.40 to 1.55 m. The average working scanning distance was 5.8 m, and the maximum distance was 12.1 m. Post-processing was performed in the Leica Cyclone software ecosystem. The procedure included visual inertial system (VIS)-assisted preliminary alignment, registration graph inspection, removal of seven weak links, global optimization, combined point cloud cleaning, and final metric verification. The resulting point cloud contained more than 100 million colorized points. The final registration root mean square error (RMSE) did not exceed 5 mm. The 95th percentile of residual errors (P95) was 18 mm, and the maximum residual was 28 mm. Independent verification showed that 18 control linear dimensions measured in the point cloud agreed with in situ tape measurements within 4–5 mm. The tape measurements were performed with a nominal accuracy of ±1 mm. The main geometric parameters of the interior were confirmed: a corridor length of 77.6 m, ceiling heights of 2.96–3.02 m, angles of 92.2–92.7°, and diameters of six engineering pipes ranging from 0.04 to 0.075 m. The resulting point cloud can be used as input data for scan-to-BIM workflows and for developing digital representations of interiors, provided that the described acquisition and quality-control protocol is followed.
Boundary layer separation at high angles of attack often limits the aerodynamic performance of airfoils. Flow control strategies are generally classified into active and passive methods, with the latter offering simple and energy-free solutions. In this study, a macro-cylinder with diameter of 4 mm and chord length of 300 mm was installed on the upper surface of a National Advisory Committee of Aeronautics (NACA) 0012 airfoil at different chord wise positions (X = 1, 2, 3, and 3.5 cm from the leading edge). NACA 0012 airfoil which has dimensions 150 mm chord and 300 mm span (symmetrical) Experiments were conducted in a subsonic wind tunnel at a free-stream velocity of 30 m/s and angles of attack ranging from 0° to 16° step 2. The results prove that Stall behavior was considerably changed by installing a state-of-the-art macro-cylinder. By energizing the boundary layer and postponing flow separation, the cylinder functioned as a passive vortex-like generator. The best overall configuration was obtained at X = 3.5 cm. The maximum lift force reached 5.45 N at 14°, while the maximum lift coefficient ($C_L$) reached 0.8378 at 12°. At 16°, the same configuration maintained a lift force of 5.38 N and $C_L$ of 0.6715, indicating improved post-stall aerodynamic behavior compared with the baseline airfoil. This improvement is attributed to the macro-cylinder’s ability to energize the boundary layer and suppress early separation.
The research analyzes whether monitoring system design, calibration management, timestamp consistency, data traceability, and verification procedures relate to the risk control of the financial aspects of methane-abatement engineering projects. An analytical case study based on a single project and involving a before-and-after comparison of the implementation of an monitoring, reporting, and verification (MRV) regime was conducted under fixed engineering and accounting conditions. This design allows the comparison to focus on differences in MRV evidence management conditions rather than on changes in physical mitigation technology. Conservative issuability was estimated using the low-confidence adjustment metric (LCAM). This analytical metric scales engineering emission reductions by evidence-related factors without supplanting registry rules or verifier judgment. With the enhanced MRV regime, the conservatively supportable fraction was 77.0% to 91.3%, while the realized price wedge declined from 0.30 to 0.12. The monitoring-to-issuance period was also shortened by 50 days.
Railway timetable planning plays a central role in the coordination and operational performance of transportation systems. Efficient timetable development remains essential for balancing infrastructure constraints, service quality, operational efficiency, and economic objectives in railway operations. The interaction among these dimensions makes timetable planning a complex decision problem for infrastructure managers and transport operators. This study aims to evaluate the relative importance of the principal criteria influencing railway timetable planning and to provide quantitative support for transportation system decision-making. A structured evaluation framework was developed using the fuzzy PIvot Pairwise RElative Criteria Importance Assessment (fuzzy PIPRECIA) method. Forty decision-makers with professional experience in railway operation, infrastructure management, engineering practice, and academia participated in the assessment process. Five main criteria were examined: railway line capacity, railway station capacity, number of passed trains, quality of train operations, and revenues of the planned timetable. The results showed that revenues of the planned timetable received the highest importance weight, followed by quality of train operations, number of passed trains, railway line capacity, and railway station capacity. The findings further showed that operational and economic dimensions exerted greater influence on timetable planning decisions than infrastructure-capacity factors. The results indicate that railway timetable planning should be approached as a system-level coordination problem rather than a capacity allocation exercise alone. This study provides a structured decision-support perspective for evaluating competing planning priorities and offers a practical basis for improving timetable development and operational performance in railway transportation systems.
Water scarcity is a major challenge in agriculture, where nearly 70% to 80% of freshwater is used for irrigation. This study reviews clay pot irrigation and its integration with drip irrigation and Internet of Things (IoT)-based monitoring for improving water use efficiency. Previous studies have reported substantial irrigation water savings under clay pot irrigation systems up to 80% compared to conventional methods by supplying water slowly near to the root zone and reducing losses. It also provides good water use efficiency and maintains acceptable crop yield under limited water conditions. Previous studies have shown that the performance of the clay pot irrigation depends on pot design, including size and shape. A few studies suggested that storing water in clay pots and the water passing through the pot walls may lead to some improvement in water quality, although detailed data is still limited. However, the integration with drip irrigation and IoT-based control can further improve water distribution and reduce manual effort.
This study examines how digital technologies shape green supply chain management (GSCM) in Vietnam’s electronics industry. Using an exploratory qualitative multi-case design, we investigated two leading Vietnamese electronics firms and triangulated evidence from company documents, field observations and 12 semi-structured interviews, including 10 interviews across the two focal firms and two interviews with external experts. Interviewees represented senior management and key functions related to environmental management, production, procurement and technology, with interview duration ranging from 80 to 120 minutes. The cases suggest an internal environmental management-led digitalization pattern in which firms first deploy digital tools for internal environmental monitoring and control and subsequently strengthen greener manufacturing and environmental cooperation, while green procurement and reverse logistics tend to lag when data integration, supplier participation and analytics capabilities remain limited. Digital adoption appears to support operational efficiency, environmental performance, employee capability development, supplier participation and faster Environmental, Social, and Governance (ESG) compliance responses, enabled by real-time sustainability information and reuse practices linked to reverse logistics. However, implementation is constrained by investment costs, skills gaps, fragmented systems and cybersecurity risks, reinforced by uncertain sustainability requirements and weak domestic green demand. The findings provide exploratory multi-case evidence from two leading Vietnamese electronics firms on practice-specific digitalization in an emerging-economy context and propose a staged digital-green capability-building roadmap. The study supports analytical rather than statistical generalization and should be interpreted as theory-building evidence for digital-green supply-chain transformation.
River transport remains central to mobility in Banjarmasin, Indonesia, where riverine settlements and uneven road access continue to shape everyday travel and regional connectivity. This study examines the factors associated with water-bus use on the Banjarmasin–Muara Teweh corridor and evaluates how these factors relate to sustainable fluvial mobility. A quantitative survey was conducted with 60 passengers of the Pancar Mas water bus at Banjar Raya Pier, supported by brief interviews on travel motives. The questionnaire covered economic, regional, and social indicators. Data adequacy was tested using the Kaiser-Meyer-Olkin (KMO) measure and Bartlett’s test, followed by principal component extraction, Varimax rotation, and confirmatory factor analysis (CFA). The results showed acceptable sampling adequacy (KMO = 0.614) and significant inter-variable correlations (Bartlett’s test, $p <$ 0.001). Three factors were retained and together explained 69.55% of the variance. Economic conditions formed the strongest factor, with income opportunity (loading = 0.879), occupation type (0.800), and job availability (0.735) as the main indicators. Regional characteristics were represented by transport availability (0.913) and accessibility (0.838), while the social dimension was reflected in housing ownership status (0.851). The CFA results also showed acceptable model fit, with $\chi^2$/$df$ = 2.15, goodness of fit index (GFI) = 0.91, comparative fit index (CFI) = 0.93, and root mean square error of approximation (RMSEA) = 0.062. The findings indicate that water-bus use in this corridor is shaped by livelihood opportunities, transport access, and settlement security. The study provides empirical evidence for maintaining river transport as part of regional connectivity and sustainable transport planning in riverine areas.
Environmental noise generated by motorcycle traffic constitutes a critical challenge for intermediate cities of the Global South, where high motorization rates intensify exposure to stressful acoustic environments. In Florencia (Caquetá, Colombia)—a city in which motorcycles represent 96.6% of the vehicle fleet—noise functions not only as an environmental pollutant but also as a psychosocial trigger associated with irritability, stress, and aggressive driving behaviors among young riders. This study evaluates urban motorcyclists’ perceived effectiveness of regulatory measures aimed at noise and traffic control, considering how education level and driving experience shape normative perceptions. Using a non-experimental, cross-sectional design, data were collected from 502 motorcyclists. Kruskal–Wallis tests and Spearman correlations revealed a significant positive association between higher education and favorable perceptions of regulatory effectiveness, while no association was observed for driving experience. An exploratory factor analysis (EFA) confirmed a two-factor structure (54.3% variance), differentiating structural/collective measures from individual/educational ones. Overall, structural and educational interventions were perceived as more effective than coercive approaches. These findings highlight the need for context-sensitive regulatory frameworks that integrate social legitimacy, cultural adaptation, and psychological determinants of behavior. The study contributes empirical evidence for designing participatory and education-centered strategies for noise management and mobility governance in structurally informal urban contexts such as Florencia.
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