Asian Journal of Engineering, Social and Health

Uncovering Nonlinear Urban Road Geometric Thresholds Using Mars Traffic Modelling

2026-05-28T07:27:27+00:00

Urban traffic facilities in densely populated metropolitan areas often experience performance degradation due to the complex interactions between road geometric characteristics and driver behavior. Conventional traffic modeling approaches generally assume linear relationships, which may fail to capture critical threshold effects in heterogeneous traffic environments. This study aims to identify nonlinear relationships between urban road geometry, driver behavior, and traffic performance, as well as to determine critical threshold values that significantly influence operational efficiency at a grade-separated u-turn facility. The research employed a quantitative approach using microsimulation and Multivariate Adaptive Regression Splines (MARS) modeling. A total of 62 simulation scenarios were developed by varying geometric parameters, including turning radius, gradient, and weaving section length, along with behavioral variables such as motorcycle proportion and time headway. Traffic performance was evaluated using average delay and through-traffic indicators, while MARS was applied to detect nonlinear interactions and critical knot points. The results demonstrate that the MARS models achieved high predictive accuracy, with generalized R-squared (GRSq) values of 0.6687 for delay and 0.9231 for through traffic. Critical thresholds were identified at a turning radius of 12 meters, a gradient of 4%, and a weaving section length of 40 meters. Motorcycle proportion and time headway were also found to significantly affect traffic performance. These findings confirm that traffic performance is influenced by nonlinear interactions between geometric design and driver behavior. Therefore, MARS provides an effective analytical framework for identifying design-sensitive thresholds and supporting more adaptive, behavior-oriented urban road planning and traffic management strategies.

Large Language Models for Intelligent Code Generation in Software Engineering: A Systematic Review and Future Research Directions

2026-05-30T02:55:08+00:00

The proliferation of Large Language Models has catalyzed a paradigm shift in software engineering automation, yet existing literature reviews predominantly evaluate functional correctness while systematically neglecting security posture, maintainability, and multi-language deployment contexts. This study addresses the critical research gap regarding the absence of unified, multi-dimensional assessment protocols for LLM-generated code in production environments. Through a PRISMA-guided systematic review of 87 primary studies published between 2020 and 2025, this research examined architectural evolution, evaluation methodologies, and deployment barriers using a dual-phase qualitative and quantitative synthesis supported by a custom quality assessment framework. A five-criterion evaluation instrument was applied to ensure methodological rigor, with strong inter-rater reliability (Cohen’s kappa = 0.84). The findings reveal that decoder-only transformer architectures have achieved dominant performance on generative benchmarks, with Claude-3 Opus attaining 74.9% Pass@1 on HumanEval. However, only 12% of evaluated studies incorporated security metrics, fewer than 8% assessed maintainability, and benchmark contamination threatens the validity of reported generalization. The novelty of this work lies in proposing a unified evaluation framework that integrates functional correctness, Common Weakness Enumeration vulnerability scanning, and maintainability metrics, alongside a standardized multi-language protocol. The implications suggest that enterprise DevSecOps pipelines must embed static security analysis and cross-language quality gates before production integration. Future research should prioritize runtime verification, longitudinal productivity studies, and contamination-free benchmarks to ensure trustworthy deployment of generative AI in mission-critical software ecosystems.

Optimization of Indonesia's Pioneer Air Transport Network: Cost Efficiency under Capacity and Minimum Service Constraints for Airports in Papua

2026-06-11T03:01:52+00:00

This study develops a two-stage optimisation model to evaluate Indonesia's pioneer passenger air transport network in Papua, including Dobo Airport in Maluku Province as a connected external node. The model addresses the need to improve public-service efficiency while preserving historical demand and satisfying airport capacity feasibility, belly cargo requirements, and minimum service levels for remote communities. The first stage applies a capacitated location-allocation formulation to assign spoke airports to existing hubs or selected potential hubs. The second stage optimises annual flight frequency on the resulting pioneer arcs under passenger capacity, belly cargo capacity, symmetric operation, and network-level load factor constraints. Network cost combines link operating cost and airport terminal cost, while commercial continuation is used as an availability indicator and emissions are evaluated after optimisation. Five scenarios were tested by varying minimum frequency and network load factor targets. The selected scenario reduces network cost from Rp649.32 billion to Rp603.96 billion, generating savings of Rp45.36 billion, or 6.99%. The average load factor increases from 74.84% to 90.28%. The optimised network serves all historical passenger demand without violating hub capacity constraints.

Mode Choice Behaviour and Fare Elasticity of Non-Vehicle Passengers in Merak-Bakauheni Ferry Services

2026-05-28T07:27:40+00:00

The Merak-Bakauheni corridor is Indonesia's most critical maritime transit route. The introduction of an Executive Ferry Service to complement the Regular Service has triggered an unbalanced passenger distribution, which persists amidst a 468% fare disparity (IDR 84,800 vs. IDR 18,100). This study aims to analyze the mode choice behavior by contrasting the absolute purchasing power (Ability to Pay/ATP) with the perceived premium service value (Willingness to Pay/WTP) of non-vehicle passengers who have previously utilized this ferry route. Utilizing a Stated Preference (SP) framework, primary data from 111 respondents from various passenger groups were modeled via Binary Logistic Regression. The optimized utility model confirms that ticket fare is the primary determinant for 51.4% of passengers. The empirical findings reveal a significant economic disparity: while the passengers' average ATP is high at IDR 871,443, their extracted WTP threshold for the Executive Service is merely IDR 69,543. Consequently, demand is highly elastic, with an estimated own-price elasticity of 2.55 and a cross-price elasticity of 1.28. The study concludes that the passenger distribution imbalance is driven not by a lack of affordability, but by the failure of the premium service's perceived value to justify the price markup. It is recommended that operators adjust their price ceiling closer to the estimated WTP threshold to restore traffic equilibrium and optimize long-term revenue across the dual-service system.

Modeling of Factors Influencing the Adoption of Digital Twins Infrastructure to Support Construction Project Automation in Indonesia

2026-06-05T03:09:37+00:00

The construction industry continues to face persistent challenges related to project delays, cost overruns, low productivity, safety risks, and limited integration of digital technologies, particularly in developing countries such as Indonesia. In response to these issues, Infrastructure Digital Twins (IDTs) have emerged as an advanced digital approach capable of integrating real-time data, simulation, prediction, and optimization to support construction project automation. This study aims to develop and test an empirical model of the factors influencing IDT adoption in Indonesian construction projects by integrating technical, organizational, and external environmental perspectives. The research applies a confirmatory quantitative approach using Partial Least Squares Structural Equation Modeling (PLS-SEM), supported by Focus Group Discussions to validate findings and formulate strategic recommendations. The study examines constructs including digital infrastructure readiness, system interoperability, data security and quality, top management support, human resource competence, organizational readiness, regulatory support, competitive pressure, external ecosystem support, adoption intention, and adoption rate. The findings indicate that IDT adoption is shaped not only by technological readiness but also by managerial commitment, organizational capacity, regulatory alignment, and industry ecosystem support. The study concludes that successful IDT implementation requires an integrated strategy involving construction organizations, project stakeholders, and policymakers to accelerate digital transformation, strengthen data-driven decision-making, and enhance sustainable construction project automation in Indonesia.

Evaluation The Optimization of Thermal Barrier Coating Stripping Methods on Inconel 939 Gas Turbine Vanes

2026-06-12T01:34:00+00:00

Thermal Barrier Coatings (TBCs) are widely used in power plants and aircraft engines. TBCs are exposed to high temperatures and repeated changes in operating parameters, which determine their service life, or Equivalent Operating Hours (EOH). This study evaluates three stripping methods — HCl 6 & 12 mol, HCOOH 6 & 12 mol, and grit blasting — for removing a Yttria-Partially Stabilised Zirconia (YPSZ) top coat and MCrAlY bond coat from Inconel 939 vanes. Stripping rate, surface roughness, microstructure, and coating completeness were compared. Grit blasting had the highest stripping rate (605 ?m/hour) but left residual bond coat and an interdiffusion zone (IDZ) with poor homogeneity. HCOOH methods were ineffective, leaving thick residues and high surface roughness (7.2–7.8 Ra). HCl 12 mol removed all layers but caused carbide coarsening. HCl 6 mol achieved complete layer removal, a stripping rate of 22.8 ?m/hour, acceptable roughness (3.33 Ra), and no significant microstructural damage. In conclusion, chemical stripping using HCl 6 mol is the most optimal method for TBC removal from Inconel 939 vanes.

Beyond Project Status: System Shift as a Structural Diagnostic Framework for Predicting Delay, Stagnation, and Adaptive Progression in Pharmaceutical Development Portfolios

2026-05-28T07:27:50+00:00

The pharmaceutical industry continues to face significant challenges in research and development (R&D) productivity, characterized by increasing development costs, extended timelines, and high rates of project delay and stagnation. Although project status classifications are widely used to monitor development progress, they often fail to explain the structural factors that cause projects to become stalled. This study aims to evaluate the System Shift framework as a structural diagnostic model for predicting delay, stagnation, adaptive progression, and success in pharmaceutical development portfolios. An exploratory empirical-methodological approach was employed using data from a pharmaceutical development portfolio consisting of 112 projects, from which 41 project-level cases were selected and coded. The framework operationalized seven dimensions: System Condition, Domain Lock, Actor Complexity, Chokepoint Severity, Position Quality, Strategy Quality, and Feedback Maturity. Data were analyzed through descriptive statistics, correlation analysis, regression models, logistic models, and cluster analysis. The findings reveal that Chokepoint Severity is the strongest predictor of project delay (r = 0.932; R² = 0.868), while Feedback Maturity and Strategy Quality significantly enhance progression and success. Furthermore, the System Shift Risk Score demonstrated superior predictive performance compared to traditional status-based classifications. The study concludes that System Shift provides a promising diagnostic framework for identifying structural constraints and adaptive capacities, thereby supporting more effective portfolio governance, decision-making, and organizational learning in pharmaceutical development environments.

The Relationship Between Knowledge, Attitudes, and Behavior and Symptoms of Scabies at the At-Taqwa Orphanage in Kalumata, Ternate City

2026-06-11T03:23:59+00:00

Scabies is a skin disease caused by the Sarcoptes scabiei var. hominis mite, with approximately 300 million cases reported annually worldwide. According to data from the Ternate City Health Office, the Kalumata Health Centre recorded 311 cases in 2023. The At-Taqwa Kalumata Orphanage in Ternate City represents a high-risk setting for scabies, which serves as the basis for this study. Knowledge, attitude, and behaviour are important factors in preventing scabies. This study aims to determine the relationship between knowledge, attitude, and behaviour and the occurrence of scabies symptoms among residents of the At-Taqwa Kalumata Orphanage in Ternate City. A cross-sectional analytic design was used, employing univariate and bivariate analyses, specifically the chi-square test. A total of 42 samples were obtained using a total sampling technique, and data were collected through interviews using a questionnaire. Of the 42 samples, knowledge was found to be in the poor category (42.9%), attitude in the poor category (40.5%), and behaviour in the sufficient and poor categories equally (38.1%). The majority of respondents were aged 11–14 years (42.9%), female (69.0%), and had completed junior high school as their highest level of education (47.6%). Bivariate analysis yielded a p-value of 0.038 for knowledge, 0.017 for attitude, and 0.012 for behaviour. These results indicate that there is a significant relationship between knowledge, attitude, and behaviour and the occurrence of scabies symptoms among residents of the At-Taqwa Kalumata Orphanage in Ternate City.