The rapid growth of religious tourism has intensified the demand for supporting transport infrastructure, particularly an efficient shared parking system integrated with on-site traffic circulation and pedestrian flow management at sacred sites. This study defines the shared parking scheme as the temporal and spatial allocation of a common facility among buses (for organized pilgrim tours) and passenger cars (for individual visitors), managed by the mosque authority across distinct worship-time windows. Three research questions are addressed: (i) whether visitor groups differ in acceptable walking distance to parking; (ii) whether a digital parking guidance system is suitable across age cohorts; and (iii) how vehicle type influences parking capacity planning. A questionnaire survey was administered to 505 respondents at the Sheikh Zayed Grand Mosque in Surakarta, Indonesia. The Pearson Chi-Square Test examined associations between categorical variables; where more than 20% of cells had expected frequencies below five, Fisher's Exact Test with Monte Carlo approximation was applied, and Cramer’s V was reported as the effect-size measure. Age was significantly associated with nearly all parking-preference variables ($p <$ 0.01), with the 17–32-year cohort showing higher receptivity to digital parking information systems. Vehicle type exhibited significant associations with five of six preference variables ($p <$ 0.05) and the largest mean Cramer’s V, indicating the most consistent though not causal demographic correlate. Travel purpose was significantly associated with visiting duration ($p$ = 0.007) and acceptable walking distance ($p$ = 0.043). Findings yield four operational recommendations: (i) segregated bus and passenger-car zones with dedicated bus-reservation slots; (ii) tiered short-stay/long-stay zoning aligned with prayer-time peaks; (iii) age-differentiated wayfinding combining digital guidance and on-site human assistance; and (iv) temporary traffic control during peak worship hours.

Traffic noise has become an increasingly important environmental concern due to rapid urbanisation and growing vehicular activity in residential areas. This study aims to identify the factors influencing traffic noise and develop a predictive framework using partial least squares structural equation modelling (PLS-SEM). Traffic noise measurements were conducted across four residential sections of Shah Alam (Seksyen 7, 9, 20, and 27) using a sound level meter (SLM) at three observation periods: morning (08:00–11:00), afternoon (12:00–15:00), and evening (16:00–19:00). Data collection included traffic volume observations, road geometry measurements, and climatic variables obtained from secondary environmental sources. A total of 504 observations were analysed using SmartPLS 4.0. The measurement model assessment demonstrated that the reflective constructs—traffic volume, road geometry, and the equivalent traffic noise level (i.e., the A-weighted equivalent continuous sound level, $L_{A\mathrm{eq}}$)—achieved acceptable reliability and validity. In contrast, climate conditions were evaluated as a formative construct to better represent the multidimensional contribution of temperature, humidity, and wind speed across observation periods. Structural model results indicated that Climate Condition exhibited the strongest influence within the model and contributed significantly to both traffic volume and $L_{A\mathrm{eq}}$, while road geometry showed a positive relationship with traffic volume. Traffic volume did not demonstrate a statistically significant direct relationship with $L_{A\mathrm{eq}}$, suggesting that residential traffic noise may be influenced by interactions among environmental and roadway conditions rather than vehicle quantity alone. The model demonstrated acceptable explanatory capability, with coefficient of determination ($R^2$) values of 0.727 for $L_{A\mathrm{eq}}$ and 0.552 for traffic volume. These findings highlight the importance of integrating climatic and roadway variables into residential traffic noise assessment and support more context-sensitive approaches for urban transport planning and environmental noise management. Future studies are recommended to incorporate additional operational traffic variables and advanced predictive techniques to improve model generalisability and prediction performance.

Healthcare supply chains face increasing challenges related to counterfeit products, fragmented information flows, limited traceability, and insufficient coordination among distributed stakeholders. Existing centralized and partially decentralized approaches still encounter difficulties in maintaining immutable records, real-time verification, and trusted operational transparency across the pharmaceutical distribution process. This study investigates a distributed medical supply chain framework that improves traceability, compliance control, and operational reliability in healthcare logistics. A blockchain-enabled architecture was developed by integrating dynamic quick response (QR)-based identification, customizable smart contracts, and a hybrid consensus mechanism combining Proof-of-Work (PoW) and Proof-of-Stake (PoS). The framework assigned a unique cryptographic identity to each medicine unit and supported end-to-end verification through blockchain-linked QR validation. Smart contracts were designed to automate ownership transfer, compliance checking, and counterfeit detection throughout the supply chain workflow. The framework was implemented and evaluated in a simulated distributed environment using pharmaceutical transaction scenarios. The experimental results showed that the proposed approach achieved average validation accuracy of approximately 98.1%, maintained transaction throughput between 150 and 320 transactions per second (TPS), and reduced consensus delay through adaptive PoW–PoS coordination. The system also demonstrated strong resistance to forgery attempts and stable operational performance across repeated validation experiments. The results indicate that integrating blockchain governance mechanisms with QR-enabled authentication can improve transparency, trust, and traceability in distributed healthcare supply chains. The proposed framework provides a scalable systems engineering solution for pharmaceutical logistics management and offers a practical foundation for compliance-oriented digital transformation in healthcare supply networks.

Building occupancy certification is a key mechanism for managing post-construction compliance, covering building safety, functional readiness, and legal operability. In Indonesia, this function is carried by the SLF (Sertifikat Laik Fungsi, Certificate of Functional Worthiness), yet fewer than 10% of buildings nationwide hold one. This study asks what drives participation in SLF certification, looking at both behavioural and system-level factors within the country’s digital building certification system. Using a sequential explanatory mixed-methods design, we analysed 270 valid survey responses from Semarang, Sidoarjo, and Bandung with partial least squares–structural equation modelling (PLS-SEM), then drew on focus group discussions (FGDs) with government officials, consultants, technical experts, and business associations to interpret the results. The quantitative results show that intention to obtain an SLF is the strongest predictor of participation, supported by knowledge and perceived ease of use of the SIMBG (Sistem Informasi Manajemen Bangunan Gedung, Building Management Information System) platform. Technical and bureaucratic barriers did not show a statistically significant negative effect in the expected direction. However, the qualitative findings reveal that high consultant costs, weak document validation, inconsistent local requirements, limited technical staff capacity, and unclear institutional coordination remain important obstacles in the certification workflow. The study contributes to engineering management by repositioning SLF participation as part of a digital building compliance management process rather than merely an administrative or public service issue. The findings indicate that improving SLF participation requires not only awareness campaigns, but also workflow-level interventions, including document pre-checking, standardised technical submission templates, cost estimation tools, application tracking, and clearer coordination between central platform managers and local technical agencies.

Large language models (LLMs) are increasingly used to turn natural-language knowledge into downstream executable rules, raising two lifecycle questions: whether a compiled rule faithfully preserves its source fragment's intended semantics, and whether later textual edits change rule behavior. We address these through a controlled benchmark based on inverted compilation: formal rules are generated programmatically, rendered into wiki-style fragments by an LLM, and reconstructed by an independent LLM pipeline, so the original rules provide automatic ground truth. The benchmark contains 2,000 rules across four business domains and 9,000 typed drift triples. For compilation verification, a slot-matched structural verifier reached 0.763 commit precision at a 32.1% commit rate, far exceeding a paraphrase-similarity baseline (0.729 precision, 2.4% commit rate). For drift classification, a slot-difference classifier was the only method that separated multiple impact categories, with F1 scores of 0.684 (condition), 0.601 (exception), and 0.419 (boundary), whereas token-level baselines collapsed to a coarse impactful-versus-cosmetic split. A complementary readiness-assessment experiment returned a negative result: surface-feature classifiers matched a majority-class baseline on synthesized fragments, indicating that readiness estimation needs authentic human-authored text or controlled degradations. Overall, slot-level structural analysis offers an effective signal for verifying and maintaining LLM-compiled rule systems, while exception extraction, cosmetic-edit discrimination, and the synthetic-to-real gap remain key limitations for future neuro-symbolic knowledge engineering.