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.

In this study we have evaluated three advanced water treatment technologies in laboratory conditions, electrochemical (EC), fluidized bed (FB) and nanocomposite-based systems. The performance of the three technologies were evaluated based on several characteristics, such as pollutant removal efficiency, operating cost (USD/m$^3$), specific energy consumption (kWh/kg), throughput (kg/h), space-time yield (STY, kg/m$^3$$\cdot$h) and energy utilization efficiency (kg/kWh). The results show that the nanocomposite system offers the best treatment efficiency (93.17% removal efficiency and very low variability (standard deviation = 0.78%), showing good stability and reliability of the process. We found that nanocomposite system had moderate operating cost of 0.109−0.116 USD/m$^3$ and specific energy consumption of 3.60−6.52 kWh/kg, with an average value of 4.70 kWh/kg. Also, it has the highest STY (0.94 kg/m$^3$$\cdot$h) and high energy utilization efficiency (0.2776 kg/kWh). In contrast, the FB system has the lowest average operating cost (0.1016 USD/m$^3$), lowest average specific energy consumption (4.20 kWh/kg) and the best energy utilization efficiency (0.2493 kg/kWh) and is the most economical option even with the lowest pollutant removal efficiency. The EC system provided the best removal efficiency (91.32%), but the highest operating cost (0.1242 USD/m$^3$) and energy consumption (6.50 kWh/kg) of the other technologies. In analysis of variance (ANOVA) and Tukey’s Honestly Significant Difference (HSD) tests, there was significant difference between all the technologies ($p$ $<$ 0.05). The nanocomposite system achieved 5.39% removal efficiency and the FB system was able to have better energy utilization than the EC and nanocomposite technology. In general, the nanocomposite technology was the best in terms of treatment efficiency, energy efficiency, and operational cost optimization and the FB system is the best choice for large-scale applications.

University students, as a key youth consumer demographic, will play a vital role in shaping sustainable purchasing behavior in the future. This study aims to uncover the factors influencing students’ intention to minimize food waste at universities using the extended norm activation theory. An online survey of 664 students examined intentions to reduce food waste on campus. Of these, 245 students used online food delivery (OFD), while 419 engaged in in-canteen dining (IC). To evaluate the empirical data, this study utilized a partial least squares structural equation model and executed measurement invariance testing within the composite model. The empirical results demonstrate that the activation of personal norms is driven by awareness of consequence and the ascription of responsibility, which consequently has a direct impact on the intention to reduce food waste. Personal norms also indirectly influence the intention to minimize food waste. Students who purchased meals only reported weaker personal norms and lower intention to reduce food waste than those who ate in the canteen. However, the OFD group showed greater awareness of consequence, which supported their efforts to reduce food waste, compared with the IC group. Overall, this study provides further insight into the psychological mechanisms underlying sustainable food consumption among university students.

Advanced driver assistance systems (ADAS) rely heavily on robust object tracking to ensure safe and autonomous navigation, especially in complex outdoor environments. Traditional Kalman filter (KF)-based methods, while effective in ideal conditions, often fall short in scenarios with high noise, asynchronous sensor data, occlusions, and varying environmental conditions. The existing tracking techniques do not adequately address the challenges of multi-object tracking under low Signal-to-Noise Ratio (SNR) or nonlinear dynamics. To bridge this gap, this work proposes Radar and Sensor-Based Tracking with Adaptive Spatial-Temporal Analysis (RASTA), a modified KF-based architecture designed to enhance multi-object tracking using mmWave radar in ADAS. The primary objective of this work was to improve tracking accuracy, handle sensor uncertainty, and enable robust performance in dynamic and noisy conditions. The methodology involved simulating ADAS motion using a discrete Langevin process with bistable dynamics, converting Cartesian trajectories to polar coordinates, and introducing noise to emulate real-world radar behavior. Experimental validation using a mmWave dataset showed that RASTA achieved up to 12.4% improvement in azimuth estimation and 10.7% in radial distance accuracy over baseline methods. The results show RASTA’s effectiveness in delivering reliable, accurate tracking.