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.

The expansion of the shipbuilding industry in coastal areas contributes substantially to economic development while simultaneously posing significant risks to air quality and community health. This study analyzed the concentration and spatial distribution of air pollutants and assessed non-carcinogenic health risks in the shipyard industrial area of Batam City. Air quality measurements were conducted for PM$_{2.5}$, SO$_2$, NO$_2$, CO, and Pb parameters at multiple receptor points at different distances from the emission source. The field measurement data were then integrated with dispersion modeling using the American Meteorological Society/Environmental Protection Agency Regulatory Model (AERMOD) based on local meteorological conditions. Health risk was evaluated using the Hazard Quotient (HQ) approach with reference to national air quality standards. Pollutant concentrations decreased consistently with increasing distance from emission sources, with PM$_{2.5}$ exhibiting the widest and most persistent spatial distribution. Although most pollutant concentrations remained below regulatory thresholds, PM$_{2.5}$ yielded HQ values exceeding 1.0 across all receptor distances up to 2000 m, indicating significant non-carcinogenic health risk at all observed distances. Model validation demonstrated strong spatial agreement between measured and simulated concentrations ($R^2$ $>$ 0.84), with a consistent tendency toward underestimation of absolute values. The integration of spatial dispersion modeling with health risk assessment offers a comprehensive analytical framework for air quality management and public health protection in coastal industrial settings.