This study presents an in-situ methodology to assess indoor ventilation performance and estimate the recommended percentage of maximum occupancy (RPMO) for reducing airborne disease transmission risks. Using carbon dioxide (CO2) as a tracer gas, Air Changes per Hour (ACH) were determined in university facilities, including classrooms, meeting rooms, auditoriums, and a concert hall, under natural and mechanical ventilation conditions. Multi-point CO2 monitoring enabled the identification of ventilation patterns and under-ventilated zones. Results demonstrated that natural cross-flow ventilation could produce ACH values up to 180% higher than mechanical systems, resulting in RPMO estimates of 95%, compared to the 30% limit implemented during the COVID-19 pandemic. In contrast, some mechanically ventilated spaces achieved 12% RPMO, despite being used at higher levels. This discrepancy highlights the importance of empirical ventilation assessments to avoid under or overestimating recommended indoor capacities. A model for estimating the recommended number of occupants (NOP) was validated during six public events in a concert hall, demonstrating good agreement between predicted and measured CO2 concentrations. The proposed methodology is simple, scalable, and operationally feasible, providing institutions with a practical tool to define evidence-based occupancy limits and improve ventilation management. The findings highlight the role of ventilation in reducing airborne transmission risks in educational and public buildings.
Combinatorial optimization problems are ubiquitous in real-world applications, yet the increasing complexity and dynamic nature of real-world problems continue to outpace traditional algorithm design based on intuitions. While heuristic and meta-heuristic frameworks offer an effective path forward, it is non-trivial to design effective and efficient ones in practice, relying more on the researcher’s expertise than on a unified methodology. This lack of structured design principles often leads to brittle solvers that fail to address those complex and hard problems. In this paper, we synthesize a set of core design lessons, which are learned from our experience in solving combinatorial optimization problems, particularly in capacitated arc routing problems as a representative case. These lessons are organized into a conceptual algorithm-design framework, comprising three core dimensions: problem characterization, search-space and representation design, and search strategy design, together with learning-enabled adaptation as a cross-cutting mechanism. Ultimately, this perspective provides an algorithm design framework for researchers to systematically investigate the next generation of robust, self-evolving optimization solvers.
Building structural models that reflect bridge geometry is important for the analysis of railway structures such as seismic-damage simulation. This study examines a method for automatically measuring major bridge dimensions from three- dimensional point cloud data. The input consists of point clouds segmented in advance by a Superpoint Transformer-based model. We then combine these segmented point clouds with geometric processing, including principal component analysis (PCA), plane fitting, clustering, and cuboid fitting, to estimate both global dimensions and local member dimensions. The method is evaluated on 18 dimensional items for the rigid-frame viaduct and seven for the single-column pier within a common framework. Of these, 16 and six items, respectively, had corresponding design-reference and observable point-cloud extents. For these comparable items, the mean item-level absolute error ratios were 4.39% for the rigid-frame viaduct and 2.14% for the single-column pier. The workflow was evaluated on one rigid-frame viaduct and one single-column-pier structure using ten and nine repeated point-cloud acquisitions, respectively, obtained under different acquisition conditions. The results demonstrate the feasibility of the workflow for the two investigated structures and characterize repeatability and sensitivity to acquisition conditions.
This study investigates vibration propagation from a single fracturing pump in a large-scale integrated fracturing vessel. Vibration testing is performed on an SCF5000 electric-driven fracturing skid to measure vibration acceleration levels (VALs) at various motor speeds. A finite element model of the fracturing cabin system is developed to analyze motion and stress fields under both harmonic and measured excitations. Results indicate that VALs across the skid base remain uniform (Coefficient of Variation (CoV) < 10%), except at two locations influenced by the motor’s large mass. VALs increase by 51% as the speed increases from 600 to 2100 rpm, with vibration energy shifting toward higher frequencies. Motion responses, such as vertical displacement (VD) and vertical acceleration (VA), attenuate in an approximately half-sinusoidal pattern, with VA amplification near the active pump reaching about 126%. An optimal startup sequence (edge pumps first, central pump last) is proposed. Under measured excitations, peak VD, VA, and deck stress increase on average by 18% with rising speed. The layout scheme with a smaller deck span and a 2–3 pump distribution is identified as superior. This study provides a validated framework for vibration control and equipment arrangement in fracturing vessels.
Wear-induced material loss is a major concern in industrial applications, driving the development of wear-resistant surface coatings. High-velocity oxy-fuel (HVOF) spraying is widely used to deposit coatings with high bond strength, low porosity, and improved wear resistance. Among these, WC–Co cermet coatings exhibit excellent hardness and tribological performance; however, wear behaviour can change significantly at elevated temperatures. The present study investigates the dry sliding wear behaviour of HVOF-deposited WC–17Co coatings from RT to 600 °C under constant load, sliding velocity, test duration, and sliding distance. The coatings were characterized before and after wear testing using SEM, EDS, XRD, and 3D surface profilometry to evaluate changes in morphology, elemental distribution, phase composition, and wear-track geometry. The coating showed a thickness of ~268.99 µm, 1.53% porosity, and an average surface roughness (Sa) of 10.2 µm. The coating showed favourable wear resistance at 200–400 °C, with minimum wear and friction at 400 °C likely due to a protective tribo-oxide layer and compacted debris. Above 400 °C, particularly at 500 and 600 °C, the wear rate increased markedly because of Co binder-phase degradation, intensified oxidation, and reduced adherence of the oxide layer, and the optimal operating range is 200–400 °C.
