Scale Model Research Articles

Experimental investigation of influence of amide polymer on loess for subgrade

The effects of moisture and drying shrinkage can lead to uneven settlement, cracking, and other diseases in loess subgrade. The objective of this study was to investigate the effects of amide polymer (AP) on the permeability, mechanical properties and crack resistance of loess by orthogonal experiments. The basic properties of AP and the permeability, mechanical properties, and dry–wet variation properties of polymer-modified loess were tested, and a scale model verification and simulation analysis were conducted. In this paper, water migration in subgrade is regulated by improving the water sensitivity of loess. By reducing the variation range of subgrade water content, the stress accumulation in subgrade caused by water is weakened. The results show that the curing time and mechanical properties of AP are directly affected by the oxidant and reducing agent, and the mechanical properties of AP are compatible with the characteristics of loess. AP filled the grain gap and reduced the permeability of loess by 34.05–280.83%. The ductility of polymer-modified loess is significantly increased, and the strain of peak strength is increased by 17.21–126.36%. AP can regulate moisture change, reduce the surface tension between particles, and reduce stress concentration. The strength loss rate was reduced by 19.98–51.21% by enhancing the cracking resistance and weakening the strength loss caused by dry and wet cycling. The increase of upper layer moisture content in the scale model of polymer-modified loess subgrade is reduced by 31.38–36.11%.

A Hybrid Finite Element Method – Kirchhoff Approximation Method for Modeling Acoustic Scattering from an Underwater Vehicle Model with Alberich Coatings with Periodic Internal Cavities

Anechoic tiles can significantly reduce the echo intensity of underwater vehicles, thereby increasing the difficulty of detecting such vehicles. However, the computational efficiency of conventional methods such as the finite element method (FEM) and the boundary element method (BEM) has its limitations. A fast hybrid method for modeling acoustic scattering from underwater vehicles with anechoic tiles with periodic internal cavities, is developed by combining the Kirchhoff approximation (KA) and FEM. The accuracy and rapidity of the KA method were validated by FEM. According to the actual situation, the reflection coefficients of rubber materials with two different structures under rigid backing are simulated by FEM. Using the KA method, the acoustic scattering characteristics of the underwater vehicle with anechoic tiles are obtained by inputting the reflection coefficients and the target’s geometric grid. Experiments on the monostatic target strength (TS) in the frequency range of 1 to 20 kHz and time domain echo characteristics of acoustic scattering on a benchmark scale model with anechoic tiles are conducted. The research results indicate that the TS values and echo characteristic curves of the KA solutions closely approximate the experimental results, which verifies the accuracy of the KA method in calculating the TS and echo characteristics of underwater vehicles with anechoic tiles.

Enhancing student engagement through emerging technology integration in STEAM learning environments

AbstractEmerging technologies can potentially transform education through student engagement. The aim of our study is threefold. Firstly, we aspired to examine the validity and reliability of Reeve and Tsengs’ 4-construct (emotional, behavioral, cognitive, and agentic) engagement scale (EBCA scale). Secondly, we aimed to examine whether and to what extent the integration of emerging technology through virtual simulations (VS) in STEAM activities can improve students’ perceived engagement. Thirdly, we strived to examine how the order of integration of VS in STEAM activities affects students’ perceived engagement. A cross-over research design was used. 84 primary school students (9–10 years old) were assigned to one of the following conditions: STA (science + technology + art); SA (science + art); STA + SA; and SA + STA. The results showed that the 4-factor EBCA scale model is aligned and fits the overall sample well. It was also observed that the longer students are involved in STEAM activities, the better their perceived engagement is, and the more they work on VS, the more they develop the values of attentive listening, directing attention, and investing effort in learning. The order of integration of VS affects perceived engagement, and students who learn with them first perceive engagement better. One of the implications of our study is to examine the metric characteristics of the EBCA scale on different samples as well. Other recommendations are stated in the discussion.

Pleasure and Satisfaction as Predictors of Future Cigarette and E-Cigarette Use: A Novel Two-Stage Modeling Approach.

Subjective experience of e-cigarettes may be an important factor in helping people who use combustible cigarettes switch completely to e-cigarettes to reduce harms from smoking. This paper describes a novel two-stage analysis using pleasure and satisfaction responses from Ecological Momentary Assessments (EMA) of both cigarette and e-cigarette use to predict future cigarette and e-cigarette tobacco use. This observational study included adult users of cigarettes and e-cigarettes who provided 7-days of EMA, capturing cigarette and e-cigarette use, followed by biweekly reports of cigarette and e-cigarette use over one year. Participants were 279 adults who provided both cigarette and e-cigarette responses during the EMA. We employed a two-stage analytic approach in which EMA data were used to predict subsequent levels of cigarette and e-cigarette use. In the first stage, EMA responses of cigarette and e-cigarette events were modeled via a mixed-effects location scale (MELS) model to yield summaries of participants' means and variability on event-related ratings of pleasure and satisfaction. These EMA summaries served as predictors in the second stage analysis of the biweekly post-EMA longitudinal cigarette and e-cigarette use data. EMA pleasure and satisfaction ratings were similar for both products and predicted both longitudinal cigarette and e-cigarette use, even after controlling for baseline cigarette and e-cigarette dependence. Relatively higher levels of satisfaction with e-cigarettes were associated with greater decreases in cigarette use over time. Pleasure and satisfaction are important predictors of subsequent cigarette and e-cigarette use. Experienced subjective pleasure and satisfaction from e-cigarettes relative to cigarettes may be an important factor in helping individuals who smoke to switch completely to e-cigarettes as a harm reduction approach. In order to help sustain complete product switching and reduce dual use or relapse to smoking, e-cigarettes may need to deliver more satisfaction to the user compared to that experienced from cigarettes.

Enhancing parental understanding of congenital heart disease through personalized prenatal counseling with 3D printed hearts.

In addition to a correct prenatal diagnosis of congenital heart disease (CHD), comprehensive parental counseling is crucial to ensure that parents are well-informed about the condition of the fetus. This study aims to investigate whether there is a significant difference in the information acquired by parents through traditional counseling, utilizing 2-dimensional (2D) illustrations and images, compared to an advanced approach utilizing personalized three-dimensional (3D) printed models of the fetal heart developed from 3D ultrasound imaging. This study, designed as a pilot randomized control trial, enrolled pregnant women with gestational ages greater than 18weeks, whose fetuses were diagnosed with CHD and referred to our center between November, 2020 and June, 2021. Two groups of patients were included in the study. The first group received standard medical counseling with 2D images and illustrations, while the second group underwent advanced counseling with 3D-printed patient-specific heart models. Both groups were then required to complete the same survey in which the knowledge of the CHD was investigated. The 3D models were created from 3D ultrasound imaging and printed using resin materials in both 1:1 and 5:1 scale. A comparison of the scores obtained from the two groups revealed that 3D visualization of the fetus's heart has the potential to increase parental knowledge about CHD and the required surgical procedures. Furthermore, all couples expressed interest in receiving a 1:1 scale model of their baby's heart. Personalized prenatal counseling with 3D-ultrasound-based heart models positively impacts parents' understanding of CHD. The use of 3D models provides a more comprehensive and accessible representation of the condition, contributing to an increased knowledge gain, and potentially helping to support informed decisions regarding their child's care.

Bonus-Malus Scale premiums for Tweedie’s compound Poisson models

Abstract Based on the recent papers, two distributions for the total claims amount (loss cost) are considered: compound Poisson-gamma and Tweedie. Each is used as an underlying distribution in the Bonus-Malus Scale (BMS) model. The BMS model links the premium of an insurance contract to a function of the insurance experience of the related policy. In other words, the idea is to model the increase and the decrease in premiums for insureds who do or do not file claims. We applied our approach to a sample of data from a major insurance company in Canada. Data fit and predictability were analyzed. We showed that the studied models are exciting alternatives to consider from a practical point of view, and that predictive ratemaking models can address some important practical considerations.

Experimental and numerical analysis of seismic performance of jacket platforms subjected to onshore and offshore earthquakes

Offshore platforms located in seismically active areas may be subjected to different seismic effects during service. The seismic performance of offshore platforms under different earthquakes was studied using shaking table tests and finite element analysis (FEA). The dynamic properties, acceleration, displacement and strain responses of the scale model under near-field, far-field and offshore earthquakes were studied. On this basis, more consideration was given to soil-pile-structure interactions (SPSIs), and this finite element (FE) model was analyzed for seismic fragility using incremental dynamic analysis (IDA). By combining the results of the tests and FEA, it was found that the dynamic response of the platform was significantly larger under offshore earthquakes than under onshore earthquakes. The collapse resistance of offshore platform under offshore earthquakes was inferior to that under onshore earthquakes. Therefore, seismic performance assessment of long-period offshore platforms based on onshore earthquakes may overestimate its seismic capacity and pose potential risks to platforms in service.

Experimental Validation of Power Output and Efficiency for an Oscillating Water Column (OWC)

With the global energy demand escalating and concerns over the environmental impact of fossil fuels, there's a pressing need for cleaner, sustainable alternatives. This study highlights the potential contribution of wave energy to the power needs of coastal structures in the context of renewable energy. The research evaluates the power output and efficiency for an Oscillating Water Column (OWC) system that can be integrated into coastal structures to meet part of their power needs. Findings from both theoretical calculations and a 1:10 scale model experiment are presented. The mechanical power output and efficiency of the system for a full-scale prototype were calculated for deep water conditions with a wave height of 3m. The water surface oscillation inside the chamber is assumed to reflect the oscillation occurring outside the chamber. The maximum average mechanical power output for the full-scale prototype, corresponding to a wavelength of 22.5 m, was determined to be 64.8 kW, achieving a mechanical efficiency of 64.4%. The overall efficiency of the system is calculated as 55% by assuming the generator efficiency to be 85%, resulting in an average power output of approximately 55 kW. A 1:10 scale model of the OWC system with a Wells turbine was constructed and tested in a tank for deep water conditions. Froude similarity and Keulegan-Carpenter similarity were used, ensuring a seamless transition from the model to the prototype. The OWC model was subjected to controlled heaving motion with a period of T=1.2 s. The power generated by the OWC model illuminated four integrated 3.4V LEDs on the Wells turbine, which were used to measure the power output produced. The power output of the model was measured to be a minimum of 0.12 W for a rotational speed of 107 rpm, which corresponds to a power output of 12 kW for the scaled-up prototype. This system has the potential for further enhancement by incorporating multiple OWCs into coastal structures exposed to wave action. Such development could facilitate meeting the power requirements of coastal structures, thereby contributing to the promotion of both renewable energy generation and a sustainable environment. Future research will focus on optimizing OWC chamber sizes for specific sites and refining the model to better capture water surface oscillation dynamics. l

Continuum insight into the effects of electrode design parameters on the electrochemical performance of lithium-ion batteries

Lithium-ion batteries energy and power density strongly depend on the type of active material and the electrode design parameters. An in-depth understanding of the effect of battery design parameters on their electrochemical performance is experimentally expensive and hence requires the utilization of cheaper continuum scale models. Here, using a lithium-ion cell composed of LiFePO4 cathode, Li4Ti5O12 anode and a porous separator filled with a liquid electrolyte as an example, we demonstrate the use of an experimentally validated pseudo-two-dimensional (P2D) model in one-dimension to explore the effects of different cell design parameters on the discharge capacity at different current rates. The model is simulated in two-dimensional to instantly visualize the concentration distribution of Li ions in the electrode at high discharge current rates. The continuum model unravels that the solution phase diffusion limitation is the main factor inhibiting the performance of thick electrodes at high current rates and the Li4Ti5O12 anode is the limiting electrode. These findings using continuum models provide guidance for and accelerate the optimization of electrode architecture for enhancing the performance of lithium-ion batteries.

De Novo Antimicrobial Peptide Design with Feedback Generative Adversarial Networks.

Antimicrobial peptides (AMPs) are promising candidates for new antibiotics due to their broad-spectrum activity against pathogens and reduced susceptibility to resistance development. Deep-learning techniques, such as deep generative models, offer a promising avenue to expedite the discovery and optimization of AMPs. A remarkable example is the Feedback Generative Adversarial Network (FBGAN), a deep generative model that incorporates a classifier during its training phase. Our study aims to explore the impact of enhanced classifiers on the generative capabilities of FBGAN. To this end, we introduce two alternative classifiers for the FBGAN framework, both surpassing the accuracy of the original classifier. The first classifier utilizes the k-mers technique, while the second applies transfer learning from the large protein language model Evolutionary Scale Modeling 2 (ESM2). Integrating these classifiers into FBGAN not only yields notable performance enhancements compared to the original FBGAN but also enables the proposed generative models to achieve comparable or even superior performance to established methods such as AMPGAN and HydrAMP. This achievement underscores the effectiveness of leveraging advanced classifiers within the FBGAN framework, enhancing its computational robustness for AMP de novo design and making it comparable to existing literature.

Experimental validation of a Kalman observer using linearized OpenFAST and a fully instrumented 1:70 model

AbstractTo enable real‐time monitoring and control strategies for floating offshore wind turbines, accurate information about the state of the system is needed. This paper details the application of a Kalman filter to the UMaine VolturnUS‐S floating wind platform to provide accurate state estimates in real time using minimal system measurements. The midfidelity nonlinear simulation tool OpenFAST was used to generate the underlying linear state‐space model for the Kalman filter. This linear model and its limitations are demonstrated through comparison with experimental data collected on a 1:70 froude‐scaled model of the floating platform and tower. Using a selection of five measurements from the real system, a Kalman filter was developed to provide estimates for the remaining system states and measurements. These estimates were then validated against the experimental values collected from testing of the scale model. Validation of the Kalman filter produced accurate estimates of surge, heave, and tower base bending moment, measurements of which were not available to the Kalman filter. Performance of the Kalman filter was tested and validated over a range of sea conditions from rated wind speed to storm events and demonstrated robustness in the Kalman filter to maintain accuracy across all operating conditions despite significant error in the underlying linear model for extreme conditions.

Scaling laws for the modelling of energy geostructures

Among the different solutions to meet the increasing need for renewable energies, energy geostructures, and particularly energy piles, are very promising. However, their dual role (structural support and energy harvester) raises design questions and safety issues. In addition, they are often placed within a groundwater flow and the influence of seepage on thermal loadings and mechanical response is still to be characterised. In this perspective, centrifuge modelling can play a significant role as in lots of geotechnical problems. In the present case, reduced scale models present a supplementary advantage since diffusion timescales are highly reduced. Scaling factor for time is 1/N² if scaling factor for lengths is 1/N. Nevertheless, the numerous phenomena to consider make it difficult to ensure the similarity between model and prototype. The present document derives and gathers all the scaling laws and attention points to keep in mind when centrifuge modelling energy piles.

A pore scale model study of mass transfer and electrochemical reaction in cathode catalytic layer of high-temperature polymer electrolyte membrane fuel cells

The mass transfer and related electrochemical reactions in the catalytic layer (CL) are crucial for the performance of high-temperature polymer electrolyte membrane fuel cells (HT-PEMFCs), but there is a lack of understanding of the optimal phosphoric acid content and Pt/C catalyst loading in the CL. In this work, we investigate the influences of Pt/C catalyst numbers (NPt/C) and phosphoric acid film thickness (δPA) on mass transfer and electrochemical reactions of the cathode CL using a pore-scale model (300 × 300 × 300 nm3). The results reveal that larger NPt/C and δPA can impede oxygen diffusion, while lower NPt/C and δPA lead to fewer reaction active sites and lower conductivity, both of which result in lower current density. Therefore, the balance of three-phase transport is extremely important for CL. There is a 3D volcanic relationship between current density, Pt/C catalysts, and phosphoric acid film. At NPt/C = 200 (7407 per μm3) and δPA = 4 nm (the volume fractions of 37.22 % and 17.87 %), the highest current density can be achieved at a potential of 0.5 V (vs. RHE), which increased with the decrease of potential. This work is valuable for understanding the optimizing the microstructure and three-phase transport pathway of the CL to improve the performance of HT-PEMFCs.

Predicting Antimicrobial Peptides Using ESMFold-Predicted Structures and ESM-2-Based Amino Acid Features with Graph Deep Learning.

Currently, antimicrobial resistance constitutes a serious threat to human health. Drugs based on antimicrobial peptides (AMPs) constitute one of the alternatives to address it. Shallow and deep learning (DL)-based models have mainly been built from amino acid sequences to predict AMPs. Recent advances in tertiary (3D) structure prediction have opened new opportunities in this field. In this sense, models based on graphs derived from predicted peptide structures have recently been proposed. However, these models are not in correspondence with state-of-the-art approaches to codify evolutionary information, and, in addition, they are memory- and time-consuming because depend on multiple sequence alignment. Herein, we presented a framework to create alignment-free models based on graph representations generated from ESMFold-predicted peptide structures, whose nodes are characterized with amino acid-level evolutionary information derived from the Evolutionary Scale Modeling (ESM-2) models. A graph attention network (GAT) was implemented to assess the usefulness of the framework in the AMP classification. To this end, a set comprised of 67,058 peptides was used. It was demonstrated that the proposed methodology allowed to build GAT models with generalization abilities consistently better than 20 state-of-the-art non-DL-based and DL-based models. The best GAT models were developed using evolutionary information derived from the 36- and 33-layer ESM-2 models. Similarity studies showed that the best-built GAT models codified different chemical spaces, and thus they were fused to significantly improve the classification. In general, the results suggest that esm-AxP-GDL is a promissory tool to develop good, structure-dependent, and alignment-free models that can be successfully applied in the screening of large data sets. This framework should not only be useful to classify AMPs but also for modeling other peptide and protein activities.

A scale model study of an engine test facility for alleviating low-frequency howl

A scale model experimental study is conducted addressing howl occurring during engine tests in the Propulsion Systems Laboratory (PSL) of NASA Glenn Research Center. The aim is to understand the sources of the howling noise and find possible remedies. It is shown that a jet discharged into the model of the PSL entrance duct gives rise to high levels of low frequency noise. This occurs apparently due to duct acoustic modes randomly excited by the jet. Wire-mesh screens of various mesh size and configuration as well as tabs, placed at the end of the model duct, are shown to affect the noise. Comparative effects of these configurations are studied, and some are found to alleviate the low-frequency noise significantly. Based on these results, the equivalent of a 4-mesh screen is recommended as the end treatment for the duct of the PSL facility.

Planing Hull Hydrodynamic Performance Prediction Using LincoSim Virtual Towing Tank

This work shows the performance of LincoSim, a web-based virtual towing tank enabling automated and standardized calm water computational fluid dynamics (CFD) data sampling, extending previous published applications to the case of a high-speed hull. The calculations are performed for a 1:10 scale model of a 43 ft powerboat hull form in the Froude number range from 0.3 to 2.0. The counterpart physical model is the experimental fluid dynamics (EFD) campaign performed at the University of Naples Federico II, where the resistance, sinkage and trim data have been measured. The EFD/CFD data comparison is performed and shown with a discussion of the spotted differences. The average percentage differences between the EFD and CFD data for the whole speed range are 1.84, 6.87 and 6.94 for the resistance, dynamic trim, and sinkage, respectively. These results confirm the maturity of the standardized and automated CFD modeling for calm water hydrodynamic analysis included in LincoSim, even at very high Froude numbers. The wetted length of the keel and chine and the wetted surface are calculated from numerical data using the advanced post-processing. Finally, as a work in progress, we test a first comparison for the same hull of the EFD and CFD data, considering two seakeeping conditions for head waves at a given wavelength for two velocity conditions. Also, this kind of analysis confirms the tight correlation between the measured and computed outcomes. This synergic interplay of EFD and CFD can link the advantages of both methods to support hull design but also requires experiment planning and final data analysis to obtain physical parameters not easily measurable in laboratory, such as the wetted surface, wetted lengths, proper viscous contribution, and pressure distribution both in calm water and in waves.

Efficiency and sustainability: Evaluating and optimizing energy use and environmental impact in cucumber production

This study assesses the energy-economic-environmental aspects of cucumber production in the face of environmental issues and resource scarcity, which threaten global food security and demand sustainable agriculture solutions. Data were collected by questionnaire and analyzed for inputs from planting to harvesting. The results indicated that diesel fuel (37.41%) and nitrate fertilizer (37.22%) constitute the primary inputs. The energy ratio, energy efficiency and net energy gain were 0.43, 0.54 kgMJ−1 and -30,153.31 MJha−1, respectively. Input optimization by Data Envelopment Analysis (DEA) reduced the carbon footprint from 178.433 to 168.900 kg CO2 per ton, the abiotic depletion from 1228.291 to 964.305 MJ and the acidification from 0.838 to 0.749 kg SO2 eq. Also, the results of DEA showed that 52.5% and 35% of the units were fully efficient under Variable Return to Scale (VRS) and Constant Return to Scale (CRS) models, respectively, while the others were inefficient to varying degrees. The average of technical, pure technical and scale efficiencies were 92.88%, 97.64%, and 95.07%, respectively. The results of economic evaluation showed that production cost is 6058.84 $ha−1 with a benefit-cost ratio of 1.71. Based on the primary results, it is suggested to use precision agriculture techniques and the exploration of alternative energy sources, such as biofuel for machinery. Additionally, using slow-release fertilizers or organic substitutes can decrease nitrogen fertilizer dependency while maintaining crop yields and improve the sustainability in this production.

International Trauma Questionnaire and Posttraumatic Cognitions Inventory-9: validity evidence and measurement invariance of their Brazilian versions

BackgroundThe International Trauma Questionnaire (ITQ) is used to measure posttraumatic stress disorder (PTSD) and complex posttraumatic stress disorder (CPTSD) symptoms, and the Posttraumatic Cognitions Inventory-9 (PTCI-9) is used to measure posttraumatic cognitions. Both tools have been translated for use in Brazil. However, the psychometric properties of the Brazilian versions were not investigated, and no study has verified the invariance of these tools for many traumatic event types.ObjectiveThis study examined the validity, reliability, and measurement invariance of the Brazilian versions of the ITQ and the PTCI-9 for trauma type, gender, race, age group, education level, and geographical region.MethodsA total of 2,111 people (67.74% women) participated in an online survey. The scale models were tested via confirmatory factor analyses and measurement invariance through multigroup analyses. Pearson’s correlation analyses were used to examine the relationships between PTSD, CPTSD, posttraumatic cognitions, and depressive symptoms.ResultsExcept for the affective dysregulation factor, the reliabilities of the ITQ and PTCI-9 dimensions were adequate. Models with six correlated dimensions for the ITQ and three correlated dimensions for the PTCI-9 showed adequate fit to the data. The ITQ and PTCI-9 exhibited scalar invariance for gender, race, age group, education level, and geographical region. The ITQ also demonstrated full invariance for trauma type. The factors of both instruments were related to each other and to depressive symptoms, with higher effect sizes for posttraumatic cognitions and complex posttraumatic stress disorder symptoms.ConclusionWe recommend using the Brazilian versions of the ITQ and PTCI-9, which are crucial tools for assessing and treating trauma-related disorders.

Evaluation Of The Accuracy Of The Generated Wave Fields In The Coastal & Ocean Basin (Cob)

The Coastal & Ocean Basin (COB) wave tank facility is located in the Flanders Maritime Laboratory at the Ostend Science Park, Belgium. The COB and its associated testing services are designed (Troch et al. 2016) to facilitate the needs of the offshore renewable energy sector, coastal and offshore engineering community and offer the opportunity to academia, companies and government agencies, to test scale models under combined action of waves and currents in any relative direction, to develop innovative designs. The exploitation is managed by the consortium Ghent University, KU Leuven and Flanders Hydraulics. The COB is operational since March 2023 and has since then successfully completed its first projects on wave energy converters (WECFarm), wave diffraction study around a monopile (PhairywinD) and floating offshore photovoltaic islands (MarineSpots).

Effects of mimicking manure removal strategies on ammonia and greenhouse gas emissions in sow pen scale models

With growing concerns about air pollution and global warming effects, the emissions of NH3 and greenhouse gases (GHGs) have become significant issues in the pig production industry. In order to discover whether optimizing manure removal strategies could alter the manure decomposition process and benefit the reduction of gas emissions, a scale model approach was used to quantify the gas emissions under controlled conditions. This study compared the gas emission reduction potential of two classic manure removal systems (scraper and pull-plug system) in three manure removal strategies: scraper-I (retaining manure for 24 h), scraper-II (retaining manure for 8 and 16 h) and pull-plug (retaining manure for 44 days). Fresh urine and faeces collected from a gestation sow house were applied to the scale models and then removed through the manure removal systems. The concentrations of gases (NH3, CH4, CO2, and N2O) within the scale model and removed slurry samples were collected and analysed (pH, electric conductivity, dry matter, total nitrogen, total ammonium nitrogen and total carbon). The results showed that emissions of CH4, CO2, and N2O from scraper-I were on average 54%, 56% and 25% lower than those from scraper-II, and 71%, 67% and 6% lower than those from pull-plug treatment, respectively. The GHGs emission rates (as CO2-equivalents) of scraper-I and scraper-II were 52% and 26.3% lower than that of pull-plug treatment respectively (P < 0.01). The emissions of NH3 displayed a temporary peak during each application of urine and removal of manure. In pull-plug treatment, the concentrations of NH3, CH4, and CO2 exponentially increased between Day 39 and Day 41. The nitrogen content, both total nitrogen and total ammonium nitrogen, within the slurry under the scraper system exhibited lower values compared to those from the pull-plug system. Conversely, the total carbon content was higher in the former system. Caution is warranted in extrapolating results to full-scale pig housing, given study limitations (e.g. small scale, mimicked scraper activity, no animals, etc.). Nevertheless, the conclusions and findings of this study provide fundamental data for understanding gas emissions from pig house manure management. This insight can guide the design and daily operations of low-emission manure removal systems in gestation sow houses.