Simulation Project Research Articles

KAJIAN PERBANDINGAN PENGARUH PENGGUNAAN BEKISTING ALUMINIUM DAN PERTH CONSTRUCTION HIRE (PCH) DALAM MEWUJUDKAN LEAN CONSTRUCTION

Technological developments have led to innovations in construction materials science, one of which is formwork materials. There are several important aspects in formwork innovation, namely cost, speed, quality, waste, and health safety environtment (HSE), but they have not fully had a positive impact on construction project development. However, it has not fully had a positive impact on construction projects. In addition, what aluminum formwork manufacturers offer so far may not necessarily suit the needs of customers. The purpose of this study is to analyze the comparison in terms of cost, time, waste, and HSE between projects using aluminum formwork and the same project simulated using Formwork Perth Construction Hire (PCH). In this study: (1) simulation on the project if the project uses PCH formwork (2) comparative analysis of products in terms of time, cost, quality, waste, HSE on real projects using aluminum formwork with projects that have been simulated using PCH formwork. The effect of using aluminum formwork when compared to a simulation project using PCH formwork: (1) in terms of the time it can provide 25.92% savings on the total duration of the simulation results project, (2) in terms of costs, it can provide a total cost savings of IDR 895,756,004,00 (3) in terms of quality, the use of aluminum formwork is stronger, durable quality, and the results of the concrete molding are tidier than using PCH formwork (4) in terms of waste, projects that use aluminum formwork produce less waste than using PCH formwork. PCH formwork. (5) in terms of HSE, aluminum formwork is safer, more environmentally friendly because it has minimal waste, and is safe for workers

Sparse-View Neutron CT Reconstruction Using a Modified Weighted Total Difference Minimization Method

Indirect neutron imaging is an effective method for nondestructive testing of spent nuclear fuel elements. Considering the difficulty of obtaining experimental data in a high-radiation environment and the characteristic of high noise of neutron images, it is difficult to use the traditional FBP algorithm to recover the complete information of the sample based on the limited projection data. Therefore, it is necessary to develop the sparse-view CT reconstruction algorithm for indirect neutron imaging. In order to improve the quality of the reconstruction image, an iterative reconstruction method combining SIRT, MRP, and WTDM regularization is proposed. The reconstruction results obtained by using the proposed method on simulated data and actual neutron projection data are compared with the results of four other algorithms (FBP, SIRT, SIRT-TV, and SIRT-WTDM). The experimental results show that the SIRT-MWTDM algorithm has great advantages in both objective evaluation index and subjective observation in the reconstruction image of simulated data and neutron projection data.

Estimation of the rice water footprint based on machine learning algorithms

It is essential to investigate the impact of climate change on the water footprint (WF) of rice from both historical simulation and future projections. In this study, four machine learning (ML) models, including random tree (RT), random forest (RF), additive regression (AR) and reduced error pruning tree (REPT) were used to model blue and green water footprint (BWFP and GWFP) for the present and future stages in the Yunnan Province, southwest China. Climate variables of daily precipitation, temperature, solar radiation, sunshine hours, wind speed, relative humidity and vapor pressure deficit data, yield and sown areas of rice were collected form 16 districts from 1990 to 2018. Six different scenarios (Sc1-Sc6) with different combinations of climate variables, crop coefficient and sown areas were used as inputs of the ML models for each of blue and green water footprint. Also, future climate projections of maximum (Tmax) and minimum (Tmin) temperatures, precipitation and sunshine were adopted for two different emission scenarios, RCP 4.5 and 8.5 from 2021to 2050, based on Geophysical Fluid Dynamics Laboratory (GFDL-ESM2M) model. For BWFP, the RT model in Sc1 with inputs of solar radiation, humidity, and vapor pressure deficit, was superior to the other scenarios with root mean square error (RMSE) and mean average percentage error (MAPE) values of 11.82 (m3 ton-1) and 0.5%, respectively. Sc4 (sown area, Tmin, sunshine hours) and Sc5 (sown area, Tmin, Tmax, crop coefficient) were the best two scenarios for all models applied for BWFP, while addition of precipitation to these two scenarios were the best for GWFP. Further, GWFP was highly negative anomaly in 2011 by 46%, 34% and 32% for the districts 5, 1 and 2 respectively, followed by 2010. Predictions of variable trends in the future showed that crop evapotranspiration during the growing season would increase in all the districts, although effective precipitation (Peff) would have both decreasing and increasing trends. Furthermore, BWFP and GWFP will have an upward trend in the future based on RCP4.5 in Sc4. This investigation addresses water footprint prediction which may assist in mitigation plans such as policies for sustainable water-use and development plans for food security.

Arctic black carbon during PAMARCMiP 2018 and previous aircraft experiments in spring

Abstract. Vertical profiles of the mass concentration of black carbon (BC) were measured at altitudes up to 5 km during the PAMARCMiP (Polar Airborne Measurements and Arctic Regional Climate Model simulation Project) aircraft-based field experiment conducted around the northern Greenland Sea (Fram Strait) during March and April 2018 from operation base Station Nord (81.6∘ N, 16.7∘ W). Median BC mass concentrations in individual altitude ranges were 7–18 ng m−3 at standard temperature and pressure at altitudes below 4.5 km. These concentrations were systematically lower than previous observations in the Arctic in spring, conducted by ARCTAS-A in 2008 and NETCARE in 2015, and similar to those observed during HIPPO3 in 2010. Column amounts of BC for altitudes below 5 km in the Arctic (>66.5∘ N; COLBC), observed during the ARCTAS-A and NETCARE experiments, were higher by factors of 4.2 and 2.7, respectively, than those of the PAMARCMiP experiment. These differences could not be explained solely by the different locations of the experiments. The year-to-year variation of COLBC values generally corresponded to that of biomass burning activities in northern midlatitudes over western and eastern Eurasia. Furthermore, numerical model simulations estimated the year-to-year variation of contributions from anthropogenic sources to be smaller than 30 %–40 %. These results suggest that the year-to-year variation of biomass burning activities likely affected BC amounts in the Arctic troposphere in spring, at least in the years examined in this study. The year-to-year variations in BC mass concentrations were also observed at the surface at high Arctic sites Ny-Ålesund and Utqiaġvik (formerly known as Barrow, the location of Barrow Atmospheric Baseline Observatory), although their magnitudes were slightly lower than those in COLBC. Numerical model simulations in general successfully reproduced the observed COLBC values for PAMARCMiP and HIPPO3 (within a factor of 2), whereas they markedly underestimated the values for ARCTAS-A and NETCARE by factors of 3.7–5.8 and 3.3–5.0, respectively. Because anthropogenic contributions account for nearly all of the COLBC (82 %–98 %) in PAMARCMiP and HIPPO3, the good agreement between the observations and calculations for these two experiments suggests that anthropogenic contributions were generally well reproduced. However, the significant underestimations of COLBC for ARCTAS-A and NETCARE suggest that biomass burning contributions were underestimated. In this study, we also investigated plumes with enhanced BC mass concentrations, which were affected by biomass burning emissions, observed at 5 km altitude. Interestingly, the mass-averaged diameter of BC (core) and the shell-to-core diameter ratio of BC-containing particles in the plumes were generally not very different from those in other air samples, which were considered to be mostly aged anthropogenic BC. These observations provide a useful basis to evaluate numerical model simulations of the BC radiative effect in the Arctic region in spring.

Anisotropic structure property based image reconstruction method for limited-angle computed tomography.

Limited-angle computed tomography (CT) may appear in restricted CT scans. Since the available projection data is incomplete, the images reconstructed by filtered back-projection (FBP) or algebraic reconstruction technique (ART) often encounter shading artifacts. However, using the anisotropy property of the shading artifacts that coincide with the characteristic of limited-angle CT images can reduce the shading artifacts. Considering this concept, we combine the anisotropy property of the shading artifacts with the anisotropic structure property of an image to develop a new algorithm for image reconstruction. Specifically, we propose an image reconstruction method based on adaptive weighted anisotropic total variation (AwATV). This method, termed as AwATV method for short, is designed to preserve image structures and then remove the shading artifacts. It characterizes both of above properties. The anisotropy property of the shading artifacts accounts for reducing artifacts, and the anisotropic structure property of an image accounts for preserving structures. In order to evaluate the performance of AwATV, we use the simulation projection data of FORBILD head phantom and real CT data for image reconstruction. Experimental results show that AwATV can always reconstruct images with higher SSIM and PSNR, and smaller RMSE, which means that AwATV enables to reconstruct images with higher quality in term of artifact reduction and structure preservation.

Projected Streamflow and Sediment Supply under Changing Climate to the Coast of the Kalu River Basin in Tropical Sri Lanka over the 21st Century

Tropical countries are already experiencing the adverse impacts of climate change. This study presents projections of climate change-driven variations in hydrology and sediment loads in the Kalu River Basin, Sri Lanka. Bias-corrected climate projections (i.e., precipitation and temperature) from three high resolution (25 km) regional climate models (viz., RegCM4-MIROC5, MPI-M-MPI-ESM-MR, and NCC-NORESM1-M) are used here to force a calibrated hydrological model to project streamflow and sediment loads for two future periods (mid-century: 2046–2065, and end of the century: 2081–2099) under two representative concentration pathways (i.e., RCPs 2.6 and 8.5). By the end of the century under RCP 8.5, all simulations (forced with the three RCMs) project increased annual streamflow (67–87%) and sediment loads (128–145%). In general, streamflow and sediment loads are projected to increase more during the southwest monsoon season (May–September) than in other periods. Furthermore, by the end of the century, all simulations under the RCP 8.5 project a shift of streamflow and sediment loads in the southwest monsoon peak from May to June, while preserving the peak in the inter-monsoon 2 (in October). The projected changes in annual sediment loads are greater than the projected changes in annual streamflow (in percentage) for both future periods.

Preservice special education teachers’ perceptions of mixed-reality simulation experiences

Special educators are expected to implement High-Leverage Practices to support diverse learners. This article describes the findings from a mixed-reality simulation project structured around the framework of connected learning. The project sought to evaluate how 30 preservice teacher candidates in special education feel about mixed-reality simulation compared to traditional classroom practice (i.e., role playing) in learning the High-Leverage Practice of system of least prompts. Results of this study indicate that those who participated in the mixed-reality simulator had significantly different social validity survey responses from the live practice group on items related to perceived performance, perceived tool value, and treatment experience; these findings can help inform teacher educators on how to incorporate mixed-reality simulation. Limitations and future research are discussed.

A real-time IGRT method using a Kalman filter framework to extract 3D positions from 2D projections

Purpose. To estimate 3D prostate motion in real-time during irradiation from 2D prostate positions acquired from a kV imager on a standard linear accelerator utilising a Kalman filter (KF) framework. The advantage of this novel method is threefold: (1) eliminating the need of an initial learning period, therefore reducing patient imaging dose, (2) more robust against measurement noise and (3) more computationally efficient. In this paper, the novel KF method was evaluated in silico using patients’ 3D prostate motion and simulated 2D projections. Methods. A KF framework was implemented to estimate 3D motion from 2D projection measurements in real-time during prostate cancer treatments. The noise covariance matrix was adaptively estimated from the previous 10 measurements. This method did not require an initial learning period as the KF process distribution was initialised using a population covariance matrix. This method was evaluated using a ground-truth motion dataset of 17 prostate cancer patients (536 trajectories) measured with electromagnetic transponders. 3D motion was projected onto a rotating imager (SID = 180 cm) (pixel size = 0.388 mm) and rotation speed of 6°/s and 2°/s to simulate VMAT treatments. Gantry-varying additive random noise (≤5 mm) was added to ground-truth measurements to simulate segmentation error and image quality degradation due to the patient's pelvic bones. For comparison, motion was also estimated using the clinically implemented Gaussian probability density function (PDF) method initialised with 600 projections. Results. Without noise, the 3D root mean square-errors (3D RMSEs) of motion estimated by the KF method were 0.4 ± 0.1 mm and 0.3 ± 0.2 mm for 2°/s and 6°/s gantry rotation, respectively. With noise, 3D RMSEs of KF estimated motion were 1.1 ± 0.1 mm for both slow and fast gantry rotation scenarios. In comparison, using a Gaussian PDF method, with noise, 3D RMSE was 2 ± 0.1 mm for both gantry rotation scenarios. Conclusion. This work presents a fast and accurate method for real-time 2D to 3D motion estimation using a KF approach to handle the random-walk component of prostate cancer motion. This method has sub-mm accuracy and is highly robust against measurement noise.

Forest Cover and Sustainable Development in the Lumbini Province, Nepal: Past, Present and Future

The analysis of forest cover change at different scales is an increasingly important research topic in environmental studies. Forest Landscape Restoration (FLR) is an integrated approach to manage and restore forests across various landscapes and environments. Such restoration helps to meet the targets of Sustainable Development Goal (SDG)–15, as outlined in the UN Environment’s sixth Global Outlook, which includes the sustainable management of forests, the control of desertification, reducing degradation, biodiversity loss, and the conservation of mountain ecosystems. Here, we have used time series Landsat images from 1996 to 2016 to see how land use, and in particular forest cover, have changed between 1996 and 2016 in the Lumbini Province of Nepal. In addition, we simulated projections of land cover (LC) and forest cover change for the years 2026 and 2036 using a hybrid cellular automata Markov chain (CA–Markov) model. We found that the overall forest area increased by 199 km2 (2.1%), from a 9491 km2 (49.3%) area in 1996 to 9691 km2 (50.3%) area in 2016. Our modeling suggests that forest area will increase by 81 km2 (9691 to 9772 km2) in 2026 and by 195 km2 (9772 km2 to 9966 km2) in 2036. They are policy, planning, management factors and further strategies to aid forest regeneration. Clear legal frameworks and coherent policies are required to support sustainable forest management programs. This research may support the targets of the Sustainable Development Goals (SDG), the land degradation neutral world (LDN), and the UN decade 2021–2031 for ecosystem restoration.

Estimating the future clinical and economic benefits of improving osteoporosis diagnosis and treatment among women in Japan: a simulation projection model from 2020 to 2040.

Japan is currently home to the world's oldest population and the 65 + years demographic is expected to grow to 35% by 2040. Thus, identifying strategies to reduce clinical and economic burden associated with osteoporosis among this at-risk population is critical. A microsimulation model was developed to project osteoporotic annual fracture incidence and costs among PMW 2020-2040. Fracture risk was estimated using a simplified Fracture Risk Assessment Tool (FRAX). Fracture estimates were based on annualized FRAX risk and treatment impact. Published literature informed inputs for direct and indirect fracture costs, DXA screening costs, and treatment costs and efficacy. Japan's current screening and treatment rates were compared against 50% increases to (1) case finding (screening rate and subsequent treatment rate) and (2) treatment rate among those at highest fracture risk. From 2020 to 2040, 21.6 million fractures are projected costing US $410.2 billion. Increased case finding scenario resulted in the prevention of 456.9 thousand primary and 340.9 thousand second + fractures saving US $4.25 billion. Increased treatment scenario led to 500.5 thousand and 435.5 thousand fewer primary and second + fractures, respectively, and reduced economic burden by $3.1 billion. Improvements to rates of osteoporosis screening and preventive treatment in Japan's aging population through disease awareness campaigns and post-fracture care programs, among others, will likely reduce osteoporosis-associated clinical and economic burden.

An Effective Project Management Simulation for Distance Education

The challenge in distance education for project management courses is how to maintain quality when team projects, traditional lectures and in-class exercises normally conducted in face-to-face courses are not practical. This research explores the desirability and effectiveness of computer-based project management simulation as a teaching tool for industrial technology students in a distance education (DE) degree program. The purpose of using the simulation was to enhance learning outcomes related to project management processes and techniques taught in the course. A survey of approximately 100 students over four semesters was used to determine how online students responded to the use of simulation to teach specific project management concepts. Additionally, the research sought to determine which project management concepts were most enhanced through this active learning activity. The results established that the SimProject® computer-based simulation improved project management education for distance students and added value to the course by reinforcing text-based content. The data established a positive relationship between the simulation and specific project management knowledge areas for scope, time, cost, risk, human resource, communication, stakeholder, and quality management. The simulation provided students with the challenge to execute a virtual project, allowing up to three separate attempts, each with the same defined goals for time, cost, and quality. Dynamic, simulation- induced, life-like obstacles based on student choices presented unique management decisions for each trial. A successful outcome required students to meet time, cost, and quality goals within 10 percent of target. Student motivation for this pedagogy was apparent as most used all three attempts and provided positive survey feedback.

Effect of glandularity on the detection of simulated cancers in planar, tomosynthesis, and synthetic 2D imaging of the breast using a hybrid virtual clinical trial.

The purpose of this study was to measure the threshold diameter of calcifications and masses for 2D imaging, digital breast tomosynthesis (DBT), and synthetic 2D images, for a range of breast glandularities. This study shows the limits of detection for each of the technologies and the strengths and weaknesses of each in terms of visualizing the radiological features of small cancers. Mathematical voxel breast phantoms with glandularities by volume of 9%, 18%, and 30% with a thickness of 53mm were created. Simulated ill-defined masses and calcification clusters with a range of diameters were inserted into some of these breast models. The imaging characteristics of a Siemens Inspiration X-ray system were measured for a 29kV, tungsten/rhodium anode/filter combination. Ray tracing through the breast models was undertaken to create simulated 2D and DBT projection images. These were then modified to adjust the image sharpness, and to add scatter and noise. The mean glandular doses for the images were 1.43, 1.47, and 1.47 mGy for 2D and 1.92, 1.97, and 1.98 mGy for DBT for the three glandularities. The resultant images were processed to create 2D, DBT planes and synthetic 2D images. Patches of the images with or without a simulated lesion were extracted, and used in a four-alternative forced choice study to measure the threshold diameters for each imaging mode, lesion type, and glandularity. The study was undertaken by six physicists. The threshold diameters of the lesions were 6.2, 4.9, and 6.7mm (masses) and 225, 370, and 399μm, (calcifications) for 2D, DBT, and synthetic 2D, respectively, for a breast glandularity of 18%. The threshold diameter of ill-defined masses is significantly smaller for DBT than for both 2D (p≤0.006) and synthetic 2D (p≤0.012) for all glandularities. Glandularity has a significant effect on the threshold diameter of masses, even for DBT where there is reduced background structure in the images. The calcification threshold diameters for 2D images were significantly smaller than for DBT and synthetic 2D for all glandularities. There were few significant differences for the threshold diameter of calcifications between glandularities, indicating that the background structure has little effect on the detection of calcifications. We measured larger but nonsignificant differences in the threshold diameters for synthetic 2D imaging than for 2D imaging for masses in the 9% (p=0.059) and 18% (p=0.19) glandularities. The threshold diameters for synthetic 2D imaging were larger than for 2D imaging for calcifications (p<0.001) for all glandularities. We have shown that glandularity has only a small effect on the detection of calcifications, but the threshold diameter of masses was significantly larger for higher glandularity for all of the modalities tested. We measured nonsignificantly larger threshold diameters for synthetic 2D imaging than for 2D imaging for masses at the 9% (p=0.059) and 18% (p=0.19) glandularities and significantly larger diameters for calcifications (p<0.001) for all glandularities. The lesions simulated were very subtle and further work is required to examine the clinical effect of not seeing the smallest calcifications in clusters.

A scanner-specific framework for simulating CT images with tube current modulation

Although tube current modulation (TCM) is routinely implemented in modern computed tomography (CT) scans, no existing CT simulator is capable of generating realistic images with TCM. The goal of this study was to develop such a framework to (1) facilitate patient-specific optimization of TCM parameters and (2) enable future virtual imaging trials (VITs) with more clinically realistic image quality and x-ray flux distributions. The framework was created by developing a TCM module and integrating it with an existing CT simulator (DukeSim). The developed module utilizes scanner-calibrated TCM parameters and two localizer radiographs to compute the mAs for each simulated CT projection. This simulation pipeline was validated in two parts. First, DukeSim was validated in the context of a commercial scanner with TCM (SOMATOM Force, Siemens Healthineers) by imaging a physical CT phantom (Mercury, Sun Nuclear) and its computational analogue. Second, the TCM module was validated by imaging a computational anthropomorphic phantom (ATOM, CIRS) using DukeSim with real and module-generated TCM profiles. The validation demonstrated DukeSim’s realism in terms of noise magnitude, noise texture, spatial resolution, and image contrast (with average differences of 0.38%, 6.31%, 0.43%, and −9 HU, respectively). It also demonstrated the TCM module’s realism in terms of projection-level mAs and resulting noise magnitude (2.86% and −2.60%, respectively). Finally, the framework was applied to a pilot VIT simulating images of three computational anthropomorphic phantoms (XCAT, with body mass indices (BMIs) of 24.3, 28.2, and 33.0) under five different TCM settings. The optimal TCM for each phantom was characterized based on various criteria, such as minimizing mAs or maximizing image quality. ‘Very Weak’ TCM minimized noise for the 24.3 BMI phantom, while ‘Very Strong’ TCM minimized noise for the 33.0 BMI phantom. This illustrates the utility of the developed framework for future optimization studies of TCM parameters and, more broadly, large-scale VITs with scanner-specific TCM.

Thermodynamic response of precipitation extremes to climate change and its impacts on floods over China

<p indent=0mm>As the planet warms, shifts in the thermodynamic and dynamic environments of the climate system exert substantial impacts on the energy budget and water cycle. Understanding the response of precipitation extremes to climate change is essential for mitigating weather-related hazards and facilitating adaptive water resources management. The moisture holding capacity of the atmosphere, governed by the Clausius-Clapeyron (C-C) relationship, increases with temperature at a rate of about 7%/°C. Thermodynamically, precipitation intensity is expected to increase exponentially and worsen flood conditions with atmospheric warming. However, both observational and satellite data have revealed a nonmonotonic (hook) scaling of precipitation and storm runoff, in which extremes strengthen with warming temperature up to a peak point (<italic>T</italic>_pp) and decline thereafter. Few studies have yet considered whether and why the hook structure might change in a future warming climate. Moreover, existing studies have largely failed to detect and attribute changes of extreme precipitation to atmospheric thermodynamics versus dynamics, limiting reliable projection of future hydrological hazards and climate simulations. Here, we build upon existing studies to understand how precipitation and storm runoff scaling might change over Chinese mainland. We use gridded observational data to examine the temperature scaling of precipitation and storm runoff extremes, extending the C-C relationship. Using the ERA5 reanalysis dataset, we diagnose the impacts of total column water vapor (TCWV), convective available potential energy (CAPE) and relative humidity on precipitation extremes. After partitioning the thermodynamic and dynamic contributions of precipitation extremes based on the energy budget and water vapor balance, we quantify the underlying physical mechanisms of hook structures. Future climate scenarios are projected by using a large ensemble from 21 global climate models (GCMs) within Coupled Model Inter-comparison Project Phase-6 (CMIP6). Future streamflow series of 151 basins are projected by forcing hydrological model with bias-corrected climate model outputs. We quantify changes in the hook structures and their <italic>T</italic>_pp, and investigate implications of these shifts for future storms and flooding. The results show that relative humidity usually decreases with warming near-surface temperatures over most areas of China, while the TCWV and CAPE exhibit positive scaling rates with temperatures. The ERA5 reanalysis data over China reveals that atmospheric dynamics are responsible for the intensification of precipitation extremes and even flooding under very warm temperatures. The daily bias correction method reduces the systematical biases of GCMs outputs adequately, and captures extreme precipitation and temperatures well. By using four different hydrological models, we find that the Xinanjiang model typically exhibits the best performance in simulating daily streamflow over Chinese mainland. Projections of future hydro-climatological scenarios indicate that the hook structures do not set a firm upper-bound constraint on the intensification of extremes; rather, both the hook structure and the <italic>T</italic>_pp shift toward warmer temperatures in a warming climate. Alongside the shifts in <italic>T</italic>_pp, future storm and flooding extremes are projected to intensify by 20%−30%. These findings highlight the urgency in enhancing societal resilience to climate change, and understanding how the intensification of weather-related hazards might affect existing infrastructure and ecosystems.

An energy system optimization model accounting for the interrelations of multiple stochastic energy prices.

The variation of and the interrelation between different energy markets significantly affect the competitiveness of various energy technologies, therefore complicate the decision-making problem for a complex energy system consisting of multiple competing technologies, especially in a long-term time frame. The interrelations between these markets have not been accounted for in the existing energy system modelling efforts, leading to a distortion of understanding of the market impact on the technological choices and operations in the real world. This study investigates the strategic and operational decision-making problem for such an energy system characterized by three competing technologies from crude oil, natural gas, and coal. A stochastic programming model is constructed by incorporating multiple volatile energy prices interrelated with each other. Oil price is modelled by the mean-reverting Ornstein–Uhlenbeck process and serves as the exogenous variable in the ARIMAX models for natural gas and downstream plastic prices. The K-means clustering method is employed to extract a handful of distinctive patterns from a large number of simulated price projections to enhance the computing efficiency without losing retaining critical information and insights from the price co-movement. The model results suggest that the high volatility of the energy market weakens the possibility of selecting the corresponding technology. The oil-based route, for example, gradually loses its market share to the coal approach, attributed to a higher volatile oil market. The proposed method is applicable to other problems of the same kind with high-dimensional stochastic variables.Supplementary InformationThe online version contains supplementary material available at 10.1007/s10479-021-04229-3.

Experiential Learning in the Classroom: An Accounting Cycle Simulation Project

Kolb and Fry (1975) discussed the dynamics of learning and suggested the four-stage experiential learning model as “a holistic approach to the learning process.” We designed a “simulation experiential learning approach.” We integrated a virtual business simulation into an accounting cycle project to incorporate the experiential learning model into a classroom activity. Specifically, students (1) created their business activities in the simulation to develop concrete business experience, (2) observed business operations and applied accounting cycle procedures to record and report these activities, (3) analyzed and evaluated business operations as captured in their accounting records and reports, and (4) used these reports to make informed decisions in their subsequent business activities. Most students considered the project effective and relevant. They found it interesting and were motivated to put forth time and effort to the project. They reported that the project required a broad range of skills. Upon completion of the project, students showed improvement along Anderson and Krathwohl’s (2001) factual, conceptual, procedural, and metacognition knowledge dimensions in learning the subject matter.

Tomography of dark-field scatter including single-exposure Moiré fringe analysis with X-ray biprism interferometry-A simulation study.

In this work, we present tomographic simulations of a new hardware concept for X-ray phase-contrast interferometry wherein the phase gratings are replaced with an array of Fresnel biprisms, and Moiré fringe analysis is used instead of "phase stepping" popular with grating-based setups. Projections of a phantom consisting of four layers of parallel carbon microfibers is simulated using wave optics representation of X-ray electromagnetic waves. Simulated projections of a phantom with preferential scatter perpendicular to the direction of the fibers are performed to analyze the extraction of small-angle scatter from dark-field projections for the following: (1) biprism interferometry using Moiré fringe analysis; (2) grating interferometry using phase stepping with eight grating steps; and (3) grating interferometry using Moiré fringe analysis. Dark-field projections are modeled as projections of voxel intensities represented by a fixed finite vector basis set of scattering directions. An iterative MLEM algorithm is used to reconstruct, from simulated projection data, the coefficients of a fixed set of seven basis vectors at each voxel representing the small-angle scatter distribution. Results of reconstructed vector coefficients are shown comparing the three simulated imaging configurations. The single-exposure Moiré fringe analysis shows not only an increase in noise because of one-eighth the number of projection samples but also is obtained with less dose and faster acquisition times. Furthermore, replacing grating interferometry with biprism interferometry provides better contrast-to-noise. The simulations demonstrate the feasibility of the reconstruction of dark-field data acquired with a biprism interferometry system. With the potential of higher fringe visibility, biprism interferometry with Moiré fringe analysis might provide equal or better image quality to that of phase stepping methods with less imaging time and lower dose.

Threshold analyses on combinations of testing, population size, and vaccine coverage for COVID-19 control in a university setting.

We simulated epidemic projections of a potential COVID-19 outbreak in a residential university population in the United States under varying combinations of asymptomatic tests (5% to 33% per day), transmission rates (2.5% to 14%), and contact rates (1 to 25), to identify the contact rate threshold that, if exceeded, would lead to exponential growth in infections. Using this, we extracted contact rate thresholds among non-essential workers, population size thresholds in the absence of vaccines, and vaccine coverage thresholds. We further stream-lined our analyses to transmission rates of 5 to 8%, to correspond to the reported levels of face-mask-use/physical-distancing during the 2020 pandemic. Our results suggest that, in the absence of vaccines, testing alone without reducing population size would not be sufficient to control an outbreak. If the population size is lowered to 34% (or 44%) of the actual population size to maintain contact rates at 4 (or 7) among non-essential workers, mass tests at 25% (or 33%) per day would help control an outbreak. With the availability of vaccines, the campus can be kept at full population provided at least 95% are vaccinated. If vaccines are partially available such that the coverage is lower than 95%, keeping at full population would require asymptomatic testing, either mass tests at 25% per day if vaccine coverage is at 63–79%, or mass tests at 33% per day if vaccine coverage is at 53–68%. If vaccine coverage is below 53%, to control an outbreak, in addition to mass tests at 33% per day, it would also require lowering the population size to 90%, 75%, and 60%, if vaccine coverage is at 38–53%, 23–38%, and below 23%, respectively. Threshold estimates from this study, interpolated over the range of transmission rates, can collectively help inform campus level preparedness plans for adoption of face mask/physical-distancing, testing, remote instructions, and personnel scheduling, during non-availability or partial-availability of vaccines, in the event of SARS-Cov2-type disease outbreaks.

Estimating the future clinical and economic benefits of improving osteoporosis diagnosis and treatment among women in China: a simulation projection model from 2020 to 2040.

Globally, aging populations drive significant increases in osteoporosis prevalence. In China, the number of women aged ≥ 65years is expected to more than double from 2020 (91.5M) to 2040 (183.6M). Using a microsimulation model, impact of increased diagnosis and treatment of postmenopausal women (PMW) with osteoporosis on anticipated reduction in fractures and associated costs in China from 2020 to 2040 was projected. A microsimulation model was developed to project annual incidence and costs of osteoporotic fractures among PMW in China from 2020 to 2040. Fracture risk was estimated using the simplified Fracture Risk Assessment Tool (FRAX). Fractures estimates were based on annualized FRAX risk and impact of treatment. Published literature informed inputs for direct and indirect fracture costs, DXA screening costs, and treatment costs and efficacy. China's current screening and treatment rates were compared against 50% increases to (i) case finding (screening rate and subsequent treatment rate) and (ii) treatment rate among those at highest fracture risk. From 2020 to 2040, 241.7M osteoporotic fractures are projected to cost $997B. Increased treatment scenario prevented 24.6M fractures and saved $56B. Increased case finding scenario prevented 26M fractures and saved $61.7B. Osteoporosis underdiagnosis and undertreatment among the aging Chinese population will considerably burden patients, caregivers, and society. Policy changes to increase screening and treatment rates may result in significant cost savings and clinical benefits.

The BACCO simulation project: a baryonification emulator with neural networks

ABSTRACT We present a neural network emulator for baryonic effects in the non-linear matter power spectrum. We calibrate this emulator using more than 50 000 measurements in a 15D parameter space, varying cosmology and baryonic physics. Baryonic physics is described through a baryonification algorithm, which has been shown to accurately capture the relevant effects on the power spectrum and bispectrum in state-of-the-art hydrodynamical simulations. Cosmological parameters are sampled using a cosmology-rescaling approach including massive neutrinos and dynamical dark energy. The specific quantity we emulate is the ratio between matter power spectrum with baryons and gravity only, and we estimate the overall precision of the emulator to be $2\!-\!3{{\ \rm per\ cent}}$, at scales $k \lt 5 \, h\, {\rm Mpc}^{-1}$ and redshifts 0 &amp;lt; z &amp;lt; 1.5. We obtain an accuracy of $1\!-\!2{{\ \rm per\ cent}}$, when testing the emulator against a collection of 74 different cosmological hydrodynamical simulations and their respective gravity-only counterparts. We also show that only one baryonic parameter, namely Mc, which sets the gas fraction retained per halo mass, is enough to have accurate predictions of most of the baryonic feedbacks at a given epoch. Our emulator is publicly available at http://www.dipc.org/bacco.