Development of a “nature run” for observing system simulation experiments (OSSEs) for snow mission development

Snow is a fundamental component of global and regional water budgets, particularly in mountainous areas and regions downstream that rely on snowmelt for water resources. Land surface models (LSMs) are commonly used to develop spatially distributed estimates of snow water equivalent (SWE) and runoff. However, LSMs are limited by uncertainties in model physics and parameters, among other factors. In this study, we describe the use of model calibration tools to improve snow simulations within the Noah-MP LSM as the first step in an Observing System Simulation Experiment (OSSE). Noah-MP is calibrated against the University of Arizona (UA) SWE product over a Western Colorado domain. With spatially varying calibrated parameters, we run calibrated and default Noah-MP simulations for water years 2010-2020. By evaluating both simulations against the UA dataset, we show that calibration decreases domain averaged temporal RMSE and bias for snow depth from 0.15 to 0.13 m and from -0.036 to -0.0023 m, respectively, and improves the timing of snow ablation. Increased snow simulation performance also improves estimates of model-simulated runoff in four of six study basins, though only one has statistically significant improvement. Spatially distributed Noah-MP snow parameters perform better than default uniform values. We demonstrate that calibrating variables related to snow albedo calculations and rain-snow partitioning, among other processes, is a necessary step for creating a nature run that reasonably approximates true snow conditions for the OSSEs. Additionally, the inclusion of a snowfall scaling term can address biases in precipitation from meteorological forcing datasets, further improving the utility of LSMs for generating reliable spatiotemporal estimates of snow.

Effects of Loading Conditions on the Pelvic Fracture Biomechanism and Discrimination of Forensic Injury Manners of Impact and Run-Over Using the Finite Element Pelvic Model

This study aimed to systematically simulate the responses of pelvic fracture under impact and run-over to clarify the effects of boundary and loading conditions on the pelvic fracture mechanism and provide complementary quantitative evidence for forensic practice. Based on the THUMS finite element model, we have validated the simulation performance of the model by a real postmortem human pelvis side impact experiment. A total of 54 simulations with two injury manners (impact and run-over), seven loading directions (0°, 30°, 60°, 90°, 270°, 300°, 330°), and six loading velocities (10, 20, 30, 40, 50, and 60 km/h) were conducted. Criteria of effective strain, Von-Mises stress, contact force, and self-designed normalized eccentricity were used to evaluate the biomechanism of pelvic fracture. Based on our simulation results, it’s challenging to distinguish impact from run-over only rely on certain characteristic fractures. Loads on the front and back were less likely to cause pelvic fractures. In the 30°, 60°, 300° load directions, the overall deformation caused a “diagonal” pelvic fracture. The higher is the velocity (kinetic energy), the more severe is the pelvic fracture. The contact force will predict the risk of fracture. In addition, our self-designed eccentricity will distinguish the injury manner of impact and run-over under the 90° loads. The “biomechanical fingerprints” based on logistic regression of all biomechanical variables have an AUC of 0.941 in discriminating the injury manners. Our study may provide simulation evidence and new methods for the forensic community to improve the forensic identification ability of injury manners.

Preparation of artificial MSW leachate for treatment studies: Testing on black soldier fly larvae process

When approaching the study of new processes for leachate treatment, each influencing variable should be kept under control to better comprehend the treatment process. However, leachate quality is difficult to control as it varies dramatically from one landfill to another, and in line with landfill ageing. To overcome this problem, the present study investigated the option of preparing a reliable artificial leachate in terms of quality consistency and representativeness in simulating the composition of real municipal solid waste (MSW) leachate, in view of further investigate the recent treatment process using black soldier fly (BSF) larvae. Two recipes were used to simulate a real leachate (RL): one including chemical ingredients alone (artificial synthetic leachate-SL), and the other including chemicals mixed with artificial food waste (FW) eluate (artificial mixed leachate-ML). Research data were analysed, elaborated and discussed to assess simulation performance according to a series of parameters, such as Analytical representativeness, Treatment representativeness (in this case specific for the BSF larvae process), Recipe relevance, Repeatability and Flexibility in selectively modifying individual quality parameters. The best leachate simulation performance was achieved by the synthetic leachate, with concentration values generally ranging between 97% and 118% of the RL values. When feeding larvae with both RL and SL, similar mortality values and growth performance were observed.

Why Students Should Be Taught Differently: Learner Characteristics, Learning Styles and Simulation Performance

Background Business simulation as an instructional tool helps in developing integrative thinking and decision making skills. It is being taught to audiences who differ considerably in age, work experience (learner characteristics) and learning styles. The use of simulations is likely to grow further with advancements in internet technology and the fact that simulations are very amenable to remote modes of instruction. Aim This study aims to assess how learner characteristics and learning styles impact business simulation performance. It further assesses the combined effect of learner characteristics and learning styles on performance in business simulations, we specifically consider the manner in which learning styles moderate the impact of learner characteristics (age) on simulation performance. Method The study was conducted with 605 students of full time MBA and executive MBA programs with age group varying from 21 years to 53 years. They were taught using the same business simulation by CAPSIM. The learning styles were measured using Felder-Solomon’s instrument ‘Index of learning style’. Regression analysis was conducted with predictor variables of learner characteristics and learning styles and outcome variable of simulation performance. The moderating effect of specific learning styles on learner characteristics was identified. Results The findings indicate that age is a significant predictor of simulation performance (younger, tech savvy students do better). Also, the use of reflective learning style enables better performance in business simulations. Older students are able to draw on experience and benefit more from reflective learning, for business simulations which involve integration across functions. Conclusion The study enhances our conceptual understanding of the factors enabling performance in business simulations and provides specific direction on how instructors must adapt facilitation approaches for different age groups of participants. Reflection is important for learning with business simulations; hence, the reflective learning style should be encouraged particularly among older students.

Integrated Semi-Supervised Clustering Method and Its Application in Rock-Typing in AA Reservoir

Rock-typing is complicated and critical for numerical simulation. Therefore, some researchers proposed several clustering methods to make classification automatic and convenient. However, traditional methods only focus in specific area such as lithofacies or petrophysical data instead of integrated clustering. Besides, all the clustering method are related to classification interval determined subjectively. Therefore, a new clustering method for rock-typing integrated different disciplines is critical for modelling and reservoir simulation. In this paper, we proposed a novel semi-supervised clustering method integrated with data from different disciplines, which can divide rock type automatically and precisely. Considering AA reservoir is a porous carbonate reservoir with seldom fracture and vug, FZI (Flow Zone Indicator) and RQI (Reservoir Quality Index) is utilized as the corner stone of the clustering method after collection and plotting for porosity and permeability data for cores from AA reservoir. Then lithofacies, sedimentary facies and petrophysical data are applied as constraints to improve FZI method. Hamming distance and earth mover distance are imported to build integrated function for clustering method. Finally, based on output results of integrated clustering method from experimental data, grid properties of model in Petrel software are imported as the input parameter for further procession. Therefore, saturation region for numerical simulation built by rock-typing is constructed. The results show that new method could make classification accurately and easily. History matching results for watercut indicate that new saturation regions improve the numerical simulation performance.

A Novel Signal Design and Performance Analysis in NavCom Based on LEO Constellation

The mega-launch of low Earth orbit satellites (LEOs) represents a critical opportunity to integrate navigation and communication (NavCom), but first, challenges related to signal design must be overcome. This article proposes a novel signal scheme named CE-OFDM-PM. Via research on the in-band or adjacent band, it was found that the proposed signal scheme was suitable for S-band and had a wide normalized power spectrum density (PSD), high peak-to-side lobe ratio (PSR), and multiple peaks in autocorrelation. In an analysis of the simulation performance evaluation in navigation and communication, it is found that the proposed signal scheme has the potential for high accuracy, a code tracking accuracy of up to 0.85 m, a small mutual influence between the proposed signal scheme and other schemes, excellent anti-interference properties, and a better performance at both short and long distances in terms of its anti-multipath capability. Furthermore, the proposed signal scheme shows the ability to communicate between satellites and the ground and is outstanding in terms of its bit error rate (BER), CNR, and energy per bit to noise power spectral density ratio (Eb/N0). From the technical, theoretical, and application perspectives, our proposed signal scheme has potential as an alternative scheme in future BDS, PNTs, and even 5G/B5G.

A Novel Active Fault-Tolerant Tracking Control for Robot Manipulators with Finite-Time Stability

Many terminal sliding mode controllers (TSMCs) have been suggested to obtain exact tracking control of robotic manipulators in finite time. The ordinary method is based on TSMCs that secure trajectory tracking under the assumptions such as the known robot dynamic model and the determined upper boundary of uncertain components. Despite tracking errors that tend to zero in finite time, the weakness of TSMCs is chattering, slow convergence speed, and the need for the exact robot dynamic model. Few studies are handling the weakness of TSMCs by using the combination between TSMCs and finite-time observers. In this paper, we present a novel finite-time fault tolerance control (FTC) method for robotic manipulators. A finite-time fault detection observer (FTFDO) is proposed to estimate all uncertainties, external disturbances, and faults accurately and on time. From the estimated information of FTFDO, a novel finite-time FTC method is developed based on a new finite-time terminal sliding surface and a new finite-time reaching control law. Thanks to this approach, the proposed FTC method provides a fast convergence speed for both observation error and control error in finite time. The operation of the robot system is guaranteed with expected performance even in case of faults, including high tracking accuracy, small chattering behavior in control input signals, and fast transient response with the variation of disturbances, uncertainties, or faults. The stability and finite-time convergence of the proposed control system are verified that they are strictly guaranteed by Lyapunov theory and finite-time control theory. The simulation performance for a FARA robotic manipulator proves the proposed control theory’s correctness and effectiveness.

Simulation performance of selected global and regional climate models for temperature and rainfall in some locations in India

Global and regional climate models are reported to have inherent bias in simulating the observed climatology of a region. This bias of climate models is the major source of uncertainties in climate change impact assessments. Therefore, use of bias corrected simulated climate data is important. In this study, the bias corrected climate data for 30 years’ period (1976-2005) from selected common fourGCMs and RCMs for six Indian locations are compared with the respective observed data of India Meteorological Department. The analysis indicated that the RCMs performance is much better than GCMs after bias correction for minimum and maximum temperatures. Also, RCMs performance is better than GCMs in simulating extreme temperatures. However, the selected RCMs and GCMs are found to either over estimate or under estimate the rainfall despite bias correction and also overestimated the rainfall extremes for selected Indian locations. Based on the overall performance of four models for the six locations, it was found that the GFDL_ESM2M and NORESM1-M RCMs performed comparatively better than CSIRO and IPSL models. After bias correction, the RCMs could represent the observed climatology better than the GCMs. And these RCMs viz., GFDL_ESM2M and NORESM1-M can be usedindividually after bias correction in the climate change assessment studies for the selected regions.