Excavation of regolith is the enabling process for many of the in-situ resource utilization (ISRU) efforts that are being considered to aid in the human exploration of the moon and Mars. Most proposed planetary excavation systems are integrated with a wheeled vehicle, but none yet have used a screw-propelled vehicle which can significantly enhance the excavation performance. Therefore, CASPER, a novel screw-propelled excavation rover is developed and analyzed to determine its effectiveness as a planetary excavator. The excavation rate, power, velocity, cost of transport, and a new parameter, excavation transport rate, are analyzed for various configurations of the vehicle through mobility and excavation tests performed in silica sand. The optimal configuration yielded a 30 kg/hr excavation rate and 10.2 m/min traverse rate with an overall system mass of 3.4 kg and power draw of less than 30 W. These results indicate that this architecture shows promise as a planetary excavation because it provides significant excavation capability with low mass and power requirements. Corresponding author(s) Email: email@example.com
Proprioception, the ability to perceive one’s own configuration and movement in space, enables organisms to safely and accurately interact with their environment and each other. The underlying sensory nerves that make this possible are highly dense and use sophisticated communication pathways to propagate signals from nerves in muscle, skin and joints to the central nervous system wherein the organism can process and react to stimuli. In a step forward to realize robots with such perceptive capability, we propose a flexible sensor framework that incorporates a novel hybrid modeling strategy, taking advantage of computational mechanics and machine learning. We implement the sensor framework on a large, thin and flexible sensor that transforms sparsely distributed strains into continuous surface shape. Finite element (FE) analysis is utilized to determine sensor design parameters, while an FE model is built to enrich the morphological data used in the supervised training to achieve continuous surface reconstruction. A mapping between the local strain data and the enriched surface data is subsequently trained using ensemble learning. This hybrid approach enables real-time, robust and high-order surface shape reconstruction. The sensing performance is evaluated in terms of accuracy, repeatability, and feasibility with numerous scenarios, which has not been demonstrated and reported on such a large-scale (A4-paper-size) sensor before.
This Supporting Information includes:Figure S1, S2, S3Supplementary Video Supplementary Video S1: Locomotion of the mobile robot. Supplementary Video S2: Vortex deforming the liquid-liquid interface. Supplementary Video S3: Locomotion of the mobile robot without electrode attached. Supplementary Video S4: Locomotion of the mobile robot with reversed polarity. Supplementary Video S5: Drawing “SIT” by controlling a floating robot with multiple electrodes. Corresponding author Email: firstname.lastname@example.org, email@example.com
This Supporting information includes:1. Component Selection and Performance of SFA2. Actuator Manufacturing and Preparation of Conductive Ink 3. Average Thickness of Conductive Coating layer on PU Foam4. Time Response of the Actuator in Different Modes 5. Characterization and Experimental Setup6. Measurement and Data Analysis7. Design Specifications of Soft Robotic Applications8. Supporting Video Corresponding author Email: firstname.lastname@example.org, email@example.com
This Supporting Information includes the extended description of the superposition state of the asymmetric double-well system in vacuum system and in solution, truth tables for the residue pairs and their corresponding quantum logic gates, and figures for the double well potential energy surfaces and transmission spectra of the residue pairs.Corresponding Authors Email: firstname.lastname@example.org and email@example.com
Identifying Clostridioides difficile infection (CDI) patients at risk of mortality or recurrence will facilitate prevention, timely treatment and improve clinical outcomes. We aim to establish an open-access web-based prediction system, which estimates CDI patients’ mortality and recurrence outcomes, and explains the machine learning prediction with patients’ characteristics. Prognostic models were developed using four various types of machine learning algorithms and statistical logistics regression model utilizing over 15,000 CDI patients from 41 hospitals in Hong Kong. The boosting-based machine learning algorithm Gradient Boosting Machine (Mortality AUC: 0.7878; Recurrence AUC: 0.7076) outperformed statistical models (Mortality AUC: 0.7573; Recurrence AUC: 0.6927) and other machine learning algorithms. The open-access prediction system for clinicians to assess and interpret the risk factors of CDI patients is now available at https://www.cdiml.care/. In this article, we explain the development of machine learning models and illustrate how to apply hyperparameter tuning with cross-validation to optimize the model accuracy.
Appendix ATable S1. Results of the WS-CNN classifier for post-HI spike transient identification in experimental data (entire 6 hours – 13 layers) Trained and validated on Sheep No. No. of patterns in the Train and Validation Dataset Tested on Sheep No. No. of patterns in the Test-set TP hits TN hits FP hits FN hits Sensitivity [%] Selectivity [%] Precision [%] Accuracy [%] 2,3,4,5,6,7 4567 1 443 152 269 1 21 87.9 99.6 99.3 95.0 1,3,4,5,6,7 4751 2 259 110 149 0 0 100 100 100 100 1,2,4,5,6,7 4731 3 279 81 196 0 2 97.6 100 100 99.3 1,2,3,5,6,7 3372 4 1638 824 806 8 0 100 99.0 99.0 99.5 1,2,3,4,6,7 4088 5 922 454 466 1 1 99.8 99.8 99.8 99.8 1,2,3,4,5,7 4466 6 544 231 312 1 0 100 99.7 99.6 99.8 1,2,3,4,5,6 4085 7 925 209 714 2 0 100 99.7 99.1 99.8 Overall performance of the 13 layers WS-CNN in the entire 6 hours 99.03±1.66
The expense of quantum chemistry calculations significantly hinders the search for novel catalysts. Here, we provide a tutorial for using an easy and highly cost-efficient calculation scheme called alchemical perturbation density functional theory (APDFT) for rapid predictions of binding energies of reaction intermediates and reaction barrier heights based on Kohn-Sham density functional theory reference data. We outline standard procedures used in computational catalysis applications, explain how computational alchemy calculations can be carried out for those applications, and then present bench marking studies of binding energy and barrier height predictions. Using a single OH binding energy on the Pt(111) surface as a reference case, we use computational alchemy to predict binding energies of 32 variations of this system with a mean unsigned error of less than 0.05 eV relative to single-point DFT calculations. Using a single nudged elastic band calculation for CH4 dehydrogenation on Pt(111) as a reference case, we generate 32 new pathways with barrier heights having mean unsigned errors of less than 0.3 eV relative to single-point DFT calculations. Notably, this easy APDFT scheme brings no appreciable computational cost once reference calculations are done, and this shows that simple applications of computational alchemy can significantly impact DFT-driven explorations for catalysts. To accelerate computational catalysis discovery and ensure computational reproducibility, we also include Python modules that allow users to perform their own computational alchemy calculations.Keywords --- Computational catalysis, density functional theory (DFT), adsorption energies, nudged elastic band calculations, binding energies, barrier heights
Counting parameters has become customary in the density functional theory community as a way to infer the transferability of popular approximations to the exchange–correlation functionals. Recent work in data science, however, has demonstrated that the number of parameters of a fitted model is not related to the complexity of the model itself, nor to its eventual overfitting. Using similar arguments, we show here that it is possible to represent every modern exchange–correlation functional approximation using just one single parameter. This procedure proves the futility of the number of parameters as a measure of transferability. To counteract this shortcoming, we introduce and analyze the performance of three statistical criteria for the evaluation of the transferability of exchange–correlation functionals. The three criteria are called Akaike information criterion (AIC), Vapnik–Chervonenkis criterion (VCC), and cross-validation criterion (CVC) and are used in a preliminary assessment to rank 60 exchange–correlation functional approximations using the ASCDB database of chemical data.
The growing generation of data and their wide availability has led to the development of tools to produce, analyze and store this information. Computational chemistry studies and especially catalytic applications often yield a vast amount of chemical information that can be analyzed and stored using these tools. In this manuscript we present a framework that automatically performs a full automated procedure consisting in the transfer of an adsorbate from a known metal slab to a new metal slab with similar packing. Our method generates the new geometry and also performs the required calculations and analysis to finally upload the processed data to an online database (ioChem-BD). Two different implementations have been built, one to relocate minimum energy point structures and the second to transfer transition states. Our framework shows good performance for the minimum point location and a decent performance for the transition state identification. Most of the failures occurred during the transition state searches needed additional steps to fully complete the process. Further improvements of our framework are required to increase the performance of both implementations. These results point to the _avoidhuman_ path as a feasible solution for studies on very large systems that require a significant amount of human resources and in consequence are prone to human errors.