DataSheet1_A data-driven artificial neural network model for the prediction of ground motion from induced seismicity: The case of The Geysers geothermal field.PDF

Ground-motion models have gained foremost attention during recent years for being capable of predicting ground-motion intensity levels for future seismic scenarios. They are a key element for estimating seismic hazard and always demand timely refinement in order to improve the reliability of seismic hazard maps. In the present study, we propose a ground motion prediction model for induced earthquakes recorded in The Geysers geothermal area. We use a fully connected data-driven artificial neural network (ANN) model to fit ground motion parameters. Especially, we used data from 212 earthquakes recorded at 29 stations of the Berkeley–Geysers network between September 2009 and November 2010. The magnitude range is 1.3 and 3.3 moment magnitude (Mw), whereas the hypocentral distance range is between 0.5 and 20 km. The ground motions are predicted in terms of peak ground acceleration (PGA), peak ground velocity (PGV), and 5% damped spectral acceleration (SA) at T=0.2, 0.5, and 1 s. The predicted values from our deep learning model are compared with observed data and the predictions made by empirical ground motion prediction equations developed by Sharma et al. (2013) for the same data set by using the nonlinear mixed-effect (NLME) regression technique. For validation of the approach, we compared the models on a separate data made of 25 earthquakes in the same region, with magnitudes ranging between 1.0 and 3.1 and hypocentral distances ranging between 1.2 and 15.5 km, with the ANN model providing a 3% improvement compared to the baseline GMM model. The results obtained in the present study show a moderate improvement in ground motion predictions and unravel modeling features that were not taken into account by the empirical model. The comparison is measured in terms of both the R2 statistic and the total standard deviation, together with inter-event and intra-event components.</p

Asymmetric Total Synthesis of (+)-Verrubenzospirolactone and (+)-Capillobenzopyranol

The first asymmetric total synthesis of (+)-verrubenzospirolactone (1), a distinctive highly fused benzosesquiterpenoid, characterized by a pentacyclic skeletal structure, is realized through a concise 10-step synthetic pathway with an impressive 22.8% overall yield. Notable highlights of this synthetic endeavor include (i) the introduction of a Ru-catalyzed ortho C–H activation step, (ii) the application of Pd-catalyzed asymmetric allylic alkylation to establish a pivotal stereocenter at C-3 with an excellent enantiomeric excess, (iii) B-alkyl Suzuki–Miyaura coupling to construct a Diels–Alder precursor, and, ultimately, (iv) the successful deployment of an intramolecular Diels–Alder reaction to complete the synthesis of (+)-verrubenzospirolactone without erosion of the enantiomeric excess

DataSheet1.pdf

<p>A hybrid walking neuroprosthesis that combines functional electrical stimulation (FES) with a powered lower limb exoskeleton can be used to restore walking in persons with paraplegia. It provides therapeutic benefits of FES and torque reliability of the powered exoskeleton. Moreover, by harnessing metabolic power of muscles via FES, the hybrid combination has a potential to lower power consumption and reduce actuator size in the powered exoskeleton. Its control design, however, must overcome the challenges of actuator redundancy due to the combined use of FES and electric motor. Further, dynamic disturbances such as electromechanical delay (EMD) and muscle fatigue must be considered during the control design process. This ensures stability and control performance despite disparate dynamics of FES and electric motor. In this paper, a general framework to coordinate FES of multiple gait-governing muscles with electric motors is presented. A muscle synergy-inspired control framework is used to derive the controller and is motivated mainly to address the actuator redundancy issue. Dynamic postural synergies between FES of the muscles and the electric motors were artificially generated through optimizations and result in key dynamic postures when activated. These synergies were used in the feedforward path of the control system. A dynamic surface control technique, modified with a delay compensation term, is used as the feedback controller to address model uncertainty, the cascaded muscle activation dynamics, and EMD. To address muscle fatigue, the stimulation levels in the feedforward path were gradually increased based on a model-based fatigue estimate. A Lyapunov-based stability approach was used to derive the controller and guarantee its stability. The synergy-based controller was demonstrated experimentally on an able-bodied subject and person with an incomplete spinal cord injury.</p

Hierarchically Structured Free-Standing Hydrogels with Liquid Crystalline Domains and Magnetic Nanoparticles as Dual Physical Cross-Linkers

Here we report a modular strategy for preparing physically cross-linked and mechanically robust free-standing hydrogels comprising unique thermotropic liquid crystalline (LC) domains and magnetic nanoparticles both of which serve as the physical cross-linkers resulting in hydrogels that can be used as magnetically responsive soft actuators. A series of amphiphilic LC pentablock copolymers of poly­(acrylic acid) (PAA), poly­(5-cholesteryloxypentyl methacrylate) (PC5MA), and poly­(ethylene oxide) (PEO) blocks in the sequence of PAA–PC5MA–PEO–PC5MA–PAA were prepared using reversible addition–fragmentation chain transfer polymerization. These pentablock copolymers served as macromolecular ligands to template Fe₃O₄ magnetic nanoparticles (MNPs), which were directly anchored to the polymer chains through the coordination bonds with the carboxyl groups of PAA blocks. The resulting polymer/MNP nanocomposites comprised a complicated hierarchical structure in which polymer-coated MNP clusters were dispersed in a microsegregated pentablock copolymer matrix that further contained LC ordering. Upon swelling, the hierarchical structure was disrupted and converted to a network structure, in which MNP clusters were anchored to the polymer chains and LC domains stayed intact to connect solvated PEO and PAA blocks, leading to a free-standing LC magnetic hydrogel (LC ferrogel). By varying the PAA weight fraction (<i>f</i>_AA) in the pentablock copolymers, the swelling degrees (<i>Q</i>) of the resulting LC ferrogels were tailored. Rheological experiments showed that these physically cross-linked free-standing LC ferrogels exhibit good mechanical strength with storage moduli <i>G</i>′ of around 10⁴–10⁵ Pa, similar to that of natural tissues. Furthermore, application of a magnetic field induced bending actuation of the LC ferrogels. Therefore, these physically cross-linked and mechanically robust LC ferrogels can be used as soft actuators and artificial muscles. Moreover, this design strategy is a versatile platform for incorporation of different types of nanoparticles (metallic, inorganic, biological, etc.) into multifunctional amphiphilic block copolymers, resulting in unique free-standing hybrid hydrogels of good mechanical strength and integrity with tailored properties and end applications

Hierarchically Structured Free-Standing Hydrogels with Liquid Crystalline Domains and Magnetic Nanoparticles as Dual Physical Cross-Linkers

Here we report a modular strategy for preparing physically cross-linked and mechanically robust free-standing hydrogels comprising unique thermotropic liquid crystalline (LC) domains and magnetic nanoparticles both of which serve as the physical cross-linkers resulting in hydrogels that can be used as magnetically responsive soft actuators. A series of amphiphilic LC pentablock copolymers of poly­(acrylic acid) (PAA), poly­(5-cholesteryloxypentyl methacrylate) (PC5MA), and poly­(ethylene oxide) (PEO) blocks in the sequence of PAA–PC5MA–PEO–PC5MA–PAA were prepared using reversible addition–fragmentation chain transfer polymerization. These pentablock copolymers served as macromolecular ligands to template Fe₃O₄ magnetic nanoparticles (MNPs), which were directly anchored to the polymer chains through the coordination bonds with the carboxyl groups of PAA blocks. The resulting polymer/MNP nanocomposites comprised a complicated hierarchical structure in which polymer-coated MNP clusters were dispersed in a microsegregated pentablock copolymer matrix that further contained LC ordering. Upon swelling, the hierarchical structure was disrupted and converted to a network structure, in which MNP clusters were anchored to the polymer chains and LC domains stayed intact to connect solvated PEO and PAA blocks, leading to a free-standing LC magnetic hydrogel (LC ferrogel). By varying the PAA weight fraction (<i>f</i>_AA) in the pentablock copolymers, the swelling degrees (<i>Q</i>) of the resulting LC ferrogels were tailored. Rheological experiments showed that these physically cross-linked free-standing LC ferrogels exhibit good mechanical strength with storage moduli <i>G</i>′ of around 10⁴–10⁵ Pa, similar to that of natural tissues. Furthermore, application of a magnetic field induced bending actuation of the LC ferrogels. Therefore, these physically cross-linked and mechanically robust LC ferrogels can be used as soft actuators and artificial muscles. Moreover, this design strategy is a versatile platform for incorporation of different types of nanoparticles (metallic, inorganic, biological, etc.) into multifunctional amphiphilic block copolymers, resulting in unique free-standing hybrid hydrogels of good mechanical strength and integrity with tailored properties and end applications