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Understanding and characterizing the intrinsic properties of charge carrier transport across the interfaces in van der Waals heterostructures is critical to their applications in modern electronics, thermoelectrics, and optoelectronics. However, there are very few published cross-plane resistivity measurements of thin samples because these inherently 2-probe measurements must be corrected for contact and lead resistances. Here, we present a method to extract contact resistances and metal lead resistances by fitting the width dependence of the contact end voltages of top and bottom electrodes of different linewidths to a model based on current crowding. These contributions are then subtracted from the total 2-probe cross-plane resistance to obtain the cross-plane resistance of the material itself without needing multiple devices and/or etching steps. This approach was used to measure cross-plane resistivities of a (PbSe)1(VSe2)1 heterostructure containing alternating layers of PbSe and VSe2 with random in-plane rotational disorder. Several samples measured exhibited a 4 order of magnitude difference between cross-plane and in-plane resistivities over the 6–300 K temperature range. We also reported the first observation of charge density wave transition in the cross-plane transport of (PbSe)1(VSe2)1 heterostructure. The device fabrication process is fully lift-off compatible, and the method developed enables the straightforward measurement of the resistivity anisotropy of most thin film materials with nm thicknesses. 

Abstract Background Individuals with hemiparesis post-stroke often have difficulty with tasks requiring upper extremity (UE) intra- and interlimb use, yet methods to quantify both are limited. Objective To develop a quantitative yet sensitive method to identify distinct features of UE intra- and interlimb use during task performance. Methods Twenty adults post-stroke and 20 controls wore five inertial sensors (wrists, upper arms, sternum) during 12 seated UE tasks. Three sensor modalities (acceleration, angular rate of change, orientation) were examined for three metrics (peak to peak amplitude, time, and frequency). To allow for comparison between sensor data, the resultant values were combined into one motion parameter, per sensor pair, using a novel algorithm. This motion parameter was compared in a group-by-task analysis of variance as a similarity score (0–1) between key sensor pairs: sternum to wrist, wrist to wrist, and wrist to upper arm. A use ratio (paretic/non-paretic arm) was calculated in persons post-stroke from wrist sensor data for each modality and compared to scores from the Adult Assisting Hand Assessment (Ad-AHA Stroke) and UE Fugl-Meyer (UEFM). Results A significant group × task interaction in the similarity score was found for all key sensor pairs. Post-hoc tests between task type revealed significant differences in similarity for sensor pairs in 8/9 comparisons for controls and 3/9 comparisons for persons post stroke. The use ratio was significantly predictive of the Ad-AHA Stroke and UEFM scores for each modality. Conclusions Our algorithm and sensor data analyses distinguished task type within and between groups and were predictive of clinical scores. Future work will assess reliability and validity of this novel metric to allow development of an easy-to-use app for clinicians. 

After stroke, many individuals develop impairments that lead to compensatory motions. Compensation allows individuals to achieve tasks but has long-term detrimental effects and represents maladaptive motor strategies. Increased use of bimanual motions may serve as a biomarker for recovery (and the reduction of reliance on compensatory motion), and tracking such motion using sensor data may provide critical data for health care specialists. However, past work by the authors demonstrated individual variation in motor strategies results in noisy and chaotic sensor data. The goal of the current work is to develop classifiers capable of differentiating unimanual, bimanaual asymmetric, and bimanual symmetric gestures using wearable sensor data. Twenty participants post-stroke (and 20 age-matched controls) performed a set of tasks under the supervision of a trained occupational therapist. Sensor data were recorded for each task. Classifiers were developed using artificial neural networks (ANNs) as a baseline, and the echo state neural network (ESNN) which has demonstrated efficacy with chaotic data. We find that, for control and post-stroke participants, the ESNN results in improved testing accuracy performance (91.3% and 80.3%, respectively). These results suggest a novel method for classifying gestures in individuals post-stroke, and the developed classifiers may facilitate longitudinal monitoring and correction of compensatory motion. 

Across coastal urban centres, underground spaces such as storage areas, transportation corridors, basement car parks, public facilities, retail & office and private spaces present a priority risk during flood events with respect to timely evacuation. However, these underground spaces are commonly not considered in urban flood prediction models, in many cases because the location and geometry of these underground spaces are often poorly known. In order to improve urban flood prediction models, various identified underground spaces have been included into the urban flood simulation presented in this paper. Here, the Software MIKE+ is adopted to simulate the coastal flood scenarios for the urban centre of the city of Belfast, Northern Ireland. In the simulation, unstructured triangular grids are used. Based on the numerical simulation, urban flood depth and flooding rates into the underground spaces can be obtained. Based on the comparison of simulated urban flood scenarios with and without underground spaces, the impact of underground spaces on street-level inundation and flood routing is evaluated. It can be observed that the inclusion of underground space has a significant impact on the flood routing process. Moreover, the underground spaces also present priority risk areas during flood events with respect to timely evacuation and to this end, underground spaces cannot be ignored in real urban flood prediction. The presented study can be used to increase communities’ emergency preparedness and flood resilience