Northbound Lagrangian Pathways of the Mediterranean Outflow Water and the Mechanism of Time-Dependent Chaotic Advection

The Mediterranean Sea releases approximately 1Sv of water into the North Atlantic through the Gibraltar Straits, forming the saline Mediterranean Outflow Water (MOW). Its impact on large-scale flow and specifically its northbound Lagrangian pathways are widely debated, yet a comprehensive overview of MOW pathways over recent decades is lacking. We calculate and analyze synthetic Lagrangian trajectories in 1980-2020 reanalysis velocity data. 16\% of the MOW follow a direct northbound path to the sub-polar gyre, reaching a 1000m depth crossing window at the southern tip of Rockall Ridge in about 10 years. Surprisingly, time-dependent chaotic advection, not steady currents, drives over half of the northbound transport. Our results suggest a potential 15-20yr predictability in the direct northbound transport, which points to an upcoming decrease of MOW northbound transport in the next couple of decades. Additionally, monthly variability appears more significant than inter-annual variability in mixing and spreading the MOW

Silicon Nanowire Sensors Enable Diagnosis of Patients via Exhaled Breath

Two of the biggest challenges in medicine today are the need to detect diseases in a noninvasive manner and to differentiate between patients using a single diagnostic tool. The current study targets these two challenges by developing a molecularly modified silicon nanowire field effect transistor (SiNW FET) and showing its use in the detection and classification of many disease breathprints (lung cancer, gastric cancer, asthma, and chronic obstructive pulmonary disease). The fabricated SiNW FETs are characterized and optimized based on a training set that correlate their sensitivity and selectivity toward volatile organic compounds (VOCs) linked with the various disease breathprints. The best sensors obtained in the training set are then examined under real-world clinical conditions, using breath samples from 374 subjects. Analysis of the clinical samples show that the optimized SiNW FETs can detect and discriminate between almost all binary comparisons of the diseases under examination with >80% accuracy. Overall, this approach has the potential to support detection of many diseases in a direct harmless way, which can reassure patients and prevent numerous unpleasant investigations

Design and Modeling of a Parent Big STAR Robot Platform That Carries a Child RSTAR

In this paper we present a wheeled robot platform for child-parent robot collaboration. The new robot, named Big STAR (BSTAR), is fitted with a tail that can act as a ramp to carry and deploy a child RSTAR that can crawl between small cracks and underneath obstacles. Both robots possess sprawling capabilities inspired from insects, enabling them to transform their external geometry and dynamics to overcome a variety of obstacles. The BSTAR can travel at speeds of up to 1.4 m/s, carry payloads of more than five kilograms and travel over rough terrains. The collaboration between the two robots substantially increases their navigability and their capability to overcome obstacles. It increases their working distance and scouting area since the larger robot can act as a charging point for the smaller one. We first describe the design of the newly developed parent BSTAR robot and provide a kinematic and dynamic analysis that determines the force requirements of the robots when collaborating, followed by an evaluation of their mechanical and electrical requirements. We show that under multiple challenging scenarios the robot pair can successfully overcome a variety of obstacles

Silicon Nanowire Sensors Enable Diagnosis of Patients <i>via</i> Exhaled Breath

Two of the biggest challenges in medicine today are the need to detect diseases in a noninvasive manner and to differentiate between patients using a single diagnostic tool. The current study targets these two challenges by developing a molecularly modified silicon nanowire field effect transistor (SiNW FET) and showing its use in the detection and classification of many disease breathprints (lung cancer, gastric cancer, asthma, and chronic obstructive pulmonary disease). The fabricated SiNW FETs are characterized and optimized based on a training set that correlate their sensitivity and selectivity toward volatile organic compounds (VOCs) linked with the various disease breathprints. The best sensors obtained in the training set are then examined under real-world clinical conditions, using breath samples from 374 subjects. Analysis of the clinical samples show that the optimized SiNW FETs can detect and discriminate between almost all binary comparisons of the diseases under examination with >80% accuracy. Overall, this approach has the potential to support detection of many diseases in a direct harmless way, which can reassure patients and prevent numerous unpleasant investigations