Electrical spin injection from Fe into ${\mathrm{A}\mathrm{l}}_x{\mathrm{G}\mathrm{a}}_{1-x}\mathrm{A}\mathrm{s}$ quantum well heterostructures is demonstrated in small ($<500\mathrm{O}\mathrm{e}$) in-plane magnetic fields. The measurement is sensitive only to the component of the spin that precesses about the internal magnetic field in the semiconductor. This field is much larger than the applied field and depends strongly on the injection current density. Details of the observed hysteresis in the spin injection signal are reproduced in a model that incorporates the magnetocrystalline anisotropy of the epitaxial Fe film, spin relaxation in the semiconductor, and the dynamic polarization of nuclei by the injected spins.

Epoxy based nanocomposites using graphene oxide (GO) sheets dispersed multi-walled carbon nanotubes (CNTs) as combination fillers were prepared using an in situ polymerization technique. A remarkable synergetic effect was observed between CNTs and GO sheets which improved the mechanical properties of the epoxy. It was confirmed by optical and field-emission scanning electron microscopy (FESEM) images that the dispersion of CNTs in epoxy matrix can be significantly improved by adding GO sheets. The overall mechanical properties of CNT–GO/epoxy composites were greatly enhanced with only adding 0.04 wt% (percent by weight) CNTs and 0.2 wt% GO sheets. Moreover, the fatigue and creep rupture lives of pure epoxy was also significantly increased by the addition of GO dispersed CNTs. Approximately a 950% improvement in fatigue life, and 400% improvement in creep rupture life were observed at the applied stress levels tested.

With the growing reliability of modern ad hoc networks, it is encouraging to analyze the potential involvement of autonomous ad hoc agents in critical situations where human involvement could be perilous. One such critical scenario is the Search and Rescue effort in the event of a disaster, in which timely discovery and help deployment is of utmost importance. This paper demonstrates the applicability of a bio-inspired technique, namely Ant Algorithms (AA), in optimizing the search time for a route or path to a trapped victim, followed by the application of Dijkstra’s algorithm in the rescue phase. The inherent exploratory nature of AA is put to use for faster mapping and coverage of the unknown search space. Four different AA are implemented, with different effects of the pheromone in play. An inverted AA, with repulsive pheromones, was found to be the best fit for this particular application. After considerable exploration, upon discovery of the victim, the autonomous agents further facilitate the rescue process by forming a relay network, using the already deployed resources. Hence, the paper discusses a detailed decision-making model of the swarm, segmented into two primary phases that are responsible for the search and rescue, respectively. Different aspects of the performance of the agent swarm are analyzed as a function of the spatial dimensions, the complexity of the search space, the deployed search group size, and the signal permeability of the obstacles in the area.

Epitaxial ferromagnetic metal-semiconductor heterostructures are investigated using polarization-dependent electroabsorption measurements on GaAs p-type and n-type Schottky diodes with embedded ${\mathrm{In}}_{1-x}{\mathrm{Ga}}_x\mathrm{As}$ quantum wells. We have conducted studies as a function of photon energy, bias voltage, magnetic field, and excitation geometry. For optical pumping with circularly polarized light at energies above the band edge of GaAs, photocurrents with spin polarizations on the order of 1% flow from the semiconductor to the ferromagnet under reverse-bias. For optical pumping at normal incidence, this polarization may be enhanced significantly by resonant excitation at the quantum well ground state. Measurements in a side-pumping geometry, in which the ferromagnet can be saturated in very low magnetic fields, show hysteresis that is also consistent with spin-dependent transport. Magneto-optical effects that influence these measurements are discussed.

Unmanned Aerial Vehicles (UAVs) with acceptable performance are becoming commercially available at an affordable cost. Due to this, the use of drones for real-time data collection is becoming common practice by individual practitioners in the areas of e.g., precision agriculture and civil defense such as fire fighting. At the same time, as UAVs become a house-hold item, a plethora of issues—which can no longer be ignored and considered niche problems—are coming of age. These range from legal and ethical questions to technical matters such as how to implement and operate a communication infrastructure to maintain control over deployed devices. With these issues being addressed, approaches that focus on enabling collectives of devices to operate semi-autonomously are also increasing in relevance. In this article we present a nature-inspired algorithm that enables a UAV-swarm to operate as a collective which provides real-time data such as video footage. The collective is able to autonomously adapt to changing resolution requirements for specific locations within the area under surveillance. Our distributed approach significantly reduces the requirements on the communication infrastructure and mitigates the computational cost otherwise incurred. In addition, if the UAVs themselves were to be equipped with even rudimentary data-analysis capabilities, the swarm could react in real-time to the data it generates and self-regulate which locations within its operational area it focuses on. The approach was tested in a swarm of 25 UAVs; we present out preliminary performance evaluation.