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Faculty at research institutions play a central role in advancing knowledge and careers, as well as promoting the well-being of students and colleagues in research environments. Mentorship from experienced peers has been touted as critical for enabling these myriad roles to allow faculty development, career progression, and satisfaction. However, there is little information available on who supports faculty and best ways to structure a faculty mentorship programme for early- and mid-career academics. In the interest of advocating for increased and enhanced faculty mentoring and mentoring programmes, we surveyed faculty around the world to gather data on whether and how they receive mentoring. We received responses from 457 early- and mid-career faculty and found that a substantial portion of respondents either reported having no mentor or a lack of a formal mentoring scheme. Qualitative responses on the quality of mentorship revealed that the most common complaints regarding mentorship included lack of mentor availability, unsatisfactory commitment to mentorship, and non-specific or non-actionable advice. On these suggestions, we identify a need for training for faculty mentors as well as strategies for individual mentors, departments, and institutions for funding and design of more intentional and supportive mentorship programmes for early- and mid-career faculty.

 

The wide deployment of wireless sensor networks has two limiting factors: the power-limited sensors and the congested radio frequency spectrum. A promising way to reduce the transmission power of sensors, and consequently prolonging their lifetime, is deploying reconfigurable intelligent surfaces (RISs) that passively beamform the sensors transmission to remote data centers. Furthermore, spectrum limitation can be overcome by spectrum sharing between sensors and radars. This paper utilizes tools from stochastic geometry to characterize the power reduction in sensors due to utilizing RISs in a shared spectrum with radars. We show that allowing RIS-assisted communication reduces the power consumption of the sensor nodes, and that the power reduction increases with the RISs density. Furthermore, we show that radars with narrow beamwidths allow more power saving for the sensor nodes in its vicinity. 

Communication over large-bandwidth millimeter wave (mmWave) spectrum bands can provide high data rate, through utilizing highgain beamforming vectors (briefly, beams). Real-time tracking of such beams, which is needed for supporting mobile users, can be accomplished through developing machine learning (ML) models. While computer simulations were used to show the success of such ML models, experimental results are still limited. Consequently in this paper, we verify the effectiveness of mmWave beam tracking over the open-source COSMOS testbed. We particularly utilize a multi-armed bandit (MAB) scheme, which follows reinforcement learning (RL) approach. In our MAB-based beam tracking model, the beam selection is modeled as an action, while the reward of the algorithm is modeled through the link throughput. Experimental results, conducted over the 60-GHz COSMOS-based mobile platform, show that the MAB-based beam tracking learning model can achieve almost 92% throughput compared to the Genie-aided beams after a few learning samples.