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Under the best of circumstances, learning to conduct qualitative research is challenging, both intellectually and emotionally. Engaging in such learning in difficult situations, such as a global pandemic, may heighten challenges while creating opportunities for truly deep learning. The purpose of this paper is to provide methodological insights to guide the growth of new qualitative researchers and inform the design of introductory methods courses based on the learning experiences of a group of graduate students conducting their first qualitative research projects. We present students’ experiences with choosing and planning a project, navigating relationships with study participants, and conducting observations and interviews. Explicit connections to qualitative methodology are offered for every stage of student research engagement. An analysis of the student authors’ experiences highlights the associated learning and innovation necessary to adapt to adversity when conducting qualitative research. Advancements in research reciprocity and human connection are presented, as experienced by the student authors. We conclude with implications and insights for teaching and learning qualitative research and ethical considerations that transcend pandemic circumstances. It is the intent of this manuscript to support the development of deep reflexive practice for new qualitative researchers, effective instructional approaches for those who teach research methods, and an insight into the power of diverse student researchers learning new skills together for the global research community. 

This work considers the problem of Distributed Mean Estimation (DME) over networks with intermittent connectivity, where the goal is to learn a global statistic over the data samples localized across distributed nodes with the help of a central server. To mitigate the impact of intermittent links, nodes can collaborate with their neighbors to compute local consensus which they forward to the central server. In such a setup, the communications between any pair of nodes must satisfy local differential privacy constraints. We study the tradeoff between collaborative relaying and privacy leakage due to the additional data sharing among nodes and, subsequently, propose a novel differentially private collaborative algorithm for DME to achieve the optimal tradeoff. Finally, we present numerical simulations to substantiate our theoretical findings. 

We develop a resilient binary hypothesis testing framework for decision making in adversarial multi-robot crowdsensing tasks. This framework exploits stochastic trust observations between robots to arrive at tractable, resilient decisionmaking at a centralized Fusion Center (FC) even when i) there exist malicious robots in the network and their number may be larger than the number of legitimate robots, and ii) the FC uses one-shot noisy measurements from all robots.We derive two algorithms to achieve this. The first is the Two Stage Approach (2SA) that estimates the legitimacy of robots based on received trust observations, and provably minimizes the probability of detection error in the worst-case malicious attack. Here, the proportion of malicious robots is known but arbitrary. For the case of an unknown proportion of malicious robots, we develop the Adversarial Generalized Likelihood Ratio Test (A-GLRT) that uses both the reported robot measurements and trust observations to estimate the trustworthiness of robots, their reporting strategy, and the correct hypothesis simultaneously. We exploit special problem structure to show that this approach remains computationally tractable despite several unknown problem parameters.We deploy both algorithms in a hardware experiment where a group of robots conducts crowdsensing of traffic conditions on a mock-up road network similar in spirit toGoogleMaps, subject to a Sybil attack.We extract the trust observations for each robot from actual communication signals which provide statistical information on the uniqueness of the sender.We show that even when the malicious robots are in the majority, the FC can reduce the probability of detection error to 30.5% and 29% for the 2SA and the A-GLRT respectively. 

Through biological activity, marine dissolved inorganic carbon (DIC) is transformed into different types of biogenic carbon available for export to the ocean interior, including particulate organic carbon (POC), dissolved organic carbon (DOC), and particulate inorganic carbon (PIC). Each biogenic carbon pool has a different export efficiency that impacts the vertical ocean carbon gradient and drives natural air–sea carbon dioxide gas (CO₂) exchange. In the Southern Ocean (SO), which presently accounts for ~40% of the anthropogenic ocean carbon sink, it is unclear how the production of each biogenic carbon pool contributes to the contemporary air–sea CO₂exchange. Based on 107 independent observations of the seasonal cycle from 63 biogeochemical profiling floats, we provide the basin-scale estimate of distinct biogenic carbon pool production. We find significant meridional variability with enhanced POC production in the subantarctic and polar Antarctic sectors and enhanced DOC production in the subtropical and sea-ice-dominated sectors. PIC production peaks between 47°S and 57°S near the “great calcite belt.” Relative to an abiotic SO, organic carbon production enhances CO₂uptake by 2.80 ± 0.28 Pg C y⁻¹, while PIC production diminishes CO₂uptake by 0.27 ± 0.21 Pg C y⁻¹. Without organic carbon production, the SO would be a CO₂source to the atmosphere. Our findings emphasize the importance of DOC and PIC production, in addition to the well-recognized role of POC production, in shaping the influence of carbon export on air–sea CO₂exchange.

 

Mental health for engineering undergraduates is an urgent topic for engineering educators. Narratives of engineering education requiring suffering may create or exacerbate problematic perceptions around stress and mental health in engineering. This study explored the roles of stress and mental health in engineering culture. We sought to explore: (1) how engineering students describe their experiences related to stress and mental health and (2) norms and expectations engineering students share about stress and mental health. Qualitative interview data were collected from 30 students who had previously responded to a college-wide survey.

Codes related to experiences with stress and mental health in engineering were organized in a bioecological systems model and analyzed for emergent themes depicting engineering culture. The study identified three themes related to stress and mental health in engineering culture: (1) engineering workload as a defining stressor, (2) specific barriers that prevent engineering students from seeking help for mental health concerns, and (3) reliance on peers to cope with stress and mental health distress.

Our analysis provided insight into how engineering students perceive norms around stress and mental health in engineering and how this impacts help-seeking for mental health challenges. These findings have important implications for developing interventions and positive cultures that support student mental health.

 

Goal-directed behavior is dependent on neuronal activity in the prefrontal cortex (PFC) and extended frontostriatal circuitry. Stress and stress-related disorders are associated with impaired frontostriatal-dependent cognition. Our understanding of the neural mechanisms that underlie stress-related cognitive impairment is limited, with the majority of prior research focused on the PFC. To date, the actions of stress across cognition-related frontostriatal circuitry are unknown. To address this gap, the current studies examined the effects of acute noise-stress on the spiking activity of neurons and local field potential oscillatory activity within the dorsomedial PFC (dmPFC) and dorsomedial striatum (dmSTR) in rats engaged in a test of spatial working memory. Stress robustly suppressed responses of both dmPFC and dmSTR neurons strongly tuned to key task events (delay, reward). Additionally, stress strongly suppressed delay-related, but not reward-related, theta and alpha spectral power within, and synchrony between, the dmPFC and dmSTR. These observations provide the first demonstration that stress disrupts the neural coding and functional connectivity of key task events, particularly delay, within cognition-supporting dorsomedial frontostriatal circuitry. These results suggest that stress-related degradation of neural coding within both the PFC and striatum likely contributes to the cognition-impairing effects of stress.