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1. In Defense of Merit in Science

   https://doi.org/10.35995/jci03010001  

   Abbot, D.; Bikfalvi, A.; Bleske-Rechek, A.L.; Bodmer, W.; Boghossian, P.; Carvalho, C.M.; Ciccolini, J.; Coyne, J.A.; Gauss, J.; Gill, P.M.W.; et al (April 2023, Journal of Controversial Ideas)

   Merit is a central pillar of liberal epistemology, humanism, and democracy. The scientific enterprise, built on merit, has proven effective in generating scientific and technological advances, reducing suffering, narrowing social gaps, and improving the quality of life globally. This perspective documents the ongoing attempts to undermine the core principles of liberal epistemology and to replace merit with non-scientific, politically motivated criteria. We explain the philosophical origins of this conflict, document the intrusion of ideology into our scientific institutions, discuss the perils of abandoning merit, and offer an alternative, human-centered approach to address existing social inequalities. 

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2. Disparities in Water Quality in Indian Country

   Conroy-Ben, O.; Richard, R. (April 2018, Journal of contemporary water research and education)

   Abstract: Tribal Nations in the United States are afflicted by a number of disparities including health, socioeconomics, education, and contaminant exposure to name a few. To understand drinking water quality disparities, we analyzed Safe Drinking Water Act violations in Indian Country found in the Environmental Protection Agency’s (EPA’s) Enforcement and Compliance History Online (ECHO) and compared them to violations in non-tribal areas of the same state for the time period 2014 – 2017. The violations assessed were total point accumulations per year per 1,000 customers, health-based maximum contaminant limit (MCL), reporting and monitoring, and public notice for each state reporting tribal data. Violation point disparities were evident, as tribal facilities acquired nearly six times the points of the national average. In some states, health-based tribal water quality was better than in non-tribal communities, however Arizona, Iowa, Idaho, Montana, Utah, and Wyoming had MCL violations affecting a greater percentage of tribal populations than non-tribal. Nation-wide, monitoring and reporting violations affected tribal communities at nearly twice the rate of non-tribal customers. Public notice reporting was high and comparable for both tribal and non-tribal facilities. Finally, a comparison of small drinking water facilities, under which ~97% of the surveyed tribal drinking water falls, confirmed state-wide disparities. Solutions for the apparent disparities in Indian Country and on non-tribal lands may be as simple as rectifying monitoring and reporting violations, though this correction will not shift the overall water quality difference. Addressing MCL and treatment violations is the next step to reduce the disparity. 

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3. Recent Advances in In Vivo Neurochemical Monitoring

   https://doi.org/10.3390/mi12020208  

   Tan, Chao; Robbins, Elaine M.; Wu, Bingchen; Cui, Xinyan Tracy (February 2021, Micromachines)

   null (Ed.)

   The brain is a complex network that accounts for only 5% of human mass but consumes 20% of our energy. Uncovering the mysteries of the brain’s functions in motion, memory, learning, behavior, and mental health remains a hot but challenging topic. Neurochemicals in the brain, such as neurotransmitters, neuromodulators, gliotransmitters, hormones, and metabolism substrates and products, play vital roles in mediating and modulating normal brain function, and their abnormal release or imbalanced concentrations can cause various diseases, such as epilepsy, Alzheimer’s disease, and Parkinson’s disease. A wide range of techniques have been used to probe the concentrations of neurochemicals under normal, stimulated, diseased, and drug-induced conditions in order to understand the neurochemistry of drug mechanisms and develop diagnostic tools or therapies. Recent advancements in detection methods, device fabrication, and new materials have resulted in the development of neurochemical sensors with improved performance. However, direct in vivo measurements require a robust sensor that is highly sensitive and selective with minimal fouling and reduced inflammatory foreign body responses. Here, we review recent advances in neurochemical sensor development for in vivo studies, with a focus on electrochemical and optical probes. Other alternative methods are also compared. We discuss in detail the in vivo challenges for these methods and provide an outlook for future directions. 

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4. Ethics in engineering or engineering in ethics?

   Fore, G. A.; Hess, J. L.; Katz, A. (June 2020, ASEE annual conference exposition)

   This paper explores how the relationship between ethics and engineering has been and could be framed. Specifically, two distinct framings will be conceptualized and explored: ethics in engineering and engineering in ethics. As with other disciplines, engineering typically subsumes ethics, appropriating it as its own unique subfield. As a framing, ethics in engineering produces specialized standards, codes, values, perspectives, and problems distinct to engineering thought and practice. These form an engineering education discourse with which engineers engage. It is epistemological in its focus, meaning that this framing constructs knowledge of proper disciplinary conduct. On the other hand, engineering in ethics as a framing device insists that engineering become a specialized articulation of ethical thought and action. Here, “engineer” and “engineering” are not nouns but verbs, referring to particular processes and technologies for transformation. One is not an “engineer;” rather, one “engineers.” One is first an ethical subject – an historical aggregate of continuous experiences/becomings – concerned with the pursuit of “the good” in the present; then, when contextually relevant, such a subject’s engineering knowledge and skills may be employed as powerful means for the becoming-good of shared worlds. In this paper, engineering in ethics is further conceptualized through a playful intermingling of an ethic of care, via the scholarship of Joan Tronto, and a Deweyian approach to ethical inquiry. Tronto’s four elements of care – attentiveness, responsibility, competence, and responsiveness – are joined with what are arguably four key components of Dewey’s process of ethical inquiry: awareness, judgment, experimentation, and iteration. This paper argues that 1) being attentive is required to achieve awareness of a given need or problem, 2) taking responsibility is a necessary practice for making and acting on one’s judgements related to the need at hand, 3) competence in a relevant skill is needed to experiment with one’s judgements, and 4) careful consideration of how others respond to how one has addressed a need is essential for the purposes of iteration. While all four contribute to the notion of engineering in ethics, the relationship between competence and experimentation is where engineering is most evidently seized as an ethical expression. How one competently wields engineering knowledge and skillfully performs disciplinary techniques is, here, foremost about actively inquiring into how to provide care for a specific need and, in doing so, creating a world aligned with one’s vision of “the good.” This paper will close with a brief consideration of the educational implications of engineering in ethics. 

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5. Uncertainty Quantification in Inverse Models in Hydrology

   Chatterjee, Somya Sharma; Ghosh, Rahul; Renganathan, Arvind; Li, Xiang; Chatterjee, Snigdhansu; Nieber, John; Duffy, Christopher; Kumar, Vipin (August 2023, ACM)

   In hydrology, modeling streamflow remains a challenging task due to the limited availability of basin characteristics information such as soil geology and geomorphology. These characteristics may be noisy due to measurement errors or may be missing altogether. To overcome this challenge, we propose a knowledge-guided, probabilistic inverse modeling method for recovering physical characteristics from streamflow and weather data, which are more readily available. We compare our framework with state-of-the-art inverse models for estimating river basin characteristics. We also show that these estimates offer improvement in streamflow modeling as opposed to using the original basin characteristic values. Our inverse model offers a 3% improvement in R2 for the inverse model (basin characteristic estimation) and 6% for the forward model (streamflow prediction). Our framework also offers improved explainability since it can quantify uncertainty in both the inverse and the forward model. Uncertainty quantification plays a pivotal role in improving the explainability of machine learning models by providing additional insights into the reliability and limitations of model predictions. In our analysis, we assess the quality of the uncertainty estimates. Compared to baseline uncertainty quantification methods, our framework offers a 10% improvement in the dispersion of epistemic uncertainty and a 13% improvement in coverage rate. This information can help stakeholders understand the level of uncertainty associated with the predictions and provide a more comprehensive view of the potential outcomes. 

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