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Data analysis workflows in many scientific domains have become increasingly complex and flexible. Here we assess the effect of this flexibility on the results of functional magnetic resonance imaging by asking 70 independent teams to analyse the same dataset, testing the same 9 ex-ante hypotheses1. The flexibility of analytical approaches is exemplified by the fact that no two teams chose identical workflows to analyse the data. This flexibility resulted in sizeable variation in the results of hypothesis tests, even for teams whose statistical maps were highly correlated at intermediate stages of the analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Notably, a meta-analytical approach that aggregated information across teams yielded a significant consensus in activated regions. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset2-5. Our findings show that analytical flexibility can have substantial effects on scientific conclusions, and identify factors that may be related to variability in the analysis of functional magnetic resonance imaging. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for performing and reporting multiple analyses of the same data. Potential approaches that could be used to mitigate issues related to analytical variability are discussed.

This review consolidates the evidence supporting endogenous HNO generation through enzymatic and non‐enzymatic pathways, emphasizing advancements in real‐time HNO sensing within living systems. Azanone (nitroxyl, HNO) is the one‐electron‐reduced congener of nitric oxide (NO•) and shares various biological actions. HNO exhibits unique properties, including positive inotropic and lusitropic effects, along with resistance to scavenging by reactive oxygen species (ROS), particularly superoxide. Research on HNO has intensified over the last two decades, focusing on its reactivity and the prospect of endogenous formation due to its potential significance. The high reactivity of HNO arises from reactions with biologically relevant species such as oxygen, NO•, and thiols, as well as self‐dimerization. Detecting and quantifying HNO has posed challenges, initially relying on nitrous oxide (N2O) detection, a byproduct of dimerization. Recent advancements, including fluorescent probes and a specific electrochemical nanomolar‐level sensor, have facilitated direct detection, enhancing understanding of HNO reactivity and formation. [ABSTRACT FROM AUTHOR]

Switching elements are crucial components in electrical and electronic systems that undergo severe degradation due to the electrical arc that is generated during breaking. Understanding the behavior of the electrical arc and modifying its characteristics via proper electrode design can significantly improve durability while also promoting optimal performance, reliability, and safety in circuit breakers. This work evaluates the feasibility of carbon nanotube (CNT)-reinforced silver and copper metal matrix composites (MMCs) as switching electrodes and the influence of CNT concentration on the characteristics of the arcs generated. Accordingly, three different concentrations per MMC were manufactured via powder metallurgy. The MMCs and reference materials were subjected to a single break operation and the electrical arcs generated using 100 W and 200 W resistive loads were analyzed. The proposed MMCs displayed promising results for application in low-voltage switches. The addition of CNTs improved performance by maintaining the arc's energy in the silver MMCs and reducing the arc's energy in the copper MMCs. Moreover, a CNT concentration of at least 2 wt.% is required to prevent unstable arcs in both metallic matrices. Increased CNT content further promotes the splitting of the electrical arc due to a more complex phase distribution, thereby reducing the arc's spatial energy density. [ABSTRACT FROM AUTHOR]

The piezoresistive effect of materials can be adopted for a plethora of sensing applications, including force sensors, structural health monitoring, motion detection in fabrics and wearable, etc. Although metals are the most widely adopted material for sensors due to their reliability and affordability, they are significantly affected by temperature. This work examines the piezoresistive performance of carbon nanoparticle (CNP) bulk powders and discusses their potential applications based on strain-induced changes in their resistance and displacement. The experimental results are correlated with the characteristics of the nanoparticles, namely, dimensionality and structure. This report comprehensively characterizes the piezoresistive behavior of carbon black (CB), onion-like carbon (OLC), carbon nanohorns (CNH), carbon nanotubes (CNT), dispersed carbon nanotubes (CNT-D), graphite flakes (GF), and graphene nanoplatelets (GNP). The characterization includes assessment of the ohmic range, load-dependent electrical resistance and displacement tracking, a modified gauge factor for bulk powders, and morphological evaluation of the CNP. Two-dimensional nanostructures exhibit promising results for low loads due to their constant compression-to-displacement relationship. Additionally, GF could also be used for high load applications. OLC’s compression-to-displacement relationship fluctuates, however, for high load it tends to stabilize. CNH could be applicable for both low and high loading conditions since its compression-to-displacement relationship fluctuates in the mid-load range. CB and CNT show the most promising results, as demonstrated by their linear load-resistance curves (logarithmic scale) and constant compression-to-displacement relationship. The dispersion process for CNT is unnecessary, as smaller agglomerates cause fluctuations in their compression-to-displacement relationship with negligible influence on its electrical performance. [ABSTRACT FROM AUTHOR]

Heuristics are simple, yet effective, strategies that people use to make decisions. Because heuristics do not require all available information, they are thought to be easy to implement and to not tax limited cognitive resources, which has led heuristics to be characterized as fast-and-frugal. We question this monolithic conception of heuristics by contrasting the cognitive demands of two popular heuristics, Tallying and Take-the-Best. We contend that heuristics that are frugal in terms of information usage may not always be fast because of the attentional control required to implement this focus in certain contexts. In support of this hypothesis, we find that Take-the-Best, while being more frugal in terms of information usage, is slower to implement and fares worse under time pressure manipulations than Tallying. This effect is then reversed when search costs for Take-the-Best are reduced by changing the format of the stimuli. These findings suggest that heuristics are heterogeneous and should be unpacked according to their cognitive demands to determine the circumstances a heuristic best applies.