Thermal Noise Measurements in a Reverberation Chamber

The letter describes the properties of thermal noise measured in an electromagnetic reverberation chamber. The consequences for the detection of weak signals in the presence of the noise are outlined.

Thermal Noise Measurements in a Reverberation Chamber

The letter describes the properties of thermal noise measured in an electromagnetic reverberation chamber. The consequences for the detection of weak signals in the presence of the noise are outlined.

Engineering chiral plasmonic nanostructures for gain-assisted plasmon amplification and tunable enhancement of circular dichroism

Circular dichroism (CD) from plasmonic nanostructures yields fascinating insights into their chiroptical properties, however, the weak signals make their investigations profoundly challenging. We have demonstrated a method for significantly improving...

Investigation of GPS Positioning Accuracy Near a Deciduous Forest Area

This article evaluates the accuracy and performance of GPS positioning near a forest area. In such cases, positions are calculated from weak signals that tend to be less accurate. Moreover, the results show that there were significant differences depending on season (May vs. October) regarding the accuracy and precision of the measured coordinates; also, accuracies were different depending on the seasonal forest characteristics. Therefore, practical recommendations for each case were established in order to help foresters select the most suitable situation. The results indicated that the season was a significant factor for the GPS surveys.

LC–MS peak assignment based on unanimous selection by six machine learning algorithms

Recent mass spectrometry (MS)-based techniques enable deep proteome coverage with relative quantitative analysis, resulting in increased identification of very weak signals accompanied by increased data size of liquid chromatography (LC)–MS/MS spectra. However, the identification of weak signals using an assignment strategy with poorer performance results in imperfect quantification with misidentification of peaks and ratio distortions. Manually annotating a large number of signals within a very large dataset is not a realistic approach. In this study, therefore, we utilized machine learning algorithms to successfully extract a higher number of peptide peaks with high accuracy and precision. Our strategy evaluated each peak identified using six different algorithms; peptide peaks identified by all six algorithms (i.e., unanimously selected) were subsequently assigned as true peaks, which resulted in a reduction in the false-positive rate. Hence, exact and highly quantitative peptide peaks were obtained, providing better performance than obtained applying the conventional criteria or using a single machine learning algorithm.

Toward a typology of weak-signal early alert systems: functional early warning systems in the post-COVID age

PurposeThe study aims to expand on the concept of an early warning system (EWS) by introducing weak-signal detection, human-in-the-loop (HIL) verification and response tuning as integral parts of an EWS's design.Design/methodology/approachThe authors bibliographically highlight the evolution of EWS over the last 30+ years, discuss instances of EWSs in various types of organizations and industries and highlight limitations of current systems.FindingsProposed system to be used in the transforming of weak signals to early warnings and associated weak/strong responses.Originality/valueThe authors contribute to existing literature by presenting (1) novel approaches to dealing with some of the well-known issues associated with contemporary EWS and (2) an event-agnostic heuristic for dealing with weak signals.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-11-2020-0513.

Swachh Survekshan Needs to Clean up its Methodology: An Assessment of Ranks as Performance Incentives for Indian Cities

The Government of India is increasingly using ranks to incentivise sub-units of government. The largest such exercise, the Swachh Survekshan, has been conducted since 2016 and aims to incentivise cities to compete on and improve waste management and sanitation outcomes. Using publicly available Swachh Survekshan data, this article suggests that the current scoring methodology provides weak signals to urban local bodies (ULBs) and citizens on performance metrics. In particular, it shows that the ranks are not consistent and stable across years, there are severe discrepancies in data between components of the awarded score, and that the current methodology favours larger cities. Caution must be exercised, therefore, in interpreting the current methodology as fostering competition. More crucially, a ranking exercise is unlikely to succeed as a policy tool unless it is implemented as one component of a broader effort to improve ULB capacity on managing administrative data.