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A Quantitative Assessment of Time, Frequency, and Time-frequency Algorithms for Automated Seizure Detection and Monitoring

Vangal et al. | Sep 28, 2020

A Quantitative Assessment of Time, Frequency, and Time-frequency Algorithms for Automated Seizure Detection and Monitoring

Each year, over 100,000 patients die from Sudden Unexpected Death in Epilepsy (SUDEP). A reliable seizure warning system can help patients stay safe. This work presents a comprehensive, comparative analysis of three different signal processing algorithms for automated seizure/ictal detection. The experimental results show that the proposed methods can be effective for accurate automated seizure detection and monitoring in clinical care.

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A Statistical Comparison of the Simultaneous Attack/ Persistent Pursuit Theory Against Current Methods in Counterterrorism Using a Stochastic Model

Tara et al. | Dec 01, 2020

A Statistical Comparison of the Simultaneous Attack/ Persistent Pursuit Theory Against Current Methods in Counterterrorism Using a Stochastic Model

Though current strategies in counterterrorism are somewhat effective, the Simultaneous Attack/Persistent Pursuit (SAPP) Theory may be superior alternative to current methods. The authors simulated five attack strategies (1 SAPP and 4 non-SAPP), and concluded that the SAPP model was significantly more effective in reducing the final number of terrorist attacks. This demonstrates the comparative advantage of utilizing the SAPP model, which may prove to be critical in future efforts in counterterrorism.

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Monitoring drought using explainable statistical machine learning models

Cheung et al. | Oct 28, 2024

Monitoring drought using explainable statistical machine learning models

Droughts have a wide range of effects, from ecosystems failing and crops dying, to increased illness and decreased water quality. Drought prediction is important because it can help communities, businesses, and governments plan and prepare for these detrimental effects. This study predicts drought conditions by using predictable weather patterns in machine learning models.

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The characterization of quorum sensing trajectories of Vibrio fischeri using longitudinal data analytics

Abdel-Azim et al. | Dec 16, 2023

The characterization of quorum sensing trajectories of <i>Vibrio fischeri</i> using longitudinal data analytics

Quorum sensing (QS) is the process in which bacteria recognize and respond to the surrounding cell density, and it can be inhibited by certain antimicrobial substances. This study showed that illumination intensity data is insufficient for evaluating QS activity without proper statistical modeling. It concluded that modeling illumination intensity through time provides a more accurate evaluation of QS activity than conventional cross-sectional analysis.

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Fractal dimensions of crumpled paper

Zhou et al. | Aug 10, 2023

Fractal dimensions of crumpled paper
Image credit: Richard Dykes

Here, beginning from an interest in fractals, infinitely complex shapes. The authors investigated the fractal object that results from crumpling a sheet of paper. They determined its fractal dimension using continuous Chi-squared analysis, thereby testing and validating their model against the more conventional least squares analysis.

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Presence of Vegetation in Relation to Slope in Yosemite Valley, California

Saltzgaber et al. | Sep 11, 2021

Presence of Vegetation in Relation to Slope in Yosemite Valley, California

This study examined the relationship between the slope of a terrain and vegetation, measured by the normalized difference vegetation index (NDVI). It was hypothesized that lower slope ranges would be more supportive of vegetation growth than higher slope ranges. Analysis showed that no slope (even as extreme as 85–90°) prohibits the growth of vegetation completely; even the steepest slopes examined contain plant life. Knowing that steep slopes can still support plant life, agriculturalists can begin to explore and start planting additional crops and plants at these extreme slopes.

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