Application of Self-Organizing Maps to the Analysis of Ignitable Liquid and Substrate Pyrolysis Samples

Review: Recent advancements and moving trends in chemical analysis of fire debris

This review describes recent advances and current trends in fire debris analysis from 2014 to 2021. Onsite analytical techniques used for fire scene investigation, identifying samples of interest for later analysis as well as onsite confirmatory techniques are examined. Laboratory techniques are reviewed both from a perspective of instrumentation and data analysis. Advances in analytical techniques include GC x GC-TOFMS, DART-MS, HS-GC-IMS. New and emerging methods of data analysis including those using machine learning are assessed. Each aspect is essential for forensic scientists to obtain the correct conclusion when collecting, examining, analysing, and interpreting fire debris. This review concludes that there is a need for the validity and certainty of all methods to be assessed if they are to be used to generate reports or draw conclusions.

Visualization and Semantic Labeling of Mood States Based on Time-Series Features of Eye Gaze and Facial Expressions by Unsupervised Learning

This study is intended to develop a stress measurement and visualization system for stress management in terms of simplicity and reliability. We present a classification and visualization method of mood states based on unsupervised machine learning (ML) algorithms. Our proposed method attempts to examine the relation between mood states and extracted categories in human communication from facial expressions, gaze distribution area and density, and rapid eye movements, defined as saccades. Using a psychological check sheet and a communication video with an interlocutor, an original benchmark dataset was obtained from 20 subjects (10 male, 10 female) in their 20s for four or eight weeks at weekly intervals. We used a Profile of Mood States Second edition (POMS2) psychological check sheet to extract total mood disturbance (TMD) and friendliness (F). These two indicators were classified into five categories using self-organizing maps (SOM) and U-Matrix. The relation between gaze and facial expressions was analyzed from the extracted five categories. Data from subjects in the positive categories were found to have a positive correlation with the concentrated distributions of gaze and saccades. Regarding facial expressions, the subjects showed a constant expression time of intentional smiles. By contrast, subjects in negative categories experienced a time difference in intentional smiles. Moreover, three comparative experiment results demonstrated that the feature addition of gaze and facial expressions to TMD and F clarified category boundaries obtained from U-Matrix. We verify that the use of SOM and its two variants is the best combination for the visualization of mood states.

Hebbian Learning on Small Data Enables Experimental Discovery of High Tg Polyimides

We report a study combining computational design and experimental evaluation of polyimides with high glass transition temperatures: T_g between 220 °C and 500 °C. The computational approach is based on the recently introduced competitive learning algorithm, supervised self-organizing maps (SUSI), which we recast as an ensemble method, e-SUSI. We use e-SUSI to solve both unsupervised and supervised/semisupervised learning tasks capturing structure-property relationships of high-T_g polyimides historically studied at Almaden Research Center. Predictors trained on historical data were applied to the combinatorial library of novel polyimides and informed selection of the candidates for synthesis and characterization. In this manner, three new polyimides were prepared with T_g values 281 °C, 282 °C, and 331 °C. The measured values closely agree with the predicted values 273 °C, 311 °C, and 335 °C, respectively. We discuss specific reasons that make the proposed computational design strategy attractive in rapid, deliverable-driven efforts with limited, small-batch data sets.

Advances in Fire Debris Analysis

The practice of forensic fire debris analysis and data interpretation in operational (i [...]