Forensic Analysis of Fiber Dyes via Surface-Enhanced Raman Spectroscopy

Fibers are a common piece of evidence found at crime scenes that may become a link between the scene and a suspect, or allow for the reconstruction of certain crime events. Although a big portion of fibers are still white cottons, the advancement of commercial fiber production and dyeing in the past century led to an increase in types of synthetic fibers and dye applications that can be found and used in forensic analyses. Nonetheless, the fiber evidentiary value is not fully explored, as for the most part, the separation and analysis of the dye on the fiber is not routinely done. This is mostly because traditional methods for dye analysis require lengthy or expensive procedures, combining extractions or hydrolysis, solvent tailoring, separation procedures such as TLC or HPLC, and potential mass-spectrometry for the extract identification. Currently, the prescribed method of fiber forensic analysis involves the fiber macro and microscopical examination, determination of optical properties, cross-sectioning, and spectrophotometric and infrared analysis. In the case where fibers of same polymer make have similar colors that cannot be separated by spectrophotometric analysis, there is still a chance that those fibers could have been dyed using differing dyes. It is possible to analyze those dyes by surface-enhanced Raman spectroscopy, a method which allows for high enhancements of low concentration, microliter volume samples, allowing the analyst to extract single fibers in small volumes of solvents.b This research focused on the comparison of solvent systems, alongside hydrofluoric acid fuming and in situ SER analysis, to develop a working routine for forensic fiber dye analysis. The research was also expanded to simulate casework, showing that the extraction methods coupled with SERS and statistical analysis can be used to differentiate and successfully classify questioned fibers when compared to known sets. Tweaks and improvements to the method of analysis are presented by either acid-addition, or use of coffee-ring – SERS analysis on high performance thin-layer chromatography plates. Methods of improvement and further research are also presented, with the scope of validating of SERS by both forensic science and the courts. Because this project presents a novel approach to the completion of forensic fiber analyses, it shows that SERS can be a valuable tool and aid in the improvement of forensic science and the criminal justice system. The presented approach shows a successful, rapid and inexpensive technique that can also be expanded and utilized for the analysis of other evidence types

Doing the right thing for the right reason: Evaluating artificial moral cognition by probing cost insensitivity

Is it possible to evaluate the moral cognition of complex artificial agents? In this work, we take a look at one aspect of morality: `doing the right thing for the right reasons.' We propose a behavior-based analysis of artificial moral cognition which could also be applied to humans to facilitate like-for-like comparison. Morally-motivated behavior should persist despite mounting cost; by measuring an agent's sensitivity to this cost, we gain deeper insight into underlying motivations. We apply this evaluation to a particular set of deep reinforcement learning agents, trained by memory-based meta-reinforcement learning. Our results indicate that agents trained with a reward function that includes other-regarding preferences perform helping behavior in a way that is less sensitive to increasing cost than agents trained with more self-interested preferences.Comment: 11 pages, 3 figure

Melting Pot 2.0

Multi-agent artificial intelligence research promises a path to develop intelligent technologies that are more human-like and more human-compatible than those produced by "solipsistic" approaches, which do not consider interactions between agents. Melting Pot is a research tool developed to facilitate work on multi-agent artificial intelligence, and provides an evaluation protocol that measures generalization to novel social partners in a set of canonical test scenarios. Each scenario pairs a physical environment (a "substrate") with a reference set of co-players (a "background population"), to create a social situation with substantial interdependence between the individuals involved. For instance, some scenarios were inspired by institutional-economics-based accounts of natural resource management and public-good-provision dilemmas. Others were inspired by considerations from evolutionary biology, game theory, and artificial life. Melting Pot aims to cover a maximally diverse set of interdependencies and incentives. It includes the commonly-studied extreme cases of perfectly-competitive (zero-sum) motivations and perfectly-cooperative (shared-reward) motivations, but does not stop with them. As in real-life, a clear majority of scenarios in Melting Pot have mixed incentives. They are neither purely competitive nor purely cooperative and thus demand successful agents be able to navigate the resulting ambiguity. Here we describe Melting Pot 2.0, which revises and expands on Melting Pot. We also introduce support for scenarios with asymmetric roles, and explain how to integrate them into the evaluation protocol. This report also contains: (1) details of all substrates and scenarios; (2) a complete description of all baseline algorithms and results. Our intention is for it to serve as a reference for researchers using Melting Pot 2.0.Comment: 59 pages, 54 figures. arXiv admin note: text overlap with arXiv:2107.0685

Patient Preferences in the Medical Product Life Cycle: What do Stakeholders Think? Semi-Structured Qualitative Interviews in Europe and the USA.

Background Patient preferences (PP), which are investigated in PP studies using qualitative or quantitative methods, are a growing area of interest to the following stakeholders involved in the medical product lifecycle: academics, health technology assessment bodies,

Factors and Situations Affecting the Value of Patient Preference Studies: Semi-Structured Interviews in Europe and the US

Objectives: Patient preference information (PPI) is gaining recognition among the pharmaceutical industry, regulatory authorities, and health technology assessment (HTA) bodies/payers for use in assessments and decision-making along the medical product lifecycle (MPLC). This study aimed to identify factors and situations that influence the value of patient preference studies (PPS) in decision-making along the MPLC according to different stakeholders. Methods: Semi-structured interviews (n = 143) were conducted with six different stakeholder groups (physicians, academics, industry representa