Non-targeted LC-MS based metabolomics analysis of the urinary steroidal profile

The urinary steroidal fraction has been extensively explored as non-invasive alternative to monitor pathological conditions as well as to unveil the illicit intake of pseudo-endogenous anabolic steroids in sport. However, the majority of previous approaches involved the a priori selection of potentially relevant target analytes. Here we describe the non-targeted analysis of the urinary steroidal profiles. The workflow includes minimal sample pretreatment and normalization according to the specific gravity of urine, a 20 min reverse phase ultra-performance liquid chromatographic separation hyphenated to electrospray time-of-flight mass spectrometry. As initial validation, we analyzed a set of quality control urines spiked with glucurono- and sulfo-conjugated steroids at physiological ranges. We then applied the method for the analysis of samples collected after single transdermal administration of testosterone in hypogonadal men. The method allowed profiling of approximately three thousand metabolic features, including steroids of clinical and forensic relevance. It successfully identified metabolic pathways mostly responsible for groups clustering even in the context of high inter-individual variability and allowed the detection of currently unknown metabolic features correlating with testosterone administration. These outcomes set the stage for future studies aimed at implementing currently monitored urinary steroidal markers both in clinical and forensic analysis

A Hybrid Methodology Approach for Fraud Detection Using Event Correlation Approach

To effectively investigate mass of events oriented data, automated methods for extracting event records and then classifying events and patterns of events into higher level terminology and vocabulary are necessary. Semantically rich representation model and automated methods of correlating event information expressed in such models are becoming a necessity. The Event Correlation for Forensics (ECF) framework was developed with the strategic objective “to develop a means by which a consolidated repository of event information can be constituted and then queried in order to provide an investigator with post hoc event correlation. Key words: Semantics, Correlation, Digital Forensic DOI: 10.17762/ijritcc2321-8169.15083

A survey on artificial intelligence-based acoustic source identification

The concept of Acoustic Source Identification (ASI), which refers to the process of identifying noise sources has attracted increasing attention in recent years. The ASI technology can be used for surveillance, monitoring, and maintenance applications in a wide range of sectors, such as defence, manufacturing, healthcare, and agriculture. Acoustic signature analysis and pattern recognition remain the core technologies for noise source identification. Manual identification of acoustic signatures, however, has become increasingly challenging as dataset sizes grow. As a result, the use of Artificial Intelligence (AI) techniques for identifying noise sources has become increasingly relevant and useful. In this paper, we provide a comprehensive review of AI-based acoustic source identification techniques. We analyze the strengths and weaknesses of AI-based ASI processes and associated methods proposed by researchers in the literature. Additionally, we did a detailed survey of ASI applications in machinery, underwater applications, environment/event source recognition, healthcare, and other fields. We also highlight relevant research directions

Temporal Models for History-Aware Explainability

On one hand, there has been a growing interest towards the application of AI-based learning and evolutionary programming for self-adaptation under uncertainty. On the other hand, self-explanation is one of the self-* properties that has been neglected. This is paradoxical as self-explanation is inevitably needed when using such techniques. In this paper, we argue that a self-adaptive autonomous system (SAS) needs an infrastructure and capabilities to be able to look at its own history to explain and reason why the system has reached its current state. The infrastructure and capabilities need to be built based on the right conceptual models in such a way that the system's history can be stored, queried to be used in the context of the decision-making algorithms. The explanation capabilities are framed in four incremental levels, from forensic self-explanation to automated history-aware (HA) systems. Incremental capabilities imply that capabilities at Level n should be available for capabilities at Level n + 1. We demonstrate our current reassuring results related to Level 1 and Level 2, using temporal graph-based models. Specifically, we explain how Level 1 supports forensic accounting after the system's execution. We also present how to enable on-line historical analyses while the self-adaptive system is running, underpinned by the capabilities provided by Level 2. An architecture which allows recording of temporal data that can be queried to explain behaviour has been presented, and the overheads that would be imposed by live analysis are discussed. Future research opportunities are envisioned