SQLite databases play a central role in mobile phone forensics. Mobile applications frequently use them for data storage. Efficient extraction and interpretation of SQLite data are crucial for reconstructing device usage and user activities. In practice, digital forensic investigators must formulate and analyse complex SQL queries to retrieve evidence from various heterogeneous databases. This task requires extensive expertise in SQL, database schemas, and application-specific data logic.In this paper, we investigate an LLM-based approach to assist digital forensic investigators by automating the generation of SQL queries for forensic analysis. This enables investigators to query SQLite databases more efficiently and with less technical effort. First, we propose a mobile forensic dataset that captures typical investigative questions and database structures. We then use this dataset to fine-tune a local LLM. We introduce ForSQLiteLM, a Llama 3.2-3B bf16 model. It is optimized on a self-defined, domain-specific dataset tailored to mobile forensic scenarios. We compare ForSQLiteLM with several state-of-the-art LLMs to evaluate its effectiveness in generating forensic queries. We show that effective forensic Text-to-SQL generation can be achieved with a locally deployable 3B-parameter LLM by combining realistic SQLite schemas, execution-based evaluation, and domain- specific fine-tuning. Finally, as a proof of concept, we demonstrate how the proposed model can be integrated into the FQLite data retrieval tool via a retrieval-augmented generation (RAG) pipeline.
Abstract The efficiency and reliability of tribologically stressed components, such as plain bearings and guide rails, are of central importance for industrial applications, as unplanned downtime can increase maintenance costs by up to 30%. This paper investigates the use of functional thermoplastics for direct, material-integrated wear measurement. The investigation focuses on two material systems: a polymer plain bearing made of polyamide with a PBT carbon black sensor material and a guide rail made of ultra-high molecular weight polyethylene (UHMW-PE). In this study, three measurement concepts – an open discrete, an open continuous and a closed continuous sensor concept – are analyzed using OpenModelica simulations. The analysis includes an investigation of the sensitivity and robustness of the measurement concepts with regard to disturbance variables such as temperature and contact resistance influences. The results show that although the discrete measurement principle is highly robust against environmental influences, it only provides binary information about the wear condition. In contrast, continuous sensor concepts enable detailed recording of the wear process and thus form the basis for predictive maintenance. The closed, continuous sensor concept offers the decisive advantage of completely eliminating measurement errors due to external contact resistances. This paper shows that the use of functional plastics makes it possible to transform conventional components into self-monitoring tribological components.
Mass spectrometers and their attached liquid chromatography (LC) systems, often referred to as LC-MS/MS instrumentation, have become an indispensable tool in biomedical research to identify and quantify proteins, metabolites, and other molecules of interest. However, these sophisticated instruments are very susceptible to malfunction or suboptimal performance, and as a result, quality control (QC) samples are typically acquired at regular intervals to assess their performance. Not surprisingly, several QC software packages have been developed in recent years to analyze and interrogate a variety of QC samples. However, existing QC software predominantly supports proteomic QC samples, with limited options for metabolomic and lipidomic QC samples. In addition, pipelines and workflows that can accommodate both types of QC samples are largely missing. To address this unmet demand, we have developed MaSpeQC, which is a free, easy-to-install, interactive and fully customizable web application to track LC-MS/MS performance across proteomic, metabolomic, and/or lipidomic workflows. MaSpeQC is vendor-agnostic and can handle any commercially available or in-house-generated QC sample from which it extracts relevant metrics. Furthermore, MaSpeQC provides an intuitive web interface for performance monitoring and early detection of issues through customizable email alerts.
