Relevance analysis using revision identifier in MS word

Electronic documents often contain personal or confidential information, which can be used as valuable evidence in criminal investigations. In the digital investigation, special techniques are required for grouping and screening electronic documents, because it is challenging to analyze relationships between numerous documents in storage devices manually. To this end, although techniques such as keyword search, similarity search, topic modeling, metadata analysis, and document clustering are continually being studied, there are still limitations for revealing the relevance of documents. Specifically, metadata used in previous research are not always values present in the documents, and clustering methods with specific keywords may be incomplete because text‐based contents (including metadata) can be easily modified or deleted by users. In this work, we propose a novel method to efficiently group Microsoft Office Word 2007+ (MS Word) files by using revision identifier (RSID). Through a thorough understanding of the RSID, examiners can predict organizations to which a specific user belongs, and further, it is likely to discover unexpected interpersonal relationships. An experiment with a public dataset (GovDocs) provides that it is possible to categorize documents more effectively by combining our proposal with previously studied methods. Furthermore, we introduce a new document tracking method to understand the editing history and movement of a file, and then demonstrate its usefulness through an experiment with documents from a real case.     

Understanding research methods,limitations, and applications of drug data collected by the National Forensic Laboratory Information System (NFLIS-Drug)

The National Forensic Laboratory Information System (NFLIS) is a drug surveillance program of the US Drug Enforcement Administration that systematically collects data on drugs that are seized by law enforcement and submitted to and analyzed by the Nation's forensic laboratories (NFLIS-Drug). NFLIS-Drug data are increasingly used in predictive modeling and drug surveillance to examine drug availability patterns. Given the complexity of the data and data collection, there are some common methodological pitfalls that we highlight with the aim of helping researchers avoid these concerns. The analysis done for this Technical Note is based on a review of the scientific literature that includes 428 unique, refereed article citations in 182 distinct journals published between January 1, 2005, and April 30, 2021. Each article was analyzed according to how NFLIS-Drug data were mentioned and whether NFLIS-Drug data were included. A sample of 37 articles was studied in-depth, and data issues were summarized. Using examples from the literature, this Technical Note highlights eight broad concerns that have important implications for the proper applications, interpretations, and limitations of NFLIS-Drug data with suggestions for improving research methods and accurate reporting of forensic drug data. NFLIS-Drug data are timely and provide key information to inform drug use trends across the United States; however, our present analysis shows that NFLIS-Drug data are misunderstood and represented in the literature. In addition to highlighting these issues, DEA has created several resources to assist NFLIS data users and researchers, which are summarized in the discussion.     

A forensic examination of four popular cross‐platform file‐sharing apps with Wi‐Fi P2P

File‐sharing apps with Wi‐Fi hotspot or Wi‐Fi Direct functions become more popular. They can work on multiple platforms and allow users to transfer files in a concealed manner. However, when criminals use these apps in illegal activities, it becomes an important issue for investigators to find digital evidence on multiple platforms. At present, there are few studies on this topic, and most of them are limited to the single platform problem. In this paper, we propose a forensic examination method for four popular cross‐platform file‐sharing apps with Wi‐Fi hotspot and Wi‐Fi Direct functions: Zapya, SHAREit, Xender, and Feem. We use 22 static and live forensic tools for 11 platforms to acquire, analyze, and classify the forensic artifacts. In our experiments, we find many useful forensic artifacts and classify them into six categories. The experimental results can support law enforcement investigations of digital evidence and provide information for future studies on other cross‐platform file‐sharing apps.     

Validation of the 4‐aminophenol color test for the differentiation of marijuana‐type and hemp‐type cannabis

The analysis of cannabis plant material submitted to seized‐drug laboratories was significantly affected by the signing of the Agricultural Improvement Act of 2018, which defined hemp and removed it from the definition of marijuana in the Controlled Substances Act. As a result, field law enforcement personnel and forensic laboratories now are in need of implementing new protocols that can distinguish between marijuana‐type and hemp‐type cannabis. Colorimetric tests provide a cost‐effective and efficient manner to presumptively identify materials prior to submission to a laboratory for analysis. This work presents the validation of the 4‐aminophenol (4‐AP) color test and demonstrates its utility for discriminating between marijuana‐type and hemp‐type cannabis (i.e., typification). Validation studies included the testing of numerous cannabinoid reference materials, household herbs, previously characterized cannabis plant samples, and real‐case samples. The 4‐AP test reliably produces a pink result when the level of Δ⁹‐tetrahydrocannabinol (THC) is approximately three times lower than the level of cannabidiol (CBD). A blue result is generated when the level of THC is approximately three times higher than that of CBD. Inconclusive results are observed when the levels of THC and CBD are within a factor of three from each other, demonstrating the limitations of the test under those scenarios.     

Evaluating the Efficiency of Primer Extension Capture as a Method to Enrich DNA Extractions,

In this study, we sought to document the efficiency of primer extension capture (PEC) as a method to enrich DNA eluates of targeted DNA molecules and remove nontarget molecules from pools containing both. Efficiency of the method was estimated by comparing number of “copies in” to “copies out” by quantitative polymerase chain reaction. PEC retention of DNA targets ranging 109‐288 base pairs (bps) in length was 15.88–2.14% (i.e., loss of 84.12–97.86% of target molecules). Experimental modifications of the PEC method resulted in no significant improvements. However, the benefit of PEC was revealed in its ability to remove most nontarget DNA molecules (99.99%). We also discovered that many (56.69%) of the target molecules are “lost” prior to their immobilization on the streptavidin‐coated beads. These estimates of methodological efficiency are directly comparable to previous ones observed following “fishing” for DNA, an alternative method for DNA enrichment.     

Family Drug Treatment Courts As Comprehensive Service Models: Cost Considerations

Literature on family drug treatment courts (FDTCs) suggests that parental participation in these courts is associated with improved substance abuse treatment and child welfare system outcomes. Despite these beneficial outcomes, FDTCs serve only 7‐10% of eligible child welfare involved families. As part of a FDTC evaluation, this FDTC site sought to provide stakeholders with information about costs and benefits. Considering the program costs alongside the cost avoidance from reduced time in foster care, this analysis determined that FDTC participation resulted in a net savings per child of $9,710. The cost component of the evaluation proved valuable, challenging, and informative.     

Evaluation of the (hu)MANid program for sex and ancestry estimation in a diverse,contemporary CT scan-based sample

Human remains from forensic and bioarcheological contexts are often fragmentary, requiring methods for estimating a forensic profile that are based upon limited skeletal features. In 2017, Berg and Keryhercz created an online application, (hu)MANid, that provides sex and ancestry estimation from mandibular morphoscopic traits and linear measurements. In this study, we examine the utility of the (hu)MANid application in a diverse, urban US adult sample (aged 20–45; n = 143) derived from computed tomography (CT) scans. We secondarily conduct a preliminary analysis of the program's utility in a sample of adolescents (aged 15–17; n = 40). Six morphoscopic, and eleven morphometric traits were recorded as directed by the literature associated with the (hu)MANid program. Percent correct classification and posterior predictive values were calculated for the sex and ancestry estimations output by the program; chi-squared tests were employed to compare self-reported and predicted ancestry. In the adult sample, sex was accurately predicted for 75.52% of the sample. Ancestry prediction, however, was less favorable ranging from 19.3% to 50% correct. For the adolescent sample, correct sex estimation (45%) did not surpass what could occur by chance alone, though ancestry prediction fared better than in the larger adult sample (percent correct prediction overall average: 47.5%, range 35.71%–71.43%). The (hu)MANid application shows utility for use with CT scan-derived adult samples for sex estimation, but caution is warranted for ancestry estimation and use with samples that may not have reached full adult maturity.