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This study investigated the influence of question repetition and question type (answerable, unanswerable, or opinion seeking) on children's recall. A total of 136 children (5-, 7-, and 9-year-olds) watched a live 15-min presentation. One week later, the children were asked 20 questions that were repeated an additional two times within the interview. Accuracy of children's responses to unanswerable questions declined with repetition. Children were more likely to change a response to an unanswerable question than to an answerable question. Overall, children maintained the same answers to only three-quarters of the repeated questions. The most common pattern of change was for children to change their answer the second time a question was asked and then to maintain that answer when questioned again. The high percentage of changed answers within a single interview has important implications for forensic interviewing.

"His handwriting checks out and his prints look pretty good. Move him to the top of the list," orders the 13-year-old captain. His co-detective makes appropriate procedural notes. "Bring the next one up. Get a foot measurement and let Andre print him before we talk." In another corner of the room, two girls administer a solemn oath to one of six appropriately evasive "suspects" while a third helps herself to a sample of his hair for microscopic analysis. Fingerprinting, measurement, and microscopy centers set up throughout the classroom, are buzzing with activity as teams of amateur sleuths work to bring a criminal to justice. The scene described is from a forensic science activity the author designed for a weekend event at the Museum of Natural Science at Louisiana State University. Originally intended to demonstrate some of the basics of forensic science through a crime-solving activity aimed at middle level students, the author later found this activity translates easily to a regular classroom setting for holidays and the end of the year. A fair amount of preparation is necessary, but several grades can participate simultaneously, as can visiting parents.

Demonstrates how forensic science can be used as a framework for generating student interest and learning in chemistry and promoting critical thinking. The "Who Dunnit?" forensic science workshop was developed by undergraduate students and is one element of a two-week residential summer outreach program that seeks to develop interest in engineering among middle school girls. (Author/MM)

Uses forensic chemical science in an elementary setting and investigates how students apply knowledge to a real-world scenario. (YDS)

Describes how forensic science programs have boomed as students decide they want to be just like the stars of "The X-Files" and "CSI." (EV)

One of the most important review topics the author teaches in middle school is the use of metric measurement for problem solving and inquiry. For many years, she had students measuring various objects around the room using the tools of metric measurement. She dutifully taught hypothesizing, data collecting, and drawing conclusions. It was hands-on, but not very exciting. This year, the author set up crime scenes for the students to analyze as a practice for measuring and to use their problem-solving skills. In the activity described in this article, students use measuring equipment, collect data, and make inferences based on the evidence collected. Most of the students are fans of the CSI television programs, so this activity generates a lot of student enthusiasm.

Describes a project involving students in forensic science and crime prevention to improve their investigative skills using a DNA fingerprinting workshop and designing burglar alarms, investigating blood splatter patterns, investigating vehicle collisions, and researching crime prevention advice on the Internet. (YDS)

The fast and nondestructive identification of mobile phone cases is a crucial task for law enforcement personnel in the field of forensic science. Attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR) has been employed in conjunction with feature extraction and deep learning algorithms to develop a method for distinguishing mobile phone cases. As preprocessing steps, multivariate scatter correction and standard normal variate were performed. Feature variables were extracted using the competitive adaptive reweighted sampling and shuffled frog leaping algorithm. Several supervised pattern recognition methods, namely ResNet50, VGG16, and multilayer perceptron (MLP), were utilized for constructing classifiers. The results indicated that the model's performance based on multivariate scattering correction (MSC) with competitive adaptive re-weighted sampling (CARS) surpassed that of other approaches. Notably, the ResNet50 model exhibited superior discriminative ability compared to the VGG16 and MLP models, achieving 100% accuracy in the differentiation of all samples. This designed method represents a potentially rapid and nondestructive approach for identifying mobile phone cases in forensic science. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) is being applied to improve the efficiency of software systems used in various domains, especially in the health and forensic sciences. Explainable AI (XAI) is one of the fields of AI that interprets and explains the methods used in AI. One of the techniques used in XAI to provide such interpretations is by computing the relevance of the input features to the output of an AI model. File fragment classification is one of the vital issues of file carving in Cyber Forensics (CF) and becomes challenging when the filesystem metadata is missing. Other major challenges it faces are: proliferation of file formats, file embeddings, automation, We leverage and utilize interpretations provided by XAI to optimize the classification of file fragments and propose a novel sifting approach, named SIFT (Sifting File Types). SIFT employs TF-IDF to assign weight to a byte (feature), which is used to select features from a file fragment. Threshold-based LIME and SHAP (the two XAI techniques) feature relevance values are computed for the selected features to optimize file fragment classification. To improve multinomial classification, a Multilayer Perceptron model is developed and optimized with five hidden layers, each layer with i × n neurons, where i = the layer number and n = the total number of classes in the dataset. When tested with 47,482 samples of 20 file types (classes), SIFT achieves a detection rate of 82.1% and outperforms the other state-of-the-art techniques by at least 10%. To the best of our knowledge, this is the first effort of applying XAI in CF for optimizing file fragment classification. [ABSTRACT FROM AUTHOR]

Contactless hand biometrics has emerged as an alternative to traditional biometric characteristics, e.g., fingerprint or face, as it possesses distinctive properties that are of interest in forensic investigations. As a result, several hand-based recognition techniques have been proposed with the aim of identifying both wanted criminals and missing victims. The great success of deep neural networks and their application in a variety of computer vision and pattern recognition tasks has led to hand-based algorithms achieving high identification performance on controlled images with few variations in, e.g., background context and hand gestures. This article provides a comprehensive review of the scientific literature focused on contactless hand biometrics together with an in-depth analysis of the identification performance of freely available deep learning-based hand recognition systems under various scenarios. Based on the performance benchmark, the relevant technical considerations and trade-offs of state-of-the-art methods are discussed, as well as further topics related to this research field. [ABSTRACT FROM AUTHOR]

Diester diterpenoid alkaloids (DDAs) are the main active ingredients of herbaceous perennial plants Aconitum. DDAs possess cardiotoxic and neurotoxic properties. Although most deaths caused by DDA poisoning are accidental, a few instances of suicide and homicide have been reported. Presented is a case of an acute aconitine (AC) poisoning following the ingestion of approximately 50 mL of homemade medicinal liquor. We described the clinical manifestations after poisoning and detailed postmortem changes, and detected the concentrations of AC and hypaconitine (HA) by liquid chromatography–tandem mass spectrometry (LC–MS/MS). The decedent experienced a burning sensation in the gastrointestinal tract after poisoning, followed by flushing and paralysis of the face and limbs, and severe cardiac arrhythmia. An autopsy revealed cyanosis of the lips and nail beds; conjunctival hemorrhage in both eyes; pulmonary edema; tissue hemorrhage and congestion in multiple organs; and inflammatory cell infiltration in the stomach, duodenum, pancreas, and cardiac muscle. The concentrations of AC and HA were as follows: cardiac blood, 38.4 ng/mL and 7.1 ng/mL; pericardial fluid, 7.3 ng/mL and 41 ng/mL; urine, 28.1 ng/mL and 574 ng/mL; bile, 38.5 ng/mL and 108 ng/mL; gastric contents, 0.06 mg and 0.56 mg; liver tissue, 10.7 ng/g and 109.6 ng/g; and medicinal liquor, 0.568 mg/mL and 0.664 mg/mL, respectively. The clinical manifestations, anatomy findings, and quantitative data on the concentrations of AC and HA in body fluids and tissues will aid forensic investigations of deaths caused by acute AC poisoning. [ABSTRACT FROM AUTHOR]

Background: Although forensic pathology age estimation of a wound is difficult, it can help reconstruct murder scenes and identify potential culprits. Forensic specialists usually focus on two aspects when evaluating wound life and determining how long ago the wound occurred. Recent developments in forensic techniques, particularly in high-throughput analyses, have allowed for the simultaneous evaluation of several markers and molecular and cellular levels of material analysis. Aim: To evaluate valuable additional information offered by each marker, summarize recent literature, and offer an update on wound-age estimation in forensic pathology. With the intention of providingsome insight for future research, prospects for evaluating wound age in forensic practice are finally examined. Conclusions: Although forensic pathologists find it difficult to determine the age of wounds, doing so can help reconstruct crime scenes and help apprehend suspects. In circumstances where several traumas are sustained by various offenders, forensic pathologists must determine the chronology and sequence of injuries because the severity of an injury usually determines the severity of punishment. Wound tracing, photographic recording (including image analysis), biophysical approaches, and/or invasive protocols requiring wound biopsies are examples of protocols that can be used to do the assessment. We give a summary of some of the most frequently required and utilized methods in this article: (a) Preclinical and animal models, such as those involving incisions, excisions, burns, and damaged wounds; (b) techniques for assessing the rate of healing, image-based wound analysis, biophysical evaluation, histological, immunological, and biochemical assays; and techniques for assessing the healing progression. [ABSTRACT FROM AUTHOR]

The development of probabilistic genotyping (PG) systems to quantitatively analyze DNA mixture samples has been transformative in forensic science. TrueAllele® Casework (TA) and STRmix™ (STRmix) are the two most widely used PG systems in the United States. The two systems were challenged with 48 two‐, three‐, and four‐person mock casework samples, for a total of 152 likelihood ratio (LR) comparisons. TA and STRmix converged on the same result (supportive, non‐supportive, or inconclusive) for ~91% of contributor‐specific comparisons. Where moderate or substantial differences in log(LR) values were observed, 9% affected the conclusion of the reference association to the mixture. The PG systems exhibited high correlations for estimated contributor‐specific template quantities (~92%) and log(LR)s produced (>88%). When the log(LR)s for only low‐template contributors (<100 pg) were compared, the R2 value dropped to ~68% and the difference became statistically significant. Of the 14 contributor comparisons where the conclusion differed, two were contradictory (supportive vs. non‐supportive) and 12 were either inconclusive versus non‐supportive or inconclusive versus supportive. The differing results were likely due to dissimilarities in the mixture input file as STRmix uses a lab‐defined analytical threshold (AT) and TA models to 10 RFUs for each electropherogram. When 7 of the 14 mixtures were reanalyzed by STRmix using a 10 RFU AT, the log(LR)s for the low‐template contributors became more similar to TAs. This study shows that while both systems may produce accurate and calibrated LRs, their results can deviate, especially for low‐template, degraded contributors, and the deviation is generally predictable. [ABSTRACT FROM AUTHOR]

The NGM Detect™ PCR Amplification Kit was designed particularly for genotyping degraded casework samples. This study aimed to validate the half‐volume amplification of the kit and to present its successful long‐term application. The validation was performed in accordance to the corresponding guidelines of the Scientific Working Group on DNA analysis methods and the European Network of Forensic Science Institutes. For validation parameters, such as sensitivity, reproducibility, and repeatability, polymerase chain reactions (PCR) were set up both manually and robotically, applying 29 cycles. For PCRs with sub‐optimal DNA input (≤0.5 ng) the cycle numbers were increased to 31. Regardless of the PCR preparation method, the optimal 0.5 ng DNA input produced optimal allelic peak heights with no allelic dropout. The first alleles that failed to amplify started to appear at the level of 0.0375 ng input DNA, although the manually prepared PCRs produced fewer missing alleles. In this case, the raised cycle number produced 1.9% and 4.4% of dropout for manually and for robotically set up PCRs, respectively. In the case of 84 degraded casework samples, PCRs were prepared only by hand. The kit was able to provide informative profiles for 78.57%, 70.37%, and 69.77% for lowly, moderately, and highly degraded samples, respectively. Allelic dropouts were 26.05%, 44.88%, and 51.23% for the same groups. According to our results, we strongly recommend using the NGM Detect™ Kit in half‐volume PCR system and encourage the usage of the kit in the particular cases when other kits fail to produce a complete DNA profile. [ABSTRACT FROM AUTHOR]

The presence of gunshot residue (GSR) in a sample can provide valuable information in forensic investigations by associating a suspect with a shooting incident. However, in order to have confidence in the integrity of the results' interpretation, the possibility of contamination by secondary transfer of GSR occurring during the transportation of a person under custody in a police vehicle should be evaluated. In order to investigate police vehicles as a source for secondary transfer of GSR particles, a total of 51 samples were collected from the rear seats of random police vehicles and used to transport arrested individuals. Results indicated that the type of upholstery of the seats plays a main role in determining the potential for secondary GSR contamination. The potential chance of coming into contact with GSR particles in police vehicles is low. GSR contamination from police vehicles is, maybe, not of a major concern but should be taken into consideration mainly when very few characteristic GSR particles are found on an analyzed sample. [ABSTRACT FROM AUTHOR]

Human decomposition studies aim to understand the various factors influencing human decay to assess the deceased and develop postmortem interval (PMI) estimation methods. These types of studies are typically conducted through physical experiments examining the deceased; however, big data systems have the potential to transform how large‐scale forensic anthropology research questions can be addressed with curated images of donors with known demographic, climatic, and postmortem historical data. This study introduces ICPUTRD (Image Cloud Platform for Use in Tagging and Research on Decomposition), a web‐based software system, which enables forensic scientists to easily access, enhance (or curate), and analyze very large photographic collections documenting the longitudinal process of human decomposition. ICPUTRD, a JavaScript‐based application, was designed and built through a combination of the Waterfall and Agile software development life‐cycle methods and provides an image search and tagging features with a predefined nomenclature of forensic‐related keywords. To evaluate the system, a user study was conducted, involving 27 participants who completed pre‐ and post‐study surveys and three research tasks. Analysis of the study results confirmed the feasibility and practicality of ICPUTRD to facilitate aspects of forensic research and casework involving large collections of digital photographs of human decomposition. It was observed that the nomenclature lacked certain law enforcement keywords, so future work will focus on expanding it to ensure ICPUTRD is suited for all its intended users. [ABSTRACT FROM AUTHOR]

Steganalysis techniques play a crucial role in forensic investigation, aiding professionals in identifying digital images with unsafe content. This work proposes a novel and improved lightweight deep-learning convolution neural network architecture called CIRNet, which provides universal steganalysis capabilities. CIRNet leverages inverted residual blocks integrated with a self-attention mechanism that effectively minimises the detection error rate and the computational complexity for steganalysis. The inverted residual blocks include lightweight depth-wise and point-wise convolutions supplemented by a self-attention module. This integration strengthens the saliency of feature maps corresponding to the embedding regions while reducing the network's parameters and the total floating-point operations. Consequently, CIRNet achieves superior accuracy, making it an excellent choice for deployment on lightweight smart mobile systems. Experimental results validate the superiority of the proposed CIRNet over several state-of-the-art deep-learning networks commonly employed in steganalysis. The results further demonstrate the enhanced generalisation ability, particularly when applied to diverse datasets generated from BOSSbase 1.01 and BOWS2 using HUGO, WOW, and S-UNIWARD steganography methods with two distinct payloads. These findings establish CIRNet as an efficient and elite performer in the domain of lightweight steganalysis. [ABSTRACT FROM AUTHOR]

When the foundation of a method is simulated data, it is paramount that the method is validated with data from physical samples when possible. This study presents the results of validating a recently developed nuclear forensics methodology with a new low-burnup plutonium sample, chemically separated from low-enriched uranium irradiated in thermal neutron flux. The nuclear forensics methodology uses machine learning models to discriminate the reactor type of origin, fuel burnup, and time since irradiation (TSI) of chemically separated plutonium samples. The machine learning models use intra-elemental isotope ratios of cesium, samarium, europium, and plutonium as features; the isotopic ratio data for training the models were generated through fuel burnup simulations of various nuclear reactor types. The methodology predicted the reactor type and fuel burnup of the plutonium sample successfully. Initial difficulties to predict the TSI were resolved with the inclusion of a new intra-elemental isotope ratio of cerium. [ABSTRACT FROM AUTHOR]

The ability to analyze blunt-force trauma is crucial for deciphering valuable clues concerning mechanisms of injury and as evidence for medico-legal investigations. The use of alternate light sources (ALS) has been studied over the past decade, and is proposed to outperform conventional white light (CWL) during bruise assessments. In response to the growing interest of the technology worldwide, a systematic review of the literature was conducted according to the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) to address the ability of ALS to detect and visualize bruising. From an initial 4055 records identified, ten studies met the eligibly criteria and were selected for this review. Evaluation also included a novel framework, referred to as SPICOT, to further systematically assess both scientific evidence and risk of bias in forensic literature. Analysis reveals that narrowband wavelengths within in the infrared or ultraviolet spectral ranges do not significantly outperform CWL in visualizing or detecting bruising. However, wavelengths within the visible spectrum, particularly 415 nm combined with longpass or bandpass yellow filters, are more effective. However, the majority of selected studies only address the sensitivity of ALS, and therefore, results may only be considered valid when the location of a bruise is known. Further investigation is required to understand the specificity of ALS, in particular how the use of topical cosmetic products, previous wounds/scar-tissue, tattoos, moles and freckles may affect detection. The ethical concern regarding the interpretation of enhanced visualized trauma should also be considered in prospect discussions prior to implementing ALS into routine practice. Nevertheless, this review finds that narrowband ALS within the visible spectrum demonstrates potential for improved injury documentation, outperforming CWL in the detection and visualization of bruising. [ABSTRACT FROM AUTHOR]

Non-prescription use of anabolic androgenic steroids (AAS) is associated with an increased risk of premature death. However, these substances are seldom screened in connection with forensic cause-of-death investigation, unless the forensic pathologist specifically suspects use, often based on a positive AAS use history. Since AAS use is often concealed from others, this practice may lead to mistargeting of these analyses and significant underestimation of the true number of AAS positive cases undergoing forensic autopsy. Thus, more accurate diagnostic tools are needed to identify these cases. The main objective of this study was to determine, whether a multivariable model could predict AAS urine assay positivity in forensic autopsies. We analyzed retrospectively the autopsy reports of all cases that had been screened for AAS during forensic cause-of-death investigation between 2016–2019 at the Finnish Institute for Health and Welfare forensic units (n = 46). Binary logistic regression with penalized maximum likelihood estimation was used to generate a nine-variable model combining circumferential and macroscopic autopsy-derived variables. The multivariable model predicted AAS assay positivity significantly better than a "conventional" model with anamnestic information about AAS use only (area under the receiver operating characteristic curve [AUC] = 0.968 vs. 0.802, p = 0.005). Temporal validation was conducted in an independent sample of AAS screened cases between 2020–2022 (n = 31), where the superiority of the multivariable model was replicated (AUC = 0.856 vs. 0.644, p = 0.004). Based on the model, a calculator predicting AAS assay positivity is released as a decision-aiding tool for forensic pathologists working in the autopsy room. [ABSTRACT FROM AUTHOR]

The decomposition of animal remains is a multifaceted process, involving ecological, biological, and chemical interactions. While the complexity is acknowledged through concepts like the necrobiome, it's unclear if this complexity is reflected in research. Appreciation of the complexity of decomposition is crucial for identifying sources of variation in estimations of time since death in medico-legal science, as well as building broader ecological knowledge of the decomposition process. To gain insights into the extent of multidisciplinary research in the field of decomposition science, we conducted an examination of peer-reviewed literature on four key drivers of variation: volatile organic compounds, microbes, drugs/toxins, and insects. Among 650 articles, we identified their scientific discipline, driver/s of variation investigated, and year of publication. We found that 19% explored relationships between two drivers, while only 4% investigated interactions between three. None considered all four drivers. Over the past three decades, there has been a steady increase in decomposition research publications, signifying its growing importance. Most research (79%) was linked to forensic science, highlighting opportunities for interdisciplinary collaboration in decomposition science. Overall, our review underscores the need to incorporate multidisciplinary approaches and theory into contemporary decomposition research. [ABSTRACT FROM AUTHOR]

In living children, the use of a wide field fundus camera such as RetCam is the gold standard practice to document retinal haemorrhages in suspected cases of abusive head trauma (AHT). In case of sudden unexpected death in infancy (SUDI), child abuse must be considered as a possible cause of death and an eye examination is required. However, no example of post-mortem fundus photograph (PMFP) of retinal haemorrhages related to AHT is yet available for clinicians. We report a SUDI case, with no external traumatic lesions or limb fractures, for which prompt PMFP showed retinal haemorrhages typical of AHT: child abuse was subsequently confirmed by the forensic investigation. We discuss why PMFP is a relevant screening test to detect retinal haemorrhages in the case of SUDI and why the use of the RetCam should be further investigated. [ABSTRACT FROM AUTHOR]

Forensic microbiology is rapidly emerging as a novel tool for human identification. The human microbiome, comprising diverse microbial communities including fungi, bacteria, protozoa, and viruses, is unique to each individual, offering a new dimension to forensic investigations. While traditional identification methods primarily rely on DNA profiling and fingerprint analysis, they face limitations when complete DNA or fingerprints profiles are unattainable or degraded. In this context, the microbial signatures of the human skin microbiome present a promising alternative due to their resilience to environmental stresses and individual-specific composition. This review explores the potential of microbiome analysis in forensic human identification, evaluating its applications, advantages, limitations, and future prospects. The uniqueness of an individual's microbial community, particularly the skin microbiota, can provide distinctive biological markers for identification purposes, while technological advancements like 16 S rRNA sequencing and metagenomic shotgun sequencing are enhancing the specificity of microbial identification, enabling detailed analysis of these complex ecological communities. Despite these promising findings, current research has not yet achieved a level of identification probability that could establish microbial analysis as a stand-alone evidence tool. Therefore, it is presently considered ancillary to traditional methods, contributing to a more comprehensive biological profile of individuals. [ABSTRACT FROM AUTHOR]

Sex and stature estimation represent two pillars in the creation of the biological profile, providing crucial demographic information that forensic anthropologists use for the identification of unknown skeletonized remains. This pilot study evaluates population data proposing a virtual sex and stature estimation method for a Hispanic population using the tibia. Ninety‐two CT scans from the New Mexico Decedent Image Database were used to generate 3D models of the left tibia (forty‐seven males, forty‐five females). Tibial length, proximal and distal breadth were the parameters taken. Intra‐observer error was assessed using an intra‐class correlation coefficient. Sex differences were explored, and discriminant function and regression analysis used to develop sex and stature estimation formulae, respectively. High repeatability was demonstrated. Sex estimation accuracies ranged between 83.7 per cent and 93.5 per cent, with proximal and distal breadth showing the highest correct classification rates. Stature estimation produced errors between 5.51 cm and 7 cm, with the validation test providing errors falling within the predicted standard error of the estimate reported by the original equations. This study suggests the potential for accurate sex and stature estimation in the Hispanic sample. Although a larger sample is needed to corroborate the preliminary results, the proposed methods might assist in the identification of future forensic cases. [ABSTRACT FROM AUTHOR]

This document is a comment submitted to the Journal of Analytical Toxicology. The authors address and clarify information presented in a previous publication regarding the development of a screening assay for drugs in blood. They discuss the evolution of standards and recommendations for toxicological investigations of drug-impaired driving, highlighting differences between the draft and final versions of the standards. The authors also compare the cutoff concentrations used in their study with the recommendations in place at the time of development and at present. They acknowledge that their method did not meet the analytical sensitivity requirements for certain analytes according to the final published standard. The article discusses the validation and limitations of a published method for screening drugs in blood samples. The authors explain that while the method met sensitivity requirements for immunoassay-based screening, it may not be possible to achieve the increased sensitivity required for confirmatory cutoffs for certain compounds. They also discuss the use of non-human blood for method development and validation, the need for additional guidance on critical validation parameters, and the evolving nature of standards in forensic toxicology. The authors emphasize the importance of considering the versioning of standards and highlight the dynamic nature of the standards environment. [Extracted from the article]

Decisions in forensic science are often binary. A firearms expert must decide whether a bullet was fired from a particular gun or not. A face comparison expert must decide whether a photograph matches a suspect or not. A fingerprint examiner must decide whether a crime scene fingerprint belongs to a suspect or not. Researchers who study these decisions have therefore quantified expert performance using measurement models derived largely from signal detection theory. Here we demonstrate that the design and measurement choices researchers make can have a dramatic effect on the conclusions drawn about the performance of forensic examiners. We introduce several performance models – proportion correct, diagnosticity ratio, and parametric and non-parametric signal detection measures – and apply them to forensic decisions. We use data from expert and novice fingerprint comparison decisions along with a resampling method to demonstrate how experimental results can change as a function of the task, case materials, and measurement model chosen. We also graphically show how response bias, prevalence, inconclusive responses, floor and ceiling effects, case sampling, and number of trials might affect one's interpretation of expert performance in forensics. Finally, we discuss several considerations for experimental and diagnostic accuracy studies: (1) include an equal number of same-source and different-source trials; (2) record inconclusive responses separately from forced choices; (3) include a control comparison group; (4) counterbalance or randomly sample trials for each participant; and (5) present as many trials to participants as is practical. [ABSTRACT FROM AUTHOR]

• S tability and regularity analysis of bare footprint measurements at different motion speeds. • Significant differences were found in length measurements (A1-A5). • MPJ and Calc width showed no significant differences across different speeds. • Mean length measurements decreased gradually as walking speed accelerated among young adults in eastern China. In some crime scenes, there may be bare footprints. Analyzing and testing the linear measurements of bare footprints in crime scenes can play an important role in personal analysis and individual identification. However, the linear measurements of bare footprints may be influenced by different motion states, leading to changes in length and width or even significant deviations. Previous studies focused on the linear differences between static and dynamic footprints, and failed to take the speed factor into consideration. This paper studied the stability and change regularities of the linear measurements of bare footprints in four different motion states: standing, normal walking, fast walking and trotting. Dust footprints of the right feet were collected from 80 healthy young adults under these four motion states. Seven linear measurements were obtained for each footprint using the Reel method, totaling 2240 data sets. One-way repeated measures ANOVA was used to assess the measurement variations across the four states. The results showed that there were statistically significant variances in the length measurements (A1-A5) between the standing state and other motion states, whereas no statistically significant variances were observed between the three dynamic states. It was found that the mean values of the five length measurements (A1–A5) increased from static to dynamic state, and then gradually decreased slightly as the walking speed increased. Additionally, no significant differences were found in the two width measurements (MPJ Width and Calc Width) between the four motion states. As a preliminary study, this study can provide a reference for the analysis of bare footprints in different motion states extracted from crime scenes. [ABSTRACT FROM AUTHOR]

The rapid growth in the usage of the Internet of Things (IoT) has expanded both in size and scope. The automation and real-time services provided by IoT devices have increased their vulnerability to ongoing attacks. One of the significant challenges in forensic analysis is the limited computational capability and storage capacity of IoT devices, which complicates the process of collecting and identifying various attack types during forensic readiness investigations process. This research proposed an Advanced Residual Network (AResNet) as an integrated model for all IoT devices, incorporating performance improvements from previous research to enhance the IoT forensic readiness framework. Explains the phase of collecting attack data into a forensic repository to identify and analyze types of attacks on IoT networks using a deep learning approach. The proposed forensic IoT readiness framework has three novel functions. First, the repository serves as a secure storage solution for data on various attacks targeting IoT devices, ensuring data integrity and providing a reliable reference source for valid digital evidence. Second, utilizes Particle Swarm Optimization (PSO) algorithms to determine the best and optimal hyperparameters, resulting in a significant improvement in the performance of the deep learning model. Third, it involves the development of the Advanced Residual Network algorithm based on deep neural networks with the collective intelligence of PSO, functioning as an Early Warning System. This system is designed to identify various types of attacks on different IoT devices with a high degree of accuracy. The AResNet model was trained and evaluated using the N-BaIoT dataset, and its performance was compared with various other deep learning models, including RNN, LSTM, CNN, 1D-CNN, and CNN-LSTM. The experimental results clearly demonstrate that the proposed model significantly outperforms previous methodologies in identifying and classifying types of attacks across all IoT devices directly, achieving an accuracy of 0.92, precision of 1.00, and recall of 1.00. Consequently, having a greatly impact on the effectiveness of the IoT forensic readiness investigation process. [ABSTRACT FROM AUTHOR]

This document provides information about the American Society for Clinical Pharmacology and Therapeutics (ASCPT) and its upcoming events and opportunities. It encourages members to renew their membership and highlights the benefits of attending the ASCPT 2025 Annual Meeting, such as professional connections, collaboration potential, and skill enhancement. The document also announces the search for the next Editor-in-Chief of Clinical Pharmacology & Therapeutics and acknowledges the recipients of various awards and grants. Additionally, it mentions the ASCPT Board and invites nominations for board positions. The document concludes with a list of new ASCPT members, showcasing the diverse perspectives and backgrounds of these individuals. [Extracted from the article]

This paper addresses a relevant problem in Forensic Sciences by integrating radiological techniques with advanced machine learning methodologies to create a non-invasive, efficient, and less examiner-dependent approach to age estimation. Our study includes a new dataset of 12,827 dental panoramic X-ray images representing the Brazilian population, covering an age range from 2.25 to 96.50 years. To analyze these exams, we employed a model adapted from InceptionV4, enhanced with data augmentation techniques. The proposed approach achieved robust and reliable results, with a Test Mean Absolute Error of 3.1 years and an R-squared value of 95.5%. Professional radiologists have validated that our model focuses on critical features for age assessment used in odontology, such as pulp chamber dimensions and stages of permanent teeth calcification. Importantly, the model also relies on anatomical information from the mandible, maxillary sinus, and vertebrae, which enables it to perform well even in edentulous cases. This study demonstrates the significant potential of machine learning to revolutionize age estimation in Forensic Science, offering a more accurate, efficient, and universally applicable solution. [ABSTRACT FROM AUTHOR]

Austroasiatic (AA) speakers constitute around 4% of the population of Thailand, while the majority (89.4%) speak Kra-Dai (KD) languages. Previous forensic and population genetic studies in various Thai populations have employed a limited number of short tandem repeats (STRs). This study aims to expand the investigation of the genetic makeup of AA populations in Thailand and their relationship to KD populations using a larger number of autosomal STRs with the VeriFiler™ Plus PCR Amplification Kit. We generated 593 new genotypes from AA-speaking groups and combined them with previously reported data from AA and KD groups. A total of 1,129 genotypes across 23 STR loci were used to construct the largest allelic frequency profile for Thai and Lao populations. However, several loci deviated from Hardy–Weinberg equilibrium, likely due to the reduced genetic diversity in some highland populations, which should be considered in forensic investigations. Beyond forensic applications, our findings reveal genetic differences between AA-speaking groups in Northern and Northeastern Thailand. The AA groups from Northeastern Thailand exhibit greater genetic homogeneity and diversity, likely due to population interactions. In contrast, reduced diversity and increased heterogeneity in AA groups from Northern Thailand are possibly driven by genetic drift and cultural and geographic isolation. In conclusion, we emphasize the usefulness of increasing the number of autosomal STRs in forensic and anthropological genetic studies. Additional Y-STR and X-STR data from various AA-speaking groups in Thailand would further enhance and strengthen forensic STR databases in the region. [ABSTRACT FROM AUTHOR]

The realm of analytical chemistry continues to struggle with defining and evaluating the limit of detection in analytical methods in the sense that a multitude of definitions, criteria, caveats, and methods have been proposed, developed, and adopted across disciplines. The last decade has seen a surge in the growth of electrochemical methods and studies in the field of forensic science and forensic chemistry. While many disciplines within forensic science have established method validation guidelines, the historical and current lack of electrochemical methods within forensic laboratories throughout the United States has left a major gap in knowledge, inhibiting the adoption and utilization of electrochemistry, which may serve as a powerful tool in many subdisciplines of forensics. As such, this work begins this discussion by focusing first on the limit of detection (LOD), with application toward both qualitative and quantitative methods. Both inorganic (ferrocyanide and lead) and organic (diphenylamine, naltrexone, and acetaminophen) target analytes were analyzed via two common voltammetry methods: cyclic voltammetry and square-wave voltammetry. The LOD for each analyte was estimated and/or calculated following a variety of literature-described methods and compared. The accuracy and reliability of these LOD characteristics based on the experimental data is described herein along with suggestions and recommendations. This manuscript is intended to compare the resulting LOD values from various methods and provide a starting point for the incorporation of electrochemistry into the forensic science laboratory, beginning a focused discussion on the development of validation guidelines and parameters needed for the adoption of this technology in forensic laboratories in order to meet the standards required by the criminal justice system. [ABSTRACT FROM AUTHOR]

For over a century, fingerprints have served as a pivotal tool for identification of individuals owing to their enduring characteristics and easily apparent features, particularly in the realm of criminal investigations. Latent fingerprints (LFPs) are "invisible fingerprints" that are most commonly available at crime scenes and require a rapid, selective, sensitive, and convenient method for detection. However, existing fingerprint development techniques harbour limitations, prompting the exploration of novel approaches that prioritize investigator safety and environmental sustainability. Leveraging the unique photophysical properties of aggregation-induced emission luminogens (AIEgens) has emerged as a promising strategy for on-site analysis of LFP visualization. In this highlight, we have presented a comparative analysis of various AIEgens (organic compounds, metal complexes, nanoparticles, and polymers) for the development and detection of LFPs. Through this examination, insights into the efficiency and potential applications of AIE-based fingerprint development techniques are provided. In addition, several strategies have been proposed for circumventing the limitations of existing AIEgens. We hope that this highlight article will encourage more researchers to investigate AIEgens in LFP detection, contributing to forensic science. [ABSTRACT FROM AUTHOR]

The Internet of Things forensics is a specialised field within digital forensics that focuses on the identification of security incidents, as well as the collection and analysis of evidence with the aim of preventing future attacks on IoT networks. IoT forensics differs from other digital forensic fields due to the unique characteristics of IoT devices, such as limited processing power and connectivity. Although numerous studies are available on IoT forensics, the field is rapidly evolving, and comprehensive surveys are needed to keep up with new developments, emerging threats, and evolving best practices. In this respect, this paper aims to review the state of the art in IoT forensics and discuss the challenges in current investigation techniques. A qualitative analysis of related reviews in the field of IoT forensics has been conducted, identifying key issues and assessing primary obstacles. Despite the variety of topics and approaches, common issues emerge. The majority of these issues are related to the collection and pre-processing of evidence because of the counter-analysis techniques and challenges associated with gathering data from devices and the cloud. Our analysis extends beyond technological problems; it further identifies the procedural problems with preparedness, reporting, and presentation as well as ethical issues. In particular, it provides insights into emerging threats and challenges in IoT forensics, increases awareness and understanding of the importance of IoT forensics in preventing cybercrimes, and ensures the security and privacy of IoT devices and networks. Our findings make a substantial contribution to the field of IoT forensics, as they not only involve a critical analysis of the challenges presented in existing works but also identify numerous problems. These insights will greatly assist researchers in identifying appropriate directions for their future research. [ABSTRACT FROM AUTHOR]

The widespread use of per- and polyfluoroalkyl substances (PFASs) with different physico–chemical properties poses a great threat to the environment and human health. Simultaneous detection of different classes of PFASs is a difficult task, especially for rapid analysis of polluted water samples in environmental forensic cases. In this study, a simple sample preparation ultrahigh-performance liquid chromatography coupled with quadrupole Orbitrap high-resolution mass spectrometry was established for the detection of PFASs in a wide range of water matrices. By optimizing the conditions of pretreatment and the parameters of the instrument, the developed method provided good linearity of calibration standards (R2 > 0.99), and demonstrated excellent MLOQ (0.008–1.2 µg/L), with spiked recoveries ranging from 57.7% to 151% for 47 targets in surface water samples, and from 45.7 to 165% for 46 targets in ground and waste water samples, respectively. This method required an injection volume of 3 µL and an analysis time of only 18 min per sample. The validation method was successfully applied to the analysis of 20 environmental water samples, in which 15 target substances with different concentrations were detected, with total concentrations of 0.082 to 262.455 μg/L. The method is simple and exclusive, and can rapidly confirm the occurrence of PFASs in different water samples, providing a convenient and fast high-throughput analysis, which is especially suitable for the application in the environmental forensic investigation of PFASs pollution. [ABSTRACT FROM AUTHOR]

The accurate classification of human activities in crime scenes during forensics (criminalistics) is of utmost importance in classifying suspicious and unlawful activities, easing their acceptability and interpretability by judges during legal procedures in courts or by other non-experts in the field of forensics. This paper implements machine learning (ML) algorithms: support vector machine (SVM) and decision tree (DT), to demonstrate with a high accuracy, how data emanating from smartphones' sensors reveal and isolate relevant information about static and dynamic human activities in criminalistics. Smartphones' data from five different sensors (accelerometer, gravity, orientation, Gyroscope and light), related to ten recurrent crime scenes activities, grouped into three classes of events (normal, felony and none-felony events) are classified by the proposed algorithms, with novelty being the classification decisions based on the entire period of the events and not instantaneous decision makings. Three independent data-subsets were made, with permutations done between them and at each time, two sets used for training and the third set used for testing. Time- and frequency-domain features were initially used separately and then combined for the model training and testing. The best average training accuracies of 100% and 97.8% were obtained for the DT and SVM, respectively, and the testing accuracies of 89.1% were obtained for both algorithms. We therefore believe that these results will serve as a solid persuasive and convincing argument to judges and non-experts of the field of forensics to accept and easily interpret computer-aided classification of suspicious activities emanating from criminalistic studies. [ABSTRACT FROM AUTHOR]

Due to the appeal of Generative Adversarial Networks (GANs), synthetic face images generated with GAN models are difficult to differentiate from genuine human faces and may be used to create counterfeit identities. However, these face images contain artifacts presented in irises owing to the irregularity of highlights between the left and right irises. Adversaries can utilize PS documents of these images trying to conceal the artifacts of images, which makes it more challenging to distinguish between pristine and fake images. In order to tackle this, our research introduces a complete dataset and analytical tools that make a substantial contribution to multimedia forensics. This allows for the authentication of documents despite common alterations in the PS documents. Owing to the lack of large-scale reference IRIS datasets in the PS scenario, this study provided a pioneering dataset aiming to set a standard for multimedia forensic investigations. Given face images, we extracted iris images using the Dlib (King, J Mach Learn Res 10(60):1755–1758, 2020) and EyeCool (Wang et al. 2021) models, as described in Guo et al. (2022). However, in some cases, the potential eyelid occlusion phenomenon resulted in incomplete iris images. We utilized a hypergraph convolution-based image inpainting technique to complete the missing pixels in the extracted iris images, thus uncovering the intricate relationships within the iris data. To evaluate the IRIS image dataset and highlight associated issues, we conducted a series of analyses using Siamese Neural Networks, including ResNet50, Xception, VGG16, and MobileNet-v2, to measure the similarities between authentic and synthetic human iris images. Our SNN model, with four different backbones, effectively differentiated between genuine and synthetic iris images. For instance, using the Xception network, we achieved 56.76% similarity in IRISes for synthetic images and 92.77% similarity in IRISes for real images. The effectiveness of our approach was demonstrated by the similarity scores obtained from all SNN architectures, which showed a significant difference between the GAN-generated images from ProGAN or StyleGAN and the original PS iris photos. The similarity scores resulting from StyleGAN are higher than those of the ProGAN architecture, but at its highest, it is 76%, while for the pristine images, it ranges from 85% to 95%. This discrepancy can be utilized in order to distinguish between pristine and GAN-generated images. [ABSTRACT FROM AUTHOR]

In the current digital era, images have become one of the key channels for communication and information. There are multiple platforms where digital images are used as an essential identity, like social media platforms, chat applications, electronic and print media, medical science, forensics and criminal investigation, the court of law, and many more. Alternation of digital images becomes easy because multiple image editing software applications are accessible freely on the internet. These modified images can create severe problems in the field where the correctness of the image is essential. In such situations, the authenticity of the digital images from the bare eye is almost impossible. To prove the validity of the digital images, we have only one option: Digital Image Forensics (DIF). This study reviewed various image forgery and image forgery detection methods based on blind forgery detection techniques mainly. We describe the essential components of these approaches, as well as the datasets used to train and verify them. Performance analysis of these methods on various metrics is also discussed here. [ABSTRACT FROM AUTHOR]

Tape is a type of trace evidence commonly seen in forensic science. Little to no research has been published regarding the optimal storage material for pressure‐sensitive adhesive (PSA) tape. The purpose of this project was to determine the substrate with the least amount of interference for storing PSA tape. Interferences such as difficulty removing tape from the substrate or substrate components leaching into the adhesive can affect the analysis. This was a long‐term study in which samples were stored on various substrates for nearly 10 years. Ten different tape samples were placed on eight different substrates for a total of 80 samples. The substrates included typical materials currently used for tape storage as well as other, less common materials. The tape samples were evaluated for ease of removal at various time intervals. The adhesives from each tape were analyzed at similar time intervals by Fourier transform infrared spectroscopy (FTIR). In addition, the adhesives were analyzed by pyrolysis gas chromatography–mass spectrometry (PyGC‐MS), scanning electron microscopy with energy‐dispersive spectroscopy (SEM‐EDS), and x‐ray fluorescence (XRF) at the last time interval (112 months) and compared to the adhesive tape samples from the original rolls of tape to determine if there was any adverse effect from the substrates during storage. The FedEx backing was the only substrate that had no adverse effects regarding ease of removal. The substrates that had the least effect on the adhesive for both short‐term and long‐term storage included FedEx backings, adhesive sheet backings, and polyester transparency sheets. [ABSTRACT FROM AUTHOR]

Lip print analysis has been proposed as a new human identification technique that allows the estimation of various individual parameters, such as population affinity. However, more consensus among the scientific community regarding this topic is needed. This study aimed to conduct a systematic review to gather evidence that might clarify whether cheiloscopy can be used in population affinity estimation. The systematic review was performed following the PRISMA guidelines. A bibliographic survey was conducted in PubMed, Scopus, and Web of Science databases, restricted to articles published between 2010 and 2022. Studies were selected according to eligibility criteria, and the study data were collected. The risk of bias in each study was assessed and applied as additional inclusion or exclusion criteria. A descriptive approach synthesized the results of the articles eligible for analysis. Out of seven included studies, six demonstrated differences in lip patterns between different population groups, and one obtained inconclusive results. Several methodological flaws in the studies and variations in the collection and analysis methodology applied, which may have conditioned the results obtained, were identified. The data gathered allowed us to conclude that no solid scientific evidence supports the use of cheiloscopy in population affinity estimation. [ABSTRACT FROM AUTHOR]