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Quantification of the stable isotopes within a compound aids forensic investigations as it provides a fingerprint which can determine that compound's source substrates, synthetic route, and possible mechanisms of degradation. Previous stable isotope studies have explored 13C and 2H measurements of the sarin precursors methylphosphonic dichloride (DC) and methylphosphonic difluoride (DF) as forensic signatures. However, these measurements required different sample preparations and measurement techniques. Orbitrap isotope ratio mass spectrometry (Orbitrap-IRMS) is a developing technique which can characterize multiple stable isotopes simultaneously. Here, we apply Orbitrap-IRMS to simultaneously observe the 13C and 2H content of methylphosphonic acid (MPA), the hydrolysis product of DC and DF, which can be used as a proxy for the isotopic content of DC and DF. Our method requires 20 min analyses and consumes ≈60 nmol of sample, with precisions of ≈0.9 ‰ (13C) and ≈3.6 ‰ (2H). We apply our method to both commercially acquired MPA and MPA obtained from the hydrolysis of commercially acquired DC. We validate our methods via comparison to elemental-analyzer isotope ratio mass spectrometry (EA-IRMS). The combined 13C and 2H measurement creates a more robust forensic tool than either isotope individually. Our results demonstrate the viability of Orbitrap-IRMS for chemical forensic measurements.

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BACKGROUND: The thresholds of mechanical inputs for bruising caused by blunt impact are important in the fields of machine safety and forensics. However, reliable data on these thresholds remain inadequate owing to a lack of in vivo experiments, which are crucial for investigating the occurrence of bruising. Since experiments involving live human participants are limited owing to ethical concerns, finite-element method (FEM) simulations of the bruising mechanism should be used to compensate for the lack of experimental data by estimating the thresholds under various conditions, which requires clarifying the mechanism of formation of actual bruises. Therefore, this study aimed to visualize the mechanism underlying the formation of bruises caused by blunt impact to enable FEM simulations to estimate the thresholds of mechanical inputs for bruising. METHODS: In vivo microscopy of a transparent glass catfish subjected to blunt contact with an indenter was performed. The fish were anesthetized by immersing them in buffered MS-222 (75-100 mg/L) and then fixed on a subject tray. The indenter, made of transparent acrylic and having a rectangular contact area with dimensions of 1.0 mm × 1.5 mm, was loaded onto the lateral side of the caudal region of the fish. Blood vessels and surrounding tissues were examined through the transparent indenter using a microscope equipped with a video camera. The contact force was measured using a force-sensing table. RESULTS: One of the processes of rupturing thin blood vessels, which are an essential component of the bruising mechanism, was observed and recorded as a movie. The soft tissue surrounding the thin blood vessel extended in a plane perpendicular to the compressive contact force. Subsequently, the thin blood vessel was pulled into a straight configuration. Next, it was stretched in the axial direction and finally ruptured. CONCLUSION: The results obtained indicate that the extension of the surrounding tissue in the direction perpendicular to the contact force as well as the extension of the thin blood vessels are important factors in the bruising mechanism, which must be reproduced by FEM simulation to estimate the thresholds.

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Food adulteration, whether intentional or accidental, poses a significant public health risk. Traditional detection methods often lack the precision required to detect subtle adulterants that can be harmful. Although chromatographic and spectrometric techniques are effective, their high cost and complexity have limited their widespread use. To explore and validate the application of nanoparticle-based sensors for enhancing the detection of food adulteration, focusing on their specificity, sensitivity, and practical utility in the development of resilient food safety systems. This study integrates forensic principles with advanced nanomaterials to create a robust detection framework. Techniques include the development of nanoparticle-based assays designed to improve the detection specificity and sensitivity. In addition, sensor-based technologies, including electronic noses and tongues, have been assessed for their capacity to mimic and enhance human sensory detection, offering objective and reliable results. The use of nanomaterials, including functionalized nanoparticles, has significantly improved the detection of trace amounts of adulterants. Nanoparticle-based sensors demonstrate superior performance in terms of speed, sensitivity, and selectivity compared with traditional methods. Moreover, the integration of these sensors into food safety protocols shows promise for real-time and onsite detection of adulteration. Nanoparticle-based sensors represent a cutting-edge approach for detecting food adulteration, and offer enhanced sensitivity, specificity, and scalability. By integrating forensic principles and nanotechnology, this framework advances the development of more resilient food-safety systems. Future research should focus on optimizing these technologies for widespread application and adapting them to address emerging adulteration threats.

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The evolution of digital media has increased the number of crimes committed using digital equipment. This has led to the evolution of the computer forensics area to digital forensics (DF). Such an area aims to analyze information through its main phases of identification, collection, organization, and presentation (reporting). As this area has evolved, many techniques have been developed, mainly focusing on the formalization of terminologies and concepts for providing a common vocabulary comprehension. This has demanded efforts on several initiatives, such as the definition of ontologies, which are a means to identify the main concepts of a given area. Hence, the existing literature provides several ontologies developed for supporting the DF area. Therefore, to identify and analyze the existing ontologies for DF, this paper presents a systematic literature review (SLR) in which primary studies in the literature are studied. This SLR resulted in the identification of ontology building methodologies, ontology types, feasibility points, evaluation/assessment methods, and DF phases and subareas ontologies have supported. These results were based on the analysis of 29 ontologies that aided in answering six research questions. Another contribution of this paper is a set of recommendations on further ontology-based support of DF investigation, which can guide researchers and practitioners in covering existing research gaps.

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BACKGROUND AND AIMS: Illicit drug consumption is associated with public health effects and criminal activities. This study aimed to estimate Illicit drug consumption and annual market in the Netherlands from wastewater analysis of drug residues. METHODS: Residues of cocaine, amphetamine and 3,4-methylene dioxymethamphetamine (MDMA) were measured between 2015 and 2022 in 30 Dutch wastewater treatment plants serving both rural and urban populations. These wastewater treatment plants covered 20% of the total Dutch population. The Dutch annual retail market was estimated by extrapolating consumption to the total Dutch population, back-calculating consumption volume, correcting for drug purity and street price collected in voluntary checking services, and accounting for the correlation of consumption and urbanity. RESULTS: The per capita MDMA and cocaine consumption correlated positively with the urbanity of the wastewater treatment plant catchments with r2 of 31% and 64%, respectively. Amphetamine did not show a significant correlation with urbanity. The three studied drugs were conservatively estimated to cover an average annual market value of 903 (95% prediction interval 829 to 987) million Euro for the studied period. Market estimations from prevalence figures and interceptions of international trade were similar. CONCLUSIONS: Illicit drug consumption in the Netherlands appears to correlate positively with urban (in contrast to rural) areas. Wastewater analysis can be used to estimate the volume and monetary value of illicit drug markets as a proof of concept.

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Our study provides the most comprehensive dataset for high-precision radiogenic isotopes of lead (Pb) in blood for the western European population. It investigates their potential for elucidating the contribution of soil Pb to blood Pb using a human biomonitoring survey involving 81 adults and 4 children living in the urban area of Liège (Belgium). Soils in the area show moderate (median of 360 mg/kg) to high (95th percentile of 1000 mg/kg) Pb concentrations, due to former metal processing activities. Blood lead levels (BLL) measured in the study population are, on average, quantitatively consistent with a âˆ¼ 20 % increase due to the exposure to Pb from soils, as estimated by a single-compartment biokinetic model. Consistently, its isotopic composition does not represent an endmember that fully accounts for the variability of Blood lead isotope (BLI) compositions measured in the study population. While some individuals show more thorogenic BLI ratios (relatively more enriched in 208Pb), which could be consistent with a greater exposure to local soils and/or by their country of birth, the BLI data mostly follow a trend roughly parallel to the European Standard Lead Pollution (ESLP) line, within the European leaded gasoline field, even two decades after the withdrawal of this source. Differences in BLI are probably associated with factors related to the presence of Pb in dwellings (pipes, paint) and drinking water distribution system, suggesting that the anthropogenic Pb in use, relevant to human exposure, may contain ore components of different origins, including the Australian Pb ore signature.

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Background: Endless ink and pixel space have been spent to validate the use of cheiloscopy classification systems in the field of identification. It is becoming evident that lip-print patterns may provide indications about an individual's gender, race, occupation, habits, blood type, and paternity. Over the course of time, much data have been collected on a substantial population in this field. In the present study, we strive to see how well a noble total lip scoring (TLS) system works in identifying a person's gender among the College of Dentistry students. We also quantified the total groove score (TGS) and compared it with each quadrant for better authentication of the set study design. Materials and Method: A cross-sectional descriptive study was conducted among 60 dental students (30 males and 30 females), with an age range of 18-30 years. Lip prints were recorded using the writing pad method and subsequently digitalized by capturing the images using Adobe Photoshop CS 6 Extended Version, counted and summed up, quadrant-wise, in both upper and lower lips. The TLS and TGS between males and females were statistically calculated using the Jamovi project. Results: The mean value for TLS for males (n = 402; SD ± 92.1) is higher than that for females (n = 348; SD ± 86.1). The correlation matrix applying a non-parametric test for non-continuous data using Spearman's ratio inferred a statistically significant correlation (-0.354) with a P-value of 0.005 for TLS, whereas a correlation (-0.162) of TGS with gender was not so significant with a P-value of 0.217. Conclusion: Our study proved the uniqueness of applying TLS as a novel approach to gender identification. The study revealed that males had a statistically higher lip score than females. Clinical Significance/Future Implications: Being unique, our study inferred the importance of TLS, which needs to be explored in detail for applying, evaluating, and validating its accuracy in forensic research that can help for errorless investigations as full-proof evidence in court.

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Scapula shape is highly variable across humans and appears to be sexually dimorphic-differing significantly between biological males and females. However, previous investigations of sexual dimorphism in scapula shape have not considered the effects of allometry (the relationship between size and shape). Disentangling allometry from sexual dimorphism is necessary because apparent sex-based differences in shape could be due to inherent differences in body size. This study aimed to investigate sexual dimorphism in scapula shape and examine the role of allometry in sex-based variation. We used three-dimensional geometric morphometrics with Procrustes ANOVA to quantify scapula shape variation associated with sex and size in 125 scapulae. Scapula shape significantly differed between males and females, and males tended to have larger scapulae than females for the same body height. We found that males and females exhibited distinct allometric relationships, and sexually dimorphic shape changes did not align with male- or female-specific allometry. A secondary test revealed that sexual dimorphism in scapula shape persisted between males and females of similar body heights. Overall, our findings indicate that there are sex-based differences in scapula shape that cannot be attributed to size-shape relationships. Our results shed light on the potential role of sexual selection in human shoulder evolution, present new hypotheses for biomechanical differences in shoulder function between sexes, and identify relevant traits for improving sex classification accuracy in forensic analyses.

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In the rapidly evolving landscape of modern technology, the convergence of blockchain innovation and machine learning advancements presents unparalleled opportunities to enhance computer forensics. This study introduces SentinelFusion, an ensemble-based machine learning framework designed to bolster secrecy, privacy, and data integrity within blockchain systems. By integrating cutting-edge blockchain security properties with the predictive capabilities of machine learning, SentinelFusion aims to improve the detection and prevention of security breaches and data tampering. Utilizing a comprehensive blockchain-based dataset of various criminal activities, the framework leverages multiple machine learning models, including support vector machines, K-nearest neighbors, naive Bayes, logistic regression, and decision trees, alongside the novel SentinelFusion ensemble model. Extensive evaluation metrics such as accuracy, precision, recall, and F1 score are used to assess model performance. The results demonstrate that SentinelFusion outperforms individual models, achieving an accuracy, precision, recall, and F1 score of 0.99. This study's findings underscore the potential of combining blockchain technology and machine learning to advance computer forensics, providing valuable insights for practitioners and researchers in the field.

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The combination of memory forensics and deep learning for malware detection has achieved certain progress, but most existing methods convert process dump to images for classification, which is still based on process byte feature classification. After the malware is loaded into memory, the original byte features will change. Compared with byte features, function call features can represent the behaviors of malware more robustly. Therefore, this article proposes the ProcGCN model, a deep learning model based on DGCNN (Deep Graph Convolutional Neural Network), to detect malicious processes in memory images. First, the process dump is extracted from the whole system memory image; then, the Function Call Graph (FCG) of the process is extracted, and feature vectors for the function node in the FCG are generated based on the word bag model; finally, the FCG is input to the ProcGCN model for classification and detection. Using a public dataset for experiments, the ProcGCN model achieved an accuracy of 98.44% and an F1 score of 0.9828. It shows a better result than the existing deep learning methods based on static features, and its detection speed is faster, which demonstrates the effectiveness of the method based on function call features and graph representation learning in memory forensics.

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Digital transformation rapidly changes how we live our lives in the post pandemic world. Unfortunately, digital technology is not limited to law abiding organisations and citizens. Criminal organisations and individuals are quick to identify new opportunities with new technologies, and digital transformation is dramatically changing the character of crimes, terror, and other threats. The fast emergence of new crimes is facilitated by possibilities brought by disruptive technologies such as AI, Internet of Things, drones, and cryptocurrencies that can be disastrous tools in the hands of criminals. Consequently, our society needs far better capacity to prevent and investigate criminal acts to protect organisations and citizens. This brings an urgent need to proactively reform digital forensics to significantly increase our capability to meet the strain on society brought by crimes evolving in the digital transformation era. The future of forensic science is already here, characterized by a mix of opportunities and challenges. It is essential to make it harder to effectively use digital technologies for criminal activities, while leveraging the possibilities of digital technologies by those affected, law enforcement agencies, business and organisations. As digital technologies continue to evolve, we need to stay up to date with the latest developments to effectively investigate and prosecute crimes in the digital age. There is an increased reliance on digital evidence, and the amount of heterogeneous digital evidence in criminal cases keep increasing. The forensic science techniques thus become more sophisticated and play an increasingly important role. However, the scientific area is extremely broad, and beyond the capability of most forensic science labs to keep up with the technology forefront development speed. Besides an urgent need to bring up the subject to the political arena, examples of how we can meet the challenges are discussed such as by extending our cooperation, encourage and facilitate cooperation for training and education to handle the extremely broad and rapid development, working out methods for explaining and visualising evidence for the treatment and legal values of digital evidence in prosecution, and cooperation between product developers and crime investigators for swift innovation of digital forensics tools and methodologies for quickly emerging threats. This paper will highlight specific examples where modern digital techniques are used to solve crimes in the physical world as well as crimes committed in the digital domain and discuss how "good AI" can be used to fight "evil AI" and finally touch on the sensitive balance between the increased power of the new digital forensic tools and private integrity.

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Extensive research has been conducted in image forensics on the analysis of double-compressed images, particularly in the widely adopted JPEG format. However, there is a lack of methods to detect double compression in the HEIF format, which has recently gained popularity since it allows for reduced file size while maintaining image quality. Traditional JPEG-based techniques do not apply to HEIF due to its distinct encoding algorithms. We previously proposed a method to detect double compression in HEIF images based on Farid's work on coding ghosts in JPEG images. However, this method was limited to scenarios where the quality parameter used for the first encoding was larger than for the second encoding. In this study, we propose a lightweight image classifier to extend the existing model, enabling the identification of double-compressed images without heavily depending on the input image's quantization history. This extended model outperforms the previous approach and, despite its lightness, demonstrates excellent detection accuracy.

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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.

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INTRODUCTION: The current situation needs to be presented with scientific data to ensure and improve the quality of specialist training. This study examined the quality of education, instructor competence, stress factors, mobbing, and their interconnections in the domain of forensic medicine specialty education in Turkey. MATERIALS AND METHODS: This cross-sectional study was conducted through an anonymous, web-based survey targeting forensic medicine specialists who have completed their specialist training in university forensic medicine departments within the last three years. The survey consisted of questions regarding issues included in the core education curriculum, educator characteristics, and perceptions of stress and mobbing. Based on the responses provided, the relationships between these factors were statistically analysed. RESULTS: 72.7 % of the study population completed the survey. Nearly half of the participants (46.3 %) did not consider the number of instructors adequate. The quality of instructors (scientific competence, educational and teaching skills, interpersonal relations, accessibility, management, and problem-solving abilities) was determined to be partially sufficient (mean = 3.36). Approximately one-third of the respondents indicated that instructors made the greatest contribution to their specialist training, whereas the proportion of those who said otherwise was much higher. Education received for topics included in the core curriculum was partially sufficient level (mean = 3.04). It was observed that there was a significant relationship between the instructor characteristics, sufficiency score of the taught topics, and perception of mobbing (p < 0.01). The sufficiency scores of instructor characteristics had a 1.02-fold impact on sufficiency ratings of the topics covered in residency training. Among the respondents, 76.9 % reported encountering stress factors during their specialty training, with the most frequently exposed stress factor (52.2 %) being disorganisation within the training program. Among the participants, 22.8 % reported experiencing mobbing. CONCLUSIONS: Education and instructor qualifications were correlated; however, stress factors and mobbing adversely affected education. A close association was observed between education, instructors, stressors, and mobbing. Thus independent and objective auditors tasked with verifying whether institutions meet the established educational standards need to be established.

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The growing non-medical use of benzodiazepines (BZs) has led to the emergence of counterfeit BZ pills and new psycho-active substances (NPS) in the BZ class on the illicit market. Comprehensive analytical methods for BZ identification are required to allow law enforcement, first aid responders and drug-checking services to analyze a variety of sample types and contents to make timely decisions on the spot. In this work, the electrochemical behavior of diazepam (DZ), clonazepam (CZ) and alprazolam (AP) is studied on graphite screen-printed electrodes, both with and without dissolved oxygen in the solution, to link their redox signals to their chemical structure. After elucidation of their reduction mechanisms using liquid chromatography coupled to high-resolution mass spectrometry, three structural classes (Class 1, Class 2 and Class 3) were defined, each with different redox centers and electrochemical behavior. Subsequently, 22 confiscated pills containing 14 different BZs were correctly assigned to these three structural classes, with the deoxygenated conditions displaying the highest class selectivity. Finally, the three classes were successfully detected after being spiked into five alcoholic beverages in the context of drug-facilitated sexual assault. For analysis in red wine, which complicated the analysis by interfering with Class 1, a dual test strategy in pH 2 and pH 7 was proposed for accurate detection. Its rapid measurements, broad scope and lack of interference from diluents or colors makes this method a promising approach for aiding various services in combating problematic BZ use.

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National forensic DNA databases are a valuable investigative tool, that have the potential to increase the efficacy of criminal investigations. Their unfettered expansion in recent years raises unsettling ethical issues that require close attention. DNA database expansion threatens the rights to privacy, non-discrimination, and equality, and can undermine public trust in government. This perspective piece relies on data from an international mapping study of Forensic DNA Databases to document the expansion of these databases, highlight the ethical issues they raise, and propose key recommendations for more responsible use of this infrastructure.

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Combating wildlife crimes in South Africa requires accurate identification of traded species and their products. Diagnostic morphological characteristics needed to identify species are often lost when specimens are processed and customs officials lack the expertise to identify species. As a potential solution, DNA barcoding can be used to identify morphologically indistinguishable specimens in forensic cases. However, barcoding is hindered by the reliance on comprehensive, validated DNA barcode reference databases, which are currently limited. To overcome this limitation, we constructed a barcode library of cytochrome c oxidase subunit 1 and cytochrome b sequences for threatened and protected mammals exploited in southern Africa. Additionally, we included closely related or morphologically similar species and assessed the database's ability to identify species accurately. Published southern African sequences were incorporated to estimate intraspecific and interspecific variation. Neighbor-joining trees successfully discriminated 94%-95% of the taxa. However, some widespread species exhibited high intraspecific distances (>2%), suggesting geographic sub-structuring or cryptic speciation. Lack of reliable published data prevented the unambiguous discrimination of certain species. This study highlights the efficacy of DNA barcoding in species identification, particularly for forensic applications. It also highlights the need for a taxonomic re-evaluation of certain widespread species and challenging genera.

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As part of a comprehensive analysis, this case report presents a possible case of child maltreatment that can serve as a basis for forensic and medical examiner investigations. This case concerns the death of an infant who was approximately two months old. During a routine examination by the pediatrician at the end of May 2021, the child was found to have a normal head circumference of 31 cm. No other abnormalities were noted. On June 19, 2021, the child died, and an autopsy revealed a head circumference of 44 cm and a subdural hematoma as the cause of death. Questions arose as to who might have abused the child and when. The only evidence was a low-quality cell phone video taken by the child's parents on June 13, 2021, six days before the child's death, in which the child could be seen lying on a pillow. It was necessary to determine whether the child in this video already had an unnatural head circumference. This study presents a novel workflow that demonstrates how to analyze and deal with low quality video to answer questions like the above. The workflow demonstrates the creation of 3D scenes from digital image and video material. These 3D scenes can be used for object measurement and to support forensic and medical investigations. In the present case, where only low quality smartphone images were available, the presented workflow was used to create a 3D scene of the child lying on the pillow. In this 3D scene, it was possible to determine the child's head circumference. These measurements support the findings of the medical examiner (dated June 24, 2021) and confirm the suspicion that possible child abuse had already taken place on June 13, 2021. The innovative approach makes it possible to identify evidence of possible abuse based on a specific point in time, in this case the child's private footage. It also demonstrates the potential of 3D scene reconstruction in complex forensic and medical scenarios.