A method was developed for the determination of etomidate and its metabolite etomidate acid in hair and urine by high performance liquid chromatography-triple quadrupole tandem mass spectrometer (HPLC-MS/MS). The hair samples were ground and extracted by ultrasonic extraction with methanol. After protein was precipitated by acetonitrile, the urine samples were centrifuged at high speed and passed through 0.22 μm filter membrane, and then Agilent Eclipse Plus C18 RRHD (3.0 mm×150 mm×1.8 μm) column was used for analysis. Gradient elution was performed with 0.1% formic acid aqueous solution-acetonitrile as mobile phase at the flow rate of 0.5 mL/min. Electrospray ion source and multiple reaction monitoring (MRM) positive ion mode were selected. The results showed that the linear relationship of the two compounds exhibited good linearity in the concentration range of 0.5 to 50 ng/mL in urine and 0.025 to 2.5 ng/mg in hair, and the R² value was greater than 0.992 5. The extraction recoveries ranged from 91.0% to 107.5%, and the intra-day and inter-day precision RSD was 0.4% to 7.4%, while the intra-day and inter-day accuracy was 91.5% to 110.8%. In the actual cases, six hair samples and 4 urine samples were tested, and the test results showed that etomidate was detected in all six hair samples, and the metabolite etomidate acid was detected in two hair samples, but the concentration was much lower than the original. Etomidate and metabolite etomidate acid were detected in two urine samples, and the metabolite concentration was much higher than the original. In conclusion, this method can be used for rapid qualitative and quantitative analysis of etomidate and its metabolite in the hair and urine of etomidate users.

Kinship analysis of grandparents and grandchildren is an important content in the field of forensic identification, which includes three subtypes: double grandparents and biological mother test (DGBM), double grandparents and alleged mother test (DGAM), and double grandparents test (DG). Until now, no systematic formula has been reported in grandparent index (GI) calculation of grandparent identification in the presence of mutation, apart from few simple calculation methods of partial gene combination in DGBM. In this study, we first created a simplified representation model of grandfather and grandmother in 5 subtypes, in the form of a set of genotypes. The general probability formula of mutation of parent genotype into child genotype (P_(DG→Z)) was derived in 12 subtypes. Furthermore, the general formulas of GI under three conditions were derived: GI of double grandparents and biological mother (GI_DGBM), GI of double grandparents and alleged mother (GI_DGAM) and GI of double grandparents (GI_DG). The detailed formulas were further refined according to genotypes of double grandparents, mother and grandchild. In addition, the percentages of mutations of 5 genetic types in the group of unrelated individuals on 30 STR loci of the EX30 kit were calculated. Finally, models of simulated kinship groups and unrelated individual groups were established respectively. The geometric mean (Π) and 95% confidence interval (CI) of CGI, the percentages of cases where CGI<10 000 in kinship groups, and the percentages of cases where CGI>0.000 1 in unrelated individual groups were calculated in the same 30 STR loci. In this study, the novel formulas can be used to calculate all kinds of GI in the presence of mutation under three conditions. The genotype combinations in all mutation cases were reviewed. Among them, the occurrence rate of both paternal and maternal mutation groups was 13.31%, which could not be ignored. In EX30 kit, the formulas can achieve firm conclusions in DGAM cases for both the kinship group and unrelated individual group; in DGBM cases identifications, all cases in the kinship group and 99.89% cases in the unrelated individual group could be correctly identified; and more genetic markers should be added for DG cases. The geometric mean of CGI in kinship groups sorted by value were as following: Π_DGAM>Π_DGOM>Π_DG, and the rule in unrelated individual groups were as following: Π_DGAM<Π_DGOM<Π_DG. The results of this study show that the here-derived GI formulas could be applied to kinship analysis of grandparents and grandchildren in the presence of mutation under three conditions, which is of high application potential.

In this article, the Raman spectrum inspection performance of six drug seizures at three different excitation wavelengths (532, 785, and 1 064 nm) is compared using Raman spectroscopy. Three to five distinctive scattering peaks in the Raman fingerprint area (100-1 800 cm^-1), with greater scattering intensity and no interference from typical adulterants, were chosen as the bass for the qualitative matching of the drug seizures when compared to the Raman spectra of the drug standards. It was discovered that in order to perform a Raman investigation of drug seizures, appropriate excitation wavelengths must be chosen based on the kind of drug involved. Additionally, if the drug seizures contain adulterated substances, the distinctive peaks of the pharmaceuticals that escape adulteration should be chosen for comparison. For seized heroin (HER), caffeine (CAF) and 3,4-methylenedioxymethamphetamine (MDMA), the near-infrared (NIR) excitation wavelengths of 785 nm and 1 064 nm are better than that of 532 nm for the Raman test, in which the 1 064 nm significantly reduces the background fluorescence of the HER seizure; for seized methamphetamine (MAMP) and ketamine (KET), the effect of the change of excitation wavelength was small, and the Raman response was good at three different excitation wavelengths, and the relative intensities of the Raman peaks in the fingerprint region and the functional group region (2 500 to 3 200 cm^-1) showed regular changes; for the Raman analysis of Magu, the excitation wavelength of 785 nm was superior, with a higher spatial resolution, which could observe more characteristic peak information while avoiding the fluorescence effect. This research work is based on Raman spectroscopy, which will provide a strong reference and basis for drug enforcement.

The aim of this study is to provide guidance and reference for further research in related fields by analyzing in-depth the current research status, emerging themes and future development trend of forensic entomology. In order to achieve this goal, the authors systematically searched the literature related to forensic entomology from CNKI (China National Knowledge Infrastructure), Wanfang database, and Pubmed database during the period from 2003 to 2023. Using CiteSpace 6.2.R4 Advanced software, the authors visualized and analyzed these literatures in terms of annual publication trends, authors, countries, institutions, and keywords. After rigorous screening, a total of 499 domestic and 2 137 foreign literatures, totaling 2 636 articles, were included in this study. The analysis results showed that although China has made some progress in the field of forensic entomology, there is still much room for improvement in international cooperation and exchange. Meanwhile, the inference of the time of death (PMI) of corpses is a research hotspot of common concern at home and abroad, but there are differences in research focus among countries. We conclude that China must strengthen empirical research and field investigation, focus on the combination of theory and practice, improve the application value and influence of research, and enhance communication and cooperation between scholars internationally to jointly promote the development of forensic entomology.

This paper explores the progress of artificial intelligence technology in the identification and reconstruction of crime scene elements. With the development of information technology, there are challenges faced by crime scene element identification and reconstruction. The paper discusses the application benefits of artificial intelligence, the relevant applications of artificial intelligence in forensic examination, and outlines the key steps of artificial intelligence in crime scene element identification and reconstruction, to explore the possibility of applying this method to crime scene element identification and reconstruction. Finally, the paper looks forward to the future development of artificial intelligence in forensic examination and suggests that it may play an important role in improving the intelligence level of crime scene examination and increasing the efficiency of case investigation. It is hoped that relevant research will provide a solution for the technical transformation of crime scene examiners and lay a foundation for the intelligent and digital development of forensic technology.

From Sanger sequencing to high-throughput sequencing, the rapid development of sequencing technology has been providing better technical support for combating crime through forensic DNA analysis. In recent years, the third-generation sequencing technology, mainly based on nanopore sequencing technology, has been widely applied in life science research, in vitro diagnosis, public health, food safety and other fields. Nanopore sequencing technology with super-long reading and real-time sequencing has great potential in the field of forensic genetics. Many authorities and experts have already realized the great potential of nanopore sequencing applications for forensic purposes, although its application in forensic science is still in its infancy. There is little relevant research literature in the field of forensic science, and we should research and explore it further. In this article, the authors attempt to describe the basic principle and characteristics of nanopore sequencing technology, and share the updates of nanopore sequencing-based STR typing, MH typing, mtDNA, DNA methylation and RNA sequencing during the past several years. Meanwhile, non-human genetic material can provide medical examiners with special evidence and clues. The past decade has witnessed the enormous potential of nanopore sequencing technology in non-human forensic genetics. Especially in the areas of microorganisms, plant, and animal forensics, the application of nanopore sequencing to species identification can exert a huge implication, and provide the vital evidence and clues for the public security. In addition, nanopore sequencing has been used to detect viruses at the scene. In the field of forensic genetics, the nanopore sequencing with portability and real-time sequencing makes it most likely to sequence directly of biological samples at the crime scene. This development of the nanopore sequencing has opened up new possibilities by bringing “the laboratory into the field”. This draw the incomparable attraction to the practical application in public security. Moreover, several problems with nanopore sequencing in forensic genetics are discussed, including complex data analysis, high error rate, high sample quality requirements, and analytical methods, and there is a certain distance from the daily application of forensic genetics, which need in-depth research. Finally, we hope that this review can provide a reference for related research and applications, opening up ideas for relevant personnel.

Rapid non-destructive testing of the formation time of blood stains at different dilution ratios under common temperature and humidity conditions was developed using UV visible reflectance spectroscopy. Dilute fresh blood with anticoagulant by 2, 4, 8, 16, and 32 times with tap water. Take 20 μL diluted blood and drop it in the center of a white cotton cloth. After drying, use the reflection accessory of a UV visible spectrophotometer (UV-2450 type) to collect the reflection spectrum, and Excel was used to do regression analysis of trough values and formation time of blood stains at different concentrations. As time increased, there was a significant blue shift in the reflected wave troughs of normal and diluted blood stains near 577.0 nm and 540.0 nm. The values of R² of the regression equation were all greater than 0.800 0, and there was a good linear relationship between the trough blue shift and the formation time of blood stain. The average speed of the trough blue shift increased with the increase of dilution ratio. Conclusion: The blue shift in the reflected wave valleys at 577.0 nm and 540.0 nm can serve as a basis for determining the formation time of normal blood stains, and combined with visual observation, it can also serve as a reference for determining the formation time of diluted blood stains.

With the swift progress of artificial intelligence (AI), the field of forensic DNA examination is witnessing a technological transformation. AI has been integrated into multiple facets of forensic DNA analysis, encompassing intelligent DNA expert systems, AI-assisted optimization of examination procedures, innovative AI-assisted DNA statistics and analysis, rapid electrophoresis data analysis powered by AI, complex mixture sample analysis, and big data inference models. These advancements have significantly enhanced the precision and efficiency of forensic DNA testing. However, the integration of AI has also introduced challenges such as data privacy, model interpretability, algorithmic bias, and legal regulation. Addressing these issues necessitates close collaboration among forensic DNA experts, bioinformatics specialists, and AI professionals. Additionally, it requires the establishment of appropriate legal and regulatory frameworks to ensure that AI applications adhere to ethical standards and effectively support judicial fairness. This article provides an in-depth examination of the application of AI in forensic DNA analysis and the challenges it presents. It analyzes specific case studies to illustrate how AI contributes to the automation and intelligence of forensic DNA analysis, while also highlighting potential risks and challenges. The paper aims to offer guidance and references for the application of AI in the forensic DNA field.

With the rapid development of the Internet of Things and artificial intelligence, people’s dependence on data in their work and life is increasing day by day, making efficient and secure storage of data increasingly important. As an effective solution for organizing multiple disk storage, RAID (redundant array of independent disks) has been widely used in recent years and is also a data storage method that public security organs pay great attention to when extracting data during the process of handling cases. However, in the process of investigation and evidence collection, investigators often need to restore the content and structure of RAID data damaged by suspects, and correctly restructuring RAID disk arrays has become an important prerequisite and necessary link for involved website reconstruction analysis and forensics. This article shared a case of illegal transaction website reconstruction based on RAID recombination, detailed the process of RAID restructuring, explained the simulation restoration method for restoring the original server environment after successful RAID restructuring, and provided the operation process for connecting the website and database in a virtual machine environment, and hopes to provide useful reference for electronic data forensics of similar cases.

In recent years, the rapid development of artificial intelligence (AI) has brought unprecedented opportunities and challenges to scientific and technological research in various fields. The cross-integration of forensic toxicology and AI has increasingly become a research hotspot of forensic science, providing new ideas and methods to solve the problems of traditional forensic toxicology. In this paper, relevant researches in recent years have been reviewed, focusing on the research progress of AI in toxicological research such as on-site investigation of poisoning, toxicant screening and qualitative and quantitative detection, toxicity prediction, toxicokinetics, toxicants interaction, as well as the identification and characterization of involved personnel in cases, and the challenges faced by AI technology in the field of forensic toxicology have also been analyzed, in order to provide reference for AI technology to better serve the research and application of forensic toxicology.

Physical evidence is the lifeline of investigation, and scientific and effective examination and identification of physical evidence can provide crucial support in reconstructing cases or events. In the context of fire scene evidence, the focus revolves around the nature of the fire and the analysis and determination of the fire’s causes. Particularly in the determination of the fire’s causes, identifying the ignition source, the fuel, and the conditions that lead to ignition are the key factors in the final analysis of the fire’s origin. During the investigation of a complex fire incident in a production workshop, difficulties were encountered in identifying the fuel and the ignition conditions. Through the application of simultaneous thermal analysis techniques to analyze the thermal properties of relevant physical evidence, parameters such as the thermal decomposition initiation temperature were determined. Upon identifying the point of origin, the heat of ignition was determined through the melting point temperature, and the material first ignited was identified through the pyrolysis temperature. Even though the pyrolysis temperature differs from the ignition temperature, the majority of combustion occurs in the gaseous phase, and combustible gas forms the basis of the combustion process. For polymeric materials, the generation of combustible gas must occur through pyrolysis, making pyrolysis a prerequisite for ignition. Only when the combustible material undergoes pyrolysis and produces a sufficient concentration of combustible gas can combustion potentially occur. Materials were collected from the point of origin in this fire investigation, and using thermal analysis techniques, the initial pyrolysis temperature was theoretically used to reasonably infer and identify the material first ignited, offering crucial reference for the analysis and identification of similar fire incidents.

With the intensive integration of deep learning and computer vision, a series of advanced technologies such as facial recognition, image (video) generation, and image classification, have made rapid progress. However, deep learning models are considered “black box models” due to their difficulty in explaining internal processes and predicting results, which poses a serious challenge to the interpretability of image evidence in the field of forensic science. Based on this, this review outlines an overview of interpretability issues based on deep learning. Emphasis was placed on the theoretical and methodological research on the interpretability of facial features based on deep learning both domestically and internationally, such as saliency maps method, perturbation-based method, and score/statistics-based method. Their applications in facial recognition and other related fields, especially in the field of forensic science portraits, were summarized. This review proposes the problems of facial feature interpretability methods based on deep learning models, and looks forward to the future development direction of facial feature interpretability based on deep learning.

Currently, capillary electrophoresis-based Y-STR genotyping kits can detect up to 40 Y-STR loci simultaneously. However, these kits only report length-based genotypes and are unable to provide STR sequence information. The STRSeqTyperY68 kit, designed for forensic male pedigree differentiation using next-generation sequencing technology, excels at genotyping 67 Y-STR loci plus a sex-determinant locus in a single-tube reaction on the MiSeq FGx sequencing platform. It simultaneously provides both length and sequence polymorphism genetic information, simplifies testing procedures, enhances efficiency, and facilitates precise differentiation of male family lineages. The ITO method is commonly used to calculate the kinship index of two individuals’ biological relationship based on Mendel’s law of genetic segregation. Additionally, it evaluates consanguineous relationships within five degrees of kinship between two individuals. The combination of next-generation sequencing technology and the ITO method can effectively narrow down the range of potential families. This paper documents a rape and murder case that remained unsolved for eight years. By cooperatively utilizing the STRSeqTyperY68 kit and the ITO method, the potential connection between crime scene evidence and reference samples was evaluated. Gradually, the investigative leads were narrowed down, leading to the resolution of the case.

This article aims to construct and valuated a novel multiplex PCR system, which consists of 21 loci and is used to identify the parentage of horses. Applying fluorescent labeling technology, HTG06, HTG07, COR022, CA425, HTG04, COR082, LEX54, COR069, AHT05, HMS03, HMS01, HMS06, HMS07, COR058, HTG10, ASB17, VHL20, HMS02, ASB02 LEX34, Amelogenin, a total of 21 equine motifs, were subjected to primer design, PCR complex amplification and CE platform sequencing to verify the sensitivity, specificity and tolerance of the system. A total of 128 actual samples were tested and compared for genetic relationship. Among them, 66 samples with unknown genetic relationship were analyzed by polymorphism analysis. The complex amplification system was able to detect 128 equine samples to obtain complete STR typing, and in the actual amplification, no non-specific peaks were generated, and no specific amplification peaks appeared for different species of animals, such as bovine, sheep, pig, chicken, cat, dog and human DNA samples. 0.075 ng of equine standard could still obtain complete typing under 10 μL system. The tolerance of the four common inhibitors could reach 200 μmol/L for heme, 200 μmol/L for hemoglobin, 100 ng/μL for humic acid and 1.2 mmol/L for EDTA, respectively. The STR multiplex amplification system established in this study is effective and reliable, which can meet the needs of laboratory for the identification of paternity of horses.

In the monitoring of the crime scene, recording can comprehensively record all kinds of sounds at the scene, including verbal conversations, abnormal sounds and other sounds that may be related to the case. The recording evidence can play a role in restoring the incident and revealing the facts of the crime. This article takes the audio recordings from the central scene of a shooting incident as the main analysis object. It introduces the method of forensic speaker recognition into the analysis of abnormal verbal cries and air gunshots related to the case. The audio-acoustic-phonetics analysis method is used to examine the cries and gunshots. A total of 142 shouts and 2-suspected shooting sounds appeared in the on-site recording of the case. Through inspection, analysis and confirmation, the shouts were all from the victim, and the two-suspected shooting sounds were air gun shots. Afterwards, combined with the on-site investigation and post-mortem examination results, the incident process was reconstructed and the sequence of events was reproduced, and the time of the incident and the license plate of the suspect’s vehicle were accurately inferred, which provided scientific basis and important clues for finding out the nature of the case, determining the direction of investigation and locking the suspect.

With the popularity of Bitcoin and Ethereum based on blockchain technology, criminals have used various excuses such as “blockchain” and “virtual currency” to engage in illegal activities. Due to the characteristics of virtual currency such as decentralization, anonymity, global convertibility, convenient transactions, and irreversibility, it is widely used in criminal activities such as fraud, gambling, and money laundering. Detecting and combating virtual currency crimes faces difficulties and challenges such as complex and diverse activity scenarios, highly concealed behaviors, and difficulty in finding evidence. Therefore, the fight against virtual currency crimes is becoming increasingly serious, and necessary and effective measures need to be taken. This study explore the current state of virtual currency-related crimes and proposed strategies to overcome the challenges faced by law enforcement agencies in combating these crimes. Investigating virtual currency crimes requires the use of technologies in various fields, including address tag library construction technology, virtual currency tracking technology, unsupervised learning and artificial intelligence technology. These technologies can help law enforcement agencies collect and process virtual currency transaction data to trace the source and flow of funds for illegal transactions. They can also analyze large amounts of data to quickly identify suspicious transactions and entities, thereby improving the investigation efficiency of law enforcement agencies. This article focuses on the current status, difficulties and response strategies of combating virtual currency crimes. The technical process of investigation, evidence collection, tracking and survey analysis by law enforcement agencies after the occurrence of virtual currency cases is discussed in detail. In the technical process of combating virtual currency crimes, this article proposes and conducts in-depth research on key technologies and their applications, bringing new directions for detecting virtual currency crimes and providing strategic ideas for combating increasingly rampant new cyber-crimes.

As one of the most commonly used reagents for amino acid detection, ninhydrin has a wide range of applications in forensic science and is a classic and effective method for displaying old fingerprints on permeable objects. However, the traditional ninhydrin display method still needs further improvement in the display effect of latent fingerprints on objects with complex background colors. Based on a review of relevant research results at home and abroad, a brief review was conducted on the composition of fingerprint substances, the mechanism of ninhydrin in fingerprint development, and the improvement of traditional ninhydrin development methods. A detailed review was also provided on the methods for enhancing the development effect since ninhydrin was applied in the field of fingerprint development. The innovation of the traditional ninhydrin solution method for enhancing visualization mainly manifests in three aspects: 1) optimization of ninhydrin solution method reagent formula, such as screening of the best solvent, exploring the optimal concentration, and discussing the influence of pH value on visualization effect; 2) The innovation of ninhydrin display methods, such as solid medium method, ninhydrin vacuum fumigation method, spray display method, etc., mainly solves the problems of carbonization interference and background ink interference in thermosensitive paper; 3) The fingerprint enhancement treatment using ninhydrin, mainly includes metal salt enhancement, rare earth-Ruhmann’s Purple violet coordination compound enhancement, and trypsin enhancement. The metal salt enhancement method and the rare earth-Ruhmann’s Purple coordination compound enhancement method have great research potential in the future development trend of latent fingerprints.

Detecting abnormal behavior is crucial for maintaining public security, especially in densely populated critical areas. Traditional target detection algorithms often struggle to deliver satisfactory results under these conditions due to challenges like dense target distribution, significant scale variation, and complex backgrounds. YOLOv8 is one of the better perforing detection models effect among the object detection models. This study introduces a novel approach to improve detection accuracy by integrating advanced mechanisms into the YOLOv8 backbone network. Firstly, the coordinate attention (CA) mechanism is incorporated into the C2f module of the backbone network. This enhances the network’s focus on targets amidst complex backgrounds by emphasizing relevant features and suppressing noise. Secondly, the swin transformer model is integrated into the YOLOv8 backbone. The swin transformer facilitates greater information interaction across the feature map, effectively utilizing the background information and improving object detection accuracy under complex scenarios. The datasets used in the experiments are described, the evaluation indexes of P, R, AP and mAP are listed, and ablation experiments and comparative experiments are carried out. Experiments demonstrate the feasibility and effectiveness of these improvements. The enhanced network is compared with several mainstream networks, showing a significant improvement in average accuracy, reaching 95.1%. Compared to the basic network YOLOv8, the average precision has been improved by 2.4%, which proves the effectiveness of this method. In summary, the innovative integration of the CA mechanism and Swin Transformer model into the YOLOv8 backbone network addresses key challenges in detecting abnormal behavior in densely populated and complex environments. These enhancements lead to improved detection accuracy, making it a promising approach for public security applications.

A gas chromatography-tandem mass spectrometry (GC-MS/MS) based method was developed for the determination of etomidate and metomidate in e-cigarette liquid. The samples were extracted with methanol and separated on a HP-5MS column (30 m×0.25 mm×0.25 μm). Etomidate and metomidate were detected by GC-MS/MS under multiple reaction monitoring (MRM) mode, and quantified by an internal standard method. The mass spectrometry parameters, including parent ions, product ions, and collision energy, were optimized in this experiment. The results showed that etomidate and metomidate exhibited good linearity within the range of 0.1 to 10 μg/mL, with R² no lower than 0.999. The recovery rates of spiked matrix were between 98% and 107%. The relative standard deviations (RSDs) for intra-day and inter-day precision were less than 8.83% and 9.34%, respectively. The concentrations of etomidate and metomidate in seven e-cigarette liquid samples ranged from 4.96% to 18.48%. This method is simple, easy to operate, with high extraction recovery, good reproducibility, and high sensitivity. It can be used for the detection and analysis of etomidate and metomidate in e-cigarette liquids.

The forensic science is undergoing a paradigm shift from the traditional paradigm to the features LR paradigm and the similarity scores LR paradigm, in an era of parallel three paradigms. Given the advantages and development opportunities brought by Bayesian LR paradigm, paradigm shift has become a major trend in forensic science in the global. However, paradigm shifts have not yet been fully realized on a global scale, and the development of the forensic paradigm is imbalance in various forensic discipline and in different countries and regions. The main reasons that hinder the paradigm shift of forensic science include limitation of the technology methods of new paradigm, misconceptions of some personnel about the new paradigms, insufficient abilities related the new paradigms, and legal application issues. Except for DNA evidence identification, the application of new paradigms and paradigm shift in China are relatively lagging behind. This article proposes an implementation path for the paradigm shift of forensic science in China, including scholars and practitioners conducting scientific research on new paradigms, regulatory authorities making paradigm shift strategies and plans, forensic lab developing and confirming LR methods, developing LR verbal scales, collecting data, training examiner and taking proficiency tests, decision-makers receiving relevant education, and legislators adjusting relevant regulations.

In recent years, with the rising prevalence of telecommunication network fraud cases, the law enforcement agencies have significantly increased their efforts to combat the development and utilization of fraudulent technologies. Consequently, criminal groups have heightened their counter-surveillance capabilities. To evade law enforcement, many criminal groups chose to set up their own instant messaging (IM) services on servers and encrypt the transmitted data to prevent authorities from obtaining incriminating evidence, such as registration information and chat logs, through the third-party companies. This paper introduces a case study involving the decryption of chat data encrypted with the Blowfish+CBC mode algorithm on a MongoDB database, aiming to provide a reference for the investigation of similar telecommunication network fraud cases.

Electronic data forensics plays a crucial role in modern criminal investigations. However, with the continuous enhancement of smartphone security mechanisms, user privacy protection features－such as privacy spaces and system clones－have posed significant challenges to forensic work, particularly when privacy passwords are unavailable. To address this challenge, this study combines the UFED tool with manual analysis methods to conduct a comprehensive investigation into the data storage mechanisms of privacy spaces and system clones in four smartphone brands: OPPO, Redmi, Vivo, and Honor. The experimental results demonstrate that data in the privacy space and system clone of some devices are stored in an unencrypted format within the file system. When the file systems were analyzed using three domestic forensic tools, data from the privacy space and system clone of some devices were not successfully parsed, revealing the data protection mechanisms in smartphones from different brands, as well as the challenges faced by domestic forensic tools. This study provides new insights into improving domestic smartphone forensic methods and offers new approaches to address the current challenges in privacy protection data extraction, with significant practical application value.

This study investigates a novel method for developing latent fingerprints by combining an ultrasonic atomization device with an aggregation-induced emission (AIE) material, TPA-1OH. Experiments were conducted to determine the operational parameters of the ultrasonic atomization device, optimize the formulation ratio of the developer TPA-1OH, and evaluate its potential impact on subsequent DNA analysis. The results demonstrate the feasibility of the proposed method for latent fingerprint development using the ultrasonic atomization device integrated with TPA-1OH. Following treatment with TPA-1OH, latent fingerprints exhibited significantly enhanced visualization under a 445 nm laser with a yellow filter, outperforming untreated controls in both clarity and contrast. Additionally, no significant DNA degradation was detected in the developer-treated specimens, indicating compatibility with downstream forensic DNA workflows.

The origins and divergence of Sino-Tibetan populations are pivotal subjects of study in linguistics, anthropology, and population genetics. While most previous research have focused on the Han and/or Tibetan populations, comprehensive studies on other Sino-Tibetan populations, such as the Qiang and Yi, are still insufficient. This study collected Y-SNP haplogroup and Y-STR haplotype data from 1151 unrelated male individuals across 12 Sino-Tibetan populations. Using methods such as principal component analysis (PCA), phylogenetic tree construction, and haplotype network analysis, this study systematically reveal the paternal genetic structure and differentiation among Sino-Tibetan populations. Analysis of 90 Y-SNP haplogroups reveals that haplogroups O-M175, D1-M174, C-M130, and N-M231 account for over 85.37% of the paternal haplogroups in the studied populations. Sub-haplogroups of each major haplogroup exhibited distinct distribution patterns across different regions and populations. PCA results indicate a strong correlation between genetic clustering, language family, and geographical distribution. Phylogenetic tree and haplotype network analyses, using 16 Y-STR markers, further support the correlation between genetic differentiation and geographical distribution, clarifying the genetic relationships among Sino-Tibetan groups. Through the combined analysis of Y-SNP and Y-STR markers, this study deeply explores the distribution, genetic substructure, and internal genetic relationships of Y-chromosome haplogroups in Sino-Tibetan populations. It provides new theoretical insights into population genetics and offers data support for Y-chromosome research applications.

With the strict regulation of etomidate, homologues of etomidate have emerged. Among them, the more common substances are metomidate, isopropoxate and propoxate. Etomidate and its homologues are very similar in structure, which requires extreme caution in analysis and detection. In this article, a qualitative and quantitative determination method for metomidate, isopropoxate, and propoxate in electronic cigarette oil, hair and urine was developed. Samples were extracted by organic solvent, gas chromatography-mass spectrometry (GC-MS) was used to verify the qualitative results of metomidate, isopropoxate and propoxate in e-liquid. Gas chromatography (GC) was used to verify the quantitative results of e-liquid. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) was used to verify the qualitative and quantitative results of hair and urine. The results showed that the limit of detection(LOD) by GC-MS for metomidate, isopropoxate and propoxate in e-liquid was 10 μg/mL; the limit of quantification(LOQ) by GC was 20 μg/mL; and the linearity ranged 20 to 100 μg/mL (R²>0.998 8). For the 3 substances in hair, LOD was 0.005 ng/mg, LOQ was 0.05 ng/mg, and the linearity was 0.05 to 5 ng/mg (R²>0.994 5). In urine, LOD was 1 ng/mL, LOQ was 5 ng/mL, and the linearity was 5 to 100 ng/mL (R² >0.999 0). The intra-day and inter-day precision RSD and accuracy were both less than 15%; the recovery rate ranged from 94.7% to 114.3%. The method is accurate, reliable and applicable in the analysis of practical samples.

In forensic pathology, Sudan III staining is used to confirm fat embolism, and its quantitative grading is of significant importance in determining the cause of death. However, manual grading based on microscopic observation is highly dependent on personal experience. To objectively quantify the degree of fat embolism, we explored a method for automatic segmentation of fat droplets in Whole Slide Images (WSI) of lung tissue stained with Sudan III. Although the colors of the Sudan III stained sections are simply consisted of transparent tissue and scarlet fat droplets, issues such as residual dye, uneven staining of the fat droplets, irregular shapes, and significant size differences can lead to missegmentation and insufficient segmentation accuracy. To address this, we propose a contrastive language-image pre-training (CLIP) model framework combined with prompt learning for fat droplet segmentation: first, feature maps output by the CLIP image encoder are fused through skip connections, guiding the model to accurately segment fat droplets using CLIP’s prior knowledge via text prompts; then, a dice loss function is used to alleviate the imbalance between the foreground and background of the image; finally, validation is performed on the slice dataset and compared with U-Net, FCN8s, and Unet++ models. The results indicate the method proposed in this article is superior to others in segmenting fat droplets on stained slice images. Moreover, the proposed cross-modal prompt learning can be integrated into other large segmentation models to perform specific target segmentation tasks.

A total of 328 soil samples were collected from Guangling County and classified into habitat-based categories, including vegetable soil, wetland soil, park soil, roadside soil, and hilly soil. The study aimed to investigate the distribution characteristics of bacterial community diversity across different land-use types at the county scale, as well as the geographic traceability of soil of unknown origin. High-throughput sequencing was conducted using the Illumina MiSeq PE250 platform, and the microbiome 16S rRNA gene amplicon sequencing data were analyzed using the Qiime 2 pipeline. Additionally, physicochemical indicators of 14 soil samples were determined. The soil bacterial community structure profiling demonstrated that, the relative abundance of dominant bacterial groups varied across different habitats. Principal coordinate analysis (PCoA) results indicated that intra-group differences among soil sample sites within the same subgroups were small, while inter-group differences among soil type sample sites from different subgroups were significant. Under this categorization, pH, TN, WC, IC, TP, Al, Mn, and Pb were identified as important environmental factors driving changes in soil microbial community composition in the region. Finally, a random forest model was established, and its parameters were optimized. Using the collected samples as a training set, the model achieved the highest prediction accuracy when utilizing soil microbial data, with an accuracy of 83.58%.

Bayes’ theorem is regarded as the most basic theory in much of the literature that discusses forensic science comparison methods. This paper analyzes the problems faced by using this theorem in forensic science, especially the significance of different representations in the theorem and the logical reasoning problem, and argues that Bayes’ theorem cannot provide meaningful support for the conclusion of feature comparison, only as a calculation tool. If the forensic science comparison method is based on Bayes’ theorem, the theoretical basis is not solid. At a time when forensic science methods are facing change, it is even more necessary to get to the bottom of the problem in theory.

The handwriting characteristics of machine imitative signature in terms of writing style, layout, writing method are almost completely consistent with the imitated signature. A general analysis of the differences between machine imitative signatures and handwritten signatures in terms of various handwriting features makes little sense. On the contrary, there are more detailed differences on some handwriting characteristic like writing strength, writing speed and so on. In this experiment, the characteristics of stroke mark are taken as the research object, and the samples of human handwritten signatures are collected and the corresponding machine imitative signatures are made. The similarities and differences between them in stroke marks are observed and summarized by microscope. The results show that there are morphological differences between machine imitative signature and handwritten signature in ink dots, indentations, stroke width and thickness and so on. Machine-writing has strong repeatability and similarity in these features, and the same features always appear in other handwriting. Because of the different structure and function of the writing robot, the writing characteristics of the machine itself also affect the stroke characteristics of the machine imitative signature. For example, ink and white line features are not observed in machine imitative handwriting. In conclusion, the stroke marks of machine imitative signature have strong regularity and high similarity, while the stroke marks of handwritten signatures have strong randomness and rich changes. Affected by machine itself, the machine imitative signature and the human writing signature have obvious and fixed differences in stroke mark. It is a feasible handwriting inspection idea to identify machine-writting handwriting from the perspective of stroke mark.

To address the over-reliance on qualitative expert judgment in identifying evidence from electrical fire, this study proposes a deep learning-based classification model for categorizing copper conductor melt marks. A total of 793 metallographic images of copper conductor melt marks collected from actual fire scenes were analyzed. Based on the specific conditions of the fire scenes and the structural characteristics of the melt marks, the images were classified by experts into four categories: fire melt marks, electric heat melt marks, primary short circuit melt marks, and secondary short circuit melt marks. Five convolutional neural network algorithms—VGG16, Inception v3, Xception, ResNet50, and EfficientNetV2S—along with two ensemble learning methods, soft voting and hard voting, were employed to train the classification model. Model performance was evaluated using five distinct metrics. The soft voting ensemble learning model, which integrates VGG16, ResNet50, and Xception, achieved the highest classification accuracy of 80.5%, outperforming the best individual model (71.1%). This study demonstrates the feasibility of applying deep learning to the intelligent classification of metallographic images of copper conductor melt marks. The proposed method provides probabilistic identification conclusions, which reduce dependence on expert judgment and promote the quantitative advancement of electrical fire evidence analysis.

This paper mainly introduces the research progress of deep learning technology in fingerprint information since 2018, including fingerprint image processing, fingerprint recognition, fake fingerprint detection technology, fingerprint dataset generation, and multimodal biometric recognition involving fingerprints. Fingerprint image processing includes fingerprint image segmentation, fingerprint image enhancement, and fingerprint image correction. Fingerprint recognition includes deep learning network-based fingerprint recognition, contactless fingerprint recognition, and fingerprint recognition based on three-level features of fingerprints. With the continuous deepening of research on deep learning technology, the study of fingerprint recognition algorithms using deep learning technology on large-scale fingerprint datasets will still be a research direction in the future. Moreover, using deep learning techniques to detect and identify fake fingerprints will be another direction for future research. The dataset of fake fingerprints needs to be dynamically updated and expanded in order to improve the ability of deep learning networks to detect and recognize fake fingerprints. In addition, one of the future research directions is how fingerprints, as an important biometric feature of the human body, can be integrated into multimodal biometric recognition based on large language models to achieve better identity recognition results.

This study investigates the detection capability of whole genome sequencing (WGS) technology for varying DNA input levels, aiming to establish a novel technical approach for SNP-based genealogical inference in forensic biological samples. On the SalusPro sequencing platform (China), WGS with a depth of 25× were performed on samples containing 0.5, 0.2, and 0.05 ng of DNA respectively. From these data, 645 199 autosomal SNP loci (Wegene GSA chip) were extracted and subjected to quality control. Genetic relationships were predicted by calculating the total length of identity by descent (IBD) fragments between individuals using IBD algorithms. The results demonstrated that when DNA input was ≥0.2 ng, the locus detection rate and genotype concordance rate exceeded 95% and 99%, respectively. The IBD fragment lengths showed no significant difference compared to the standard samples (P>0.05), with an average confidence interval accuracy of 91.40% for 1st- to 7th-degree relationships. In contrast, at 0.05 ng DNA input, the locus detection rate and genotype concordance rate dropped below 80% and 97%, respectively. The IBD fragment lengths and confidence interval accuracy were significantly lower than those of the standard samples (P=0.004), rendering this input level unsuitable for reliable genealogical inference.

In the field of forensic genetics, the interpretation and analysis of mixed STR profiles have been a focal point of research due to their complexity and challenges, playing a crucial role in the investigation of difficult cases. To improve the accuracy and speed of mixed STR profile separation and to adapt to the effects of low template quantities under complex profile noise, domestic and internationall scholars have conducted a series of studies on mixed STR profile separation. In recent years, artificial intelligence methods have shown promise as a new solution for mixed STR profile separation. This paper reviews the research progress related to mixed STR profile separation, highlights the key and challenging aspects of the separation process, and proposes potential applications of artificial intelligence algorithms in this field. This paper aims to provide reference and insights for solving the problem of mixed STR profile separation using artificial intelligence methods.

GC-MS and GC-MS/MS analytical methods were developed for the simultaneous detection of three 4(3H)-quinazolinone abused substances, including methaqualone, etaqualone, and 2-methoxyqualone, and applied to the analysis of suspected drugs and the hair of abusers, respectively. The standard solutions of methaqualone, etaqualone, and 2-methoxyqualone were analyzed by using the selected ion monitoring (SIM) and multiple reaction monitoring (MRM) modes, respectively, to determine the characteristic fragment ions and characteristic ion pairs of the three substances. The so established two methods were used to test the suspected drugs and hair samples in the actual cases. The established GC-MS analytical method detected components of methaqualone, etaqualone, and 2-methoxyqualone in the suspected drugs at levels of 11 μg/mg, 4.3 μg/mg, and 2.5 μg/mg, respectively. The established GC-MS/MS analytical method detected methaqualone, etaqualone, and 2-methoxyqualone in the hair at levels of 148 ng/mg, 565 pg/mg, and 58.2 ng/mg, respectively. The detection methods of GC-MS and GC-MS/MS established in this study are simple, rapid, and accurate, which can provide a reliable scientific basis for the detection of methaqualone, etaqualone, and 2-methoxyqualone in the suspected drugs and the hair of abusers.

Fingerprint, one of the most reliable and valuable evidence in the crime scenes, has long been recognized as a powerful tool for personal identification and worldwide law-enforcing departments to fight against relevant crimes. For many years, in practice, fingerprint analysis has been developed based on the latent fingerprint visualization, primarily. However, most of these fingerprints in crime scenes have been ambiguous, deformed or fragmentary, which contributed to the difficulty in fingerprint analysis. So, recently, some researchers have gradually paid increasing attention on the fingerprint age for fingerprint analysis, though their researches were almost taken placed in the lab. To improve the accuracy and reliability of fingerprint age analysis, and effectively promoting the application of fingerprints age in practice, in this article, according to these researchers’ reports, the morphological characteristics of fingerprints related to the fingerprint age, including two-dimensional (2D) morphological characteristics (e.g. ridge widths and color contrast between ridges and furrows) and three-dimensional (3D) morphological characteristics (e.g. ridge heights), were reviewed, respectively. Furthermore, fingerprint residues which are transferred onto the object surfaces when fingertips touch object surfaces, reveal a wealthy of information, especially fingerprint age. Therefore, the changes of fingerprint residues involving electrical effect, the optical characteristics, as well as the change of compositions such as squareness, wax esters and fatty acids, were also comprehensively summarized in this review. Particularly, the future research directions and prospects were discussed about the methods and the reagents of latent fingerprint development, the equipment and the technologies of fingerprint detection, the composition and the degradation rates of fingerprint residues, and the influencing factors model of age determination of fingerprint.

Within the realm of law enforcement, the utilization of WeChat data has emerged as an indispensable investigation tool, extensively employed in crime investigations and clues tracking. This paper focuses on the information shared by WeChat users in their Moments, with particular attention to interactions between friends. A method for extracting and analyzing clues based on the WeChat Moments relationship network is proposed. Firstly, social connections between users and friends are extracted by analyzing interactions such as likes and comments in WeChat Moments. The WeChat Moments relationship network is then constructed using force-directed graph techniques, providing a visual representation of the relationships between users and their friends. Subsequently, in-depth analysis is conducted through graph clustering and centrality analysis methods. By identifying closely connected individuals, potential associated groups and social circles are revealed, offering key leads for subsequent investigative work. Lastly, focusing on these closely connected individuals, a thorough analysis of their chat records is performed using word cloud technology and the TextRank algorithm. By mining keywords and topics, a more comprehensive understanding of communication content is obtained, aiding in the accurate assessment of the activities and intentions of individuals involved in the case. Through application and validation in real cases, this method demonstrates the ability to rapidly construct the WeChat Moments relationship network, identify closely connected individuals, and perform targeted analysis of their chat records. The results of the experiments show significant achievements in improving the efficiency, accuracy, and depth of lead acquisition, providing robust support for law enforcement investigations. The proposed method, based on the WeChat Moments relationship network, offers new perspectives and technological means for law enforcement investigations. Future work may involve further optimizing algorithms and enhancing the capability to handle large-scale data to adapt to the complex and dynamic nature of criminal environments, thereby providing more effective support for investigative efforts.

Postmortem interval (PMI) refers to the interval between the discovery or examination of the body and the occurrence of death. Estimation of postmortem interval is one of the important research contents in forensic pathology，and it has always been the focus and hot spot of research work. Different techniques are used to evaluate and analyze the changes in the human body after death to estimate postmortem interval. The traditional methods of estimation of postmortem interval are based on postmortem phenomena such as algor mortis, rigor mortis, livor mortis, etc. These methods rely on the subjective experience of forensic pathologists, and the estimated time of death is a relatively wide range, and the estimated result is susceptible to subjective judgment. In recent years, postmortem computed tomography (PMCT) has become increasingly influential in the field of forensic pathology. Postmortem computed tomography is a non-invasive, rapid, and objective auxiliary means of autopsy, which can significantly improve the quality and efficiency of autopsy and can find imaging features that may not be observed in traditional autopsy. After the death of the human body, the body will undergo a series of postmortem changes according to a certain time law. A series of characteristic imaging changes in cadaver organs and tissues with the passage of time of death can be observed through the application of postmortem computed tomography, and these imaging changes can be quantitatively and objectively described, The potential ideal imaging indicators can be used to estimate postmortem interval and the correlation between these indicators and the postmortem interval can be analyzed. This paper systematically reviewed the research on the correlation between postmortem imaging features of organs and tissues (such as brain, heart, aorta, lung, liver, etc.) after postmortem computed tomography of various parts of the cadaver (head, chest, abdomen) and the estimation of postmortem interval, in order to provide new ideas for the study of the estimation of postmortem interval and further expand the application of virtual anatomy technology in the field of forensic pathology.

Human body odor arises from the secretion of various glands on the skin's surface, which, when acted upon by microorganisms, evaporate to produce a distinct scent. This odor contains valuable biological information, with certain compounds exhibiting strong stability and individual specificity, serving as “odor fingerprinting” that can distinguish between different populations. Machine learning is an important method for human odor research, which can not only explore the characteristic components of odor in different populations, but also investigate the differences between different individuals. This paper discusses the application of “odor fingerprinting” in individual identification and feature characterization, drawing upon recent literature. It outlines the data processing procedures involved in human odor analysis, highlights the challenges encountered, and explores current research trends. Finally, the application trends of the recognition of human odor are discussed in order to provide reference for odor recognition research.

The research progress of artificial intelligence (AI) in forensic pathology was systematically reviewed, which focuses on the key technical challenges frequently encountered in forensic practice, including histopathological diagnosis, cause of death analysis, postmortem interval estimation, injury mechanism analysis, and bloodstain pattern analysis. By sorting out the intersections of forensic pathology examination and AI, this study aims to promote the application of relevant research findings in forensic practice and empowers the formation of new-quality combat effectiveness in public security, as well as the improvement of the intelligence level of forensic work.