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.

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.

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.

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.

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.

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 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 this paper, the characteristics of allele typing deletion of Y-STR loci and its correlation with AZF (azoospermia factor) deletion were discussed, which could provide reference for forensic practice. Y-STR kits (Yfiler Platinum, SureID PathFinder Plus) were used to analyze the blood samples of 23 461 male family members. A total of 14 cases with 4 or more Y-STR allele-dropout samples were found. Meanwhile, Sequence-tagged site (STS) was detected with Y chromosome microdeletion detection kit, and the deletion of AZF region of Y chromosome was evaluated according to the deletion STS. The results showed that the proportion of multiple Y-STR typing missing was 0.059 7% (14/23461), with 1 case of short arm and 13 cases of long arm, which were from different families with different types. STS deletion was detected in the AZF region in 13 long arm multiple typing deletion samples, and no abnormality was detected in 1 short arm multiple typing deletion sample. This study suggests that there is a correspondence between multiple STR allele dropouts in the long arm of Y chromosome and microdeletions in AZF region, and the biological basis of sterility exists in these typing individuals.

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.

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.

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.

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.

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.

Differences in handwriting composition are crucial for recognizing the existence of alterations in questioned document examination. The ambient ionization mass spectrometry is increasingly being recognized for its utility in detecting ink changes, due to its benefits such as high speed, rich information, minimal destruction of the sample. In this paper, the desorption electrospray ionization mass spectrometry imaging (DESI-MSI), a prominent technique within ambient ionization mass spectrometry, was used to examine the suspicious handwriting to determine whether the document was altered. The authenticity of the number “1”, suspected of being added to change“2”into“12”, was scrutinized. The mass spectra of strokes of ink were obtained, and the suspicious“1”could differentiate from other numbers. Based on the MS data, the questioned part could be visualized through imaging, clearly demonstrating the evidence of tampering. Additionally, chemometrics were applied to cluster the handwriting composition data, aiding in the identification of the alteration fact. Compared with the traditional optical detection and spectroscopic method, DESI-MSI offered a more comprehensive analysis of the handwriting’s material properties, showcasing its potential for practical application. This technique provides an innovative perspective for examining such cases.

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.

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.

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.

Objective and accurate assessment of visual impairment has been one of the challenging tasks in forensic identification. In forensic practice, visual impairments are commonly seen as caused by physical factors, but rarely by chemical factors such as methanol poisoning. Methanol and its metabolites can directly damage eye tissues and cause vascular paralysis and dilatation, damaging the nervous system. Visual impairment can appear after a few hours to 2~3 days of poisoning, manifesting as diplopia, blurred vision, and eyeball swelling and pain, followed by rapid vision loss and even blindness. Ophthalmologic examination reveals dilated and fixed pupils and diminished or absent light reflexes. Funduscopic examination reveals dilated veins, optic nerve papilla congestion, and edema, followed by pallor or optic nerve atrophy. Optic nerve damage rarely returns to normal completely. The mechanism of visual function impairment in methanol poisoning is mainly its metabolite formic acid or format, which is excreted very slowly in vivo and has a selective affinity for the eye’s neural tissues, inhibiting the oxidative phosphorylation process, affecting cellular respiration, and inhibiting ATP synthesis in mitochondria, leading to tissue toxicity and hypoxia and then degenerative changes of the cells, oligodendrocytes and astrocytes swelling myelin sheath loss, optic nerve edema and pressure on the optic nerve in the posterior sieve plate region, thus resulting in toxic optic neuropathy. Toxic optic neuropathy occurs when the optic nerve is compressed in the posterior region of the sieve plate, etc. Optic nerve atrophy often occurs after 1 to 2 months, and irreversible damage to the optic nerve and visual function can occur at a later stage. Two injury degree identification cases of visual impairment after methanol poisoning were reported. The methanol poisoning confirmation, the objective assessment of visual impairment, as well as the causal relationship between methanol poisoning and visual impairment, were discussed and comparatively analyzed, with a view to similar cases to provide a reference clue.

The AGCU X Plus STR system is a newly developed multiplex PCR kit that detects 32 X-chromosomal STR loci simultaneously. These are DXS6807, DXS9895, linkage group 1 (DXS10148, DXS10135, DXS8378), DXS9902, DXS6795, DXS6810, DXS10159, DXS10162, DXS10164, DXS7132, linkage group 2 (DXS10079, DXS10074, DXS10075), DXS981, DXS6800, DXS6803, DXS6809, DXS6789, DXS7424, DXS101, DXS7133, GATA172D05, GATA165B12, linkage group 3 (DXS10103, HPRTB, DXS10101), GATA31E08 and linkage group 4 (DXS8377, DXS10134, DXS7423). A major advantage of this kit is that it takes into account linkage between loci, in addition to detecting more X-STR loci. In order to evaluate the forensic application of 32 X-STR fluorescence amplification system, PCR settings, sensitivity, species specificity, stability, DNA mixtures, concordance, stutter, sizing precision, and population genetics investigation were evaluated according to the Scientific Working Group on DNA Analysis Methods(SWGDAM) developmental validation guidelines. The study showed that the genotyping results of each locus were significantly accurate when the DNA template was at least 62.5 pg. Complete profiles were obtained for the 1∶1 and 1∶3 combinations. A total of 209 unrelated individuals from Southern Chinese Han community, consisting of 84 females and 125 males, were selected for population studies, and 285 allele profiles were detected from 32 X-STR loci. The polymorphism information content (PIC) ranged from 0.272 1 in DXS6800, to 0.910 5 in DXS10135, with an average of 0.679 8. DXS10135（PIC = 0.910 5）was the most polymorphic locus, with discrimination power（DP）of 0.916 4 and 0.987 1 for the male and female. The cumulative PD_F, PD_M, MEC_trio and MEC_duo values were all greater than 0.999 999 999. There were 78 different DXS10103-HPRTB-DXS10101 haplotypes among the 125 males, and the haplotype diversity was 0.9810. There was no significant difference in the cumulative PD_F, PD_M, MEC_trio and MEC_duo values whether considering linkage or not. In summary, the new X-STR multiplex typing system is effective and reliable, which can be useful in human genetic analysis and kinship testing as a potent complement to autosomal STR typing.

To solve the disputes on road rights caused by the invasion of traffic width by different traffic participants in the mixed traffic network, a method for measuring road surface distance in videos based on direct linear transformation theory is proposed. This method uses a one-dimensional direct linear transformation algorithm and relies on three characteristic points in the video that are collinear with the point to be measured to determine the direct linear transformation relationship between the image-side distance and the object-side distance, and then calculate the target distance. The measurement errors of this method are evaluated using the measurement values obtained from on-site scanning by Faro Focus^3D laser 3D scanner. Experimental results demonstrate that this method can effectively measure the road distance in videos, with a measurement accuracy reaching the centimeter level, and it is equally applicable to video images taken from different shooting angles. The application of this method can determine the traffic width of different parties involved in accidents in the mixed traffic network through video images, providing technical support for judicial appraisal and accident handling.

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.

DNA test of aged skull is always a difficult problem in the field of forensic science, as the skull contains little DNA, and the DNA degrades badly. Here, an improved method is introduced for DNA test of aged skulls. Taking the skulls in recent cases for example, the optimization was carried out from the selection of materials, concentration and recovery of demineralized solution, high volume extraction and purification of DNA, etc. Finally, the results of autosomal and Y chromosome STR polymorphism were obtained successfully. The results showed that petrosal part of temporal bone could be the preferred extraction site for the test of aged skulls, with a higher success rate compared with other parts of the skull; in addition, the use of Amicon Ultra-15 10K centrifugal ultrafiltration tubes could remove a large number of small molecules, such as water and ions in the decalcification solution, so as to retain and recycle the large molecules of DNA, which effectively reduced the amount of DNA loss; furthermore, the quality of DNA was poor in aged skulls, so increasing the amount of bone powder used and increasing the extraction and purification system could also significantly improve the amount of DNA recovered. This method improves the quantity and quality of DNA recovered from aged skull, and can provide reference for the follow-up DNA test of similar aged bones and teeth.

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.

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.

Bloodstain is one of the most important forensic evidences in criminal cases. How to identify the bloodstains and obtain some potential evidence is of great significance to solve the criminal case. In this paper, a hand-held near-infrared (NIR) spectrometer was used to collect the spectral data of different species of bloodstains samples on cotton fabrics with different colors including human blood, chicken blood and pig blood. After collecting the spectral data, standard normal variables (SNV) pre-processing operation was implemented on the spectral data to eliminate the common offset and scaling effects. Then, the training models were established via extreme learning machine (ELM) algorithm to identify the species of bloodstain. Next, the testing samples were predicted by means of using the built specie identification bloodstain model. Meanwhile, the traditional support vector machine (SVM) and genetic algorithm-back propagation (GA-BP) classification algorithms were also used to build the identification model and the prediction results were also compared with ELM algorithm. The experimental results showed that the prediction accuracy of ELM algorithm was 98.48%, which was higher than that of GA-BP algorithm (84.62%) and SVM algorithm (73.84%). Meanwhile, the precision, sensitivity and specificity of the prediction results using ELM algorithm were also much higher than those of SVM and GA-BP algorithms. The above results showed that the accuracy of the identification model built by ELM algorithm was the highest and the overall performance of the model was the best. The research results of the paper showed that he rapid detection method based on a handheld NIR spectrometer and ELM algorithm could identify the types of the bloodstains efficiently, non-destructively, quickly and accurately and it provided a new technical reference for bloodstains detection and identification in criminal cases.

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 detection method for simultaneously analyzing 10 bufotoxins (arenobufagin, gammabufotalin, resibufagin, desacetylcinobufagin, resibufogenin, cinobufotalin, bufotalin, cinobufagine, bufalin, cinobufaginol) in biological tissues samples was established using ultra-high performance liquid chromatography-quadrupole/electrostatic field orbital trap high resolution mass spectrometry (UPLC-Q/Exactive MS) with liver as the sample. 0.8 mL of acetonitrile was added in 0.2 g of liver to precipitate proteins, and then was purified with a hybrid solid phase column. ACQUITY UPLC HSS T3 column (2.1 mm×100 mm×1.8 µm) was selected. The mobile phase A consisted of 0.1% formic acid in water and B consisted of acetonitrile with a gradient elution program for separation. The mass spectrum was analyzed by electrospray ion source with positive ion mode. The detection limit of 10 bufotoxins in liver could be less than 10 ng/mL, and the lowest could reach 0.1 ng/mL. These 10 bufotoxins showed good linearity within the range of 20 ng/mL to 400 ng/mL (R²>0.996). The extraction recoveries of 10 bufotoxins from liver ranged from 62.9% to 93.2%, and the matrix effect ranged from 90.9% to 109.0%. The intraday precision was within 9.2%, and the interday precision was within 14.6%. The stabilities of 10 bufotoxins in the matrix were good under repeated freezing condition, thawing condition and room temperature condition, and after extraction processing, with the RSD of concentration all within 15%. After gastric administration, the distribution order of content in male rats was as follows: liver>lung>heart>kidney>genital organ>muscles; in female rats, the distribution order of content was as follows: lung>genital organ>liver>heart>kidney>muscles. This study established a detection method for bufotoxins in biological tissues and revealed the distribution pattern of bufotoxins in animal bodies tentatively, providing a reference for the examination of bufotoxins and the selection of related specimens.

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 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.

In this article, propoxate and isopropoxate were reported for the first time as drug substitutes. In order to systematically explore the structural characteristics of the propyl derivatives of etomidate, propoxate and isopropoxate were synthesized. Liquid chromatography, nuclear magnetic resonance spectroscopy, gas chromatography-mass spectrometry, ultra performance liquid chromatography-quadrupole time-of-flight mass spectrometry, infrared spectroscopy and Raman spectroscopy were used to analyze etomidate, propoxate and isopropoxate, respectively. The structural commonalities and differences of etomidate, propoxate and isopropoxate were compared. The similarities and differences of the three substances in chromatography, mass spectrometry and spectrum were analyzed. The ultra-high pressure liquid chromatography and gas chromatography were used to compare the retention times of the three substances. In the hydrogen nuclear magnetic resonance spectrum, ethyl of etomidate, propyl of propoxate and isopropyl of isopropoxate have significant differences in the chemical shift regions δ=0-5 ppm of ¹H NMR, and δ=10-70 ppm of ¹³C NMR. In gas chromatography-mass spectrometry, three substances can be quickly distinguished through fragment ion m/z 216.1 and molecular ion peaks. In ultra performance liquid chromatography-quadrupole time-of-flight mass spectrometry, three substances can be distinguished by comparing the quasi-molecular ion peaks of the primary mass spectrum and the abundance ratio of the fragment peaks of the secondary ion mass spectrum. The proposed fragmentation pattern of the three substances in the electron ionization of gas chromatography-mass spectrometry and in the electrospray Ionization of ultra performance liquid chromatography-quadrupole time-of-flight mass spectrometry were studied. Analyzing the distinct peaks in infrared spectroscopy and Raman spectra is challenging because the characteristics of peaks of FT-IR spectra and Raman spectra are complex. However, creating a database for the pure substances of the three substances enables direct comparison of spectral libraries. These studies provide fundamental data characterization for forensic toxicology and similar fields, providing technical support for law enforcement to identify substitutes of etomidate.

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.

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.

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.

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.

Calculation of the degree of lung compression by Mimics software remains the “gold standard”. In the forensic sphere, due to the complexity of the Mimics software, many people do not utilize this method in forensic practice. They may calculate degree of lung compression by visual observation, represent the result of degree of lung compression by some slicer of CT. These factors will lead to inaccuracies of calculated results. The aim of this study is to develop a model for automatic calculation of lung compression degree based on deep learning semantic segmentation technology, and explore the feasibility of deep learning for lung compression measurement by comparing the results of automatic calculations with Mimics software. In this study, 42 cases of the computed tomography (CT) data including pneumothorax diagnosis in DICOM format were collected each cases has about 350 images with a thickness of 1 mm. Among them, 32 cases used for training and 10 cases used for validation. The air-containing regions of 1943 images were manually annotated. An additional five chest CT cases were selected for external testing. The degree of lung compression was calculated by both the deep learning model and Mimics software, and the correlation between the results of the two methods and the calculation errors were analyzed. In the validation set, the average error between the deep learning model calculation results and the manual method was 2.4%, and the model processed an average of 356 per case with an average time of 60.04 s, while the average error in the test set was 4.4%. The aforementioned results lead to the following conclusions: The deep learning model constructed in this study has the potential to be applied in the automated measurement of the lung compression degree due to pneumothorax, which can provide a reference for the calculation of the lung compression degree due to pneumothorax in forensic practice.

The formation of bullet cartridge marks, stemming from the processes of loading, firing, and ejection, serves as a varied indicator of specific gun characteristics. This study employs microscopic analysis to meticulously observe and interpret the shell casing marks left by six internationally utilized 9 mm NATO standard bullets (CBC-9mm-15, FC LUGER 9MM, GCED LUGER 9mm, WIN LUGER 9MM, CLA 9MM 16, S&B 11 9×19) fired from a Glock-19 pistol. The findings reveal a remarkable consistency and prevalence in the characteristics exhibited by comparable shell casing marks across various ammunition types. Specifically, the morphological details, location, and frequency of marks including firing pin marks, firing-pin hole marks, tongue-like marks, ejector marks, extractor marks, and the fan-shaped scraping marks at the mouth of the cartridges, all underscore the distinctive and stable signature of the Glock-19 pistol in its firing mechanism. Consequently, this research offers valuable insights and serves as a reference tool for forensic firearms identification and gun traceability in judicial contexts.

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.