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.

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.

With the advancement of paperless offices, handwritten electronic signatures have become widely adopted across various sectors, including banking, government affairs, and commerce. These signatures refer to the textual impressions created by fingers or stylus pens on electronic screens, such as smartphones and digital tablets. Concurrently, dynamic attributes like writing duration, stroke sequence, and pen pressure are captured and stored electronically on computers. From an implementation perspective, handwritten electronic signatures fall within the broader category of electronic signatures. However, they differ from digital signatures in terms of implementation technology, presentation format, verification methods, and technological requirements. Given the distinct verification methodologies, handwritten electronic signatures should not be subject to the authentication framework outlined in The Electronic Signature Law. Instead, their unique handwritten characteristics should be analyzed from a morphological angle. Nevertheless, shifts in writing devices and mediums have diminished certain static characteristics, often resulting in imperfections in the identification process. This complexity is further compounded by difficulties in leveraging dynamic features for identification, challenges in distinguishing copied and pasted handwriting, and a scarcity of experienced authenticators. Consequently, during the identification process, it is crucial to: collect comparable original specimens, specify the writing devices used, gather samples from identical or similar sources as the specimens, and utilize identification expertise to identify stable traits with individual variations. By adopting a comprehensive quantitative analysis approach and corroborating with electronic data, the accuracy of identification conclusions can be significantly enhanced.

To establish a high performance liquid chromatography-tandem hybrid triple-quadrupole linear ion trap mass-spectrometry method for qualitative and quantitative determination of etomidate in blood and hair. Deuterium cocaine was selected as the internal standard, and blood sample was extracted by acetonitrile (V/V, 1/6), then centrifuged at high speed; hair sample (about 20 mg) was ground by a ball mill, then extracted by methanol and passed through the organic membrane. Using 0.1% (V/V) formic acid aqueous solution and 0.1% (V/V) formic acid acetonitrile as mobile phase, the analyte was separated and analyzed by an ACQUITY UPLC^®C18 (2.1 mm×100 mm×1.7 μm) column. Electrospray positive multiple reaction monitoring/information-dependent acquisition / enhanced product ion scanning (MRM-IDA-EPI) and secondary library retrieval were used for analysis. The results showed that etomidate in blood had a good linear relationship in the range of 1.0 to 100.0 ng/mL (r>0.995). The linear relationship of etomidate in hair was good in the range of 0.05 to 5.0 ng/mg (r>0.995). The detection limits (S/N≥3) for blood and hair were 0.2 ng/mL and 0.002 ng/mg, and the quantitative limits (S/N≥10) were 0.5 ng/mL and 0.005 ng/mg respectively. The recoveries of 1.0, 10.0, 100.0 ng/mL in spiked blood were 97.1% to 103.4%, and 0.5, 2.5, 5.0 ng/mg in spiked hair were 84.0% to 99.8%. The relative standard deviation was less than 15%. This method can be applied to the qualitative and quantitative analysis of etomidate in blood and hair in the juridical practice.

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.

Currently, smartphone screenshot images, as a swift and convenient tool for capturing and sharing information, have found widespread applications in daily life and professional work. However, simultaneously, the associated security and privacy concerns regarding these images have become increasingly salient. Due to the significant differences in their generation mechanisms between smartphone screenshots and captured images, this fact poses challenges for screenshot image traceability and forensics. In the current research and practical applications, technical research on tracing the sources of screenshot images remains in its nascent stage, with accurate tracing and identifying the source devices of specific images emerging as a pivotal research topic in the field of digital image forensics. This study focuses on developing a recognition methodology and system based on metadata features. By collecting screenshot samples from 52 smartphones of prevalent mobile phone brands in the market, metadata is extracted from the sample images, and a metadata feature database is subsequently established. The source device of the image is matched and identified by comparing multi-dimensional features, including model, device manufacturer, profile creator, rotation angle, file type, image width, and image height. Experimental results demonstrate that the proposed method exhibits high accuracy and reliability in identifying the origins of images for traceability purposes, thereby offering a novel technical approach for the field of digital forensics.

In recent years, Android system applications (hereinafter referred to as ‘APPs’) have become one of the primary ‘tools’ used by criminals for fraud. Criminals develop fraudulent apps and distribute their installation packages, known as Android application packages or APK files, to victims. After downloading and installing these apps, victims are deceived through their interactions within the apps. Therefore, the functional analysis of apps on Android devices has become a crucial source of for analyzing the processes of fraudulent activities and identifying the perpetrators of such crimes. With the development of protective technologies in recent years, an increasing number of fraudulent application files now employ various protective measures to prevent virtual machine executing and packet capturing, making dynamic analysis of these APPs increasingly difficult. This paper introduces common anti-packet capture techniques, including APK environment detection, packet capture detection, and certificate verification detection, and starts with reverse code analysis of APKs, dynamic packet capture analysis, and the underlying system code of Android, which explores the feasibility of bypassing dynamic detection and anti-packet capture mechanisms. The study of these methods for evidence collection provides valuable insights for the analysis of various types of fraudulent and malicious APPs.

Diphenidol is a non-phenothiazine anti-vertigo and anti-emetic over-the-counter drug, an analogue of trihexyphenidyl hydrochloride, and usually in the form of its hydrochloride, which was first approved in the USA in 1967. Diphenidol can improve the blood flow of cerebral vertebral artery, reduce the vertigo stimulation of vestibular nerve, inhibit the labyrinth function of inner ear, and block the vomiting center or the medullaoblongata emetic chemosensory area. Then, this drug has good anti-vertigo effect, less adverse reactions, high safety and so on, so it has been widely used in clinical practice, especially employed as an anti-emetic agent in the treatment of vomiting and vertigo associated with surgery, radiotherapy, chemotherapy, Meniere’s disease, and other labyrinthine disorders. Diphenidol is generally considered a relatively safe drug, which is inexpensive and easy to purchase. However, ingesting large doses intentionally or accidentally can cause serious toxic effects. The main symptoms of diphenidol poisoning include dry mouth, irritability, hallucinations, headache, euphoria and temporary hypotension. In severe cases, respiratory failure may result from respiratory depression, hypotensive shock or arrhythmia. In recent years, there have been a number of suicides and accidental poisonings related to diphenidol in China, including preschool children being poisoned by ingestion and adults attempting suicide. In forensic practice, diphenidol poisoning can be difficult to diagnose. Additionally, clinical symptoms and signs of diphenidol poisoning vary and are easily confused with epilepsy, tetanus rabies, hysteria or rodenticide poisoning. In addition, if there are no tablets or bottles left on the scene, a definitive diagnosis of diphenidol poisoning may be overlooked by the forensic doctor. Therefore, the physicochemical properties, pharmacological and toxicological effects, metabolic pathways and products in vivo, human pharmacokinetics, zoological experiments, analysis and detection techniques and other aspects of diphenidol were described in this paper, in order to provide some theoretical references for related cases.

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 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.

In recent years, telecommunications fraud cases have become increasingly prevalent, with criminals continuously refining their fraud tactics. From initially exploiting mainstream instant messaging platforms like WeChat and QQ, perpetrators have shifted to luring victims into specially crafted apps. To streamline development and reduce costs, criminals embed third-party SDK interface codes into these illicit apps, with IM (Instant Messaging) services being a common type. In these novel fraud scenarios, conducting both dynamic and static analyses of the chat-focused apk files involved can yield valuable forensic leads regarding IM services and access databases containing crucial information for investigation and prosecution. This article, taking the Android system as an example, meticulously outlines the process of mining forensic clues from instant messaging apps and subsequent data analysis, encompassing technical principles, analysis and processing steps, and case applications. It emphasizes techniques such as extracting key values through apk static analysis, validating those keys via dynamic packet capture, and utilizing SQL queries to sift through and analyze chat logs, thereby offering a professional methodological reference for evidence gathering in related cases.

Gamma-hydroxybutyrate (GHB) is a psychotropic drug listed in the United Nations 1971 Convention on Psychotropic Substances and China’s Regulations on the Administration of Narcotic Drugs and Psychotropic Substances. GHB has a strong inhibitory effect on the central nervous system, which can cause temporary memory loss, nausea, vomiting, hypersexuality, hallucination and even death. It is a more common chemical synthetic drug in entertainment venues. In recent years, GHB and its precursor γ-butyrolactone (GBL) are often used by criminals, leading to intentional injury, traffic accidents, rape and other malignant cases, causing a series of serious public health problems and social security problems. GHB is a normal endogenous substance in human body, which can be increased by exogenous intake. This means that timely sampling is needed to ensure the accuracy of the test results. If the concentration of the biological material cannot be detected in time, it cannot be proved that the victim or suspect had exogenous GHB ingestion. At the same time, the metabolism of GHB in human body is fast, the metabolites are complex, and the test window is short, which brings challenges to the police inspection and identification. In the face of the social reality that the cases of “anesthesia rape” and “anesthesia robbery” involving GHB continue to occur frequently, it is particularly important to study the testing and identification technology of GHB. This paper introduces the abuse and control of GHB at home and abroad, points out the difficulties in the detection and identification of GHB, and then summarizes the research progress in the detection and identification technology of GHB in blood, saliva, hair and urine. The endogenous levels of GHB in the biological specimens above and the critical concentrations of GHB after exogenous intake are enumberated, which can help forensic experts to exclude false positive results and assist in determining GHB abuse, in order to provide reference for the anti-drug work of public security in China.

This paper presents a case study of suicide involving multiple axe blows to the head, systematically analyzed through forensic pathology, bloodstain dynamics, and crime scene reconstruction techniques to provide empirical evidence for distinguishing the manner of death in sharp-force craniocerebral injuries. The deceased exhibited 28 densely distributed parallel transverse wounds on the head, with left-sided injuries demonstrating greater severity than the right. The scalp wound showed a comminuted skull fracture with brain tissue leakage. Two superficial neck incisions displayed characteristic hesitation marks, while blood spatter patterns on the right sleeve exterior corresponded with axe-swinging trajectories. Through systematic analysis of wound distribution patterns (the left-deep/right-shallow injury gradient conforming to biomechanical characteristics of right-handed axe strikes), tool-trauma morphology compatibility (axe weight, blade sharpness, and consistency with cranial injuries), and bloodstain dynamics (vertical drip patterns and elevated-arm cast-off characteristics), combined with critical scene evidence - absence of climbing/battle traces, intact entry points, and exclusive DNA presence on the tool - further excluded homicide.

Etomidate is a non-barbiturate intravenous anesthetic. After intravenous administration, it binds to plasma albumin and distributes into brain and heart tissues rapidly. Etomidate has the characteristics of rapid onset, short duration, and stable circulation. It is mainly used in clinical anesthesia induction or short-term surgical anesthesia. Due to its anesthetic properties, abusers make it into smoke powder, smoke oil, etc., and add them to e-cigarettes, which have widespread hazards and high concealment. Long-term use of etomidate will cause damage to the endocrine system, liver and nervous system. The toxicity to nerve cells is particularly prominent. In severe cases, apnea or even death may occur. At present, the abuse of etomidate has become a global issue, and a certain abuse trend also exists in China. Because etomidate is easy to obtain, teenagers account for the vast majority of users, which seriously endangers their physical and mental health. On September 6, 2023, the State Drug Administration, the Ministry of Public Security and the National Health Commission jointly issued the Announcement on the Adjustment of the List of Narcotic Drugs and Psychotropic Substances, which included etomidate (except for etomidate-containing drug preparations approved for marketing in China) on the list of category II psychotropic drugs, but the phenomenon of abuse is still frequent. At present, the research on etomidate mainly focuses on clinical medication, adverse reactions and detection methods. However, the research on toxicokinetics, toxicological mechanism and abuse or addiction mechanism is still insufficient. In this paper, the pharmacokinetics, anesthesia mechanism, toxicity, and abuse status of etomidate were retrospectively analyzed, and the detection methods such as gas chromatography-mass spectrometry (GC-MS), high performance liquid chromatography-mass spectrometry (HPLC-MS) and quantitative nuclear magnetic resonance spectroscopy (NMR) were summarized, in order to provide reference for the in-depth study and forensic identification of etomidate.

This study explored the utilization of low-altitude unmanned aerial vehicle (UAV) imagery combined with ground-based handheld photography to create a multi-scale, variable-resolution 3D realistic model of high falling incident scenes. Initially, a low-altitude UAV is employed to capture imagery data of the orientation, general overview, and fall space of the high falling incident site. Subsequently, a digital single-lens reflex (DSLR) camera is utilized to obtain detailed image data of key areas such as the starting and ending points of the fall. In the SMART 3D reconstruction software, feature points are marked and matched between the UAV imagery and the handheld camera images, aiming to rigidly correlate the aerial and ground data, thereby fusing these diverse sources to create a refined 3D model. The results demonstrate that the 3D model, constructed through the fusion of the two data sources, offers a multi-scale, comprehensive representation of the high falling incident scene. It enables users to observe both the overall orientation and layout from a distance, as well as to scrutinize key areas and detailed features up close. This technical methodology provides a direct and authentic representation of the location, texture, size, and other physical attributes of trace evidence at the scene. It offers a novel means for digitally documenting and preserving the high falling incident scene, which can then aid in reconstructing the fall sequence and enable accurate determination of the case's nature.

Determining the sequence of intersecting lines between laser printing and stamped impression is one of the key contents of questioned document examination. Fluorescence microscope is considered to be one of the most effective ways to examine the sequence of intersecting seal and toner lines of questioned documents. However, due to various factors, determining the sequence of crossing lines has always been a challenge to forensic document examiners. Considering the influence of types of factors such as toner morphology, stamped impression fluorescence, and paper, a systematic analysis of laser printing handwriting and print timing issues was conducted using the ZMSX-05 vermilion ink timing instrument to excite fluorescence. The fluorescence phenomenon on the surface of toner was observed under two different timing conditions (“printing before stamping” and “stamping before printing”). Experiments have shown that the morphology of ink powder plays an important role in determining the sequence of intersecting lines. When the toner powder is compact and piled up on the surface of the paper and the seal ink has strong fluorescence, the sequence of the intersecting lines can be determined quickly and accurately; however, when the toner powder is non-compact and penetrates into the paper fibers, the seal ink has weak fluorescence, making it relatively difficult to determine the sequence of the intersecting lines. However, by comparing the transmitted light and fluorescence test images, it is possible to accurately determine the sequence between laser printing and stamp impression under most conditions, especially for compact and non-compact toner examination, for which 100% accuracy rate and 90% detection rate were achieved in the blind test, but some examiners made errors in determining the sequence under the interference of paper fibres, which indicates that the conclusions drawn by examiners with different professional abilities are different. This also reminds us that in actual identification, examiners should understand the principle of the fluorescence method, distinguish the effect of each element on the sequence of intersecting lines, and scientifically apply examination methods in order to make correct identification opinions. In addition, time should be another important element in determining the sequence of intersecting lines, but the time interval between laser printing and stamping, as well as the changes that occur over time after the formation of the two time sequences, require long-term detection. For example, how the fluorescence of the printed text on the surface of the toner powder changes over time, etc. This should be an important line of research for the future, so that experimental data can provide reference and clarification for document examiners.

For the first time, a general ¹H-quantitative nuclear magnetic resonance spectrometry (¹H-qNMR) method for the simultaneous quantification of heroin hydrochloride, cocaine, cocaine hydrochloride, and 7 adulterants was established after the discussion of some key quantitative parameters. According to the solubility and stability of heroin hydrochloride, cocaine, and cocaine hydrochloride in different deuterated solvents, and the spectra comparison of seized samples and reference materials, dimethylsulfoxide-d6 was selected as the solvent, and 1,3,5-trimethoxybenzene was selected as the internal standard. The signals at δ_H 6.09 ppm for internal standard 1,3,5-trimethoxybenzene; at δ_H 6.87, 6.71, 2.24 ppm for heroin hydrochloride; at δ_H 7.90, 7.64, 5.12, 3.61 ppm for cocaine; and at δ_H 7.88, 5.49, 2.79 ppm for cocaine hydrochloride were selected as quantitative peaks．The established ¹H-qNMR method was applied for the quantification of 168 seized samples, the content of heroin and cocaine were basically consistent with the results of High-performance liquid chromatography. This method is simple, fast, accurate, does not require standard samples, and shows good tolerance and high reproducibility. It can provide new ideas for the quantification and profiling analysis of seized drug samples.

In recent years, Chinese patent medicines and health care products have become more popular, but some businessmen have added illegal additions in them for profit. At present, there are many kinds of illegal additions. The objects of this study mainly include seven categories: medicines for regulating blood pressure, medicines for blood sugar regulation, medicines for blood lipid regulation and so on. These drugs may lead to a series of problems in cardiovascular, hepatic/renal function, which can be life-threatening in serious cases. In order to protect public health and rights, China has issued many standards and relevant guidelines. The main detection techniques are: immunoassay, spectroscopy, chromatography and the combination of various technologies and real-time direct analysis mass spectrometry. This paper summarized the categories of illegal additions in recent years, the commonly used sample pretreatment technology and the corresponding detection technology, and briefly introduced the characteristics of each technology. Finally, an outlook is given about the management of new illicitly added chemicals, the direction of development on related detection technologies, and the measures to deal with new illicitly added chemicals.

With the full advancement of the low-altitude economic strategy, low-altitude flight safety has increasingly become a top priority for national stability and security. Drone crimes are rapidly evolving from potential risks to real threats, with both the number of cases and the level of harm rising exponentially. In response to the critical challenges of drone governance, such as “difficulties with low-altitude surveillance, target identification, and timely disposal”, this article proposes to build a four-dimensional linkage technical investigation and prevention pathway of “global intelligent sensing network - three-dimensional forensic examination system - precise source tracing technology - coordinated rapid disposal”. This system, for the first time, proposes the focus of investigations on drone-related incidents, provides technical support for the high-quality development of drones, and provides systematic solutions to global non-traditional security governance.

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.

Gun-related cases pose extreme societal harm and urgently demand efficient and precise detection methods. This study aims to integrate the reflectance transformation imaging (RTI) technique with deep learning to apply it to the field of gun and bullet recognition. In the experiment, a total of 1 500 samples of fired cartridge cases were selected from five QSZ92 9 mm pistols. Detailed images of the markings on the base of the cartridge cases were captured using the DTV3.1 intelligent imaging system to obtain their normal maps. Thereafter, the pre-trained ResNet-50 network extracted features from the normal maps and underwent classification training. The model’s performance was evaluated by outputting AUC values, accuracy on the test set, and a confusion matrix. The experimental results reveal a total AUC value of 0.98 across the five guns, with gun No. 2 achieving the highest accuracy of 97.66% and gun No. 1 the lowest at 93.75%. This study demonstrates that the automatic recognition method of cartridge case marks based on RTI technology and deep learning yields significant results, offering valuable reference for the identification of other traces in the field of trace inspection.

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.

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.

Exosomes, which are extracellular vesicles with a double-layer membrane, play significant roles in biological processes. They act as tiny carriers, capable of transporting a wide range of genetic materials and metabolites, including DNA, RNA, proteins, lipids, etc. Hypoxia, a state of low oxygen availability, is a powerful environmental factor that triggers significant changes in exosomes. Their composition, secretion process and overall functionality will all be drastically affected under hypoxic conditions. In the realm of forensic pathology, the precise identification of death from mechanical asphyxia (DMA) has always been a major challenge. The corpses of individuals who have died from mechanical asphyxia often lack distinct and characteristic signs that can directly point to this particular cause of death. This absence of specific indicators makes it extremely difficult for forensic pathologists to achieve precise diagnosis, which brings trouble to the criminal investigation and court trial of DMA cases. In this paper, we conducted a comprehensive review of the hypoxia-induced changes of exosomes and discussed the benefits, limitations, and feasibility of application of these changes to precise identification of DMA. Through this in-depth discussion, we hope to offer new perspectives and directions for relevant studies, thereby contributing to the advance of forensic pathology.

In order to further improve the accuracy of speaker recognition and avoid the complicated process of manual feature extraction required by traditional speaker recognition methods, this paper proposes an end-to-end speaker recognition method based on CBAM attention mechanism and deep neural network. CBAM, a lightweight general module, is introduced into the deep neural network structure and seamlessly integrated into the network architecture. After it is added into the first layer of deep neural network convolution in this paper, the features of speech signals first pass through the CBAM channel attention module to strengthen the model’s attention to the channel dimension of speech features. Then CBAM spatial attention module is used to improve the model’s attention to the spatial dimension of speech features, further improve the model’s sensitivity to important feature information, and use the end-to-end loss function to train the whole model as a whole. At the same time, an embedded court speaker recognition method based on generalized end-to-end loss function training is proposed, and the likelihood ratio is obtained by using the embedded cosine similarity score trained by the improved network model, so as to intuitively and accurately judge whether it is the same speaker, thus providing intuitive and powerful evidence for the court. Finally, taking deep neural network BILSTM and GRU as examples, the mainstream data set CN-Celeb was used to train the model to ensure that the model can achieve better combat effects in a complex and rich voice environment. Zhaishell, a subset of Zhvoice, and the audio of actual combat cases collected by ourselves were used for combat test, to ensure that the model in this paper has a good recognition effect for both Mandarin and dialect. The results show that the method proposed in this paper can effectively improve the recognition accuracy, quickly construct the model and improve the generalization ability.

Against the backdrop of the rapid development of big data and artificial intelligence, the field of footprint examination urgently requires advancements in professionalization and informatization. Currently, research on the identification methods of barefoot footprints has made certain progress, but the automatic recognition of shod footprints remains a tough challenge in the field. Therefore, this study introduces the Surf algorithm to explore its potential application in the automatic recognition of shod footprints. More specifically, the study employs the Surf algorithm to perform feature matching on four types of footprint images (i.e., the same person with the same shoe, the same person with different shoes, different people with the same type of shoes, and different people with different shoes), and conducts an in-depth analysis of the similarity between footprints by mapping the matching points through geometric transformations. The results show that footprints from the same person with the same shoe exhibit a significantly greater number of Surf feature point matches compared to other types; after geometric transformation mapping, footprint images from the same person, the same shoe and same time have a greater number of matching points, with accurate matching positions, while footprints from different people exhibit fewer matches. Additionally, even when considering footprints from the same person, those formed in closer time intervals have more matching points than those formed further apart in time. In summary, the Surf algorithm exhibits efficiency and reliability in recognizing footprints from the same person with the same shoe.

The identification of the characteristics of shooting bullet trace image is the main content of gunshot trace inspection, and also one of the challenges. This article introduces an advanced automatic annotation method for shooting bullet trace features based on the High-resolution networks (HRNet) framework, which can achieve automatic labeling of the land-engraved trace area, groove-engraved trace area, and slippage trace area. A database of 5 985 images containing seven different sizes of shooting bullet traces extracted by BalScan (3D trace image scanning system) was constructed and divided into training, validation, and testing datasets at a ratio of 7:1.5:1.5. The training dataset was manually annotated to identify the land-engraved trace area, groove-engraved trace area, and slippage trace area, which were used to train the high-resolution network model. Then, the unlabeled testing dataset was input into the trained model for automatic annotation of the feature areas. Finally, the annotation results were manually reviewed and the accuracy was recorded. The results showed that the proposed method achieved an average accuracy of 94.1% in the automatic annotation task, demonstrating its effectiveness. This annotation algorithm for shooting bullet trace images without manual annotation can significantly reduce the workload of inspectors and provide a feasible new approach to improve the efficiency of firearm trace inspection.

Forensic document examination, a pivotal branch of forensic science, involves the meticulous analysis and authentication of various document forms, including handwriting, printed materials, and seal impressions. With the relentless progression of technology, the integration of deep learning methodologies has significantly accelerated the automation and intelligence in this field. Specifically, the employment of complex multi-layered neural network models within deep learning has facilitated a heightened level of document image recognition and analysis, surpassing the capabilities of conventional approaches. This technological breakthrough has not only enhanced the accuracy and efficiency of forensic document examination but also substantially reduced the influence of human error and subjectivity, thereby bolstering the credibility of results. This article provides a thorough review of the contributions by domestic and international researchers in leveraging deep learning for different aspects of document examination. It delves into the advancements in handwriting analysis, which involves the identification and comparative assessment of individual writing styles; printed document verification, which focuses on the authenticity of printed materials; and seal impression inspection, where the authenticity and source of seal marks are scrutinized. The discussion includes an overview of the foundational principles underpinning these methodologies, the specific applications of deep learning in these areas, and the cutting-edge research findings propelling the field forward.In addition to highlighting these advancements, the article also critically examines the existing obstacles and constraints in applying deep learning to forensic document examination. These include the demand for more robust and generalizable models capable of accommodating the extensive variability encountered in real-world documents, the necessity of extensive and diverse datasets to train these models, and the challenges associated with integrating deep learning tools into established forensic workflows. The article concludes by offering insights into the future directions for research and application, emphasizing the potential for deep learning to further revolutionize forensic document examination as the technology continues to mature.

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.

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 anonymity and decentralization of Bitcoin make it a significant medium for illicit transactions, posing challenges for traditional detection methods in handling complex transaction network structures. This study proposes a graph neural network model based on a pre-trained Conditional Variational Autoencoder (CVAE) to enhance the efficiency and accuracy of Bitcoin illicit transaction detection. The model generates K−1 feature vectors through the CVAE, which have the same number as the input features, and then combines these generated K−1 feature vectors with the original feature vector to ultimately form K feature vectors. Each feature vector undergoes multi-channel aggregation and max pooling, resulting in multiple feature vectors. These vectors are subsequently processed through linear layers and layer normalization, followed by another round of max pooling to obtain a global feature vector. Finally, the feature vectors are further processed through graph convolutional layers and linear layers to generate the final classification result. The model integrates input feature vectors at the output layer through a skip mechanism. Experimental results demonstrate that this model performs excellently in Bitcoin illicit transaction detection, significantly improving detection accuracy and robustness.

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.

Forensic SNP genealogy can infer distant kinship based on SNP chip data. In order to clarify the ability of genealogical inference technology based on SNP chip data to detect trace DNA in forensic field, in this study Illumina CGA microarray was used to detect the samples. The samples were preliminarily evaluated based on the DNA input, detection rate, sample heterozygosity and other indicators, then the IBS and IBD algorithms were used for forensic SNP genealogy. The classification consistency was compared with the reference samples, and the factors affecting the prediction accuracy were analyzed, the detection ability of the technology system for different input amounts of DNA were determined, and finally the accurate SNP typing data in the low-quality data were screened based on the signal ratio and other indicators, so as to improve the use value of the trace DNA detection data. The results show that when the DNA input was higher than 1.95 ng, the IBS algorithm had an average confidence interval accuracy of 94.33% and 91.96% for IBD, and when the input was 488－781 pg, the IBS algorithm had an accuracy of 23.11% for the average confidence interval for 1－5 kinship, while the IBD algorithm reached 30.13%. When the DNA input is less than 488 pg, both the IBS and IBD algorithms are unable to make genealogical inferences. Allele insertion is a major factor affecting the accuracy of pedigree inference, and when the homozygous error reaches 22.5%, the sample cannot be used for pedigree inference. By screening the signal ratio, the heterozygous SNP loci with a signal ratio greater than 1.5 can be removed, which can improve the genealogical inference ability of low-input samples. Based on the real family data of Illumina CGA chips, this study analyzed the influence of sample input on the accuracy of genealogical inference, and optimized the SNP data by signal ratio, so as to improve the application value of low-input samples in genealogical inference.

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.

With the wide application of surveillance systems, there is an increasing concern about public safety and security issues. Among them, the rapid detection and recognition of fighting behavior is very important for maintaining social order and security. However, traditional monitoring systems often face many challenges when dealing with large-scale video streams, including high computational complexity and resource-limited environments. In order to cope with these challenges, this paper proposes an improved fighting behavior detection model based on YOLOv5s, which reduces the number of parameters of the model and the computational complexity, so that the model can operate more efficiently in the resource-limited environment and detect various fighting behaviors more accurately. First of all, the open source interactive markup tool Labelimg was used to annotate the data set and train the network model with a large amount of data. Secondly, considering the need for rapid and accurate solutions in public security practice, lightweight network MobileNetv3 is used as the backbone network by comparing various convolutional structures to replace the original backbone network of YOLOv5s model, so as to reduce the number of parameters and calculation amount of the model and improve the model detection accuracy. By setting ablation experiments, the improved model is compared with other models and the original model. The experimental results show that compared with the original network, the detection accuracy of the improved model is increased from 92% to 94.4%, the computational load is reduced from the original 15.8 G to 3.1 G, and the detection speed of the algorithm can reach 0.153 s at the fastest, meeting the real-time requirements. And the detection accuracy is the highest among the three models. This model is suitable for public security practical application scenarios with high precision and limited memory and computing power.

The human ear possesses a complex three-dimensional structure and exhibits relatively high stability. Research on the morphological characteristics of ear images holds significant importance for individual identification in cases of occluded or incomplete facial images in videos and photographs. This paper categorizes the visible ear features in frontal and profile facial images into three levels: global feature, local feature, and detailed feature. Based on a self-compiled ear image dataset from the Institute of Forensic Science, Ministry of Public Security, P.R.C. (comprising 2 078 individuals and 44 826 images), the study further subdivides each feature type, providing illustrations and reference markers to depict the corresponding characteristics, thereby establishing a relatively comprehensive feature system. Using morphological comparison and statistical analysis methods, the frequencies of global features (36 feature items), local features (58 feature items), and detailed features were separately analyzed. The statistical results indicate that morphological features such as the earlobe and crus of the helix are the most frequently observed in ear images and remain relatively stable under varying lighting conditions, angles, and resolutions. Some features are relatively rare (e.g. multiple helical notches or an antitragus that is narrower at the top and wider at the bottom), and their presence can provide valuable support for individual identification.

With the improvement of living standards and an increasing awareness of personal health, the demand for health foods has surged. Consequently, the market has expanded continuously, presenting numerous regulatory challenges for the relevant authorities. The illegal addition of drugs in health foods poses a significant threat to consumer health and impedes the sustainable development of the health foods industry. Therefore, it is imperative to establish comprehensive methods for detecting these unlawful substances in health foods and to develop robust regulatory systems. Among these, health foods that claim to relieve physical fatigue are particularly vulnerable to such illegal adulterations, which primarily fall into two categories. The first is often marketed with alleged aphrodisiac or sexual enhancement benefits, while the second claims to improve cognitive function. Many unscrupulous vendors illegally add phosphodiesterase 5 inhibitors (PDE5i) or synthetic nootropics to enhance these claimed benefits. This paper provides a summary of the types, hazards, and prevalence of illicit drug additives in health foods designed for aphrodisiac or cognitive improvement, and gives an overview of the latest advancements in detection methodologies, including high-performance liquid chromatography (HPLC), liquid chromatography-mass spectrometry (LC-MS), spectroscopy, immunoassays, and electrochemical analysis. The primary methods for detecting illegal drug additives in aphrodisiac health foods include HPLC, LC-MS, spectroscopy, mass spectrometry (MS), immunoassay, and electrochemical analysis. For brain-tonifying health foods, the main detection methods are HPLC and LC-MS. Finally, this article explores the future of detecting illicit drug additives in health foods from the perspectives of standardizing preprocessing methods, expanding the application of portable instruments, employing various techniques for non-targeted screening, and utilizing artificial intelligence and machine learning technologies. The intent of this paper is to offer valuable insights and reference for law enforcement and regulatory agencies in the pursuit of monitoring and ensuring the safety of these health foods.

In order to improve the development effect of latent blood fingerprint, this paper attempts to introduce fluorescent labeling technology into latent blood fingerprint development. By taking advantage of the characteristics of strong esterification activity of N-hydroxy-succinimide ester, easy reaction with the amino group of protein to form amide chemical bond and stable structure, a new fluorescence derivative 5-carboxyfluorescein-N-hydroxy-succinimide ester (CFSE) was synthesized and applied to latent blood fingerprint development area for the first time. In this paper, the developing ability of CFSE on latent blood fingerprint will be systematically explored. By setting a series of gradients, using comparative experiments, hypothesis testing and other methods, the operation method and optimal reagent formula of the fluorescein to develop latent blood fingerprints were determined. CFSE can be widely used in the development of latent blood fingerprints on non-permeable complex backgrounds and dark background objects, and has little effect on sample DNA extraction, and compared with acid yellow, the results showed that the fineness of the lines and the contrast with the background were higher. In addition, the study also determined the order of using CFSE in combination with the common methods for developing latent blood fingerprints. CFSE has the advantages of strong fluorescence brightness, high fineness, obvious contrast with the background, less fluorescence stains, and easy washing, and the overall effect is better, which provides a new method for latent blood fingerprints development.

As technology advances, mobile device forensics becomes increasingly challenging in the context of constantly updated operating systems and enhanced data encryption techniques. This paper, taking the TikTok application data extraction from a Huawei mobile phone in an actual case as an example, delves into the difficulty of extracting data from specific applications in new Android phones. To tackle this challenge, this study proposes a method utilizing a root-privileged phone to clone data from the source device and subsequently extract the required information, thereby achieving successful data retrieval. Furthermore, recognizing the inefficiencies and time-consuming nature of traditional manual timestamp conversion methods during targeted database analysis, this study has developed a novel database retrieval tool. This tool automates the process of swiftly retrieving and analyzing data from key time periods across multiple databases within a predefined directory, significantly enhancing processing speed and efficiency. Thus, our study not only offers a solution to the challenges of data extraction and analysis but also serves as a valuable methodological reference for mobile device forensics in similar cases.

This paper reports a parentage testing case in which Mendelian inheritance at STR loci was violated between mother and child because the child’s chromosome 2 showed paternal uniparental disomy of the mosaic type. After analysis with three commercial autosomal STR multiplex kits, non-Mendelian patterns were observed at the loci D2S1338 and D2S441. Subsequent SNP profiling demonstrated that the child’s chromosome 2 was entirely paternal in origin and mosaic, providing a reference for recognizing mosaic uniparental disomy in parentage testing.