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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.
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The presence of close non-matches poses inherent risks to fingerprint identification, and exploring their occurrence patterns in different regions of the fingerprint can enhance the risk awareness of identification personnel. To investigate this, 30 fingerprints from the central and branch regions of loops were selected as research subjects. Each fingerprint was queried 15 times within a database of ten million individuals to examine the occurrence of homologous fingerprints and close non-matches when 6 to 20 minutiae were marked. The results revealed that close non-matches could be found in both the central and branch regions of loops when 6 to 20 minutiae were marked, and notably, the number of close non-matches in the branch region was significantly higher than in the central region. The proportion of homologous fingerprints ranked higher than close non-matches in the central and branch regions of loops, with respective percentages of 98.4% and 60.9%. Even when 20 minutiae were marked, there were still instances where homologous fingerprints did not appear in the branch region of the loop. Frequency analysis of similar regions indicated that the side of the central region facing the loop and the side of the branch region facing the branch were more susceptible to having close non-matches. Additionally, high-level close non-matches with over 10 matching points were identified in both the central and branch regions of loops. Therefore, fingerprint identification personnel must handle close non-matches with caution to prevent them from interfering with accurate identification.
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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.
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As digital currencies, notably, Bitcoin, gain widespread adoption, the detection of illicit transactions poses a pressing challenge that requires prompt resolution. This research introduces a novel approach for detecting illicit Bitcoin transactions that integrates diverse features to enhance both detection efficiency and accuracy. Firstly, a comprehensive feature set is constructed by amalgamating conventional data features with those uniquely derived from LSTM, RandomWalk, and PageRank algorithms, enabling the capture of intricate patterns within transaction data. Secondly, to address the class imbalance inherent in Bitcoin transaction datasets, FocalLoss is adopted as the loss function, strengthening the model's ability to discern minority classes (i.e., illicit transactions). Finally, the model is validated on the Elliptic dataset using a multilayer perceptron (MLP) architecture with a single hidden layer, and its performance is compared with current mainstream Bitcoin illegal transaction detection models (GAT, GCN). Experimental results demonstrate that the proposed method achieves significant improvements in crucial metrics such as F1 score and recall rate compared to traditional methods, validating the effectiveness of the multi-feature fusion strategy and the utilization of FocalLoss in tackling the challenge of illegal Bitcoin transaction detection.
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This paper aims to confirm the new psychoactive substance 1-(4-fluorobenzyl)-4-methylpiperazine (4F-MBZP) through a combination of multiple technologies. After ultrasonic extraction with methanol and separation by preparative liquid chromatography, the target was analyzed using a combination of gas chromatography-mass spectrometry (GC-MS), liquid chromatography-quadrupole/time-of-flight tandem mass spectrometry (LC-Q-TOF-MS/MS), Fourier transform infrared spectroscopy (FTIR) and nuclear magnetic resonance hydrogen spectroscopy (1H-NMR), respectively. Through GC-MS analysis, the main characteristic fragment ions of the unknown compound with a retention time of 9.78 min were m/z 208.1, 164.1, 109.1 and 99.1; By means of LC-Q-TOF-MS/MS detection, the quasi-molecular ion peak of the unknown compound was determined to be m/z 209.286 4, and the main characteristic ion fragments obtained by MS2 were m/z 189.183 0, 109.176 9, 99.452 0 and 83.387 1; The infrared characteristic absorption peaks of the target measured by FTIR analysis were 3 517.1, 2 981.5, 2 943.4, 1 603.9, 1 517.3, 1 232.0, 1 168.6, 1 080.2, 827.8 and 766.9 cm-1, indicating that the molecular structure of the suspicious compound contains functional groups including benzene ring, methyl, methylene, and C-N; After further analysis of proton attribution in the unknown compound structure by 1H-NMR and comparison with relevant literature that has been publicly reported, it was confirmed that the unknown compound is a new psychoactive substance called 4F-MBZP. This method is expected to be used in qualitative analysis of 4F-MBZP in seized drugs with simple operation and high accuracy, while providing reference for the detection and identification of new drugs.
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This study investigates the method of using coaxial light to differentiate between laser printer and detect the addition of printed documents. Documents printed by laser printers of different brands and models are inspected using coaxial light, with the reflection of the printed text and graphics, specifically the degree of brightness or darkness, serving as a means to distinguish the printer models. The same method is used to inspect documents that have undergone additional printing on the same printer model as well as on different printer models, and the differences in the coaxial light response after the additional printing are analyzed. The experimental results show that the coaxial light response of documents printed by the same model in a single run and at different times remains basically stable, with minimal influence from paper quality and toner amount, making it a viable feature for differentiating laser printers. The coaxial light response varies significantly among documents printed by different models, with a high degree of differentiation; Moreover, the differences in the coaxial light response of the text and graphics in the added printed documents are even more pronounced, enhancing discrimination further. Overall, the study concludes that the coaxial light inspection method is a non-destructive, fast, and efficient method for forensic analysis of laser printed documents.
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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.
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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.
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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.
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In this study, poppy was used as the research object to construct a TaqMan probe real-time fluorescence quantitative PCR detection system for rapid and accurate detection of poppy origin, and to verify its application in actual samples. According to the ITS gene sequence of Papaver somniferum published in GenBank, the specific primers and probes were designed, and the amplification system was constructed and optimized. The specificity, sensitivity and the detection performance on actual samples were compared with the Sanger sequencing method to evaluate its feasibility. The results showed that the optimized real-time fluorescence quantitative PCR detection system had strong specificity, and the detection limit was 0.4 ng/μL. The poppy-derived components were detected in 18 samples to be tested, which were consistent with the results of DNA sequencing. The establishment of this research method has certain application potential for rapid detection of illegal cultivation of opium poppy-derived plants.
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This study established an HPLC-MS/MS method for qualitative and quantitative analysis of mifepristone and N-demethylmifepristone in blood samples. The appropriate pretreatment methods were investigated to pretreat the blood samples. HPLC was applied for separation. MRM mode was adopted to analyze. The precision, matrix effect, detection limit, recovery rate, and linear relationship of the method were all tested. The results indicated that mifepristone and N-demethylmifepristone in blood were separated well, with good chromatographic peak shape and good linear relationship in the range of 10 to 200 ng/mL. The detection limit concentration of mifepristone in blood was 0.5 ng/mL, the lower limit of quantification was 1.5ng/mL, the matrix effect was 94.6% to 98.2%, and the recovery rate was 82.7% to 92.4%; The detection limit concentration of N-demethylmifepristone was 1ng/mL, the lower limit of quantification was 2 ng/mL, the matrix effect was 91.2% to 97.3%, and the recovery rate was 81.8% to 91.7%. This method is simple, efficient, stable and easy to operate. It demonstrates satisfactory results for both qualitative and quantitative tests of mifepristone and N-demethylmifepristone, making it suitable for applying to the daily inspection and identification of related cases.
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A gas chromatography-mass spectrometry (GC-MS) method for detection the peroxide explosive hexamethylene triperoxide diamine (HMTD) was established to address the difficulty of HMTD detection in practical cases. The samples were extracted with acetone, followed by centrifugation to obtain the supernatant which was then concentrated and diluted. Separation was achieved using an HP-5MS chromatographic column (30 m×0.25 mm×0.25 μm) at a flow rate of 4.0 mL/min. The samples were analyzed using electron impact ionization (EI) mode and quantified by an external standard method. HMTD exhibited good linearity within the concentration range of 1 to 100 μg/mL, with a correlation coefficient (R2) of at least 0.995. When employing blank dust as the matrix, the method demonstrated a detection limit of 0.4 μg/g in full scan mode (SCAN). However, in selective ion scanning mode (SIM), the detection limit was significantly lower at 0.01 μg/g, with a quantitation limit of 0.04 μg/g. The average recovery rate ranged from 89.15% to 95.41%, indicating good reliability of the method. Additionally, the intra-day precision ranged from 4.67% to 6.26%, while the inter-day precision varied between 4.59% and 12.69%, demonstrating favorable reproducibility of the assay. This method is simple, reliable, and highly sensitive, and has been successfully applied to detect HMTD in practical cases. It is suitable for the detection of HMTD in suspected explosives and explosive soil, meeting the needs of actual case investigations.
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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.
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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.
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In recent years, fire accidents in hazardous chemical warehouses have occurred frequently, often triggering secondary incidents such as explosions and toxic substance leaks. These accidents not only result in catastrophic losses of life and property but also release hazardous pollutants and toxic gases during high-temperature processes, posing severe threats to the ecological environment. Therefore, advancing fire scene investigation techniques for such incidents is crucial for accurately determining fire causes, preventing similar accidents, enhancing emergency response efficiency, and formulating effective safety measures. This paper introduces the characteristics and underlying causes of fire accidents in hazardous chemical warehouses, and provides a comprehensive review of recent research progress in fire scene investigation techniques. By offering insights into standardized investigative methods, this study aims to strengthen theoretical foundations and provide practical guidance for improving emergency response strategies and mitigating the risk of future occurrences.
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Virtual autopsy technology is a derivative of modern CT technology, which combines CT imaging techniques with principles of systemic anatomy to quickly obtain and preserve information about the inside and outside of a body. It is a current hot topic in the field of forensic pathology and offers advantages that traditional autopsy cannot match in forensic practice, including its non-invasive nature, repeatability, and integrity. This paper highlights these advantages by discussing a special case of a work-related accident. This accident where the deceased was exposed to an instantaneous high-pressure environment with minor superficial injuries, making it difficult to accurately determine the cause of death. Therefore, we used virtual autopsy technology to discover serious subcutaneous gas accumulation and important organ damage in the deceased without destroying the body. In addition, through the analysis of virtual anatomical data by multi-planar reconstruction (MPR), we made reasonable inferences about the pathways of gas entering various important organs of the deceased. We also believe that the popularity of virtual autopsy technology in forensic practice is not high, but it will have extremely important application value in the field of forensic examination that requires rapid determination of the cause and mechanism of death, such as in work-related accidents and traffic accidents.
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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.
