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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.
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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.
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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.
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This study established an analytical method suitable for the rapid on-site detection of trace methamphetamine (MET) in sewage by integrating magnetic solid phase extraction (MSPE) with immunochromatographic technology. Using magnetic nanospheres conjugated with monoclonal antibodies against methamphetamine (MET) as an immunomagnetic probe, solid-phase extraction was employed to enrich and extract methamphetamine drugs in sewage. Subsequently, the extracted methamphetamine was then quantified using colloidal gold immunochromatographic test strips. The quantitative analysis of methamphetamine was carried out by optimizing various parameters such as the amount of labeled antibody, the pH of desorption solution, the desorption time, the water sample volume, and the water sample pH. The experimental results showed that the regression equation of the standard curve of the prepared test strip was 0.999 6, and the detection limit was 0.13 ng/mL.The combination of immunomagnetic probes and colloidal gold immunochromatography enabled the detection of trace methamphetamine drugs in sewage samples within 30 minutes. The method utilized a sewage sample volume of 50 mL, achieving a concentration factor of 50 times, with a quantitative detection limit of 8 ng/L.This combined method is simple to operate, time-efficient, and has low dependence on specialized equipment. Additionally, it does not require the use of organic solvents. It is suitable for the rapid on-site detection of trace amounts of methamphetamine drugs in sewage. Additionally, it can serve as an auxiliary tool for laboratory detection of sewage samples and be applied to the monitoring of urban sewage toxicity, thus playing an important role in relevant fields.
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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.
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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.
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Degradation of samples is one of the difficulties in forensic evidence examination. Affected by environmental factors such as high temperature, humidity, exposure, and microorganisms, the structure of DNA molecules is destroyed and fragmented. Large fragments of DNA are prone to failure during amplification, and complete amplification products cannot be obtained. The miniSTR technology can provide an effective solution to such problems. In this paper, a double multiplex amplification system for 25 miniSTR loci was established. Its performance indicators were tested, and its application value in degraded samples was evaluated. The technical performance indicators of the system, including balance, sensitivity, species specificity, and consistency, were compared with the VeriFiler™ Plus PCR Amplification Kit to test its detection ability for both simulated degraded samples and case work samples. The results showed that the average intra-color and inter-color balance of the miniSTR double multiplex amplification system were both more than 0.7, and the detection sensitivity for the standard 9948 DNA reached 0.05 ng. No specific amplification was observed in species detection. All 100 extracted blood samples achieved accurate typing, and all simulated degraded samples were successfully detected. The detection rate of case work samples was higher than that of the VeriFiler™ Plus PCR Amplification Kit. The miniSTR double multiplex amplification system exhibits good balance, high sensitivity, strong species specificity, and accurate typing. Its detection ability for simulated degraded samples and trace degraded samples is superior to that of the VeriFiler™ Plus PCR Amplification Kit. Particularly, the use of this amplification system can significantly increase the detection of loci in large, severely degraded fragments that are otherwise difficult to detect. At present, there are few commercial miniSTR products available, making this research even more valuable for advancing forensic DNA analysis.
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This study integrates hyperspectral imaging technology with chemometric methods to establish a quick and non-destructive technique for identifying brands and models of blue ballpoint pen inks. Hyperspectral images were acquired from ink traces of nine different brands and models of blue ballpoint pens. Sixty regions of interest (ROIS) were selected per sample, extracting a total of 540 averaged spectral curves. Initially, three preprocessing methods-savitzky-golay (SG) smoothing, standard normal variate (SNV) transformation, and their combination-were compared to build classification models using support vector machine (SVM) and light gradient boosting machine (LightGBM). The optimal preprocessing method was determined based on model performance. Following this, feature wavelengths were extracted using successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), uninformative variable elimination (UVE), and their combinations to establish the classification models. The results demonstrated that the (CARS + SPA)-SVM model achieved the highest classification accuracy of 92.66% for the nine different brands and models of ballpoint pen inks. This research highlights the potential of combining hyperspectral imaging technology with machine learning as an efficient and non- destructive approach to identifying types of ballpoint pen inks.
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To detect whether four harmful heavy metals As, Pb, Cd and Hg content in fishery products and meat exceed the permitted levels, this paper provided a method for the on-site and rapid detection of these heavy metal elements. It resulted that the limit of detection (LOD) of four metals in fish were 0.03 mg/kg, 0.02 mg/kg, 0.02 mg/kg and 0.06 mg/kg; the LOD of four metals in bivalves were 0.15 mg/kg, 0.03 mg/kg, 0.30 mg/kg and 0.03 mg/kg; the LOD of four metals in meat were 0.03 mg/kg, 0.06 mg/kg, 0.03 mg/kg and 0.02 mg/kg. Besides, the relative error was As < 10%, Pb < 25%, Cd < 15%, and Hg < 30%. The linear correlation coefficient R2 between the detection value and the reference method (ICP-MS) was ≥0.94. The reproducibility accuracy was investigated by comparative test in multiple laboratories, which was As < 10%, Pb < 20%, Cd < 10%, and Hg < 20%. Collectively, this method can meet the requirements of rapid detection of heavy metal elements in fishery products and meat, and the equipment is portable with simple operation, and the detection efficiency has been greatly improved.
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This paper introduces a new method for fingerprint development. This method utilizes sulfur-nitrogen polymer as the developing reagent and employs special development equipment to enhance fingerprints on various metal object surfaces. The sulfur-nitrogen polymer fingerprint development method can achieve the following: the development of latent fingerprints on the surfaces of metal objects; the appearance of latent fingerprints on the surfaces of metal objects after the soaking process; the development of latent fingerprints on the surfaces of metal objects after being washed. In addition, compared with the fingerprint development methods using ‘502’ glue and vacuum deposition systems, the sulfur-nitrogen polymer method offers superior development effects. Research shows that the sulfur-nitrogen polymer method for fingerprint development is effective for latent fingerprinting on metal surfaces. It also has a certain effect on difficult fingerprinting on metal surfaces after treatment involving soaking, detergent washing, and sponge wiping.
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This article investigates the post-mortem redistribution pattern of metformin in rats. Twenty-one rats were randomly divided into 7 groups, with 1 group serving as the control group and the remaining 6 groups as the experimental group. The experimental group rats were gavaged with 1mg/kg metformin, while the control group rats were gavaged with the same dose of physiological saline. After 2 hours, they were euthanized and placed in a right supine position at room temperature (15°C). Heart blood, heart, liver, spleen, lung, kidney, brain, testes, and muscles were collected at 0, 6, 12, 24, 48, and 72 h after death. The metformin content in each tissue and organ was extracted using protein precipitation method and detected by liquid chromatography-mass spectrometry. This study found that the distribution of metformin content in various time points, tissues and organs of rats showed different patterns. Metformin exhibits post-mortem redistribution in rats after gavage, therefore, the heart, muscle, and lungs are suitable samples for forensic identification of drug concentration at the time of death.
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Routine external examination of dead body can provide information to estimate the postmortem interval (PMI) roughly by observing postmortem phenomena in situ such as corneal opacity. With the development of computer vision, these traditional direct observation methods have developed with more objective, accurate, standardized, and convenient approaches. By reviewing the relevant literature published in recent years, this article summarized the application and improvement of PMI estimation based on corneal images, from aspects of observing or detecting indicators, equipment, evolving data processing methods, models, etc., in order to show application and research progress of computer vision technology applied in the field of forensic medicine. It introduced the main animal model research and human cadaver research, focused on several parts such as research materials, methods and results, and provided some comparative analysis. Then it extracted out several key points including the influence of eyelid opening or closing, the effect of light source, the interference of diseases and injuries, the selection of regions of interest in images, and the understanding of data processing methods, etc. Lastly, several advice were put forward to make a better understanding of the future development trend which may offer reference and ideas to colleagues.
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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.
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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.
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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.
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Determining the formation method of signature handwriting is the first step of handwriting examination, which plays a vital role in whether the correct appraisal opinion can be issued. This paper examines the formation of signature handwriting in a case involving 58 contracts, and sums up a method to mutually verify and analyze the formation of signature handwriting from various angles, such as writing style, ink shape, defect exposure, ink defect, pressure characteristics and spectral morphology, which has achieved good results in practical application and can provide reference for the inspection and identification of related cases.
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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.
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Cases of visual impairment following injury, as presented by subjects under examination, are common during forensic clinical evaluations. Certain subjects maintain a negative attitude, such as feigning blindness or visual impairment during visual activity tests, which complicates the forensic clinical evaluation process aimed at establishing the facts. Therefore, forensic experts should organize the medicolegal expertise process, deduce the mechanism of injury to the ocular region based on relevant data, consider and discuss the subjectivity of the subjects alongside the objectivity of ophthalmological laboratory tests, and ultimately reach an expert conclusion in accordance with the Technical Specification for Forensic Clinical Identification of Visual Dysfunction (SF/Z JD0103004-2016). This article presents a case regarding the forensic clinical evaluation of post-injury visual impairment and provides insights for similar medicolegal assessments.
