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

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

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

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

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

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

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

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

Body fluid stains are common biological materials at crime scenes. The accurate determination of their tissue sources can help with crime scene reconstruction, case nature determination and trial. The analysis of cell-specific mRNA expression has been proposed as promising method for the identification of body fluids. Conventional strategy of mRNA profiling requires reverse transcription, PCR amplification, and electrophoresis. The one-step RT-PCR detection technology can complete reverse transcription and PCR of mRNA in one reaction, which can reduce experimental time and simplify experimental operations. In this study, we subjected the one-step multiplex reverse transcription PCR strategy to mRNA profiling with the inclusion of 10 tissue specific biomarkers in the F13plex system targeting peripheral blood (HBA, HBB), menstrual blood (MMP7, MMP10), vaginal secretion (HBD1, CYP2B7P), saliva (STATH, HTN3) and semen (PRM2, SEMG1), and 3 housekeeping genes (ACTB, GAPDH and RPL19). We verified the system’s specificity, sensitivity, and ability to detect mixed and aged samples. In terms of specificity, most of the selected genes had good specificity, but there were some cross-reactions that were hard to avoid. In order to ensure the accuracy of identification, we determined that the target body fluid was contained only when the three housekeeping genes and two specific mRNA markers were simultaneously detected. In terms of sensitivity, we found that different types of samples had different sensitivities. For example, when using 10 ng RNA for vaginal secretions and menstrual blood samples, some specific target genes were not detected and could not be correctly determined; for blood samples, even when 0.01 ng RNA was used, the RFU value of the target gene is still above 10 000. However, there are only a small amount of test materials in actual cases; it is difficult to quantify the extracted RNA. For five kinds of body fluids, 2 μL of RNA extracted from a 1 mm2 sample could all detect housekeeping genes and corresponding target genes, and the correct body fluid could be determined (Except 1 vaginal secretion sample). The target genes of the mixed components were detected in all 16 mixed samples, and correct mixed component determinations could be made, proving the system’s good ability to identify mixed samples. Almost all housekeeping genes could be detected in 14 aged samples, but only 7 were correctly identified. Because no specific target genes were detected in the remaining samples, correct determinations could not be made. Consequently, the system needs to be further optimized. For HBD1 and MMP7 with poor specificity, other vaginal secretion and menstrual blood-specific genes will be screened and verified for replacement. The instability of mRNA results in relatively poor test results for aged samples. In practical applications, other genetic markers with better stability should be used to determine the results. In general, lots of studies have been demonstrated the usability of mRNA profiling to the identification of forensic relevant body fluid. According to the comprehensive assessment of the one-step RT-PCR strategy in this study, the one-step profiling assays can be a reliable and economical method for the simplified, accurate, and simultaneous analysis of tissue-specific biomarkers for the discrimination of body fluid origin. It shows good application potential in forensic body fluid identification work.

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.

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

Latent fingerprints are of the most important physical evidence in forensic identification though most of them are invisible, requiring physical or chemical treatments to enhance their visualization. To date, various of methods have been exploited for latent fingerprints to enhance their visualization. However, with the update of criminal methods, the traditional methods of developing latent fingerprints are limited. Among them, the physical developing methods are not sensitive enough to the latent fingerprint on the surface of complex object, and during the developing operation, some powders or toxic substances are easy to float in the air, which destroy the health of the experimenters. The chemical developing methods also have the disadvantages including cumbersome operation, high cost, toxicity and impact on DNA detecting. As specialized materials, nanomaterials have been widely used in medical equipment, electronic equipment and coatings. In the field of latent fingerprint development, nanomaterials have also attracted extensive attention from researchers because of their good adsorption, non-floatability in the air, wide applicability and less impact on DNA detecting. The research progresses home and abroad has been comprehensively reviewed in this article about various nanomaterials in latent fingerprints development. These nanomaterials mainly in latent fingerprints development cover metal nanomaterials, non-metallic nanomaterials, metal oxide nanomaterials and the other nanomaterials, including the mechanism, synthesis methods and adaptability. In addition, the future trends of nanomaterials in the development of latent fingerprints were suggested, in order to further promote the practical application of nanomaterials to develop latent fingerprints.

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

This paper aims to validate the multiplex amplification system of 9 CpG sites reported in the literature, and explore its applicability in the Chinese population. The SNaPshot multiplex amplification system was validated in terms of accuracy, analysis of the initial amount of converted DNA templates, and detection of mixed samples. A total of 236 samples of five types of body fluids including saliva, semen, blood, vaginal secretion, and menstrual blood were selected. The SNaPshot multiplex amplification system was used to detect the methylation values of 9 CpG sites. The detection threshold of CpG sites was that the methylation value is greater than 0.1. Analyze the starting amount of transformed DNA templates in this system after converting DNA using sodium bisulfite (template amount ranging from 0.5 ng to 10 ng). DNA extracted from four body fluids, including saliva, semen, blood, and vaginal secretion, were mixed in the following ratios: 1∶1, 1∶5, 1∶10, and 1∶20. Finally, the detection data set of 232 samples of the five types of body fluids was used to optimize the existing body fluid source determination method. The train set (n=162) was used to construct a random forest model, and the test set (n=70) was used to predict the body fluid type and evaluated the predictive performance of the model. Furthermore, an external data set (n=40) was added to validate the prediction model. In saliva, semen, blood, vaginal secretion samples, and menstrual blood, the body fluid type was determined directly based on the specific sites of body fluids, and the accuracy rates of body fluid identification were 100%, 98%, 98%, 94% respectively. Due to the influence of the menstrual cycle, some sites were missing, and the average accuracy of menstrual blood identification was 21%. This system could effectively detect the amount of transformed DNA from 1 ng to 10 ng. Among the mixed sample, both body fluid sources were correctly identified in all 1∶1 mixed samples. The main components could be detected in the other mixed samples (ratio 1∶5, 1∶10 and 1∶20), while there was a significant difference in the detection of secondary components. A random forest model was built from 232 samples, and the accuracy of identifying the five fluid sources in both the test and validation sets was 100%. The above results show that the multiplex amplification system has high accuracy for the identification of saliva, semen, blood and vaginal secretion, and is suitable for the identification of trace samples, mixed samples (ratio 1∶1) or main components of other ratios. Compared with direct interpretation based on body fluid specific peaks, the new random forest model can better identify menstrual blood. In summary, the multiplex amplification system for tissue identification of five types of forensic body fluids based on DNA methylation is potential for good forensic application.

Hydatidiform mole (HM) is a kind of gestational trophoblastic disease caused by abnormal fertilization. The presence of abundant paternal genetic information plays a key role in the pathogenesis of hydatidiform mole. Paternity testing involving hydatidiform mole tissue is relatively rare in forensic practice. In the case discussed, the aborted tissue originating from a female victim under 14 at the time of her pregnancy was diagnosed with hydatidiform mole. It was morphologically consistent with hydatidiform mole, and showed typical genotyping features of a complete hydatidiform mole (CHM) fertilized by single sperm, i.e. homozygous in all 23 loci. The alleged suspect was not actually the biological father of the aborted embryonic tissue. The author consulted a large number of literatures to analyze the experimental results. Unexplained paternity incompatibility suggests a different biological father, who was finally found through comparison and retrieval in the DNA database. Further STR testing was performed using VeriFiler^TM Plus kit, SureID^® X37 kit and Yfiler^TM. Platinum Casework kit. One allele was detected in 23 autosome and 35 X chromosome STR loci from embryonic tissue, which was found in the genotype of the focused suspect. The STR typing technology could determine the type of hydatidiform mole and provide clues and evidence for the identification. This paper may provide enlightenment for the handling of similar cases.

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

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

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

At present, in genetic identification, the detection of autosomal STR loci and the calculation of half sibling index using the ITO method can infer whether there is a genetic relationship among the identified individuals. However, the ITO method sometimes cannot provide a clear judgment basis for calculating likelihood rates. Therefore, multiple genetic markers can be detected to assist in judgment and different calculation methods can be used for comprehensive analysis to obtain evidence support for determining genetic relationships. Half sibling identification is a difficult point in genetic relationship identification, especially in heterosexual half sibling relationships, which cannot be assisted by the genetic rules of X-STR or Y-STR, which further increases the risk and difficulty of identification. In the case where only two individuals participate in identification, it is also impossible to analyze through family reconstruction methods. Therefore, in a case of half sibling relationship identification, this article detected genetic markers such as autosomal STR, mtDNA, and SNP, and comprehensively used various analysis methods such as ITO, discriminant function, mitochondrial high variability region polymorphism, and state consistency based on allele frequency estimation to infer the degree of kinship. Through mutual verification of multiple genetic marker detection results, reliable identification results were finally obtained.

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

This paper aims to establish a solid-phase support liquid-liquid extraction-high performance liquid chromatography/triple quadrupole tandem mass spectrometry (SLE-HPLC-MS/MS) method for the detection of scopolamine, anisodamine and atropine in blood and urine. The effects of protein precipitated method, solid phase extraction method and solid-phase supported liquid-liquid extraction method on the extraction of drugs from blood and urine samples were investigated. Scopolamine, anisodamine and atropine, in blood and urine samples were analyzed by high performance liquid chromatography triple quadrupole tandem mass spectrometry. The results showed that the recovery rate of solid-phase supported liquid-liquid extraction was the highest. The linear relationship between the concentration of scopolamine, anisodamine and atropine in blood and urine and the peak area (r>0.999 2) was good in the range of 0.1－100 ng/mL (blood) and 0.5－100 ng/mL (urine). The minimum detection limit of scopolamine, anisodamine and atropine in blood was 0.01 ng/mL, and the quantitative limit was 0.1ng/mL; The minimum detection limit of scopolamine, anisodamine and atropine in urine was 0.05 ng/mL, and the quantification limit was 0.5 ng/mL. The solid-phase supported liquid-liquid extraction liquid chromatography/tandem mass spectrometry method is characterized by simple operation, less solvent usage and high recovery rate. It is suitable in the detection of scopolamine, anisodamine and atropine in blood and urine for three types of drug poisoning cases (incidents).

Dried blood spot (DBS) is a sampling technique in which a small volume of blood is collected on a specific type of filter paper. Since the 1960s, DBS has been primarily used for the newborn screening of metabolic diseases. With the development of highly sensitive analytical instruments, the dried blood spot technique allows for accurate quantification of ethanol, stimulants, drugs of abuse and heavy metal elements in biological samples. Due to its obvious advantages, the application of DBS in forensic toxicology has witnessed significant growth in the past decade, which is highlighted and presented in this article. Some challenges and suggestions of dried blood spot applications were summarized for the further research as well.

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

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.

The means and forms of drug transmission are becoming increasingly diverse, posing significant challenges to public security investigations, laws and regulations supervision, and identification technology. In terms of identification technology, the detection of isomers, which can easily lead to misjudgments, remains particularly challenging. The comprehensive analysis of multidimensional detection methods has emerged as a trend in addressing the identification and regulation of new psychoactive substances. Currently, a notable synthetic cathinone called 2-dimethylamino- 1-[3,4-(methylenedioxy)phenyl]-1-pentanone(N,N-dimethylpentylone) warrants attention. This paper aims to develop an analytical method for identifying N,N-dimethylpentylone as a new synthetic cathinone. The processed unknown samples are analyzed using a combination of gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS), nuclear magnetic resonance (NMR) including ¹H-NMR, ¹³C-NMR, H-H COSY, HSQC, HMBC, and Fourier transform infrared spectroscopy (FTIR). GC-MS analysis results show that the qualitative ion fragments are m/z 100.1, m/z 58.1, m/z 121.1, m/z 149.0, with a retention time at 11.888 minutes. LC/MS analysis results show that the parent ion is m/z 250.05, and the product ions are m/z 100.10, m/z 205.10, and m/z 135.10, with a retention time at 9.772 minutes. FTIR analysis results show that there area carbonyl absorption peak at 1 676 cm^-1, and a benzene ring vibration absorption peak at 1 506 and 1 441cm^-1. One-dimensional nuclear magnetic spectrum results show 9 hydrogen signals with different chemical shifts and 14 carbon signals with different chemical shifts in the unknown compound. Two-dimensional NMR analysis results show that the unknown compound conforms to the structure of N,N-dimethylpentylone. In summary, N,N-dimethylphenylone components are detected in the unknown sample.

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

The subjective likelihood ratio paradigm of pattern features is the theory and method for the interpretation of evidence significance of feature findings of pattern evidence. Subjective likelihood ratio (LR) of pattern features is the probability ratio of occurrence of pattern features assigned under the two opposing propositions representing the prosecution and defense respectively, based on expert knowledge or the combination of expert knowledge and data. The proposition hierarchy for evaluating the subjective LR of pattern features includes source level and active level. Subjective LR of pattern features expresses the relative support direction and intensity of the pattern features findings for the propositions of the prosecution and defense, providing qualitative evidence value for decision makers to determine disputed factual propositions. Decision makers will determine the propositions facts based on subjective LR opinions of pattern features, or based on the posterior probabilities of the propositions derived from LR through Bayes’ theorem, combined with other evidence in the case, in the way to exclude reasonable doubts. The subjective LR paradigm of pattern features differs significantly from the traditional paradigm we are accustomed to in terms of scientific logic and the formation, expression, understanding, and reasoning application of opinions, and it also differs from the objective LR paradigm of DNA feature. This presents new requirements and challenges for forensic examiner and decision-makers.

The likelihood ratio paradigm of facial similarity score is the theory and method for interpreting the evidential significance of score finding from facial comparison. Facial similarity score likelihood ratio is the latest method of Bayesian likelihood ratio paradigm for forensic science. The likelihood ratio (LR) of facial similarity score is the ratio of the occurring probability of the facial score finding quantitatively assigned based on the probability distribution data of facial scores, under a pair of conflicting propositions that usually represent the claims of the prosecution and defense parties. The propositions typically deal with the question of whether a facial image with unknown identity collected at a crime scene comes from a suspect with known identity. The face score LR expresses the relative support direction and strength of the face score finding for the propositions of the prosecution and defense parties, providing quantitative evidence value for decision-makers to determine the disputed fact of the face source. The decision maker determines the fact of the facial source proposition based on the facial score LR opinion, or the posterior probability of the facial source proposition derived from the LR and the prior odds through Bayesian law, combined with other evidence, to exclude reasonable doubt. The likelihood ratio paradigm of facial similarity score is completely different from the traditional paradigm we are accustomed to in terms of scientific logic, opinion formation, expression, understanding, and reasoning applications. It also differs from the widely used LR paradigm of DNA feature findings, which poses new requirements and challenges for forensic examiner and decision-makers.

The interpretation paradigm of forensic findings is undergoing a shift from the traditional paradigm of categorical conclusions to the likelihood ratio paradigm of forensic findings. The forensic likelihood ratio paradigm, DNA evidence as a typical one, has improved the validity of forensic opinions and transformed the reasoning and proof mode of forensic opinions. The forensic likelihood ratio paradigm is based on the Bayesian likelihood ratio framework. The likelihood ratio (LR) of the forensic findings is assigned under a pair of opposing source or active propositions advocated by the prosecution and defense, and the LR is used as the evidence value of forensic findings relatively supporting the propositions of both prosecution and defense parties. It provides LR opinions of forensic findings to help decision-makers infer and determine disputed propositional facts on the source or active level. The forensic likelihood ratio paradigm is completely different from traditional paradigms in terms of scientific basis, opinion formation, expression, understanding, and reasoning application, which poses new requirements and huge challenges for forensic examiner and decision-makers in criminal proceedings.

Footprint features, as one of the biological features of the human body, play an important role in the field of personal identification. At present, most research on footprint recognition focuses on footprint images as experimental data, using deep learning algorithms as the foundation and relying on auxiliary algorithms to complete high-precision footprint recognition tasks. However, there is a problem with models built on footprint images. Due to the similarity of footprints of different people, as the number of samples increases, the differences between the features of footprints of different people will continue to decrease, leading to an increasing false detection rate of the model. In order to reduce the interference of similarity between footprints on model recognition ability, this paper takes dynamic footprints as the research object and proposes a dynamic footprint retrieval method based on multi-class feature fusion. The proposed method uses a spatio-temporal fusion module to integrate the spatio-temporal information of footprints, so that the footprint recognition method is not limited to the apparent information of footprints. Firstly, the convolutional neural network is used to extract the frame level features of dynamic footsteps, and then the feature fusion module calculates the complete apparent features of the fused dynamic footprints through a trainable weight matrix and frame level features. Secondly, the temporal aggregation branch of the spatio-temporal feature fusion module is used to extract long-term temporal features within frame level features, and then the long-term temporal features are fused with frame level features through orthogonal fusion calculation method to form spatio-temporal features. Finally, the visual features and spatio-temporal features are fused for dynamic footprint retrieval. A comparative experiment is conducted on a dynamic footprint dataset of 200 people with existing deep learning algorithms, and the experimental results shows that this method achieved better performance, with Rank1 and mAP being 85.39% and 55.28%, respectively.

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

The objective likelihood ratio (LR) paradigm of DNA feature is the theory and method for interpreting the evidential significance of DNA analysis findings. It is a typical representative of the forensic Bayesian likelihood ratio paradigm. The DNA feature’s objective LR is the ratio of the probability of DNA feature findings quantitatively assigned based on model and data, in the context of two alternative propositions typically advocated by both of the prosecution and defense. The hierarchy of propositions includes the sub-sub source level, sub-sources level, sources level, and activity level. The LR of DNA feature findings expresses the relative support direction and strength of the DNA findings for the propositions of the prosecution and defense, providing quantitative evidence value for decision-makers to determine disputed propositional facts. Decision makers will determine the propositional facts without reasonable doubt, based on the LR opinions of DNA findings or the posterior probability of the propositions derived from LR opinions and Bayesian laws, and combined with other evidence in the case. The DNA feature’s objective LR paradigm is completely different from the traditional paradigm that we are used to in terms of scientific logic, opinion formation, expression, understanding, and reasoning applications, which poses new requirements and challenges for forensic examiner and decision-makers.

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.

There are few cases of human death caused by rocks striking the human body. Generally, such cases occur in remote mountainous areas, lacking DNA, videos, and other direct evidence, so forensic pathologists play a crucial role in providing essential references for these cases. This article analyzes the injuries resulting from a fatal case in which a massive rock struck a person’s right upper arm and right pars lumbalis. It can be found that the rocks caused irregular longitudinal pectinate exfoliation on the posterolateral right upper arm, irregular fusiform lacerations, transverse cuneiform humeral fracture, multiple inward-type rib fractures in the right axillary segment, penetrating lacerations in the right lung. The authors sort out injury characteristics, summarize the injury mechanisms, and distinguish them from injuries with similar features, such as those caused by high falling and traffic accidents. The purpose is to provide an analytical framework for similar injury cases and then serve as a basis for forensic examination.

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

In 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.

When dealing with major emergencies, it poses the greatest challenge for public security law enforcement departments to timely, accurately and intelligently process the massive and complicated multi-source heterogeneous police data transmitted by various front-end collection devices. Under the background of major emergencies, the effectiveness of police work hinges on the real-time data collection, the accuracy of processing, and the intelligence of decision-making. This necessitates a police data system capable of rapid response, accurate analysis, and intelligent decision-making. However, current police data processing systems still rely on manual screening and analysis, resulting in inefficiencies during major incidents. Therefore, how to leverage massive police data and fully extract valuable information from it is one of the difficult problems to be solved in the process of police modernization. This paper first conducts a thorough analysis of the current situation and problems of police data application in China’s police work during major emergencies, followed by the proposal of countermeasures for the in-depth application of police data. Ultimately, digital twin and knowledge graph technology are combined to realize the intelligent analysis and application of massive police data, thereby establishing a new policing model with capabilities for security risk perception and situation analysis.

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

ENFSI has released a series of important documents, including Vision of the European Forensic Science Area 2030, ENFSI Strategic Plan 2023-2026, and ENFSI Action Plan 2023-2024, which collectively chart the future direction and strategy of forensicc science in Europe. The field of court science in Europe is facing the challenges and opportunities of emerging technologies, in particular the application of advanced technologies such as Artificial Intelligence, Big Data Management, aiming to improve the reliability and efficiency of court science outcomes. ENFSI is committed to promoting innovation and development of forensicc science in Europe by means of clear objectives and action plans. The development strategy of European forensic science has important inspiration for the progress of forensic science in China. Through in-depth understanding and drawing on the European experience, combined with the actual situation in China, we can formulate a more scientific and reasonable development path to promote China’s forensic science to a new height, and provide strong support for the construction of the rule of law and judicial justice.