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Abstract Considerable research shows that crime is concentrated at a few proprietary places: addresses and facilities. Emerging research suggests that proprietary places may radiate crime: activities at a place increase the risk of crime in the area around it. Weaknesses in the research create uncertainty about radiation, so we need more rigorous research. To conduct this research, we need a theory of crime radiation that operates at two spatial levels: the proprietary place and the area. This paper describes such a theory. Our theory states that crime radiation stems from the interaction between place management decisions at the place and offenders searching for opportunities in the area. Place managers create crime opportunities inside and outside their places. Offenders exploit place managers’ creations by deliberately searching for opportunities or by chancing upon the opportunities. The ways place managers and offenders interact gives rise to three types of crime radiation: hot dot, veiled dot, and cold dot. Finally, we propose questions crime scientists should answer to better understand crime radiation.

Abstract. Gallbladder polypoid lesions (GPLs) refer to any elevated lesion of the mucosal surface of the gallbladder wall, and the prevalence is estimated to be between 0.9% and 12.1%. GPLs include benign polyps and malignant polyps. Benign polyps are further classified as non-neoplastic polyps and neoplastic polyps. Cholesterol polyps are the most common benign polyps and adenocarcinoma is the main type of malignant polyp. Hepatitis B virus infection, liver function abnormalities, dyslipidemia, and obesity are the main risk factors for GPLs. Studies of biological mechanisms have focused on malignant gallbladder polyps, the development of which is regulated by hormone levels in vivo, gut microbiota, inflammation, oxidative stress, Salmonella typhimurium, and related molecules. Diagnostic modalities include chemical examination and imaging examination, with imaging examination currently being the mainstay. Treatment of patients with GPLs is based on the presence or absence of symptoms, age, size of the polyps, tendency of the polyp to increase, and risk factors for symptomatic malignancy to determine whether surgery should be performed.

Abstract Background Diurnal and nocturnal mammals have evolved distinct pathways to optimize survival for their chronotype-specific lifestyles. Conventional rodent models, being nocturnal, may not sufficiently recapitulate the biology of diurnal humans in health and disease. Although diurnal rodents are potentially advantageous for translational research, until recently, they have not been genetically tractable. The present study aims to address this major limitation by developing experimental procedures necessary for genome editing in a well-established diurnal rodent model, the Nile grass rat (Arvicanthis niloticus). Results A superovulation protocol was established, which yielded nearly 30 eggs per female grass rat. Fertilized eggs were cultured in a modified rat 1-cell embryo culture medium (mR1ECM), in which grass rat embryos developed from the 1-cell stage into blastocysts. A CRISPR-based approach was then used for gene editing in vivo and in vitro, targeting Retinoic acid-induced 1 (Rai1), the causal gene for Smith-Magenis Syndrome, a neurodevelopmental disorder. The CRISPR reagents were delivered in vivo by electroporation using an improved Genome-editing via Oviductal Nucleic Acids Delivery (i-GONAD) method. The in vivo approach produced several edited founder grass rats with Rai1 null mutations, which showed stable transmission of the targeted allele to the next generation. CRISPR reagents were also microinjected into 2-cell embryos in vitro. Large deletion of the Rai1 gene was confirmed in 70% of the embryos injected, demonstrating high-efficiency genome editing in vitro. Conclusion We have established a set of methods that enabled the first successful CRISPR-based genome editing in Nile grass rats. The methods developed will guide future genome editing of this and other diurnal rodent species, which will promote greater utility of these models in basic and translational research.

The solidification process of Ni-based superalloy K465 was examined using DTA and X-ray diffraction. The impact of melt structure on solidification process and the morphology of MC carbide and γ′-phase was analyzed. It was observed that an increase in melt peak temperature led to a significant decrease in precipitation temperature for carbides, but only a slight increase in the formation temperature of matrix and eutectic microstructure. The X-ray diffraction results showed that some ordered structures were detected in the sample with melt peak temperature of 1450 °C and 1500 °C, but not in the sample with melt peak temperature of 1360 °C. Furthermore, the ordered structure displayed similar crystalline features with M6C carbide. As the melt peak temperature increased, the carbides changed from block type to script type morphology. This phenomenon was likely caused by the formation of ordered structures at high temperatures. However, the microstructure of γ matrix and γ′ precipitates remained unaffected by the melt peak temperature.

Goaf instability poses significant hazards, affecting mine safety and public welfare. This study aims to evaluate the risk of goaf instability to enhance safety measures in mining operations. Thirteen key indicators were identified to construct a comprehensive evaluation index system. By integrating game theory, we combined subjective and objective weights to develop a constant weight model, which was subsequently improved by considering data distribution characteristics to develop a local variable weight model. The variable weight intervals were determined through cumulative frequency analysis of normalized factor indices, and the Monte Carlo method was employed to define weight adjustment parameters. Using the cloud model, we assessed the instability risk of goafs. Our results indicate that the variable weight model provides higher evaluation accuracy compared to the constant weight model, offering clearer and more distinguishable membership degrees for the evaluation outcomes, suggesting its potential for more precise risk assessments in mining operations.

Purpose: The psycho-social adaptation of cancer patients is very important, which affects the treatment, rehabilitation process and prognosis of patients, and is closely related to the subjective well-being and quality of life of patients. However, the key factors affecting the psycho-social adjustment of cancer patients are not clear yet. This study aims to evaluate the psycho-social adaptation of cancer patients and its influencing factors based on a meta-analysis. Basic procedures: The Systematic review was conducted following the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) checklist and guided by the society-to-cell model framework. Literature retrieval was conducted from the construction of the library to December 2023. Main findings: Fourteen pieces of literature were included in this study, with a total sample size of 2922 cases. Among the 14 literatures included, 9 were in English and 5 were in Chinese, published between 1991 and 2021. All of the 14 literatures were cross-sectional studies. According to the society-to-cells model framework, the influencing factors are divided into 5 levels: society, community, family, individual, and physiology. However, studies related to the cellular level are lacking. Principal conclusions: The psychosocial adaptation of cancer patients is affected by physiology, individual, family, community and society, among which age, education level, disease uncertainty, hope level, psychological pain, self-efficacy, social support, coping styles (facing, avoidance, submission, and emotion-oriented) are the main factors affecting the psychosocial adaptation of cancer patients. However, studies related to the cellular level are lacking. This may be due to the fact that most of the factors from the individual to the society level are intervenable, and most studies focus more on the mining of these levels of factors. However, the biological basis is crucial to the occurrence and development of diseases, and needs to be paid attention to by nursing staff, and further research on this level needs to be strengthened in the future.

ObjectiveNivolumab, recently proven in a phase 3 clinical trial (CheckMate 901) to enhance survival when combined with gemcitabine-cisplatin for advanced urothelial carcinoma. This study aimed to assess its cost-effectiveness against gemcitabine-cisplatin alone, from US and Chinese payers’ perspectives.MethodsA partitioned survival model was established to assess the life-years, quality-adjusted life-years (QALYs), lifetime costs, and incremental cost-effectiveness ratios (ICERs) of nivolumab plus gemcitabine-cisplatin versus gemcitabine-cisplatin alone as first-line treatment for advanced urothelial carcinoma. Univariate, two-way, and probabilistic sensitivity analyses were conducted to assess the model’s robustness. Additionally, subgroup analyses were performed.ResultsNivolumab plus gemcitabine-cisplatin and gemcitabine-cisplatin achieved survival benefits of 4.238 life-years and 2.979 life-years for patients with advanced urothelial carcinoma, respectively. Compared with gemcitabine-cisplatin, nivolumab plus gemcitabine-cisplatin resulted in ICERs of $116,856/QALY in the US and $51,997/QALY in China. The probabilities of achieving cost-effectiveness at the current willingness-to-pay thresholds were 77.5% in the US and 16.5% in China. Cost-effectiveness could be reached if the price of nivolumab were reduced to $920.87/100mg in China. Subgroup analyses indicated that the combination had the highest probability of cost-effectiveness in patients under 65 or with an Eastern Cooperative Oncology Group (ECOG) performance-status score of 0 in the US and China.ConclusionNivolumab plus gemcitabine-cisplatin first-line treatment for advanced urothelial carcinoma results in longer life expectancy than gemcitabine-cisplatin, but is not cost-effective in China at current price. However, cost-effectiveness is likely to be achieved in most patient subgroups in the US.

Early-stage rheumatoid arthritis (RA) often harbors a hidden threat: sarcopenia, a silent contributor to osteoporosis and fractures. Assessing muscle mass and quality, particularly during active RA periods when joint mobility hinders strength and function evaluations, becomes crucial. Imaging techniques emerge as invaluable tools in this diagnostic puzzle, offering accurate quantification of muscle status in RA patients. This article delves into advancements of various imaging modalities in unraveling RA-related sarcopenia.

Abstract Background Due to the diversity and high sensitivity of the treatment, there were difficulties and uncertainties in the breast cancer surgical decision‐making process. We aimed to describe the patient's decision‐making behaviour and shared decision‐making (SDM)‐related barriers and facilitators in breast cancer surgical treatment. Methods We searched eight databases for qualitative studies and mixed‐method studies about breast cancer patients' surgical decision‐making process from inception to March 2021. The quality of the studies was critically appraised by two researchers independently. We used a ‘best fit framework approach’ to analyze and synthesize the evidence. Results Twenty‐eight qualitative studies and three mixed‐method studies were included in this study. Four themes and 10 subthemes were extracted: (a) struggling with various considerations, (b) actual decision‐making behaviours, (c) SDM not routinely implemented and (d) multiple facilitators and barriers to SDM. Conclusions Patients had various considerations of breast surgery and SDM was not routinely implemented. There was a discrepancy between information exchange behaviours, value clarification, decision support utilization and SDM due to cognitive and behavioural biases. When individuals made surgical decisions, their behaviours were affected by individual‐level and system‐level factors. Therefore, healthcare providers and other stakeholders should constantly improve communication skills and collaboration, and emphasize the importance of decision support, so as to embed SDM into routine practice. Patient and Public Contribution This systematic review was conducted as part of a wider research entitled: Breast cancer patients' actual participation roles in surgical decision making: a mixed method research. The results of this project helped us to better analyze and generalize patients' views.

Acute gastrointestinal bleeding (AGIB) is a common and serious condition that requires prompt diagnosis and effective treatment. Multi-phase contrast-enhanced computed tomography (CT) can rapidly identify the location and cause of active gastrointestinal bleeding, providing crucial information for subsequent treatment decisions, and has become a potential first-line tool for evaluating AGIB. In this report, we describe four cases of AGIB with clinical presentations of hematemesis and melena, accompanied by symptoms such as post-exertional palpitations, weakness, hypotension, confusion, and coma. Some patients also had abdominal distension, pain, dizziness, and chest tightness. The laboratory results showed anemia and positive fecal occult blood tests. Multi-phase contrast-enhanced CT allowed for the accurate localization of the active bleeding sites in the digestive tract. Among the four patients, two showed significant symptom improvement after treatment, one was transferred to a higher-level hospital due to critical illness and died from multiple organ failure, and one patient died from hemorrhagic shock due to lymphoma and multiple organ failure. This article discusses the value of multi-phase contrast-enhanced CT for the clinical diagnosis of AGIB and provides a literature review to summarize the imaging characteristics of AGIB on multi-phase contrast-enhanced CT to provide reliable evidence for the timely diagnosis of AGIB.

To ensure the safety of operations in the airfield area, it is crucial to address the increased conflict risks resulting from the growing number of vehicles and aircraft. Based on the complex network theory, this study takes aircraft and vehicles in the airfield area as nodes and selects five different indicators (average degree, average node weight, average weighted clustering coefficient, network density, and network efficiency) to characterize the operation state of the airfield area, so as to identify conflict risks. Building on this framework, an ATT-Bi-LSTM innovation prediction model based on LSTM network architecture is established to forecast the evolution of network indicators over time. By leveraging the algorithm to predict the temporal evolution of indicators, valuable insights into the future evolution of conflict risk can be gleaned from the prediction results. Real operational data from Xi’an Xianyang Airport are utilized as a demonstrative example in this study. The results of the experiments illustrate that the analytical approach proposed in this study achieves a precise identification of the indicators. The experimental results are then compared with data from other predictive models that operate on the same data set. Compared to alternative prediction models, the accuracy is increased by nearly 10%, reaching 89.78%. The results of the study help to accurately identify conflict risks in the airfield area in advance and provide strategic conflict avoidance strategies for relevant staff. This is essential to ensure the security of airfield area.

Goaf has become one of the most significant sources of hazard affecting the safety of metal and nonmetal mines. Evaluation of goaf stability is of paramount importance for mine safety production. First, 13 indices such as rock mass structure, geological structure, and goaf volume are selected based on engineering experience and literature review to assess the stability of goaf. These indices are classified according to the characteristics of each factor, and a stability evaluation system for underground mine goaf is constructed. Second, the analytic hierarchy process method based on group decision theory is utilized to calculate the subjective weight of each index. Additionally, the CRITIC method is used to calculate the objective weight of each index. Finally, game theory is used to combine the subjective and objective weights, thereby improving the accuracy of the index weight. The stability grade of the goaf is calculated using the normal cloud model. The FLAC3D numerical simulation is used to analyze the stability of the goaf and verify the accuracy of the model. The abovementioned model is utilized for assessing the stability of the goaf in the Duimenshan mine section. The results indicate that 90% of the goaf area is in a stable or relatively stable condition, while the remaining 10% is unstable. The evaluation outcomes were compared with FLAC3D numerical simulations, highlighting a scientific and reliable method with an accuracy rate of 90%.

Objective: This study aimed to evaluate the burden of lung involvement in patients with coronavirus disease 2019 (COVID-19) infection using four semi-quantitative measurement methods; compare the diagnostic performance of the four methods and consistency of results over time among observers; and find the fastest and most accurate evaluation method. Methods: Data of 157 patients with COVID-19 infection confirmed by real-time reverse transcription polymerase chain reaction (RT-PCR) using nasopharyngeal swab samples and hospitalized were retrospectively analyzed. According to the "Diagnosis and Treatment Plan for COVID-19 Infection (Tenth Edition)" issued by the China's National Health Commission, 87 patients were classified as medium type, 66 as severe, and 4 as critical. Based on the patients' chest CT images, two radiologists independently evaluated the severity of lung involvement using four semi-quantitative scoring systems and recorded the time taken by each scoring system. The intra-group correlation coefficient (ICC) was used to test the evaluation results consistency between the two radiologists using four semi-quantitative evaluation methods. Result: There were statistically significant differences in D-dimer, lymphocyte percentage, lymphocyte count, lactate dehydrogenase, and C-reactive protein between the severe and non-severe groups. The scoring results of the four semi-quantitative evaluation methods showed that the average score in the severe group was higher than that in the non-severe one. The consistency of the four semi-quantitative evaluation methods used by the two radiologists was poor, of which the ICC of the four methods was less than 0.75. The total severity score (T-SS) method showed the shortest average time. Conclusion: The semi-quantitative assessment method for detecting severity of pulmonary lesions in patients infected with COVID-19 based on vision and subjective experience has some limitations. The development of a quantitative assessment model based on artificial intelligence with good generalization in the future has important clinical application value.

AbstractBackground Bright light therapy (BLT) is widely used for treating Seasonal Affective Disorder (SAD). However, the neural mechanisms underlying the therapeutic effects of BLT remain largely unexplored. The present study used a diurnal rodent (Nile grass rats; Arvicanthis niloticus) to test the hypothesis that the therapeutic effects of BLT could be, in part, due to reduced neuroinflammation and/or enhanced neuroplasticity. Our previous research has demonstrated that compared to grass rats housed in a summer-like daytime bright light condition (1000 lux), those housed in a winter-like daytime dim light condition (50 lux) showed increased depression- and anxiety-like behaviours, as well as impaired sociosexual behaviours and spatial memory, similar to what is observed in patients suffering from SAD.Materials and Methods In the present study, male and female grass rats were housed under the winter-like dim daytime light condition (lights on 600–1800 hr, 50 lux). The experimental groups received daily 1-h early morning BLT from 0600-0700 using full-spectrum light (10,000 lux), while the control groups received narrowband red light (λmax, 780 nm). Following 4 weeks of treatment, the expression of several neuroinflammatory or plasticity markers was examined in the medial prefrontal cortex (mPFC), basolateral amygdala (BLA), and the CA1 of the dorsal hippocampus.Results For the neuroinflammatory markers, BLT reduced TNF-α in the BLA of females, and upregulated CD11b in the mPFC and IL6 in the BLA in males. For the neuroplasticity markers, BLT downregulated BDNF in the CA1 and TrkB in all three brain regions in females but upregulated BDNF in the BLA and CA1 in males.Conclusions These results indicate that the therapeutic effects of BLT on sleep, mood, and cognition may be attributed in part to mechanisms involving neuroinflammation and neuroplasticity in corticolimbic brain regions. Moreover, these effects appear to vary between sexes.

Background Shared decision-making is a practice that fully reflects the idea of patient-centered care, but its clinical implementation process is not ideal. Physicians are main participants to promote the implementation of shared decision-making, but there are few studies on their behaviors in shared decision-making. Objective To explore the process and main behavioral characteristics of physicians' participation in shared decision-making, offering evidence for the promotion and implementation of shared decision-making in clinical practice. Methods A mixed-methods convergent design was used to collect quantitative and qualitative data. In the quantitative study, convenience sampling method was used to select in-service physicians (n=360) from a grade A tertiary hospital in Shanghai to attend a questionnaire survey from May to December 2020 to understand the status of their participation in shared decision-making. K-means clustering was conducted to analyze the distribution characteristics of physicians' participation in shared decision-making. In the qualitative study, 23 physicians selected from the participants of the quantitative study using convenience sampling and purposive sampling from June to December 2020 were recruited to attend semi-structured interviews to explore the process and experience of their participation in shared decision-making and identify the main behavioral characteristics of them in each part of the process of shared decision-making. Results Quantitative data analysis: in all, 325 (90.3%) of the physicians who returned responsive questionaries were included for analysis. The average total score of their participation behavior in shared decision-making was (80.44±14.88) . The further analysis found that physicians had the highest participation in behaviors of "Explain the advantages and disadvantages of the treatment options to my patient" (4.38±0.74) and "I told my patient that there are different options for treating his/her medical condition" (4.30±0.84) ; physicians had the lowest participation in behaviors of "I made clear to my patient that a shared decision needs to be made" (3.72±1.22) and "My patient and I selected a treatment option together" (3.74±1.03) . The results of cluster analysis showed that the behavioral characteristics of physicians' participation in shared decision-making could be divided into three groups, namely informed consent group, partial patient participation group and shared decision-making group. Qualitative data analysis: physicians' participation behaviors in shared decision-making included building up the awareness of shared decision-making, providing the patient with information, examining patient comprehension of the information, clarifying patient values, co-assessment (of the feasibility of the options) , reaching a decision and decision implementation. Analysis of the mixed-methods research results showed that in the decision-making process, physicians paid more attention to the provision of information, and ignored the behavioral factors of promoting patient participation at the level of doctor-patient communication. The behavioral characteristics of physicians' participation in shared decision-making were different. Conclusion The physicians' participation behavior in shared decision-making was limited. And they may have misunderstandings about the behavior process of shared decision-making. To promote the practical implementation of shared decision-making in clinical practice, it is suggested to help physicians clarify the process of shared decision-making via interventions enhancing their knowledge and attitudes regarding shared decision-making, deepen their understanding of shared decision-making through scenario simulation and role-playing, and improve their recognition of patient values in decision-making.

The radio frequency fingerprint (RFF)-based device identification is a promising physical layer authentication technique. However, the wireless channel significantly affects the RFF features of the wideband wireless devices. In this paper, we extensively investigate the impact of channel variation on RFF identification using 20 MHz IEEE 802.11 signal. A time domain least mean square (LMS) equalization-based feature extraction method has been proposed. This method progressively restores the transmitted signal and preserves more details of RFF features than the classical frequency domain equalization (FDE) method. Moreover, a hybrid identifier is proposed to take advantage of both LMS-based and FDE-based methods. With the equalized samples, a four-layer convolutional neural network is designed for device identification. An experimental system has been set up to capture the waveform of 68 802.11 devices at different positions. The experimental results show that the LMS-based method outperforms others when the acquisition positions of the training dataset are the same as those of the testing dataset. On the other hand, the FDE-based method is shown to be more effective when the acquisition positions of the training dataset do not fully include those of the testing dataset. Moreover, the hybrid identifier achieves an improvement of 2% for overall identification accuracy.

Radio frequency fingerprint (RFF) identification is a promising technique for identifying Internet of Things (IoT) devices. This paper presents a comprehensive survey on RFF identification, which covers various aspects ranging from related definitions to details of each stage in the identification process, namely signal preprocessing, RFF feature extraction, further processing, and RFF identification. Specifically, three main steps of preprocessing are summarized, including carrier frequency offset estimation, noise elimination, and channel cancellation. Besides, three kinds of RFFs are categorized, comprising I/Q signal-based, parameter-based, and transformation-based features. Meanwhile, feature fusion and feature dimension reduction are elaborated as two main further processing methods. Furthermore, a novel framework is established from the perspective of closed set and open set problems, and the related state-of-the-art methodologies are investigated, including approaches based on traditional machine learning, deep learning, and generative models. Additionally, we highlight the challenges faced by RFF identification and point out future research trends in this field.

Abstract Despite decades of research into social disorganization theory, criminologists have made little progress developing community programs that reduce crime. The lack of progress is due in part to faulty assumptions in the theory: that neighborhoods are important; that residents are the primary source of control; and that informal social controls are emergent. In this paper we propose an alternative: the neighborhoods out of places explanation (NOPE). NOPE starts with property parcels (i.e., proprietary places), rather than neighborhoods. It focuses on the power and legal authority of people and institutions that own property, rather than on residents. It posits that control is intentional and goal driven, rather than emergent. We refer to those who own and control as creators. This small group of elites shape city areas and residents must adapt to the environments that suppress or facilitate crime. We discuss how shifting our focus to creators provides important new implications for theory, research, and policy in criminology.

BackgroundShared decision-making has promoted the transformation of the role of patients from passive recipients of health care to active participants and supervisors, deeply reflecting the idea of patient-centered care. Research on shared decision-making in China is still in its infancy. Physicians are major participants in decision-making, but their perspectives on shared decision-making have been rarely studied.ObjectiveTo explore physicians' perspective on shared decision-making, offering evidence for the implementation of shared decision-making.MethodsFrom May to July 2020, we conducted semi-structured interviews with 15 physicians selected by convenience sampling method from a grade A tertiary hospital in Shanghai for investigating their actual decision-making process, their views and attitudes about shared decision-making, and the obstacles to clinical implementation of shared decision-making. The interview results were analyzed by Colaizzi's method of data analysis.ResultsAmong the 15 physicians, 10 were male and 5 were female; aged 28-53 years old, with an average age of (38.4±7.0) years old; 4 residents, 5 attending physicians, 4 associate chief physicians, and 2 chief physicians; 5 physicians, 7 surgeons and 3 oncologists. Four themes including 15 subthemes were identified: differences in physicians thederstanding of policies related to shared decision-making; unclear understanding of physicians and patients' roles in shared decision-making; acknowledging the importance of shared decision-making; obstacles to clinical implementation of shared decision-making.ConclusionOur study suggests that improving physicians' understanding level of shared decision-making, and creating conditions facilitating clinical implementation of shared decision-making may promote the sound development of shared decision-making.

This paper studies the performance of location-based beamforming with the presence of artificial noise (AN). Secure transmission can be achieved using the location information of the user. However, the shape of the beam depends on the number of antennas used. When the scale of the antenna array is not sufficiently large, it becomes difficult to differentiate the performance between the legitimate user and eavesdroppers nearby. In this paper, we leverage AN to minimize the area near the user with eavesdropping risk. The impact of AN is considered for both the legitimate user and the eavesdropper. Closed-form expressions are derived for the expectations of the signal to interference plus noise ratios (SINRs) and the bit error rates. Then, a secure beamforming scheme is proposed to ensure a minimum SINR requirement for the legitimate user and minimize the SINR of the eavesdropper. Numerical results show that, even with a small number of antennas, the proposed beamforming scheme can effectively degrade the performance of eavesdroppers near the legitimate user.

The fifth-generation (5G) mobile cellular network is vulnerable to various security threats. Radio frequency fingerprint (RFF) identification is an emerging physical layer authentication technique which can be used to detect spoofing and distributed denial of service attacks. In this paper, the performance of RFF identification is studied for 5G mobile phones. The differential constellation trace figure (DCTF) is extracted from the physical random access channel (PRACH) preamble. When the database of all 64 PRACH preambles is available at the gNodeB (gNB), an index-based DCTF identification scheme is proposed, and the classification accuracy reaches 92.78% with a signal-to-noise ratio of 25 dB. Moreover, due to the randomness in the selection of preamble sequences in the random access procedure, when only a portion of the preamble sequences can be trained, a group-based DCTF identification scheme is proposed. The preamble sequences generated from the same root value are grouped together, and the untrained sequences can be identified based on the trained sequences within the same group. The classification accuracy of the group-based scheme is 89.59%. An experimental system has been set up using six 5G mobile phones of three models. The 5G gNB is implemented on the OpenAirInterface platform.