刑事辨认证据规则初探

刑事辨认证据的认定与运用是各国司法实践中面临的相当复杂的问题。目前,我国的刑事辨认证据的运用尚处于无序、混乱的状态之中。辩认结论应当是一种独立的证据形式,应将辨认结论规定为法定证据种类之一,同时应确立非法辨认的排除规则,明确重复辨认的证据效力,确立辨认结论的补强规则等。     

北京汉族群体9个STR位点的频率分布及法医学应用

提供北京汉族群体9个STR基因座的频率分布资料，了解其在法医学中的应用价值。应用PCR技术对9个STR基因座分3组进行复合扩增，经PAG电泳分离、银染，扫描仪扫描，计算机判读并保存结果，对北京地区汉族无关个体9个基因座的基因频率分布进行调查。结果显示，上述9个基因座的杂合度为0．6419～0．8092，多态性信息总量为0．9999，鉴别机率为0．9999，匹配机率为2．0×10-9和非父排除率为0．9985。STR3组9个基因座的综合检验可应用于法医学个体识别和亲子鉴定，并达到同一认定的标准。     

复合扩增STR位点片段长度多态性的研究

本文介绍了以三个短串联重复序列（STR）位点为基础的复合扩增进行个体识别的技术。其特点是在同一扩增体系内对彼此独立的三个STR位点，即HUMTH01[AATG]nHUMFABP[AAT]nHUMARA[AGC]n进行复会扩增，扩增产物用聚丙烯酰胺凝胶电泳分离。经研究发现这三个STR位点具有高度多态性，扩增产物分子量在190～310bP之间，各位点等位基因数分别为6、9、16个，杂合度78．2％、85，0％、89．0％，个体识别率0．918、0．960、0．905，其累积的个体识别率达0．9997，高于pMCT118等位点，且扩增时间短，操作简便，为法医物证检验提供了一种高效的个体识别手段。     

根据胸骨形态变化推算年龄的研究

<正> 本文运用多元线性回归分析的方法,对74例成人汉族男性胸骨(19～48岁)的形态变化与年龄的关系进行了研究。通过胸骨上选定的7个形态变化标志点,依其各自的形态变化规律,制定出相应的等级评分标准。根据此标准对74例胸骨标本进行了观察评分。然后将所得原始数据和年龄一起输入电子计算机,求出由胸骨形态变化,计算年龄的多元回归方程: =10.78+0.82X_1+0.85X_2+0.80X_3+0.38X_4+2.51X_5+0.64X_6+3.02X_7±2.45 经方差分析表明P<0.01,回归高度显著。本文提出的胸骨判定年龄的方法,在无名尸骨的法医学鉴定中,具有实用价值。     

Links,Mitte, Rechts: Worauf grã¼ndet die ideologische Selbstidentifikation der Schweizer und Schweizerinnen?

Die Links‐Rechts‐Selbsteinschã¤tzung wird in Stimmverhaltensstudien hã¤ufig als ein äusserst zuverlã¤ssiger Prã¤diktor des individuellen Entscheids ausgewiesen. Dabei ist jedoch keineswegs klar, wie sie auf den Urnenentscheid Einfluss nimmt. Ã?ber ihre Verhaltensrelevanz besitzen wir deshalb nur wenig gesichertes Wissen, da nach wie vor umstritten ist, worauf die ideologische Selbsteinstufung fusst. Ist sie ein Surrogat der Parteisympathie, wird sie aus grundlegenden gesellschaftspolitischen Ã?berzeugungen hergeleitet oder ist sie â‐“ zumindest teilweise â‐“ ein Datenartefakt? Die empirische Analyse zeigt, dass eine Unterscheidung zwischen Parteigebundenen und ‐ungebundenen sowie der Einbezug der intervenierenden Variablen der politischen Informiertheit nã¶tig ist, um den Determinanten der ideologischen Selbstidentifikation auf die Spur zu kommen. Dies ermã¶glicht die Differenzierung von vier Analysegruppen, fã¼r die unterschiedliche Aussagen Geltung haben.     

Accuracy of comparison decisions by forensic firearms examiners

This black box study assessed the performance of forensic firearms examiners in the United States. It involved three different types of firearms and 173 volunteers who performed a total of 8640 comparisons of both bullets and cartridge cases. The overall false-positive error rate was estimated as 0.656% and 0.933% for bullets and cartridge cases, respectively, while the rate of false negatives was estimated as 2.87% and 1.87% for bullets and cartridge cases, respectively. The majority of errors were made by a limited number of examiners. Because chi-square tests of independence strongly suggest that error probabilities are not the same for each examiner, these are maximum-likelihood estimates based on the beta-binomial probability model and do not depend on an assumption of equal examiner-specific error rates. Corresponding 95% confidence intervals are (0.305%, 1.42%) and (0.548%, 1.57%) for false positives for bullets and cartridge cases, respectively, and (1.89%, 4.26%) and (1.16%, 2.99%) for false negatives for bullets and cartridge cases, respectively. The results of this study are consistent with prior studies, despite its comprehensive design and challenging specimens.     

三七中人参炔醇的提取、分离和鉴定

目的:提取、分离和鉴定三七中人参炔醇.方法:采用超临界CO2萃取三七脂溶性成分,硅胶柱层析、制备薄层分离,并通过波谱技术(UV、IR、MS、1HNMR、13CNMR)分析鉴定人参炔醇化学结构.结果:从三七中分离并鉴定了人参炔醇成分,得率为0.077%.结论:应用超临界CO2萃取三七中脂溶性成分并分离其中的人参炔醇,得率较高.     

Quantitative evaluation of latent fingermarks with novel enhancement and illumination

A variety of suspended silica and metal nanoparticles have been used over the last 20 years to enhance latent fingermarks. This study quantitatively evaluates enhancement of natural and sebum-enriched fingermarks from three adult subjects acquired with a consistent applied force on glass with a fingermark press using suspended commercially available polystyrene (PS) particles. Images of the enhanced fingermarks acquired with total internal reflection (TIR), or standard overhead white light (WL), illumination are compared with fingermarks enhanced with conventional methods including cyanoacrylate fuming. The different enhancement and illumination methods are quantified based on the brightness and contrast of the fingermark images, as well as the number of minutiae that can be identified and matched to those on an inked manually acquired “template” fingermark using automatic fingerprint identification system (AFIS) software. Enhanced fingermarks acquired with the press are shown to be more consistent than manually acquired fingermarks based on these metrics. The results demonstrate that TIR illumination from a large-area illuminator built in house gives enhanced fingermark images with more matched minutiae and contrast superior to that for WL illumination for all types of enhancement. “Wet-powdering” with PS particles gives fingermark images that are for the most part comparable in terms of the number of matched minutiae to fingermarks enhanced with more conventional methods, suggesting that this novel enhancement method has a performance comparable to conventional enhancement methods. Interestingly, the age of the fingermark appears to have almost no effect on this new type of enhancement; sebum-enriched fingermarks ranging in age from 12 h to 435 days appear to have statistically identical numbers of matched minutiae.     

Performance of forensic facial comparison by morphological analysis across optimal and suboptimal CCTV settings

Facial comparison is an important yet understudied discipline in forensics. The recommended method for facial comparison in a forensic setting involves morphological analysis (MA) with the use of a facial feature list. The performance of this approach has not been tested across various closed-circuit television (CCTV) conditions. This is of particular concern as video and image data available to law enforcement is often varied and of subpar conditions. The present study aimed at testing MA across two types of CCTV data, representing ideal and less than ideal settings, also assessing which particular shortcomings arose from less-than-ideal settings. The study was conducted on a subset of the Wits Face Database arranged in a total of 225 face pools. Each face pool consisted of a target image obtained from either a high-definition digital CCTV camera or a low-definition analogue CCTV camera in monochrome, contrasted to 10 possible matches. The face pools were analysed and scored using MA and confusion matrices were used to analyse the outcomes. A notably high chance corrected accuracy (CCA) (97.3%) and reliability (0.969) was identified across the digital CCTV sample, while in the analogue CCTV sample MA appeared to underperform both in accuracy (CCA: 33.1%) and reliability (0.529). The majority of the errors in scoring resulted in false negatives in the analogue sample (75.2%), while across both CCTV conditions false positives were low (digital: 0.3%; analogue: 1.2%). Even though hit rates appeared deceptively high in the analogue sample, the various measures of performance used and particularly the chance corrected accuracy highlighted its shortfalls. Overall, CCTV recording quality appears closely associated to MA performance, despite the favourable error rates when using the Facial Identification Scientific Working Group feature list.     

Presentation of acoustic characteristics with network models for dialect identification

Objective To explore the presentation of acoustic characteristics with network models for dialect identification so as to screen out the optimal singular model for automatic dialect classifier. Methods Four selected typical neural network models for acoustic feature extraction, SOM (self-organizing feature Map), RNN (recurrent neural network), LSTM (long short-term memory network) and CNN (convolutional neural network), were individually simulated through python. With the dataset containing typical dialects (6036 samples of 105 persons’ spoken voices) from 13 cities in Jiangsu province, three aggregates were respectively built up for purpose of training, verification and test at the division ratio of 6:2:2. The test aggregate was then edited into sub-aggregates of 3 and 10 seconds, having each further added of white noise to form the sub-aggregates owning signal-to-noise ratio (SNR) of 3 and 10 dB. Thus, 4 test aggregates were thereby produced, with each containing 1207 samples. The appropriate classifiers were chosen to evaluate the performance of four above-selected models into their operations of training, verification and test. For the dialect identification, every selected network model was verified of its ability to extract features from the test aggregates owning different SNR and duration. Results With the previously-normalized data and network parameters, the confusion matrices of models were obtained from the output data of 4 neural network models processing into 4 test aggregates, having resulted in the Macro-F1 and Micro-F1 scores that are useful and eligible for evaluation of multi-classification problem. The results showed that LSTM and CNN are significantly better of performance than SOM and RNN. SOM is obviously more sensitive to the SNR of test samples, though having poor identification accuracy with the 3dB test aggregate. RNN has the improved accuracy for dialect identification, yet having the insufficient representation ability to key information of long-term samples. LSTM achieves the optimal evaluation scores of 93.1% (Macro-F1)/92.7% (Micro-F1) with 10dB/10s test aggregate, excelling in overcoming the bug of RNN with its characteristic structural unit. CNN is stable of identification accuracy, not easily affected with the length of speech fragments, thereby having better performance in noise-resistibility for substandard recordings. Owning the nonlinear transformation operations of convolution and pooling, CNN model is of good nonlinear expression ability to demonstrate nice fitting performance for information representation in dialect classification although it is incompetent in real-time presentation with the identified material. Conclusions LSTM+CNN framework is of better acoustic characteristics performance and robustness, capable of meeting the further updating development and application of automatic dialect identification. Besides, the audio sample duration and SNR are still the key for a model (singular or coalesced from two or more) to improve its identification accuracy. © 2021, Editorial Office of Forensic Science and Technology. All rights reserved.