Composite Machine Learning Algorithm for Material Sourcing,

This study developed a composite machine learning algorithm for attribution of materials of forensic interest (like ammonium nitrate) to original sources. k-nearest neighbor and random forest models were used for source elimination and classification, respectively, in a two-step, composite algorithm based on particle color, size/shape, and trace element concentration features. Novel approaches for simulation to supplement within-source reference features based on empirically measured multi-lot analyses, an improved hold-one-lot-out method for cross-validation, an assessment of the likelihood of the presence of a reference sample, fusion of the source probabilities from the respective classification models, and the calculation of metrics for assessing ensemble sourcing performance are described. Excellent sourcing predictions were obtained; the sourcing algorithm identified the correct source as the top choice 89% of the time, and the correct source was identified to be an average of 2.7 times more likely than the most likely incorrect source.     

Explaining non‐participation in deliberative mini‐publics

This article investigates citizens’ refusal to take part in participatory and deliberative mechanisms. An increasing number of scholars and political actors support the development of mini‐publics – that is, deliberative forums with randomly selected lay citizens. It is often argued that such innovations are a key ingredient to curing the democratic malaise of contemporary political regimes because they provide an appropriate means to achieve inclusiveness and well considered judgment. Nevertheless, real‐life experience shows that the majority of citizens refuse the invitation when they are recruited. This raises a challenging question for the development of a more inclusive democracy: Why do citizens decline to participate in mini‐publics? This article addresses this issue through a qualitative analysis of the perspectives of those who have declined to participate in three mini‐publics: the G1000, the G100 and the Climate Citizens Parliament. Drawing on in‐depth interviews, six explanatory logics of non‐participation are distinguished: concentration on the private sphere; internal political inefficacy; public meeting avoidance; conflict of schedule; political alienation; and mini‐public's lack of impact on the political system. This shows that the reluctance to take part in mini‐publics is rooted in the way individuals conceive their own roles, abilities and capacities in the public sphere, as well as in the perceived output of such democratic innovations.     

Assessing Stability and Change in Criminal Offending: A Comparison of Random Effects, Semiparametric, and Fixed Effects Modeling Strategies

An important theoretical problem for criminologists is an explanation forthe robust positive correlation between prior and future criminaloffending. Nagin and Paternoster (1991) have suggested that the correlationcould be due to time-stable population differences in the underlyingproneness to commit crimes (population heterogeneity) and/or thecriminogenic effect that crime has on social bonds, conventionalattachments, and the like (state dependence). Because of data andmeasurement limitations, the disentangling of population heterogeneityand state dependence requires that researchers control for unmeasuredpersistent heterogeneity. Frequently, random effects probit models havebeen employed, which, while user-friendly, make a strong parametricassumption that the unobserved heterogeneity in the population follows anormal distribution. Although semiparametric alternatives to the randomeffects probit model have recently appeared in the literature to avoid thisproblem, in this paper we return to reconsider the fully parametric model. Viasimulation evidence, we first show that the random effects probit modelproduces biased estimates as the departure of heterogeneity from normalitybecomes more substantial. Using the 1958 Philadelphia cohort data, we thencompare the results from a random effects probit model with a semiparametricprobit model and a fixed effects logit model that makes no assumptions aboutthe distribution of unobserved heterogeneity. We found that with this dataset all three models converged on the same substantive result—evenafter controlling for unobserved persistent heterogeneity, with models thattreat the unobserved heterogeneity very differently, prior conduct had apronounced effect on subsequent offending. These results are inconsistentwith a model that attributes all of the positive correlation between priorand future offending to differences in criminal propensity. Sinceresearchers will often be completely blind with respect to the tenabilityof the normality assumption, we conclude that different estimationstrategies should be brought to bear on the data.     

误收假币后故意使用行为的刑事责任问题初探

误收假币后故意使用的行为具有刑事可罚性,应当以使用假币罪定罪处刑。在犯罪认定时,客观要件之中的“使用”行为不应以置于流通为必要;只要能够根据基础事实予以合理推定其主观上为“明知”,就足以认定其符合本罪的主观要件。由于该行为的期待可能性较小,因此处刑时应结合我国针对误收假币后故意使用行为的立法变动情况,对于数额较大的情形原则上只判处罚金刑,而是否侵犯公私财产权这一随机客体也具有影响量刑的功能。     

误收假币后故意使用行为的刑事责任问题研究

误收假币后故意使用的行为侵犯了货币的法定地位、货币的公共信用，并可能侵犯公私财产权，因而具有刑事可罚性，应当以使用假币罪定罪处刑。在犯罪认定时，客观要件之中的“使用”行为不应以置于流通为必要；只要能够根据基础事实予以合理推定其主观上为“明知”，就足以认定其符合本罪的主观要件。由于该行为的期待可能性较小，因此处刑时应结合我国针对误收假币后故意使用行为的立法变动情况，对于数额较大的情形原则上只判处罚金刑，而是否侵犯公私财产权这一随机客体也共有影响量刑的功能。     

猪瘟病毒E2蛋白B细胞识别基序的筛选

根据猪瘟病毒(classical swine fever virus,CSFV)E2蛋白的序列设计并人工合成18条重叠多肽,与载体蛋白BSA偶联后,利用抗CSFV E2蛋白单克隆抗体SWmAb8筛选该蛋白上的B细胞表位;同时用12肽噬菌体随机肽库淘选E2单抗识别多肽序列。结果显示,多肽序列VSPTTLRTEV能被单抗SWmAb8识别;含SPTxLR基序的噬菌体能被单抗SWmAb8结合,且能被病毒抗原竞争性抑制。证实SPTxLR基序很可能是E2蛋白的线性表位之一,可诱导机体产生中和抗体。     

再论医疗事故争议纠纷的诉讼管辖

本文从正反两方面论述了医疗纠纷中以“侵权结果发生地”确定管辖权的合理性及必要性。并从司法实践出发,就如何从证据法学角度认定“结果发生地”的相关事实阐述了作者的观点。     

平板玻璃受热后折射率的变化规律初探

目的探索平板玻璃受热后折射率的变化规律。方法首先采用差示扫描量热仪测定玻璃样品的转变温度Tg,将样品加热至不同的温度(温度范围从室温到Tg以上),自然冷却至室温后再用GRIM3测量折射率。结果加热温度在Tg以下时,玻璃的折射率基本不变,加热温度在Tg以上时,折射率有明显变化。结论对于从火灾和爆炸现场提取的玻璃,在进行折射率比对时应当考虑现场高温对测量值的影响,并进行必要的修正。     

When random assignment fails: Some lessons from the Minneapolis Spouse Abuse Experiment

In this paper, we consider what may be done when researchers anticipate that in the implementation of field experiments, random assignment to experimental and control groups is likely to be flawed. We then reanalyze data from the Minneapolis Spouse Abuse Experiment in a manner that explicitly models violations of random assignment. As anticipated, we find far larger treatment effects than previously reported. The techniques developed should be useful in a wide variety of settings when random assignment is implemented imperfectly.