基于胜任力的测查式公务员培训新探——以湖北省省直机关处级公务员为例

本研究以湖北省省直机关处级公务员为例,探索构建以胜任力为导向的测查式公务员培训模式。本文运用胜任力理论和素质测评技术,构建省直机关处级公务员胜任力模型,依据胜任力指标对处级公务员能力现状进行测查和分析,探求其实际能力与岗位胜任力之间的差距,在突出科学测查环节基础上确定培训需求,设计多样化的培训方案,以提升公务员胜任力水平从而提升其工作绩效。     

National Council of Juvenile and Family Court Judges Passes a Set of Practice Reform Resolutions

The National Council of Juvenile and Family Court Judges (NCJFCJ) recently passed resolutions and policy statements on how to improve the lives of youth and families involved with juvenile or family courts. These resolutions address the needs of homeless youth and their families, juvenile probation and adolescent development, and the need for independent oversight of youth confinement facilities.     

THEORIES OF REGULATORY IMPACT: THE ROLES OF THE REGULATOR, THE REGULATED, AND THE PUBLIC

Berry reviews the major theories for why legislative intent differs from regulatory output including: (1) theories of commission inadequacy, (2) group theories of political science, and (3) group theories of regulation offered by economists. He reviews the salient literature in each type and concludes that none alone incorporates a sufficient explanation for the wide range of regulatory outcomes. Such an explanation, he maintains, requires an analysis of all three actors, and of the variation in the incentives, resources, and goals of each.     

No Five-Finger Exercise: Digital Law to Manage Legal Complexity

This article considers the following. Digital coding of information is far less extensive than the complexity and much less radical than the digital coding of selection among information which establishes the legal system. Niklas Luhmanns theory is the principal example of an appreciation for the latter. Luhmann presented the design for understanding law as autopoietic, a law legitimated and sustained by legal procedure.     

Body Morph: Feasibility Testing of an Interactive CD-ROM to Teach Young Adolescents about Puberty

The aim of this pilot study was to examine the feasibility of an interactive computer program among 34 sixth and seventh grade children and to assess the potential for knowledge acquisition about puberty. Based on a developmental self-esteem approach to teach children about their bodies, the Body Morph program was designed to maximize the educational experience by: (1) tailoring puberty information to boys or girls, and (2) creating a confidential experience. Preliminary results indicated that students significantly improved their knowledge about puberty after using the program. Satisfaction among boys and girls for this type of educational approach suggests that it is a compelling format from which to learn about puberty.This research project was funded with the support of the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) 1R43DK061269-01A2 to develop an educational computer program to improve body image in middle schoolers.     

Evaluation and classification of fentanyl-related compounds using EC-SERS and machine learning

Multiple analytical techniques for the screening of fentanyl-related compounds exist. High discriminatory methods such as GC–MS and LC–MS are expensive, time-consuming, and less amenable to onsite analysis. Raman spectroscopy provides a rapid, inexpensive alternative. Raman variants such as electrochemical surface-enhanced Raman scattering (EC-SERS) can provide signal enhancements with 10¹⁰ magnitudes, allowing for the detection of low-concentration analytes, otherwise undetected using conventional Raman. Library search algorithms embedded in instruments utilizing SERS may suffer from accuracy when multicomponent mixtures involving fentanyl derivatives are analyzed. The complexing of machine learning techniques to Raman spectra demonstrates an improvement in the discrimination of drugs even when present in multicomponent mixtures of various ratios. Additionally, these algorithms are capable of identifying spectral features difficult to detect by manual comparisons. Therefore, the goal of this study was to evaluate fentanyl-related compounds and other drugs of abuse using EC-SERS and to process the acquired data using machine learning—convolutional neural networks (CNN). The CNN was created using Keras v 2.4.0 with Tensorflow v 2.9.1 backend. In-house binary mixtures and authentic adjudicated case samples were used to evaluate the created machine-learning models. The overall accuracy of the model was 98.4 ± 0.1% after 10-fold cross-validation. The correct identification for the in-house binary mixtures was 92%, while the authentic case samples were 85%. The high accuracies achieved in this study demonstrate the advantage of using machine learning to process spectral data when screening seized drug materials comprised of multiple components.