In the present study, we examined performance rating correlates of the Selection Validation Survey (SVS), an informant rating form used to describe the characteristics of newly hired public safety personnel following their initial training period. We correlated SVS ratings for = 174 police officers with aggregate scores derived from daily performance observation ratings provided by their field training officers (i.e., senior law enforcement officers assigned to train, observe, and evaluate police recruits during a formal 16-week training period). Results generally indicated moderate to strong associations between conceptually similar SVS ratings and field training officer scores, providing evidence that the SVS variables validly summarize performance-relevant data accrued during the field training period. For example, a single SVS item asking the rater to characterize the officer's overall field performance correlated highly in the expected direction (Spearman's rho = -.69) with a composite of daily ratings describing the officer's observed field performance and problem-solving skills. Taken together, these findings indicate that the SVS meaningfully and efficiently captures a range of important information regarding the performance and professional skills of new police officers, providing a useful validation criterion for predictors of police officer performance.
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http://dx.doi.org/10.1080/00223891.2023.2182692 | DOI Listing |
Sci Rep
December 2024
Department of Diagnostic Radiology, Dalhousie University, Halifax, Canada.
The goal of this study was to determine how radiologists' rating of image quality when using 0.5T Magnetic Resonance Imaging (MRI) compares to Computed Tomography (CT) for visualization of pathology and evaluation of specific anatomic regions within the paranasal sinuses. 42 patients with clinical CT scans opted to have a 0.
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December 2024
Jihua Laboratory, Foshan, 528000, China.
Surface-enhanced Raman scattering (SERS) technology has attracted more and more attention due to its high sensitivity, low water interference, and quick measurement. Constructing high-performance SERS substrates with high sensitivity, uniformity and reproducibility is of great importance to put the SERS technology into practical application. In this paper, we report a simple fabrication process to construct dense silver-coated PMMA nanoparticles-on-a-mirror SRES substrates.
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December 2024
Medical Image Analysis, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
Immune checkpoint inhibitor (ICI) treatment has proven successful for advanced melanoma, but is associated with potentially severe toxicity and high costs. Accurate biomarkers for response are lacking. The present work is the first to investigate the value of deep learning on CT imaging of metastatic lesions for predicting ICI treatment outcomes in advanced melanoma.
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December 2024
Department of Mechanical Engineering, Sejong University, Seoul, Republic of Korea.
Nonthermal plasma has been extensively utilized in various biomedical fields, including surface engineering of medical implants to enhance their biocompatibility and osseointegration. To ensure robustness and cost effectiveness for commercial viability, stable and effective plasma is required, which can be achieved by reducing gas pressure in a controlled volume. Here, we explored the impact of reduced gas pressure on plasma properties, surface characteristics of plasma-treated implants, and subsequent biological outcomes.
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December 2024
Department of Electrical and Electronic Engineering, Bangladesh University of Engineering and Technology, Dhaka, 1205, Bangladesh.
Prediction and discovery of new materials with desired properties are at the forefront of quantum science and technology research. A major bottleneck in this field is the computational resources and time complexity related to finding new materials from ab initio calculations. In this work, an effective and robust deep learning-based model is proposed by incorporating persistent homology with graph neural network which offers an accuracy of and an F1 score of in classifying topological versus non-topological materials, outperforming the other state-of-the-art classifier models.
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