Forensic examiners regularly testify in criminal cases, informing the jurors whether crime scene evidence likely came from a source. In this study, we examine the impact of providing jurors with testimony further qualified by error rates and likelihood ratios, for expert testimony concerning two forensic disciplines: commonly used fingerprint comparison evidence and a novel technique involving voice comparison. Our method involved surveying mock jurors in Amazon Mechanical Turk (N = 897 laypeople) using written testimony and judicial instructions. Participants were more skeptical of voice analysis and generated fewer "guilty" decisions than for fingerprint analysis (B = 2.00, OR = 7.06, p = <0.000). We found that error rate information most strongly decreased "guilty" votes relative to no qualifying information for participants who heard fingerprint evidence (but not those that heard voice analysis evidence; B = -1.16, OR = 0.32, p = 0.007). We also found that error rates and conclusion types led to a greater decrease on "guilty" votes for fingerprint evidence than voice evidence (B = 1.44, OR = 4.23, p = 0.021). We conclude that these results suggest jurors adjust the weight placed on forensic evidence depending on their prior views about its reliability. Future research should develop testimony and judicial instructions that can better inform jurors of the strengths and limitations of forensic evidence.
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http://dx.doi.org/10.1111/1556-4029.14323 | DOI Listing |
Syst Rev
January 2025
Pharmacy Department, Hamad Medical Corporation, Doha, Qatar.
Introduction: Medication errors occur at any point of the medication management process and are a major cause of death and harm globally. The perioperative environment introduces challenges in identifying medication errors due to the frequent use of time-sensitive, high-alert medications in a dynamic and intricate setting. Pharmacists could potentially reduce the occurrence of these errors because of their training and expertise.
View Article and Find Full Text PDFNeural Netw
January 2025
City University of Hong Kong Shenzhen Research Institute, Shenzhen, China; Department of Mathematics, City University of Hong Kong, Hong Kong, China. Electronic address:
We consider kernel-based supervised learning using random Fourier features, focusing on its statistical error bounds and generalization properties with general loss functions. Beyond the least squares loss, existing results only demonstrate worst-case analysis with rate n and the number of features at least comparable to n, and refined-case analysis where it can achieve almost n rate when the kernel's eigenvalue decay is exponential and the number of features is again at least comparable to n. For the least squares loss, the results are much richer and the optimal rates can be achieved under the source and capacity assumptions, with the number of features smaller than n.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Mathematics and Engineering Physics, Faculty of Engineering, Mansoura University, Mansoura, Egypt.
This paper focuses on modeling Resistor-Inductor (RL) electric circuits using a fractional Riccati initial value problem (IVP) framework. Conventional models frequently neglect the complex dynamics and memory effects intrinsic to actual RL circuits. This study aims to develop a more precise representation using a fractional-order Riccati model.
View Article and Find Full Text PDFBackground: Polysomnography (PSG) is resource-intensive but remains the gold standard for diagnosing Obstructive Sleep Apnea (OSA). We aimed to develop a screening tool to better allocate resources by identifying individuals at higher risk for OSA, overcoming limitations of current tools that may under-diagnose based on self-reported symptoms.
Methods: A total of 884 patients (490 diagnosed with OSA) were included, which was divided into the training, validation, and test sets.
Comput Struct Biotechnol J
December 2024
Systems Biology Center, Key Laboratory of Engineering Biology for Low-carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China.
DNA holds immense potential as an emerging data storage medium. However, the recovery of information in DNA storage systems faces challenges posed by various errors, including IDS errors, strand breaks, and rearrangements, inevitably introduced during synthesis, amplification, sequencing, and storage processes. Sequence reconstruction, crucial for decoding, involves inferring the DNA reference from a cluster of erroneous copies.
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