Improved Measurement of the Newtonian Gravitational Constant

The Newtonian gravitational constant, G, is one of the oldest known fundamental constants in nature, and yet it is known with the least precision of all other fundamental constants. The research group at IUPUI, in collaboration with Cal Poly Humboldt, will use multiple approaches within a singular torsion pendulum apparatus to precisely determine G. Specifically, measurements will be made using the angular acceleration feedback and time of swing methods in the same apparatus, which was carefully designed for reduced error in both techniques. We expect to obtain a measurement at the 2 ppm level using these new methods

The Effects of Sex-Segregation on Physical Education Fitness-Testing Results

The purpose of this study was to determine how sex-segregated fitness-testing impact PACER scores

Geospatial Temporal Crime Prediction Using Convolution and LSTM Neural Networks: Enhancing the Las Vegas Cardiff Model

According to the Department of Justice, more than half of violent crimes go unreported to law enforcement in the United States (Kollar et al., 2018). This data gap reduces the opportunity to implement proven solutions in the areas with the greatest need. In 1996, Dr. Shepherd developed the Cardiff Model with the aim of bringing together hospitals, law enforcement, and community leaders through the sharing of data. We partnered with ongoing efforts to implement the Cardiff Model in Las Vegas, Nevada. Our goal was to provide a geospatial temporal model that can predict the next 30 days of crime. By utilizing the Metropolitan Police Department\u27s (LVMPD) violent crime database, we were able to use a combination of long short-term memory (LSTM) and convolutional neural network (CNN) models to predict where and when violent crimes are likely to occur. Our total crime LSTM model produced an RMSE of 8.621 over a 30-day horizon. When incorporating the spatial component, our CNN and LSTM model produces an MSE of 0.0009 over the same horizon. These findings show that with sufficient latitude and longitude tracked violent crime data, we’re able to accurately produce predictive heat maps. This establishes a framework to expand on the current process which develops heat maps aggregated over historical time periods. By adding to the existing drug overdose heat maps built by Grard et al. (2023), we hope to provide local leadership with the necessary tools to achieve similar reductions in violent crimes seen in Cardiff Projects across the globe

How well do computer-generated faces tap face expertise?

The use of computer-generated (CG) stimuli in face processing research is proliferating due to the ease with which faces can be generated, standardised and manipulated. However there has been surprisingly little research into whether CG faces are processed in the same way as photographs of real faces. The present study assessed how well CG faces tap face identity expertise by investigating whether two indicators of face expertise are reduced for CG faces when compared to face photographs. These indicators were accuracy for identification of own-race faces and the other-race effect (ORE)-the well-established finding that own-race faces are recognised more accurately than other-race faces. In Experiment 1 Caucasian and Asian participants completed a recognition memory task for own- and other-race real and CG faces. Overall accuracy for own-race faces was dramatically reduced for CG compared to real faces and the ORE was significantly and substantially attenuated for CG faces. Experiment 2 investigated perceptual discrimination for own- and other-race real and CG faces with Caucasian and Asian participants. Here again, accuracy for own-race faces was significantly reduced for CG compared to real faces. However the ORE was not affected by format. Together these results signal that CG faces of the type tested here do not fully tap face expertise. Technological advancement may, in the future, produce CG faces that are equivalent to real photographs. Until then caution is advised when interpreting results obtained using CG faces

Disrupted neural activity patterns to novelty and effort in young adult APOE-e4 carriers performing a subsequent memory task

Introduction: The APOE e4 allele has been linked to poorer cognitive aging and enhanced dementia risk. Previous imaging studies have used subsequent memory paradigms to probe hippocampal function in e4 carriers across the age range, and evidence suggests a pattern of hippocampal overactivation in young adult e4 carriers. Methods: In this study, we employed a word-based subsequent memory task under fMRI; pupillometry data were also acquired as an index of cognitive effort. Participants (26 non-e4 carriers and 28 e4 carriers) performed an incidental encoding task (presented as word categorization), followed by a surprise old/new recognition task after a 40 minute delay. Results: In e4 carriers only, subsequently remembered words were linked to increased hippocampal activity. Across all participants, increased pupil diameter differentiated subsequently remembered from forgotten words, and neural activity covaried with pupil diameter in cuneus and precuneus. These effects were weaker in e4 carriers, and e4 carriers did not show greater pupil diameter to remembered words. In the recognition phase, genotype status also modulated hippocampal activity: here, however, e4 carriers failed to show the conventional pattern of greater hippocampal activity to novel words. Conclusions: Overall, neural activity changes were unstable in e4 carriers, failed to respond to novelty, and did not link strongly to cognitive effort, as indexed by pupil diameter. This provides further evidence of abnormal hippocampal recruitment in young adult e4 carriers, manifesting as both up and downregulation of neural activity, in the absence of behavioral performance differences

Using Hospital Bed Capacity Prediction During COVID-19 to Determine Feature Importance

The COVID-19 pandemic has exacerbated existing hospital capacity limitations in the United States, causing hospitals in certain regions to hit maximum capacity. The purpose of this study is to investigate key features of COVID-19 related admissions to help create a higher level of public understanding and help guide healthcare management professionals and governments when considering preventive measures. The introduction of preventative measures and new regulations during the pandemic have led to the generation of multiple types of models and feature selection methods in the field of Machine Learning that are increasingly complicated. This study focuses on the exploration of feature selection through building multiple models, one simple linear model and one decision tree model for prediction on inpatient hospitalization rates. This will result in a highly interpretable model that can be more readily understood and easily used

Identifying Locations of Drug Overdose in Las Vegas to Implement the Cardiff Violence Prevention Model

This paper will provide an innovative approach to drug overdose prevention programs. Using data from Las Vegas emergency departments, this paper will analyze geospatial trends of drug overdoses. Leveraging the Cardiff Violence Prevention Model, the information is shared with local law enforcement agencies and decision makers to empower them to make evidence-based strategies. This paper highlights the efficacy of a data-driven model in addressing public health issues and underscoring its ability for even broader implementation in urban settings. Findings will suggest significant implications for policymaking, crime prevention, and public health initiatives, demonstrating a step towards a safer Las Vegas

Identifying Locations of Violent Injuries in Las Vegas to Implement the Cardiff Violence Prevention Model

Public violence in the United States is a major health concern. Incidents involving violent crimes are often not reported to law enforcement. The Cardiff Model is a violence prevention program developed in the UK to identify and enable data sharing of violent injury locations between Emergency Rooms (ER) and local law enforcement to help identify areas for community improvements. The model is now in use in several major cities in the US to reduce violence. Las Vegas has seen an increase in public violence in recent years. As a result, researchers from the Southern Nevada Health District (SNHD) and University of Las Vegas (UNLV) believe the Cardiff Model is a viable solution to address this public health crisis. This research explores natural language processing and machine learning models to extract violent injury location information from ER records in preparation for introducing the Cardiff Violence Prevention Model in Clark County, Nevada