Information Dissipation in Random Quantum Networks

We study the information dynamics in a network of spin-$1/2$ particles when edges representing $XY$ interactions are randomly added to a disconnected graph accordingly to a probability distribution characterized by a "weighting" parameter. In this way we model dissipation of information initially localized in single or two qubits all over the network. We then show the dependence of this phenomenon from weighting parameter and size of the network.Comment: 9 pages, 5 figure

The Effects of Inaccurate and Missing Highway-Rail Grade Crossing Inventory Data on Crash and Severity Model Estimation and Prediction

Highway-Rail Grade Crossings (HRGCs) present a significant safety risk to motorists, pedestrians, and train passengers as they are intersections where roads and railways intersect. Every year, HRGCs in the US experience a high number of crashes leading to injuries and fatalities. Estimations of crash and severity models for HRGCs provide insights into safety and mitigation of the risk posed by such incidents. The accuracy of these models plays a vital role in predicting future crashes at these crossings, enabling necessary safety measures to be taken proactively. In the United States, most of these models rely on the Federal Railroad Administration\u27s (FRA) HRGCs inventory database, which serves as the primary source of information for these models. However, errors or incomplete information in this database can significantly impact the accuracy of the estimated crash model parameters and subsequent crash predictions. This study examined the potential differences in expected number of crashes and severity obtained from the Federal Railroad Administration\u27s (FRA) 2020 Accident Prediction and Severity (APS) model when using two different input datasets for 560 HRGCs in Nebraska. The first dataset was the unaltered, original FRA HRGCs inventory dataset, while the second was a field-validated inventory dataset, specifically for those 560 HRGCs. The results showed statistically significant differences in the expected number of crashes and severity predictions using the two different input datasets. Furthermore, to understand how data inaccuracy impacts model estimation for crash frequency and severity prediction, two new zero-inflated negative binomial models for crash prediction and two ordered probit models for crash severity, were estimated based on the two datasets. The analysis revealed significant differences in estimated parameters’ coefficients values of the base and comparison models, and different crash-risk rankings were obtained based on the two datasets. The results emphasize the importance of obtaining accurate and complete inventory data when developing HRGCs crash and severity models to improve their precision and enhance their ability to predict and prevent crashes. Advisor: Aemal J. Khatta

Cytomorphological investigations in Oxyria digyna Hill. from the Kashmir Himalaya, India

In the present paper, detailed cytomorphological investigations in Oxyria digyna Hill. from Kashmir Himalaya—India have been reported for the first time. All the 14 investigated populations of O. digyna are diploid based on x = 7. Out of these in two populations 0–2B chromosomes have been recorded for the first time while 6 populations differed significantly in their meiotic characteristics. Meiotic abnormalities during male meiosis observed include inter PMC chromatin transfer (cytomixis). Non-synchronous disjunction of some bivalents, laggards and bridges at anaphases and telophases. Consequent to these meiotic anomalies, microsporogenesis in meiocytes is abnormal resulting in to dyads, triads and polyads with or without micronuclei. The overall effect is seen in reduced pollen fertility. Unreduced pollen grains were observed in some populations, which differed significantly in their size compared to the normal (reduced) pollen grains. It is observed that a smaller frequency of pollen grains differed morphologically in Aharbal and Yosmarg populations. The remaining eight populations showed regular meiotic course, normal microsporogenesis and high percentage of pollen fertility (95.09–99.09 %).Приводятся детальные цитоморфологические ис-следования Oxyria digyna Hill из Кашмира (Гималаи, Индия). Все 14 изученных популяций являются диплоидными, где x = 7. Из них в двух популяциях впервые описаны 0–2B хромосомы, тогда как шесть популяций сильно различались по своим мейотическим характеристикам. Аномалии мейоза при микроспорогенезе включали цитомиксис, несинхронное расхождение некоторых бивалентов, задержки и мосты в анафазах и телофазах. Возникающий в связи с этим аномальный микроспорогенез приводит к формированию диад, триад и полиад как с микроядрами, так и без них. Общим эффектом является снижение фертильности пыльцы. В некоторых популяциях наблюдали нередуцированные пыльцевые зерна, которые по величине значительно отличались от нормальных. В популяциях Aharbal и Yosmarg некоторые пыльцевые зерна отличались морфологи-чески. Оставшиеся восемь популяций проявляли нормальный ход мейоза, нормальный микроспорогенез и высокий процент фертильности пыльцы (95,09–99,09 %).The authors are grateful to the University Grants Commission, New Delhi for providing financial assistance under the DRS SAP III and DST programmes. Thanks are also due to the Head, Department of Botany, Punjabi University, Patiala for necessary laboratory facilities

ChatGPT Performance on Standardized Testing Exam -- A Proposed Strategy for Learners

This study explores the problem solving capabilities of ChatGPT and its prospective applications in standardized test preparation, focusing on the GRE quantitative exam. Prior research has shown great potential for the utilization of ChatGPT for academic purposes in revolutionizing the approach to studying across various disciplines. We investigate how ChatGPT performs across various question types in the GRE quantitative domain, and how modifying question prompts impacts its accuracy. More specifically this study addressed two research questions: 1. How does ChatGPT perform in answering GRE-based quantitative questions across various content areas? 2. How does the accuracy of ChatGPT vary with modifying the question prompts? The dataset consisting of 100 randomly selected GRE quantitative questions was collected from the ETS official guide to GRE test preparation. We used quantitative evaluation to answer our first research question, and t-test to examine the statistical association between prompt modification and ChatGPT's accuracy. Results show a statistical improvement in the ChatGPT's accuracy after applying instruction priming and contextual prompts to the original questions. ChatGPT showed 84% accuracy with the modified prompts compared to 69% with the original data. The study discusses the areas where ChatGPT struggled with certain questions and how modifications can be helpful for preparing for standardized tests like GRE and provides future directions for prompt modifications

A Heterogeneity-Based Temporal Stability Assessment of Pedestrian Crash Injury Severity Using an Aggregated Crash and Hospital Data Set

This study utilized a unique approach to crash data analysis by examining the temporal stability of pedestrian crash injury severity and its contributory factors. Police-reported crash data and EMS-related injury data from Nebraska were obtained from 2014 to 2018, and random parameter ordered probit models for injury severity were estimated for each year to account for unobserved heterogeneity. Four discrete levels of injury severity were considered for model estimation: fatality, disabling injury/suspected serious injury, visible injury/possible injury, and no injury. Data were filtered based on several important variables of interest, such as pedestrian characteristics, crash characteristics, environmental and weather characteristics, road surface characteristics, pedestrian location of crash, pre-crash pedestrian conditions, contributory circumstances of a crash, presence of work zones, and time gap between actual crash-time and police-reported time. A series of likelihood ratio tests were used to determine the temporal stability of factors over the course of two consecutive years and then over all individual time periods. The likelihood ratio tests showed temporal instability among explanatory variables for different time periods as well as for consecutive years. The random-parameters ordered probit models estimated a random distribution for the following indicators: old pedestrian indicator, pedestrian not visible due to dark clothing indicator, marked crosswalk at intersection indicator, time gap of 10-30 minutes between actual crash-time and police-reported time, chest area injury, work zone indicator, and ice on road indicator. This exploratory research suggests significant policy implications to help improve pedestrian safety

The Effects of Inaccurate and Missing Highway-Rail Grade Crossing Inventory Data on Crash Model Estimation and Crash Prediction

ABSTRACT: Most highway-rail grade crossing (HRGC) crash models in the US rely on the Federal Railroad Administration’s (FRA) highway/rail crossing inventory database. Any errors and/or incomplete information in this database affects the estimated crash model parameters and subsequent crash predictions. Using 560 HRGCs in Nebraska, this study illustrates differences in crash predictions obtained from the FRA’s new (2020) Accident Prediction and Severity (APS) model when: 1) using the unaltered, original FRA HRGC inventory dataset as input, and 2) using a field-validated inventory dataset for those 560 HRGCs as input to the new APS model. Results showed that the predictions using the two different input datasets were statistically significantly different. HRGC hazard rankings from the two predictions as well as FRA’s Web Accident Prediction System (WBAPS) were different from each other. Estimation of new zero-inflated negative binomial models using 5-year reported HRGC crashes and the two inventory datasets for the 560 HRGCs enabled model parameter estimate and marginal value comparisons showing differences in estimated coefficients’ expected-magnitudes and average marginal effects. The conclusions were that erroneous and missing data in the unaltered FRA HRGC inventory dataset led to statistically different crash predictions compared to corrected and complete (field validated) HRGC inventory dataset and estimated crash prediction model parameters and their respective marginal values were different for comparative models based on the two different HRGC inventory datasets

Road Work Zone Safety: Investigating Injury Severity in Motor Vehicle Crashes Using Random Effects Multinomial Logit Model

Work zones serve the purpose of facilitating maintenance and rehabilitation activities on roadways. However, these areas can also present unforeseen conditions to drivers, including narrowed right-of-way, lane shifts, and traffic disruptions. These conditions frequently contribute to vehicular crashes within work zones, resulting in property damage, injuries, and even loss of life. This paper aims to highlight work zone related crash data insights and presents statistical estimates of significant determinants of injury severity by analyzing ten-year crash data (2008-2018) from Nebraska, USA. The examination of crash data helped in highlighting work zone attributes that are empirically associated with serious injury crashes and fatalities. Crash data analysis evaluated the relationship of injury severity in work zones with key crash variables such as time of crash, road classification, crash location, road surface conditions, weather conditions and road characteristics. The crash data revealed that 2016 had the highest (1326/11.28%) whereas 2011 had the lowest (739/6.3%) recorded work zone crashes. Also, most of work zone crashes were recorded in activity and transition areas. A standard multinomial logit model and random effects multinomial logit model was estimated and compared. The estimated model showed that higher crash injury severity was associated with highways and interstates, curved and steep road conditions, lane closure and intermittent type work zones, activity and termination areas in work zones, presence of workers, time of day and certain crash attributes. Identifying these key factors related to work zone crashes helped suggest several mitigation strategies to reduce the severity of such incidents. This research is exploratory in nature, and the findings are anticipated to contribute to future studies on work zone safety

Exploring Statistical and Machine Learning-Based Missing Data Imputation Methods to Improve Crash Frequency Prediction Models for Highway-Rail Grade Crossings

Highway-rail grade crossings (HRGCs) are critical spatial locations of transportation safety because crashes at HRGCs are often catastrophic, potentially causing several injuries and fatalities. Every year in the United States, a significant number of crashes occur at these crossings, prompting local and state organizations to engage in safety analysis and estimate crash frequency prediction models for resource allocation. These models provide valuable insights into safety and risk mitigation strategies for HRGCs. Furthermore, the estimation of these models is based on inventory details of HRGCs, and their quality is crucial for reliable crash predictions. However, many of these models exclude crossings with missing inventory details, which can adversely affect the precision of these models. In this study, a random sample of inventory details of 2000 HRGCs was taken from the Federal Railroad Administration’s HRGCs inventory database. Data filters were applied to retain only those crossings in the data that were at-grade, public and operational (N=1096). Missing values were imputed using various statistical and machine learning methods, including Mean, Median and Mode (MMM) imputation, Last Observation Carried Forward (LOCF) imputation, K-Nearest Neighbors (KNN) imputation, Expectation-Maximization (EM) imputation, Support Vector Machine (SVM) imputation, and Random Forest (RF) imputation. The results indicated that the crash frequency models based on machine learning imputation methods yielded better-fitted models (lower AIC and BIC values). The findings underscore the importance of obtaining complete inventory data through machine learning imputation methods when developing crash frequency models for HRGCs. This approach can substantially enhance the precision of these models, improving their predictive capabilities, and ultimately saving valuable human lives