539 research outputs found
Information Dissipation in Random Quantum Networks
We study the information dynamics in a network of spin- particles when
edges representing 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 parametersβ 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 HimalayaβIndia 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 0β2B 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 (95.09β99.09 %).ΠΡΠΈΠ²ΠΎΠ΄ΡΡΡΡ Π΄Π΅ΡΠ°Π»ΡΠ½ΡΠ΅ ΡΠΈΡΠΎΠΌΠΎΡΡΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΈΡ-ΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ Oxyria digyna Hill ΠΈΠ· ΠΠ°ΡΠΌΠΈΡΠ° (ΠΠΈΠΌΠ°Π»Π°ΠΈ, ΠΠ½Π΄ΠΈΡ). ΠΡΠ΅ 14 ΠΈΠ·ΡΡΠ΅Π½Π½ΡΡ
ΠΏΠΎΠΏΡΠ»ΡΡΠΈΠΉ ΡΠ²Π»ΡΡΡΡΡ Π΄ΠΈΠΏΠ»ΠΎΠΈΠ΄Π½ΡΠΌΠΈ, Π³Π΄Π΅ x = 7. ΠΠ· Π½ΠΈΡ
Π² Π΄Π²ΡΡ
ΠΏΠΎΠΏΡΠ»ΡΡΠΈΡΡ
Π²ΠΏΠ΅ΡΠ²ΡΠ΅ ΠΎΠΏΠΈΡΠ°Π½Ρ 0β2B Ρ
ΡΠΎΠΌΠΎΡΠΎΠΌΡ, ΡΠΎΠ³Π΄Π° ΠΊΠ°ΠΊ ΡΠ΅ΡΡΡ ΠΏΠΎΠΏΡΠ»ΡΡΠΈΠΉ ΡΠΈΠ»ΡΠ½ΠΎ ΡΠ°Π·Π»ΠΈΡΠ°Π»ΠΈΡΡ ΠΏΠΎ ΡΠ²ΠΎΠΈΠΌ ΠΌΠ΅ΠΉΠΎΡΠΈΡΠ΅ΡΠΊΠΈΠΌ Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊΠ°ΠΌ. ΠΠ½ΠΎΠΌΠ°Π»ΠΈΠΈ ΠΌΠ΅ΠΉΠΎΠ·Π° ΠΏΡΠΈ ΠΌΠΈΠΊΡΠΎΡΠΏΠΎΡΠΎΠ³Π΅Π½Π΅Π·Π΅ Π²ΠΊΠ»ΡΡΠ°Π»ΠΈ ΡΠΈΡΠΎΠΌΠΈΠΊΡΠΈΡ, Π½Π΅ΡΠΈΠ½Ρ
ΡΠΎΠ½Π½ΠΎΠ΅ ΡΠ°ΡΡ
ΠΎΠΆΠ΄Π΅Π½ΠΈΠ΅ Π½Π΅ΠΊΠΎΡΠΎΡΡΡ
Π±ΠΈΠ²Π°Π»Π΅Π½ΡΠΎΠ², Π·Π°Π΄Π΅ΡΠΆΠΊΠΈ ΠΈ ΠΌΠΎΡΡΡ Π² Π°Π½Π°ΡΠ°Π·Π°Ρ
ΠΈ ΡΠ΅Π»ΠΎΡΠ°Π·Π°Ρ
. ΠΠΎΠ·Π½ΠΈΠΊΠ°ΡΡΠΈΠΉ Π² ΡΠ²ΡΠ·ΠΈ Ρ ΡΡΠΈΠΌ Π°Π½ΠΎΠΌΠ°Π»ΡΠ½ΡΠΉ ΠΌΠΈΠΊΡΠΎΡΠΏΠΎΡΠΎΠ³Π΅Π½Π΅Π· ΠΏΡΠΈΠ²ΠΎΠ΄ΠΈΡ ΠΊ ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΡ Π΄ΠΈΠ°Π΄, ΡΡΠΈΠ°Π΄ ΠΈ ΠΏΠΎΠ»ΠΈΠ°Π΄ ΠΊΠ°ΠΊ Ρ ΠΌΠΈΠΊΡΠΎΡΠ΄ΡΠ°ΠΌΠΈ, ΡΠ°ΠΊ ΠΈ Π±Π΅Π· Π½ΠΈΡ
. ΠΠ±ΡΠΈΠΌ ΡΡΡΠ΅ΠΊΡΠΎΠΌ ΡΠ²Π»ΡΠ΅ΡΡΡ ΡΠ½ΠΈΠΆΠ΅Π½ΠΈΠ΅ ΡΠ΅ΡΡΠΈΠ»ΡΠ½ΠΎΡΡΠΈ ΠΏΡΠ»ΡΡΡ. Π Π½Π΅ΠΊΠΎΡΠΎΡΡΡ
ΠΏΠΎΠΏΡΠ»ΡΡΠΈΡΡ
Π½Π°Π±Π»ΡΠ΄Π°Π»ΠΈ Π½Π΅ΡΠ΅Π΄ΡΡΠΈΡΠΎΠ²Π°Π½Π½ΡΠ΅ ΠΏΡΠ»ΡΡΠ΅Π²ΡΠ΅ Π·Π΅ΡΠ½Π°, ΠΊΠΎΡΠΎΡΡΠ΅ ΠΏΠΎ Π²Π΅Π»ΠΈΡΠΈΠ½Π΅ Π·Π½Π°ΡΠΈΡΠ΅Π»ΡΠ½ΠΎ ΠΎΡΠ»ΠΈΡΠ°Π»ΠΈΡΡ ΠΎΡ Π½ΠΎΡΠΌΠ°Π»ΡΠ½ΡΡ
. Π ΠΏΠΎΠΏΡΠ»ΡΡΠΈΡΡ
Aharbal ΠΈ Yosmarg Π½Π΅ΠΊΠΎΡΠΎΡΡΠ΅ ΠΏΡΠ»ΡΡΠ΅Π²ΡΠ΅ Π·Π΅ΡΠ½Π° ΠΎΡΠ»ΠΈΡΠ°Π»ΠΈΡΡ ΠΌΠΎΡΡΠΎΠ»ΠΎΠ³ΠΈ-ΡΠ΅ΡΠΊΠΈ. ΠΡΡΠ°Π²ΡΠΈΠ΅ΡΡ Π²ΠΎΡΠ΅ΠΌΡ ΠΏΠΎΠΏΡΠ»ΡΡΠΈΠΉ ΠΏΡΠΎΡΠ²Π»ΡΠ»ΠΈ Π½ΠΎΡΠΌΠ°Π»ΡΠ½ΡΠΉ Ρ
ΠΎΠ΄ ΠΌΠ΅ΠΉΠΎΠ·Π°, Π½ΠΎΡΠΌΠ°Π»ΡΠ½ΡΠΉ ΠΌΠΈΠΊΡΠΎΡΠΏΠΎΡΠΎΠ³Π΅Π½Π΅Π· ΠΈ Π²ΡΡΠΎΠΊΠΈΠΉ ΠΏΡΠΎΡΠ΅Π½Ρ ΡΠ΅ΡΡΠΈΠ»ΡΠ½ΠΎΡΡΠΈ ΠΏΡΠ»ΡΡΡ (95,09β99,09 %).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 Administrationβs (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 FRAβs 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 FRAβs 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 coefficientsβ 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
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