Total Number Of Violations Research Articles

A restrictive, parsimonious theory of footing in directional Harmonic Serialism

AbstractThis paper develops a theory of footing in Harmonic Serialism (HS; Prince & Smolensky 1993/2004; McCarthy 2000, 2016) where Con contains only directionally evaluated constraints (Eisner 2000, 2002; Lamont 2019, 2022a, 2022b). Directional constraints harmonically order candidates by the location of violations rather than the total number of violations. A central result of adopting directional evaluation is that the constraint Parse($\sigma$) not only motivates iterative footing but also determines where feet surface. This obviates the need for alignment constraints (McCarthy & Prince 1993; McCarthy 2003; Hyde 2012a, 2016), which determine where feet are parsed in HS with constraints that count loci (Pruitt 2010, 2012). The theory uses fewer constraints, is empirically adequate, and makes more restrictive predictions than HS with counting constraints and parallel Optimality Theory (Prince & Smolensky 1993/2004) with directional constraints.

Оценка влияния высоких температур окружающей среды на частотные и структурные характеристики нарушений правил дорожного движения

Abstract. In the hot season, there is a positive relationship between the number of accidents per thousand registered vehicles and air temperature, due to the fact that high temperatures and solar activity can have a significant negative impact on the condition of drivers. As a rule, the creation of an emergency is preceded by a violation of traffic rules. Therefore, the identification of the type and number of violations committed by drivers at high ambient temperatures is relevant. The purpose of the work is to assess the impact of high ambient temperatures on the structure and frequency of traffic violations. Based on a literature review, the article states that at present there is no methodology for assessing the impact of high ambient temperatures on the characteristics of traffic violations. As an information platform, data from the Wialon — GPS/Glonass system are used, which allow you to objectively assess the characteristics of driving activity and the number of traffic violations. The work uses methods of mathematical statistics. The main result of the work, which constitutes the scientific novelty of the study, is the revealed increasing dependence of the total number of traffic violations on the average monthly ambient temperature. The structure of such violations is determined. The most significant, based on the impact on the accident rate, violations are: exceeding the speed limit and abrupt and dangerous maneuvers. It has been found that the total number of violations of the rules increases during periods with high ambient temperatures by 94% compared to months with moderate temperatures. The largest number of violations consists in exceeding the speed limit — 97.8% of their total number. The practical significance of the work consists in obtaining objective information about accidents at high ambient temperatures, on the basis of which it becomes possible to develop organizational and technological measures aimed at improving road safety. The areas of further research are related to the creation of an information platform for theoretical and methodological tools aimed at preventing high accidents of road transport in the hot season.

Evaluating water quality using social media and federal agency data.

United States Environmental Protection Agency (USEPA) drinking water violation report is currently one of the most reliable measures of evaluating United States drinking water quality. While states continuously strive to comply with federal water quality standards making this documentation continuously relevant, consumers are likely to perceive water quality through sensory aesthetics or physical and virtual social networks. This research quantifies the relationship between consumer perceptions and government-reported drinking water quality to provide insights to state water managers and policymakers. We evaluated consumer perceptions of tap water using weekly social media data. The online search returned 898,709 mentions and 799,035 posts. Net sentiment, measured as the number of negative posts minus the number of positive posts divided by the number of posts expressing sentiment, was determined and ranged from -100 to 100. Net sentiment was uncorrelated with USEPA weekly water quality violations for most states. Net sentiment was correlated with violations related to arsenic standards (-0.223) and a total number of violations (-0.220) for Washington. For California, net sentiment was correlated with violations related to disinfectants and other organic compounds (-0.295). In many cases, water violations in one city became national news, which eclipsed local water issues circulating on social media.

Automatic Social Distance Monitoring system using Deep Learning Algorithms

In recent months, the worldwide outbreak of Corona Virus Disease (COVID-19) has created a panic situation for all human beings irrespective of the geographic location and economic status of the countries. The communal spread of the deadly virus can be prevented by maintaining the social distance between people and the wearing of face masks. This paper provides a prominent solution for monitoring using the Open-Source Computer Vision Library (Open-CV) and Deep Learning Algorithm to course people in public areas and to avoid crowding. This proposed project can be implemented using images taken from the Closed Circuit Television (CCTV). The distance between the people is detected with help of an object detection algorithm. The Euclidean distance is calculated and compared with the standard distance given. The persons with enough distance will be indicated in the green-colored bounding box and others with a red-colored bounding box. The total number of violations is also shown at the output feed. This project provides a solution to eradicate the spread of the deadly virus to an extent with minimum human effort and reduced risk of infection to the local athourities.

From trait anger to aggressive violations in road traffic

Drivers' beliefs and attitudes are known to be precursors of driving behavior, mediating the relationship between it and personality traits. However, the mediation effect of these beliefs on the relationship between trait anger and aggressive driving is not fully understood. The present study examined how the variance in the total numbers of violations, ordinary violations, and aggressive violations was explained by anger and behavioral, normative and control beliefs, and how much of this variation was explained by a linear combination of anger and any of these beliefs. A sample of 224 participants completed a series of instruments tapping into trait anger, attitude, subjective norms, descriptive norms, perceived behavioral control, intention, total number of violations, aggressive violations, and ordinary violations. The results showed that trait anger controlled approximately 9% of the variation in the total number of violations and aggressive violations and 6.5% of ordinary violations, while behavioral beliefs (through attitude) added a further 9.2–11.6%. The intention, which prior research linked to the manifestation of the behavior, explained a further 21.6% in aggressive violations in our study. Attitude mediated the relationship between trait anger and behavioral outcomes, the linear combination of trait anger and attitude accounting for 40.7% in intention, 17.6% in aggressive violations, 22.5% in the total number of violations and 25% in ordinary violations. The role of behavioral beliefs was greater when the level of aggressive violation decreased. Aggressive violations are less intentional than other behaviors that lead to violations, making it very important to consider both attitude toward behavior and trait anger to reduce them. Limitations and directions for further research are also discussed.

Mask Assignment and DSA Grouping for DSA-MP Hybrid Lithography for Sub-7 nm Contact/Via Holes

Directed self assembly (DSA) is a very promising candidate for the sub-7 nm technology nodes. To print such small dimensions, multiple patterning (MP) is likely to be used to print the guiding templates for DSA. Therefore, algorithms are required to perform the DSA grouping at the same time as the mask assignment. In this paper, we present an optimal integer linear program (ILP) to solve this problem for two schemes of hybrid DSA-MP process. Scalable heuristic algorithms are also proposed to solve the same problem. In comparison to the ILP, the proposed heuristics are $4\boldsymbol \times$ – $213\boldsymbol \times$ faster, and result in an increase of total number of violations by 4%–29%.

Inspection Frequency, Sociodemographic Factors, and Food Safety Violations in Chain and Nonchain Restaurants, Philadelphia, Pennsylvania, 2013-2014.

We explored how restaurant inspection frequency and restaurant neighborhood sociodemographic characteristics are related to food safety inspection outcomes in chain and nonchain restaurants to better understand external factors that may influence inspection outcomes. We categorized the results of restaurant inspections in Philadelphia, Pennsylvania, in 2013 and 2014 by restaurant type (chain or nonchain), inspection frequency (1, 2, or ≥3 per 2-year study period), and violation type (total number of violations, foodborne-illness risk factor violation, or good retail practice violation). We collected 2013 US Census block group sociodemographic data for each restaurant neighborhood. We used nested mixed-effects regression analyses to determine the association between restaurant inspection frequency and inspection violations, as well as between inspection violations and restaurant neighborhood sociodemographic variables, stratified by restaurant type. Compared with nonchain restaurants, chain restaurants had significantly fewer total violations per inspection (mean [SD]: 6.5 [4.6] vs 9.6 [6.8] violations, P < .001). For nonchain restaurants, an increase from 1 to 2 inspections resulted in 0.8 ( P < .001) fewer mean violations per inspection, and an increase from 1 to ≥3 inspections resulted in 1.6 ( P < .001) fewer mean violations; this association was not seen in chain restaurants. For nonchain restaurants, a higher proportion of black residents in a restaurant neighborhood was associated with 0.6 ( P < .001) fewer mean foodborne-illness risk factor violations but 1.0 ( P < .001) more mean good retail practice violations per inspection. A risk-based stratified approach to restaurant food safety inspection frequency, based on whether or not restaurants are part of chains, could reduce the frequency of violations, particularly in restaurants with the most violations.

Softening the Rule of Five—where to draw the line?

In order to improve the discovery and development of new drugs, a broad effort is being made to assess the ‘drug-like’ properties of molecules in early stages of the discovery-research process. Although there are numerous approaches to this problem, perhaps the simplest and most widespread one is that developed by Chris Lipinski and his co-workers at Pfizer, which is generally referred either as the Lipinski Rules or the Rule of Five (ROF). The ROF is based on four properties of molecules, namely, molecular weight (MW), logP, number of hydrogen bond donors (HBD), and the number of hydrogen bond acceptors (HBA). A ‘flag’ is set if the value of a given property exceeds the chosen threshold value for that property—MW 500Da, logP 5, the number of HBDs 5, and the number of HBAs 10. Each flag corresponds to an ROF violation. The total number of violations is the ROF-Score, which lies between ‘0’ and ‘4’. Molecules with ROF-Scores greater than one are considered to be marginal for further development. The difficulty with this approach is that two molecules with nearly identical property values can, nonetheless, possess ROF-Scores that can differ by two or more. Thus, one molecule could be considered for further studies while the other, nearly identical molecule (in terms of its four ROF properties), would most likely not be. This problem arises because of the sharp thresholds imposed by the present formulation of the ROF, which is based upon classical sets. In the current work an alternative approach based on the use of utility functions, within the framework of the analytic hierarchy process (AHP), are employed to ‘soften’ the sharp boundaries inherent in classical sets. This provides a more realistic assessment of compounds in terms of their potential suitability in drug-discovery research programs.

Detection of Various Performance Enhancing Substances in Specimens Collected from Race Horses in Illinois: A Five-Year Experience

In order to protect the integrity of horse racing in Illinois, a complex testing of urine and blood specimens collected post-race from winning and special designation horses is continuously conducted. The initial screening by immunoassays was followed by the confirmation on presumptive positive samples. Instrumental screening was also conducted. Perimortem and postmortem specimens and special exhibits (syringes, needles, etc.) were also analyzed. The administration of alkalinizing agents was detected by measuring the total plasma carbon dioxide concentration. The laboratory analyzed specimens collected post race from winning horses and special designation horses at eight race tracks in the State of Illinois over the five-year time period (2004-2009). The total number of specimens collected was 91,808, comprising 45,210 urine specimens and 46,598 blood specimens. The total number of violations was 413 (0.45% of the total number of specimens analyzed); 207 were blood specimens (0.44% of the total blood specimens analyzed), and 206 were urine specimens (0.45% of the total urine specimens analyzed). A total of 220 violations were reported for harness horses, and 193 were reported for Thoroughbred horses. The number of reported violations of the total tested specimens in Illinois was small, but a wide variety of performance-enhancing drugs was shown.

The Prevalence, Correlates, and Impact of Logically Inconsistent Preferences in Utility Assessments for Joint Health States in Prostate Cancer

Variations in health state utilities can impact cost-effectiveness analyses. One potential source of error is when joint health state (JS) utilities are rated higher than the embedded single state (SS) utilities. Knowing when and in whom this occurs can improve cost-effectiveness analyses. Men (n = 323) were surveyed at the time of prostate biopsy. Time tradeoff SS and JS utilities for prevalent prostate cancer (PCa) health states were collected. JS utilities assessed included those most prevalent for PCa. "Inconsistency" was defined in the following 3 ways: (1) any size rank order violation; (2) total number of violations; and (3) differences greater than 1 standard deviation (SD). Regression analysis assessed independent patient characteristics associated with inconsistent responses. Aggregate JS utilities were consistent. At the individual level, 36% to 41% of responses violated rank order and 12% to 14% were larger than 1 SD. In all, 69% of respondents had at least 1 JS inconsistency, and 24% had >1 SD inconsistencies. Being married and feeling anxious were independently correlated with giving all types of inconsistent ratings, and lower education correlated with making >SD errors. SS utilities, and not JS utilities, were significantly lower for the inconsistent group. "Correcting" JS inconsistencies decreased aggregate utilities 1 to 9 units. Inconsistent JS utilities for PCa are prevalent in men at biopsy. Being married, more anxious, and having less education are correlated with inconsistencies. It is the SS utilities, rather than the JS utilities, that differ between consistent and inconsistent raters. Better understanding of the source of these inconsistencies is needed.

Self-Policing and the Environment: Predicting Self-Disclosure of Clean Air Act Violations Under the U.S. Environmental Protection Agency's Audit Policy

ABSTRACT The U.S. Environmental Protection Agency's Self-Policing or Audit Policy waives or reduces penalties when regulated entities voluntarily discover, disclose, and correct environmental violations. This study uses a case-control design to determine if traditional regulatory efforts, organizational factors, and/or case characteristics are associated with the odds of disclosing a Clean Air Act violation under the policy. The results suggest that Audit Policy use is related to within-industry regional inspection levels. We find no evidence that company size directly influences Audit Policy use. Instead, company size appears to be mediated by case characteristics. Specifically, we suggest that larger companies may be more likely to use the Audit Policy because they violate multiple environmental laws. This finding indicates that the Audit Policy may expand the total number of violations that ultimately become known to the U.S. Environmental Protection Agency.