Business Against Drunk Driving: The Neoliberal State, Labatt Brewery, and the Creation of the “Responsible Drinker”

This paper examines the motivations and consequences of Labatt’s anti–drinking and driving campaign. The paper considers the economic and political conditions that enabled Canada’s largest brewer to execute a cause-advertising campaign and to establish itself as a “responsible corporation”—even when its leadership cared less about the deleterious effects of Labatt products and more about the company’s earnings. It examines neoliberal governance and the relationship between the public and private sector in tackling a prominent social problem—impaired driving—and how a for-profit business used its influence to create a new subjectivity: the “responsible drinker,” who did not drive while under the influence. It seeks to situate Labatt’s campaign within an increasingly neoliberal, individualistic political economy. This paper argues that Labatt’s actions were part of the neoliberal agenda toward “responsibilization” that shifted the responsibility for drunk driving away from regime-based institutions and onto the individual, allowing the neoliberal state to govern from a distance. It demonstrates that contrary to neoliberal rhetoric the state did not shrink during the late twentieth century but rather took on new regulatory functions.

In-Vehicle Alcohol Detection Using Low-Cost Sensors and Genetic Algorithms to Aid in the Drinking and Driving Detection

Worldwide, motor vehicle accidents are one of the leading causes of death, with alcohol-related accidents playing a significant role, particularly in child death. Aiming to aid in the prevention of this type of accidents, a novel non-invasive method capable of detecting the presence of alcohol inside a motor vehicle is presented. The proposed methodology uses a series of low-cost alcohol MQ3 sensors located inside the vehicle, whose signals are stored, standardized, time-adjusted, and transformed into 5 s window samples. Statistical features are extracted from each sample and a feature selection strategy is carried out using a genetic algorithm, and a forward selection and backwards elimination methodology. The four features derived from this process were used to construct an SVM classification model that detects presence of alcohol. The experiments yielded 7200 samples, 80% of which were used to train the model. The rest were used to evaluate the performance of the model, which obtained an area under the ROC curve of 0.98 and a sensitivity of 0.979. These results suggest that the proposed methodology can be used to detect the presence of alcohol and enforce prevention actions.

Effect of the New Zero-Tolerance Drinking and Driving Law on Hospital Admissions Due to Road Traffic Accidents in Brazil: an Interrupted Time Series Study From 2008 to 2019 Short Title New Zero-tolerance Drinking and Driving Law on Hospitalization in Brazil

We investigated the effect of the new zero-tolerance drinking and driving law (Law 12760) on hospital admissions due to road traffic accidents in Brazil by using interrupted time series from 2008 to 2019. We used ICD-10 codes for land transport accidents (V00-V89). The hospitalization rate was calculated per 100,000 inhabitants. The sources were the Hospital Information System and the Brazilian Institute for Geography and Statistics. For Brazil, the hospitalization rate decreased by 0.400841 (p=0.052; 95% CI -0.8060128 to -0.0043307) in the first month of the intervention (December 2012), followed by a significant change in the hospitalization trend. Compared to the period prior to the intervention, the monthly trend was reduced by 0.048215 (p<0.01; 95% CI 0.0614524 to 0.0349775) in the post period. These results stand in agreement with subgroup analyses for the Brazilian regions, although North and Northeast regions did not immediately reduce hospitalization rates (level change). Our results suggested that 436,581 hospitalizations for land transport accidents were averted by the new zero-tolerance drinking and driving law from Dec. 2012 to Dec. 2019 in Brazil.

Categorizing Metatheologies

The approaches I am focusing on are representatives in a category scheme derived from the twentieth century concern that led to the rise of metaethics, a concern over what to make of the different kinds of declarative sentences in natural language. Some such sentences are straightforwardly descriptive (“The cat is on the mat), while others are evaluative (“Ice cream is the best dessert”), and still others are normative (“Drinking and driving is forbidden”). Here we see how these categories line up with our fundamental metatheologies, with Creator Theology being descriptive, Perfect Being Theology being evaluative, and Worship-Worthiness Theology being normative.

Driving While Impaired and Riding with an Impaired Driver among Recent Latinx Immigrants

The overarching aim of this study is to assess driving while impaired by alcohol (DWI) and riding with an impaired driver (RWID) rates among young adult Latinx immigrants to Miami-Dade County, Florida, within a year of arrival in the U.S.A. More specifically, this study aims to: (i) describe the demographic and socioeconomic characteristics of Latinx immigrants to Miami-Dade County within a year of arrival; (ii) examine their alcohol use, DWI, and RWID; and (iii) identify factors influencing these behaviors. This study uses baseline data from an ongoing National Institutes of Health-funded longitudinal study examining drinking and driving trajectories among young adult recent Latinx immigrants to Miami-Dade County. During the baseline assessment, retrospective pre-immigration data and post-immigration data were obtained via personal interviews. Inclusion criteria included being a Latinx immigrant, 18–34 years old, who recently immigrated (within one year before baseline assessment) to the U.S.A. from a Latin American country with the intention of staying in the U.S.A. for at least three years beyond baseline. Respondent-driven sampling was applied. Results showed that since arriving in the U.S.A. approximately 6.3% of all participants had engaged in DWI at least once and 20% reported RWID. Although household income and being male were significant factors, the factor that influenced post-immigration DWI/RWID the most was pre-immigration DWI (odds ratio = 13.1) and pre-immigration RWID (odd ratio = 24.5). Interventions aimed to prevent recent immigrants from engaging in DWI and RWID should take cultural factors and pre-immigration behaviors into account.

Drink and Drive Prohibitor System With Biometric Authentication And Accident Alerting System Using IOT

Drinking and driving is already a serious public health problem ,which is likely to emerge as one of the most significant problems in near future .the system implemented by us aims at reducing the road accident in the near future due to drunk and drive . This project present the progress in using the alcohol detector ,a device that senses a change in the alcoholic gas content of the surrounding air these device is more commonly referred to as a breath analysis, as it analysis the alcohol content from person’s breath. The system detects the presence of alcohol in the vehicle and immediately locks the engine of the vehicle. System starts on fingerprint authentication and also track vehicle with the help of GSM/GPS module in case of accident occurs.

Real Time Driver Drowsiness Detection App using ML

Every year road accidents are increasing rapidly as technological and mechanical advancements in vehicles permits drivers to drive at high speed. Approximately 1.35 million people die each year as a result of road accidents in India alone 151 thousand casualties were recorded last year. From this nearly 78% road accidents are caused due to driver's fault. Main factors for this accidents are drowsiness, drunk and drive and over speeding from which nearly 40% of the accidents caused due to drowsiness. People are conscious about the risk of drinking and driving but don’t realize the dangerous of drowsiness because no instruments exist to measure the driver drowsiness. If the Driver fails to concentrate on driving it reduces the driver reaction time and impairs steering behavior To solve this problem we are going to use the power of machine learning to identify if the driver is drowsy or not. Generally when someone feels drowsy his\hers eye blinking speed decreases by specifying threshold value we can detect if the driver is drowsy or not. This programme provides a mechanism for scanning facial landmarks and then using the essential landmarks for eye tracking after recognising the face. this insures that the driver is in full control of his vehicle. The system make use of device’s front camera to monitor drivers’s face to detect drowsiness and alarms the driver if system founds him drowsy. More functionality will be added to the system, such as issuing an SOS if something occurs to a car in a remote location. The software also has emergency numbers, so that in the event of an emergency, the driver may call the appropriate authorities as needed. This system may also be used for navigating by utilising the app's map functionality. Flutter will be utilised to provide a native and user-friendly system interface. As a result, the software will be available for both Android and iOS smartphones. The use of as little hardware as possible will ensure seamless processing. This technology may be employed in a variety of circumstances, including cab services, state transportation and on-road goods delivery.