Drive Sober or Get Pulled OverPython

Java Python Drive Sober or Get Pulled Over

Introduction

Drunk driving remains one of the most critical public safety issues the society is concerned about and its dangerous consequences on road safety. According to the National Highway Traffic Safety Administration (NHTSA), one person is killed in a drunk-driving crash every 39 minutes, resulting in over 13,000 lives lost each year. Drunk driving is a major risk factor for a large number of accidents, casualties, and injuries on the roads. According to data, 1,013 lives lost in drunk-driving crashes in December 2021, 4,561 people killed in December drunk-driving crashes from 2017-2021. The “Drive Sober or Get Pulled Over” campaign aims to change individual behaviors by raising awareness about the dangers of driving under the influence of alcohol and encouraging safe driving practices through strategic communication and heightened enforcement. This campaign targets drivers across the United States, aiming to highlight the serious legal consequences of drunk driving with the slogan “Drive Sober or Get Pulled Over.” It addresses the issue of drunk driving by highlighting the consequences of irresponsible driving behavior. and encouraging individuals to make responsible choices when it comes to alcohol consumption and driving. By leveraging a robust media strategy and high-visibility enforcement efforts, the campaign seeks to create a deterrent effect and attempt to induce drivers to change their drunk driving behaviors, ultimately saving lives and reducing the societal burden of alcohol-related crashes.

Drink, drive, go to jail? A study of police officers arrested for drunk driving

Abstract

The purpose of the current study is to provide empirical data on cases of police driving under the influence (DUI) of alcohol and/or drugs. The study identifies events that may have influenced the decision to arrest, including associated traffic accidents, fatalities, officer resistance, the refusal of field sobriety tests, and the refusal of blood alcohol content tests. The study is a quantitative content analysis of news articles identified through the Google News search engine using 48 automated Google Alerts queries. Data are analyzed on 782 DUI arrest cases of officers employed by 511 nonfederal law enforcement agencies throughout the United States. The study is the only study known to describe police officer DUI arrests as they occur within police agencies across the United States.

A ‘Hands on’ Public Service Program to Help People Stay Sober and Safer on the

Roadway

Abstract

Despite the existence of many different “Don’t drink and drive” programs and campaigns over the past 30 years, alcohol intoxication has continued to account for approximately one quarter to one third of all Drive Sober or Get Pulled OverPython traffic crashes and crash-related deaths in the United States. The present study describes a new ‘hands on’ evidence-based approach involving real alcohol-intoxicated subjects using a virtual reality (VR) driving ‘game’ to educate the public more effectively about the dangers of drunk driving. A single demonstration subject ‘drove’ a VR-based portable driving simulator on multiple occasions before (Pre) and at 30 min intervals for up to six hours after either vehicle (no alcohol), two, four or six ‘drinks’ (3, 6, or 9 ounces of 80 proof vodka). The defensive driving task was a choice reaction crash avoidance steering maneuver in which the driver’s task was to determine which way to turn to avoid a crash and then aggressively steer away to avoid a crash. The primary dependent variable was the latency to initiate an avoidance steering response. Blood alcohol concentration (BAC) determinations (estimations) were conducted immediately prior to driving tests using BAC Track portable breathalyzers. Control drives (Pre-Treatment and Vehicle treatment) were characterized by an approximately 300–320 ms reaction time to initiate a crash avoidance. Alcohol increased crash-avoidance reaction time. Peak BAC values were 35, 78 and 120 mg/dL for two, four and six drinks, respectively; the decline in BAC was comparable and linear for all three treatments. There was a strong correlation (r = 0.85) between pre-drive BAC level and reaction time across all of the alcohol-related drives. There was a significant increase in crash avoidance reaction time when the BAC was 50–79 mg/dL, which is below the legally defined BAC limit (80 mg/dL) currently used in most states in the US. These results demonstrate that (1) this VR-based driving simulator task could be a useful ‘hands on’ tool for providing public service demonstrations regarding the hazards of drinking and driving and (2) a BAC concentration of 50 mg/dL represents a reasonable evidence-based cut-off for alcohol-impaired driving.

Effectiveness of Sobriety Checkpoints for Reducing Alcohol-Involved Crashes

Abstract

The goal of sobriety checkpoints is to deter drinking and driving by systematically stopping drivers for assessment of alcohol impairment, thus increasing the perceived risk of arrest for alcohol-impaired driving. This review examines the effectiveness of random breath testing (RBT) checkpoints, at which all drivers stopped are given breath tests for blood alcohol levels, and selective breath testing (SBT) checkpoints, at which police must have reason to suspect the driver has been drinking before demanding a breath test. A systematic review of the effectiveness of sobriety checkpoints in reducing alcohol-involved crashes and associated injuries and fatalities was conducted using the methodology developed for the Guide to Community Preventive Services (Community Guide) . Substantial reductions in crashes were observed for both checkpoint types across various outcome measures and time periods. Results suggest that both RBT and SBT checkpoints can play an important role in preventing alcohol-related crashes and associated injuries         

【顶级EI完整复现】【DRCC】考虑N-1准则的分布鲁棒机会约束低碳经济调度(Matlab代码实现)内容概要:本文介绍了名为《【顶级EI完整复现】【DRCC】考虑N-1准则的分布鲁棒机会约束低碳经济调度(Matlab代码实现)》的技术资源,聚焦于电力系统中低碳经济调度问题,结合N-1安全准则与分布鲁棒机会约束(DRCC)方法,提升调度模型在不确定性环境下的鲁棒性和可行性。该资源提供了完整的Matlab代码实现,涵盖建模、优化求解及仿真分析全过程,适用于复杂电力系统调度场景的科研复现与算法验证。文中还列举了大量相关领域的研究主题与代码资源,涉及智能优化算法、机器学习、电力系统管理、路径规划等多个方向,展示了广泛的科研应用支持能力。; 适合人群:具备一定电力系统、优化理论和Matlab编程基础的研究生、科研人员及从事能源调度、智能电网相关工作的工程师。; 使用场景及目标:①复现高水平期刊(如EI/SCI)关于低碳经济调度的研究成果;②深入理解N-1安全约束与分布鲁棒优化在电力调度中的建模方法;③开展含新能源接入的电力系统不确定性优化研究;④为科研项目、论文撰写或工程应用提供可运行的算法原型和技术支撑。; 阅读建议:建议读者结合文档提供的网盘资源,下载完整代码与案例数据,按照目录顺序逐步学习,并重点理解DRCC建模思想与Matlab/YALMIP/CPLEX等工具的集成使用方式,同时可参考文中列出的同类研究方向拓展研究思路。
### 三级标题:SOBER 序列的定义与技术背景 SOBER 序列是一种伪随机数生成器(PRNG)家族中的输出序列,广泛用于流密码(stream cipher)的设计中。该序列由 Philip Hawes 和 Martin Boesgaard 在 2003 年提出,作为 SOBER 系列流密码的一部分,旨在提供高安全性与高效性能的结合。SOBER 系列包括多个变种,如 SOBER-t16、SOBER-t32 等,分别对应不同的寄存器位宽与非线性函数(NLF)设计。 SOBER 序列的核心结构由一个线性反馈移位寄存器(LFSR)和一个非线性函数(NLF)组成。LFSR 负责生成长周期的伪随机序列,而 NLF 则用于对 LFSR 的状态进行非线性变换,以提高序列的不可预测性与抗线性攻击能力。在无口吃(non-decimation)模式下,每个 NLF 输出直接作为密钥流符号输出,从而形成最终的 SOBER 序列[^2]。 ### 三级标题:SOBER 序列的算法结构 SOBER 系列算法的典型结构包括以下关键组件: - **LFSR 结构**:采用可逆的 LFSR 设计,确保状态空间的完全覆盖,周期接近最大值。 - **非线性函数(NLF)**:对 LFSR 的某些位进行非线性组合,通常使用布尔函数或 S 盒。 - **口吃机制(Decimation)**:部分变种中引入,用于跳过某些输出以增强安全性。 - **初始化过程**:结合密钥与初始向量(IV)进行多次迭代,确保初始状态的随机性。 以下是一个简化的 SOBER-t32 算法的伪代码示例: ```python def nlf(s0, s1, s2, s3): # 非线性函数示例,实际中更复杂 return (s0 ^ s1) & (s2 | s3) def sober_t32(lfsr_state, key_stream_length): key_stream = [] for _ in range(key_stream_length): # 更新 LFSR new_bit = lfsr_state[0] ^ lfsr_state[3] ^ lfsr_state[4] ^ lfsr_state[5] lfsr_state = [new_bit] + lfsr_state[:-1] # 应用 NLF key_stream.append(nlf(*lfsr_state[:4])) return key_stream ``` ### 三级标题:SOBER 序列的应用场景 SOBER 序列主要用于流密码系统中,适用于需要高效生成伪随机密钥流的场景,如: - **无线通信加密**:例如在移动网络或物联网设备中,要求加密算法具有低功耗与高速度。 - **嵌入式系统安全**:资源受限的环境中,SOBER 系列因其较小的硬件实现面积而受到青睐。 - **安全协议设计**:作为密钥流生成模块,参与构建端到端加密通信协议。 由于其良好的统计特性与抗线性/差分攻击能力,SOBER 序列在特定安全标准与协议中被推荐使用,尤其是在对性能与安全性均有较高要求的场合。
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