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Men lose 17 minutes of life with every cigarette they smoke while a woman’s life is cut short by 22 minutes with each cigarette, experts have estimated. This is more than previous estimates, which suggest that each cigarette shortens a smoker’s life by 11 minutes. The new estimates, which suggest that each cigarette leads to 20 minutes loss of live on average across both genders, are based on more up-to-date figures from long-term studies tracking the health of the population. Researchers from University College London said that the harm caused by smoking is “cumulative” and the sooner a person stops smoking, and the more cigarettes they avoid smoking, the longer they live. The new analysis, commissioned by the Department for Health and Social Care, suggests that if a 10-cigarettes-a-day smoker quits on January 1, then by January 8 they could “prevent loss of a full day of life”. By February 20, their lives could be extended by a whole week. And if their quitting is successful until August 5, they will likely live for a whole month longer than if they had continued to smoke. The authors added: “Studies suggest that smokers typically lose about the same number of healthy years as they do total years of life. Make 2025 the year you quit smoking for good. There’s lots of free support available to help you. Find out more 🔽 https://t.co/J0ehnoRM1D pic.twitter.com/LQpUp6HJBm — WHH 🏥 (@WHHNHS) December 27, 2024 “Thus smoking primarily eats into the relatively healthy middle years rather than shortening the period at the end of life, which is often marked by chronic illness or disability. “So a 60-year-old smoker will typically have the health profile of a 70-year-old non-smoker.” The analysis, to be published in the Journal of Addiction, concludes: “We estimate that on average, smokers in Britain who do not quit lose approximately 20 minutes of life expectancy for each cigarette they smoke. “This is time that would likely be spent in relatively good health. “Stopping smoking at every age is beneficial but the sooner smokers get off this escalator of death the longer and healthier they can expect their lives to be.” Dr Sarah Jackson, principal research fellow from the UCL Alcohol and Tobacco Research Group, said: “It is vital that people understand just how harmful smoking is and how much quitting can improve their health and life expectancy. “The evidence suggests people lose, on average, around 20 minutes of life for each cigarette they smoke. “The sooner a person stops smoking, the longer they live. “Quitting at any age substantially improves health and the benefits start almost immediately. “It’s never too late to make a positive change for your health and there are a range of effective products and treatments that can help smokers quit for good.” There are so many reasons to quit smoking this New Year – for your health, for more money, and for your family. Make a fresh quit for 2025 – find tips and support at https://t.co/GyLk65o8kS or https://t.co/iW6WLxTL00 pic.twitter.com/KxPZ5N378y — North Tees and Hartlepool NHS Foundation Trust (@NTeesHpoolNHSFT) December 27, 2024 Health officials have said that smokers can find advice, support and resources with the NHS Quit Smoking app, as well as the online Personal Quit Plan. Public health minister Andrew Gwynne said: “Smoking is an expensive and deadly habit and these findings reveal the shocking reality of this addiction, highlighting how important it is to quit. “The new year offers a perfect chance for smokers to make a new resolution and take that step.” Commenting on the paper, Professor Sanjay Agrawal, special adviser on tobacco at the Royal College of Physicians, said: “Every cigarette smoked costs precious minutes of life, and the cumulative impact is devastating, not only for individuals but also for our healthcare system and economy. “This research is a powerful reminder of the urgent need to address cigarette smoking as the leading preventable cause of death and disease in the UK.”
New Canadians, non-traditional demographics boost minor hockey uptake in B.C.Police hunt for UnitedHealthcare CEO's masked killer after 'brazen, targeted' attack on NYC streetBLOOMINGTON — Former McLean County Chair Catherine Metsker, whose term on the board was set to expire in 2026, announced her resignation on Wednesday. McLean County Clerk Kathy Michael confirmed the departure of Metsker, who had served on the County Board for the last 10 years and had served as chair since September 2023 . Metsker could not immediately be reached for comment Wednesday afternoon. However, once the board had a 12-8 Democratic majority following the November election, it voted to replace Metsker with former Vice Chair Elizabeth Johnston. Johnston said Metsker has always been very passionate about and dedicated to county issues and that will be missed on the County Board. With Metsker's departure, the board is expected to make an official announcement of the vacancy during its Dec. 12 meeting. At that point, applicants interested in filling Metsker's seat have until the end of the first full week of January to submit their resume and application to McLean County Administrator's Office. Applicants must be Republican and must reside in the District 1, which covers most of the northern half of McLean County outside of Bloomington and Normal. Selected candidates will be interviewed by the McLean County Executive Committee during its Jan. 13 meeting, and the full board is expected to vote to appoint the chosen candidate on Jan. 16. Metsker Contact Drew Zimmerman at 309-820-3276. Follow Drew on Twitter: @DZimmermanLee Get Government & Politics updates in your inbox! Stay up-to-date on the latest in local and national government and political topics with our newsletter. Government Reporter {{description}} Email notifications are only sent once a day, and only if there are new matching items.
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Unlikely battleground California plays key role -- again -- in setting US House's political balanceShare Tweet Share Share Email Predictive analytics, a subset of advanced analytics, uses statistical algorithms, machine learning techniques, and data mining to analyze historical data and make predictions about future outcomes. In finance, this approach has become a cornerstone for enhancing decision-making, reducing risks, and identifying opportunities. But how does predictive analytics contribute to financial health? And why is seeing the big picture crucial ? The Importance of Financial Health Financial health reflects the stability and sustainability of an individual, business, or economy. For individuals, it entails consistent income, manageable debt, and savings for future needs. For businesses, financial health involves maintaining profitability, ensuring liquidity, and managing risks effectively. Predictive analytics provides tools to assess, monitor, and improve these aspects by offering data-driven insights. Why Predictive Analytics Matters in Financial Management Proactive Decision-Making: Instead of reacting to financial issues, predictive analytics allows entities to anticipate and mitigate problems before they escalate. Risk Management: By analyzing historical patterns, businesses can identify potential risks and devise strategies to counteract them. Optimized Investments: Insights derived from predictive models can help optimize investment decisions, ensuring maximum returns. Key Applications of Predictive Analytics in Financial Health Credit Scoring and Risk Assessment Credit scoring is one of the most common applications of predictive analytics in finance. By evaluating historical borrowing and repayment data, predictive models can determine the likelihood of a borrower defaulting on a loan. Furthermore, This information helps lenders make informed decisions, minimizing potential losses. For example, financial institutions use machine learning models to evaluate creditworthiness by considering multiple factors, such as income, credit history, and spending habits. These models go beyond traditional credit scoring methods, providing a comprehensive risk profile for borrowers. Fraud Detection Financial fraud poses a significant threat to individuals and organizations alike. Predictive analytics plays a pivotal role in identifying unusual patterns that may indicate fraudulent activities. Machine learning algorithms analyze vast amounts of transactional data, flagging anomalies in real time. For instance, if a credit card is suddenly used in a different country or for unusually large purchases, predictive models can detect these anomalies and trigger alerts. This proactive approach enhances security and reduces financial losses. Portfolio Management Investors and portfolio managers rely on predictive analytics to make data-driven decisions. By analyzing market trends, economic indicators, and historical performance, predictive models provide insights into the future performance of assets. This enables better asset allocation, diversification, and risk management. For example, robo-advisors use predictive analytics to recommend personalized investment strategies based on an investor’s goals, risk tolerance, and financial situation. These automated tools make investing accessible and efficient. Cash Flow Forecasting Accurate cash flow management is vital for businesses to ensure liquidity and avoid financial crises. Predictive analytics helps companies forecast cash inflows and outflows by analyzing historical data, seasonal trends, and market conditions. This foresight allows businesses to plan expenditures, manage debts, and seize growth opportunities effectively. Personal Financial Planning Individuals can leverage predictive analytics for budgeting and long-term financial planning. In as much as, Tools like budgeting apps and financial planning software use predictive models to analyze spending habits and forecast future financial needs. These insights empower users to make informed decisions, save effectively, and achieve financial goals. The Role of Data in Predictive Analytics Data is the backbone of predictive analytics. Without accurate and comprehensive data, predictive models cannot deliver reliable insights. In financial health, data sources include: Historical Financial Data: Past transactions, income statements, and balance sheets provide the foundation for analysis. Market Trends: Economic indicators, stock market data, and industry trends help predict future scenarios. Behavioral Data: Insights into consumer behavior, such as spending habits and preferences , enrich predictive models. Challenges in Implementing Predictive Analytics Data Quality and Accessibility The accuracy of predictive models depends on the quality of data. Incomplete, outdated, or biased data can lead to flawed predictions. Additionally, accessing sensitive financial data often involves navigating complex privacy regulations and security concerns. Integration with Existing Systems Implementing predictive analytics requires integrating advanced tools with existing financial systems. This process can be challenging and time-consuming, especially for organizations with legacy systems. Expertise and Costs Building and maintaining predictive models demand skilled data scientists and significant investments in technology. Moreover, For smaller businesses or individuals, these costs can be prohibitive. Best Practices for Leveraging Predictive Analytics Define Clear Objectives Identify specific financial goals and align predictive analytics efforts with these objectives. Whether it’s reducing credit risks, improving savings, or optimizing investments, clarity in goals ensures effective implementation. Invest in Quality Data Ensure access to accurate, comprehensive, and up-to-date data. Implement robust data governance practices to maintain data integrity and compliance. Embrace Automation Automation simplifies predictive analytics processes, making them more accessible and efficient. Tools like artificial intelligence (AI) and machine learning can automate data analysis, model building, and insights generation. Monitor and Update Models Predictive models must evolve with changing market conditions and new data. Thus, Regular monitoring and updates ensure models remain relevant and reliable. Future Trends in Predictive Analytics for Financial Health The field of predictive analytics continues to evolve, driven by advancements in technology and increasing demand for data-driven insights. Key trends include: Integration with Artificial Intelligence AI-powered predictive analytics can process vast amounts of data at unprecedented speeds, uncovering deeper insights and enhancing accuracy. Additionally, AI-driven tools like natural language processing and deep learning are expected to revolutionize financial analytics. Real-Time Analytics Real-time predictive analytics enables faster decision-making by providing immediate insights. This capability is particularly valuable in volatile markets or fraud detection scenarios. Increased Accessibility As technology becomes more affordable, predictive analytics tools are becoming accessible to small businesses and individuals. Furthermore, This democratization of analytics empowers more users to benefit from data-driven insights. Conclusion Predictive analytics is transforming financial health by enabling proactive decision-making, enhancing risk management, and optimizing financial strategies. Additionally, By seeing the big picture, individuals and businesses can navigate uncertainties, seize opportunities, and achieve sustainable growth. While challenges exist, the future of predictive analytics in finance holds immense potential, promising greater accessibility, efficiency, and impact. Furthermore, Embracing this innovative approach is not just an option—it’s a necessity for thriving in an increasingly data-driven world. Related Items: Offload Real-Time Analytics , Predictive Analytics for Financial Health , Seeing the Big Picture Share Tweet Share Share Email Recommended for you Offload Real-Time Analytics from MongoDB Using Elasticsearch Comments
The ChatGPT-maker and Anduril will focus on improving defenses against drone attacks, the companies said in a joint release. The partnership comes nearly a year after OpenAI did away with wording in its policies that banned use of its technology for military or warfare purposes. Founded in 2017, Anduril is a technology company that builds command and control systems and a variety of drones, counting the United States, Australia and the United Kingdom among its customers, according to its website. OpenAI said in October that it was collaborating with the US military's research arm DARPA on cyber defenses for critical networks. "AI is a transformational technology that can be used to strengthen democratic values or to undermine them," OpenAI said in a post at the time. "With the proper safeguards, AI can help protect people, deter adversaries, and even prevent future conflict." The companies said the deal would help the United States maintain an edge over China, a goal that OpenAI chief Sam Altman has spoken of in the past. "Our partnership with Anduril will help ensure OpenAI technology protects US military personnel, and will help the national security community understand and responsibly use this technology to keep our citizens safe and free," Altman said in Wednesday's release. Anduril was co-founded by Palmer Luckey, after Facebook bought his previous company Oculus VR in a $2 billion deal. The new partnership will bring together OpenAI's advanced AI models with Anduril systems and software, according to the companies. "Our partnership with OpenAI will allow us to utilize their world-class expertise in artificial intelligence to address urgent Air Defense capability gaps across the world," Anduril co-founder and chief executive Brian Schimpf said in the release. Schimpf said the collaboration would allow "military and intelligence operators to make faster, more accurate decisions in high-pressure situations." gc/aha