Flooding can damage pavement infrastructure significantly, causing both immediate and long-term structural and functional issues. This research investigates how flooding events affect pavement deterioration, specifically focusing on measuring pavement roughness by the International Roughness Index (IRI). To quantify these effects, we utilized 20 years of pavement condition data from TxDOT's PMIS database, which is integrated with flood event data, including duration and spatial extent. Statistical analyses were performed to compare IRI values before and after flooding and to calculate the deterioration rates influenced by flood exposure. Moreover, we applied Explainable Artificial Intelligence (XAI) techniques, such as SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME), to assess the impact of flooding on pavement performance. The results demonstrate that flood-affected pavements experience a more rapid increase in roughness compared to non-flooded sections. These findings emphasize the need for proactive flood mitigation strategies, including improved drainage systems, flood-resistant materials, and preventative maintenance, to enhance pavement resilience in vulnerable regions.

Flooding is one of the most disastrous natural hazards, responsible for substantial economic losses. A predictive model for flood-induced financial damages is useful for many applications such as climate change adaptation planning and insurance underwriting. This research assesses the predictive capability of regressors constructed on the National Flood Insurance Program (NFIP) dataset using neural networks (Conditional Generative Adversarial Networks), decision trees (Extreme Gradient Boosting), and kernel-based regressors (Gaussian Process). The assessment highlights the most informative predictors for regression. The distribution for claims amount inference is modeled with a Burr distribution permitting the introduction of a bias correction scheme and increasing the regressor's predictive capability. Aiming to study the interaction with physical variables, we incorporate Daymet rainfall estimation to NFIP as an additional predictor. A study on the coastal counties in the eight US South-West states resulted in an $R^2=0.807$. Further analysis of 11 counties with a significant number of claims in the NFIP dataset reveals that Extreme Gradient Boosting provides the best results, that bias correction significantly improves the similarity with the reference distribution, and that the rainfall predictor strengthens the regressor performance.

Potential advancements in artificial intelligence (AI) could have profound implications for how countries research and develop weapons systems, and how militaries deploy those systems on the battlefield. The idea of AI-enabled military systems has motivated some activists to call for restrictions or bans on some weapon systems, while others have argued that AI may be too diffuse to control. This paper argues that while a ban on all military applications of AI is likely infeasible, there may be specific cases where arms control is possible. Throughout history, the international community has attempted to ban or regulate weapons or military systems for a variety of reasons. This paper analyzes both successes and failures and offers several criteria that seem to influence why arms control works in some cases and not others. We argue that success or failure depends on the desirability (i.e., a weapon's military value versus its perceived horribleness) and feasibility (i.e., sociopolitical factors that influence its success) of arms control. Based on these criteria, and the historical record of past attempts at arms control, we analyze the potential for AI arms control in the future and offer recommendations for what policymakers can do today.

This week's list of top data news highlights covers July 30, 2022 to August 5, 2022 and includes articles on practicing complex surgeries with virtual reality and using an AI system to create an advertising campaign. India's National Tiger Conservation Authority (NCTA) has used over 26,000 cameras to capture over 24 million images of tigers around the country. Conservationists are using an AI system to identify tigers found in the images and quantify the total tiger population in the country. The NCTA plans to use an AI system to map patrol routes throughout sanctuaries to better monitor tigers next. Researchers at the University of New Orleans, Louisiana Department of Environmental Quality, and Jefferson Parish Department of Environmental Affairs have used a supercomputer to simulate the diffusion and dispersion of chemical compounds that can deodorize a landfill.

In 2019, Walmart started working with a company called Gatik to test autonomous delivery trucks on a two-mile route between a fulfillment center and a store in Bentonville, Arkansas. After those vehicles logged more than 70,000 miles with a human driver there to make sure nothing went wrong, Walmart and Gatik say they're ready for a new challenge. Next year, there won't be any human drivers in the trucks. That milestone will make Gatik one of the first companies in the space to operate a fully autonomous route in this way. As the startup itself is quick to point, it has its simplified approach to thank for the achievement.

The word "Disagree" is seen on the hand of Julia Grabowski during a town hall meeting for Republican U.S. Senator Bill Cassidy in Metairie, Louisiana. News about President Donald Trump -- including an apparently neglected vegetable garden that once belonged to former first lady Michelle Obama -- is inescapable. As The New York Times' Farhad Manjoo wrote, "he is no longer just the message. In many cases, he has become the medium." Mental health professionals in the U.S. have reported that the all-encompassing coverage of the president has induced anxiety and depression, or post-election stress, in many of their patients.