Elon Musk's artificial intelligence company is stirring controversy in Memphis, Tennessee. That's where he's building a massive supercomputer to power his company xAI. Community residents and environmental activists say that since the supercomputer was fired up last summer it has become one of the biggest air polluters in the county. But some local officials have championed the billionaire, saying he's investing in Memphis. The first public hearing with the health department is scheduled for Friday, where county officials will hear from all sides of the debate.

Extreme weather events and other vulnerabilities are causing blackouts with increasing frequency, disrupting traffic control systems and posing significant challenges to urban mobility. To address this growing concern, we introduce \model{}, a naturalistic driving dataset collected during blackouts at complex intersections. Beacon provides detailed traffic data from two unsignalized intersections in Memphis, TN, including timesteps, origin, and destination lanes for each vehicle over four hours. We analyze traffic demand, vehicle trajectories, and density across different scenarios. We also use the dataset to reconstruct unsignalized, signalized and mixed traffic conditions, demonstrating its utility for benchmarking traffic reconstruction techniques and control methods. To the best of our knowledge, Beacon could be the first public available traffic dataset that captures naturalistic driving behaviors at complex intersections.

After 25 years as a pediatric infectious diseases specialist, Asunción Mejías is too familiar with the deadly unpredictability of respiratory syncytial virus (RSV), an infection that hospitalizes up to 80,000 children under the age of 5 every year in the US. "It's a disease which can change very quickly," says Mejías, who works at St. Jude Children's Research Hospital in Memphis, Tennessee. "I've always told my colleagues that for every two children that are admitted, one can go to the ICU in the next three hours and the other one may go home the next day. RSV infections are very common, to the point that nearly every child will have one before they turn 2 years old. Most children experience symptoms similar to a cold, like coughing and sneezing, but some can develop severe lung disease: RSV is responsible for more than 100,000 infant deaths globally every year, nearly half of which are in babies under 6 months of age.

Elon Musk's AI company has caught the attention of environmentalists in Memphis, Tennessee for reportedly operating gas turbines without a permit. On Monday, environmentalist groups Memphis Community Against Pollution, Young, Gifted & Green, Sierra Club Chickasaw Group, and Sierra Club Tennessee Chapter sent a letter to the Shelby County Health Department calling for an investigation of the xAI data center, which powers the X chatbot Grok. According to the letter, xAI has installed at least 18 gas combustion turbines, which "have the capacity to emit about 130 tons of the ozone-precursor nitrogen oxides (NOx) per year." The groups allege that the xAI data center, which may be contributing to Memphis' existing smog problem, "has not applied for any air permits for these turbines." The vast amount of electricity and computing power required to run AI models is an emissions nightmare.

In a small room tucked into a US navy facility outside Memphis, Tennessee, uniformed personnel sit hunched over monitors, their eyes focused on screens as they speak into headsets with clipped efficiency. Computer towers and glowing red keyboards crowd their desks. This is top-of-the-line gear, used for executing combat missions and coordinating strategy – but not with fleets stationed across the world. These sailors are playing video games. On the other end of their headsets and screens are young gamers they hope to inspire. "In 2019, we did a big look at where we were spending our money, looking at where the next generation is," says Lt Aaron Jones, captain of the navy's esports team, as we sit in his office after touring the facility. "This is where they are," Jones continues. "Whether it's Twitch or YouTube or Facebook Gaming, this is what they love."

Analyzing the behavior of complex interdependent networks requires complete information about the network topology and the interdependent links across networks. For many applications such as critical infrastructure systems, understanding network interdependencies is crucial to anticipate cascading failures and plan for disruptions. However, data on the topology of individual networks are often publicly unavailable due to privacy and security concerns. Additionally, interdependent links are often only revealed in the aftermath of a disruption as a result of cascading failures. We propose a scalable nonparametric Bayesian approach to reconstruct the topology of interdependent infrastructure networks from observations of cascading failures. Metropolis-Hastings algorithm coupled with the infrastructure-dependent proposal are employed to increase the efficiency of sampling possible graphs. Results of reconstructing a synthetic system of interdependent infrastructure networks demonstrate that the proposed approach outperforms existing methods in both accuracy and computational time. We further apply this approach to reconstruct the topology of one synthetic and two real-world systems of interdependent infrastructure networks, including gas-power-water networks in Shelby County, TN, USA, and an interdependent system of power-water networks in Italy, to demonstrate the general applicability of the approach.

Detroit carjackings are up 40 percent compared to last year - and the ages of the kids doing them – have become unbelievable. A Tennessee man stands accused of carjacking his Tinder date at gunpoint last year and trying to sell her car back to her for $500. Elijah Darius Scott, 25, of Memphis, Tennessee, landed in the Shelby County Jail on Tuesday after being charged with carjacking and aggravated robbery, as well as employment of a firearm during a dangerous felony, according to local outlet WREG. The alleged victim informed officers that after agreeing to meet a man she knew only as Darius, Scott entered the passenger side of the vehicle, placed a gun next to her and demanded her phone and money while threatening to shoot her. Elijah Darius Scott, 25, allegedly carjacked a Memphis woman and offered to sell her car back to her for $500.

Role requiring'No experience data provided' months of experience in Memphis Pay if you succeed in getting hired and start work at a high-paying job first. Get Paid to Read Emails, Play Games, Search the Web, $5 Signup Bonus. Voca has an immediate contract FOR HIRE position opening (contract term approximately 12 months) for an IT Software Developer resource for our insurance industry client, located in Memphis, TN. This resource can work remotely, with a slight preference for candidates residing in MEMPHIS, TN or COLUMBUS, OH! To develop, maintain, test and debug software to meet generally defined requirements in a Windows client/server environment utilizing software development languages/environments such as PL/SQL, Oracle, Progress 4 GL Application Development Environment (ADE), .Net, Microsoft SQL, or other platforms; to formulate and define system scope and objectives through research and fact-finding for the purpose of developing or modifying moderately complex information systems; to prepare detailed specification from which programs will be written; and to design, code, test, debug, document and maintain programs. Becomes familiar with most aspects of the application including reports, parameters, claims management, intake services, carrier/client interfaces and vendor/business partner interfaces.

Patient monitoring is vital in all stages of care. We here report the development and validation of ICU length of stay and mortality prediction models. The models will be used in an intelligent ICU patient monitoring module of an Intelligent Remote Patient Monitoring (IRPM) framework that monitors the health status of patients, and generates timely alerts, maneuver guidance, or reports when adverse medical conditions are predicted. We utilized the publicly available Medical Information Mart for Intensive Care (MIMIC) database to extract ICU stay data for adult patients to build two prediction models: one for mortality prediction and another for ICU length of stay. For the mortality model, we applied six commonly used machine learning (ML) binary classification algorithms for predicting the discharge status (survived or not). For the length of stay model, we applied the same six ML algorithms for binary classification using the median patient population ICU stay of 2.64 days. For the regression-based classification, we used two ML algorithms for predicting the number of days. We built two variations of each prediction model: one using 12 baseline demographic and vital sign features, and the other based on our proposed quantiles approach, in which we use 21 extra features engineered from the baseline vital sign features, including their modified means, standard deviations, and quantile percentages. We could perform predictive modeling with minimal features while maintaining reasonable performance using the quantiles approach. The best accuracy achieved in the mortality model was approximately 89% using the random forest algorithm. The highest accuracy achieved in the length of stay model, based on the population median ICU stay (2.64 days), was approximately 65% using the random forest algorithm.

The study of adverse childhood experiences and their consequences has emerged over the past 20 years. In this study, we aimed to leverage explainable artificial intelligence, and propose a proof-of-concept prototype for a knowledge-driven evidence-based recommendation system to improve surveillance of adverse childhood experiences. We used concepts from an ontology that we have developed to build and train a question-answering agent using the Google DialogFlow engine. In addition to the question-answering agent, the initial prototype includes knowledge graph generation and recommendation components that leverage third-party graph technology. To showcase the framework functionalities, we here present a prototype design and demonstrate the main features through four use case scenarios motivated by an initiative currently implemented at a children hospital in Memphis, Tennessee. Ongoing development of the prototype requires implementing an optimization algorithm of the recommendations, incorporating a privacy layer through a personal health library, and conducting a clinical trial to assess both usability and usefulness of the implementation. This semantic-driven explainable artificial intelligence prototype can enhance health care practitioners ability to provide explanations for the decisions they make.