Combining Ad Hoc Text Mining and Descriptive Analytics to Investigate Public EV Charging Prices in the United States

Electric vehicle (EV) charging infrastructure is present all over the United States, but charging prices vary greatly, both in amount and in the methods by which they are assessed. For this paper, we interpret and analyze charging price information from PlugShare, a crowd-sourced EV charging data platform. Because prices in these data exist in a semi-structured textual format, an ad hoc text mining approach is used to extract quantitative price information. Descriptive analytics of the processed dataset demonstrate how the prices of EV charging vary with charging level (Direct Current Fast Charging versus Level 2), geographic location, network provider, and location type. Our research indicates that a great deal of diversity and flexibility exists in structuring the prices of EV charging to enable incentives for shaping charging behaviors, but that it has yet to be widely standardized or utilized. Comparisons with estimates of the levelized cost of EV charging illustrate some of the challenges associated with operating and using these stations.

Blockchain Network Propagation Mechanism Based on P4P Architecture

Blockchain is a mainstream technology in which many untrustworthy nodes work together to maintain a distributed ledger with advantages such as decentralization, traceability, and tamper-proof. The network layer communication mechanism in its architecture is the core of the networking method, message propagation, and data verification among blockchain nodes, which is the basis to ensure blockchain’s performance and key features. When blocks are propagated in peer-to-peer (P2P) networks with gossip protocol, the high propagation delay of the protocol itself reduces the propagation speed of the blocks, which is prone to the chain forking phenomenon and causes double payment attacks. To accelerate the propagation speed and reduce the fork probability, this paper proposes a blockchain network propagation mechanism based on proactive network provider participation for P2P (P4P) architecture. This mechanism first obtains the information of network topology and link status in a region based on the internet service provider (ISP), then it calculates the shortest path and link overhead of peer nodes using P4P technology, prioritizes the nodes with good local bandwidth conditions for transmission, realizes the optimization of node connections, improves the quality of service (QoS) and quality of experience (QoE) of blockchain networks, and enables blockchain nodes to exchange blocks and transactions through the secure propagation path. Simulation experiments show that the proposed propagation mechanism outperforms the original propagation mechanism of the blockchain network in terms of system overhead, rate of data success transmission, routing hops, and propagation delay.

The No Surprises Act: A Bipartisan Achievement to Protect Consumers from Unexpected Medical Bills

The No Surprises Act, passed by Congress at the end of 2020, offers significant protections to most Americans with private health insurance. Insured Americans are vulnerable to receiving surprise medical bills when they receive services from out-of-network providers. Protections for consumers against such bills initially emerged in several states that passed laws. The varying approaches taken in different state laws ultimately offered a foundation for federal legislation. Although there was always a broad consensus among stakeholders for protecting consumers during both state and federal deliberations, it was a challenge to identify a means of determining the amount that an insurer should pay to the out-of-network provider. But the Congress eventually reached a compromise that became law, which goes into effect in January 2022.

Self-Learning Controllers in the Oil and Gas Industry

Recently, solving the optimization-control problems by using artificial intelligence has widelyappeared in the petroleum fields in exploration and production. This paper presents the stateof-the-art reinforcement-learning algorithm applying in the petroleum optimization-controlproblems, which is called a direct heuristic dynamic programming (DHDP). DHDP has twointeractive artificial neural networks, which are the critic network (provider acritique/evaluated signal) and the actor network (provider a control signal). This paper focuseson a generic on-line learning control system in Markov decision process principles.Furthermore, DHDP is a model-free learning design that does not require prior knowledgeabout a dynamic model; therefore, DHDP can be appllied with any petroleum equipment ordevise directly without needed to drive a mathematical model. Moreover, DHDP learns byitself (self-learning) without human intervention via repeating the interaction between anequipment and environment/process. The equipment receives the states of theenvironment/process via sensors, and the algorithm maximizes the reward by selecting thecorrect optimal action (control signal). A quadruple tank system (QTS) is taken as a benchmarktest problem, that the nonlinear model responses close to the real model, for three reasons:First, QTS is widely used in the most petroleum exploration/production fields (entire system orparts), which consists of four tanks and two electrical-pumps with two pressure control valves.Second, QTS is a difficult model to control, which has a limited zone of operating parametersto be stable; therefore, if DHDP controls on QTS by itself, DHDP can control on otherequipment in a fast and optimal manner. Third, QTS is designed with a multi-input-multioutput (MIMO) model for analysis in the real-time nonlinear dynamic system; therefore, theQTS model has a similar model with most MIMO devises in oil and gas field. The overalllearning control system performance is tested and compared with a proportional integralderivative (PID) via MATLAB programming. DHDP provides enhanced performancecomparing with the PID approach with 99.2466% improvement.

PEER INFLUENCE AS DETERMINANTS OF UNIVERSITY- COMMUNITY PREFERENCE FOR GSM SERVICE PROVIDERS IN ONDO STATE, NIGERIA

The study investigated the strength of peer influence as determinants of university-community preference for GSM service providers in Ondo State, Nigeria. Two research questions were raised to guide the study. The study adopted a descriptive research survey. The population for this study consisted of the undergraduate students, academics staff and non-teaching staff of universities in Ondo State who were subscribers to each of the various popular service providers in Ondo State, Nigeria, namely, MTN, Globacom (Glo), Airtel and Etisalat (now 9mobile). The four universities have 34,246 students, 1,642 academic staff and 2,743 non-teaching staff totaling 38,631. Random sampling technique was used to select the sample of three thousand, and eight hundred and sixty-five (3,865) respondents. Questionnaire was used for data collection; the reliability was done by using Gultman Split-half method with a r-value of 0.859. Completed copies of the questionnaire were retrieved from the respondents, 3,863 copies of the instrument were distributed 3,671 copies were collected and used for analysis. The data generated was analyzed using descriptive statistics, charts, mean, standard deviation and percentage. The findings of the study revealed that majority of the respondents are students and friends pressurized themselves to use the network they have been using, it was recommended that, the students should be careful not to be influenced by friends to choose a wrong network provider that might affect them in their academics. KEY WORDS: Peer Influence, Network Providers and University-Community

Who Is Liable When a Driverless Car Crashes?

Autonomous vehicles (AVs) will revolutionize mobility in the future. However, accidents will still happen and it will affect the practices of today’s tort laws. This work discusses all those aspects which should be considered in order to find out who is liable, i.e., an operator, owner, manufacturer, government entity, software provider, network provider, original equipment manufacturer (OEM), etc., as traditional tort rules will not help to find out the liability in case of an AV accident. The work comprehensively discusses different liabilities ranging from legal, civil, operator, criminal, moral, product, insurance, etc., to find out who is liable in case of an AV accident, as compared to the existing literature which generally discusses one or two aspects only. The work also presents the current state of legislation and discusses legal challenges to the lawmakers, insurance companies, consumer, and manufacturers, etc. The future mobility models and different scenarios of AV accidents have also been discussed in terms of legal liability and third party insurance claims. The role of regulatory bodies and different challenges has also been discussed along with recommendations. Finally, the work also proposes a new novel liability attribute model with a particular focus on ethical issues. The research proposes that liability should be attributed in such a way that it benefits everyone and everybody feels justified in case of an AV accident. The research also concludes that product liability will be the major issue in terms of insurance issues and the manufacturer should be held liable for product failure unless other evidence favors the manufacturer.

On Nash Equilibria in Non-Cooperative All-Optical Networks

We consider the problem of determining a routing in all-optical networks, in which some couples of nodes want to communicate. In particular, we study this problem from the point of view of a network provider that has to design suitable payment functions for non-cooperative agents, corresponding to the couples of nodes wishing to communicate. The network provider aims at inducing stable routings (i.e., routings corresponding to Nash equilibria) using a low number of wavelengths. We consider three different kinds of local knowledge that agents may exploit to compute their payments, leading to three corresponding information levels. Under complete information, the network provider can design a payment function, inducing the agents to reach a Nash equilibrium mirroring any desired routing. If the price to an agent is computed only as a function of the wavelengths used along connecting paths (minimal level) or edges (intermediate level), the most reasonable functions either do not admit Nash equilibria or admit very inefficient ones, i.e., with the largest possible price of anarchy. However, by suitably restricting the network topology, a constant price of anarchy for chains and rings and a logarithmic one for trees can be obtained under the minimal and intermediate levels, respectively.

Network Functions Virtualization (NFV)

Network functions virtualization (NFV) is consolidating as one of the base technologies for the design, deployment, and operation of network services. NFV can be seen as a natural evolution of the trend to cloud technologies in IT, and hence perceived as bringing them to the network provider environments. While this can be true for the simplest cases, focused on the IT services network providers rely on, the nature of network services raises unique requirements on the overall virtualization process. NFV aims to provide at the same time an opportunity to network providers, not only in reducing operational costs but also in bringing the promise of easing the development and activation of new services, thereby reducing their time-to-market and opening new approaches for service provisioning and operation, in general. In this chapter, the authors analyse these requirements and opportunities, reviewing the state of the art in this new way of dealing with network services. Also, the chapter presents some NFV deployments endorsed by some network operators and identifies some remaining challenges.

Abstract 15760: Survival Following Durable Ventricular Assist Device Implantation is Associated With Provider Teamwork: A Social Network Analysis

Introduction: Health systems with more robust provider teamwork have lower mortality rates for coronary artery bypass grafting. Patients undergoing durable ventricular assist device (VAD) therapy have complex comorbidities and develop adverse events requiring multidisciplinary provider teamwork. Hypothesis: Provider teamwork is associated with 180-day mortality following durable VAD implantation. Methods: Data (linked Medicare claims+INTERMACS) were analyzed from primary durable VADs implanted across 119 hospitals over a five-year period. Claims were used to identify provider interactions (cardiac surgeons, cardiologists, anesthesiologists, intensivists, nurse practitioners, physician assistants) for previously shared VAD patients during the 1-year period ending 6-months before each patient’s VAD implant admission. Provider teamwork was assessed at the hospital level using a clustering coefficient (range: 0-1; higher values = greater teamwork), an established and validated measure capturing the prevalence of tightly connected groups in a network. Provider teamwork level was associated with a patient’s 180-day mortality rate using multivariable regression adjusting for baseline characteristics reported to INTERMACS. Results: The study cohort included 2,807 VAD patients (mean age 63 years, 53% for destination therapy, 19% female, 74% white) with 10.8% identified as INTERMACS Profile 1. There were 468 deaths (16.7%) within 180 days of implantation. Provider teamwork (clustering coefficient) was inversely associated with 180-day mortality (beta=-0.79; 95%CI: -1.26, -0.11). A 1SD increase in provider teamwork was associated with a 13.4% decrease in the predicted probability of 180-day mortality, Figure. Conclusions: Social networks characterized by greater provider teamwork are important determinants of mortality following VAD implantation and may serve as targets for optimizing outcomes and greater adoption for this therapy.