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In the fall of 1969 the Libyan monarchy was overthrown by a group of young military officers headed by Mouamir Quadafi. In the months prior to the revolution the tribal-based Monarchy had found it difficult to cope with the stress of rapid social and economic change, and had found itself immobilized by the conflict between Libya's traditional tribal and religious elite and a small but influential modernizing elite composed of students, technocrats and younger military officers. This new elite, a byproduct of Libya's oil wealth, had become particularly distressed by the arbitrariness and inefficiency of the Monarchy, the severe maldistribution of the oil revenues, and the existence of what they felt to be a poorly planned and managed development program which had largely failed to penetrate Libya's predominatly rural areas.

The transportation sector is a major contributor to greenhouse gas (GHG) emissions and is a driver of adverse health effects globally. Increasingly, government policies have promoted the adoption of electric vehicles (EVs) as a solution to mitigate GHG emissions. However, government analysts have failed to fully utilize consumer data in decisions related to charging infrastructure. This is because a large share of EV data is unstructured text, which presents challenges for data discovery. In this article, we deploy advances in transformer-based deep learning to discover topics of attention in a nationally representative sample of user reviews. We report classification accuracies greater than 91% (F1 scores of 0.83), outperforming previously leading algorithms in this domain. We describe applications of these deep learning models for public policy analysis and large-scale implementation. This capability can boost intelligence for the EV charging market, which is expected to grow to US$27.6 billion by 2027.

The internet has been used by individuals, organizations, and governments for business, sports, health, banking, advertisement, education, and other services. Many websites have been developed and designed in the last several decades. However, most have not been developed and designed according to a shared set of design standards. Consequently, there is a need for an approach to evaluate the effectiveness of a website. A literature review was conducted to develop such an approach. Four experts were then consulted to inspect and evaluate the approach, and a questionnaire was completed by three categories: Internet users, website developers, and others to determine its final version. This research resulted in the development of an approach to evaluate website effectiveness, composed of three major criteria: design, content, and functionality, and 17 sub-criteria. The significance of this new approach is that it allows stakeholders to evaluate their websites and determine how to improve them in order to achieve their vision and mission.

Abstract
The overall project objective at the Iron King Mine Superfund site is to determine the level and potential risk associated with heavy metal exposure of the proximate population emanating from the site's tailings pile. To provide sufficient size-fractioned dust for multi-discipline research studies, a dust generator was built and is now being used to generate size-fractioned dust samples for toxicity investigations using in vitro cell culture and animal exposure experiments as well as studies on geochemical characterization and bioassay solubilization with simulated lung and gastric fluid extractants. The objective of this study is to provide a robust method for source identification by comparing the tailing sample produced by dust generator and that collected by MOUDI sampler. As and Pb concentrations of the PM10 fraction in the MOUDI sample were much lower than in tailing samples produced by the dust generator, indicating a dilution of Iron King tailing dust by dust from other sources. For source apportionment purposes, single element concentration method was used based on the assumption that the PM10 fraction comes from a background source plus the Iron King tailing source. The method's conclusion that nearly all arsenic and lead in the PM10 dust fraction originated from the tailings substantiates our previous Pb and Sr isotope study conclusion. As and Pb showed a similar mass fraction from Iron King for all sites suggesting that As and Pb have the same major emission source. Further validation of this simple source apportionment method is needed based on other elements and sites.