Impacted First and Second Permanent Molars: Overview

Impaction of a permanent tooth is a relatively common clinical occurrence in the human dentition. First mandibular molars and maxillary second molars are rarely impacted with a reported prevalence of 0–2.3% for second molars, 0.02% for the maxillary first molar, and of less than 0.01% for the mandibular first molar. The failures in their eruption mechanism may occur due to an obstacle such as the presence of a supernumerary tooth or an odontoma, lack of adequate space in the arch, an abnormal eruption path, or with idiopathic etiology. It is an asymptomatic pathology which is usually a casual discovery. Early diagnosis and treatment of permanent molars eruption disturbances contributes to optimal outcomes and favorable long-term prognosis by reduction of complication. The purpose of this is chapter is (1) to define prevalence and etiopathogeny of impacted first and second permanent molars, (2) to pinpoint the needs of earlier diagnosis, and finally (3) to highlight the treatment options

Bioavailability of Ruminally or Abomasally Infused L-carnitine in Holstein Heifers

This study evaluated the relative bioavailability of carnitine delivered by different methods in dairy cattle. Four Holstein heifers were used in a split-plot design to compare ruminally or abomasally infused L-carnitine. The study included 2 main-plot periods, with infusion routes allocated in a crossover design. Within main-plot periods, each of 3 subplot periods consisted of 4-d infusions separated with 4-d rest periods. Subplot treatments were infusion of 1, 3, and 6 g L-carnitine daily. Doses were increased within a period to minimize carryover. Treatments were delivered in two 10-h infusions daily. Blood was collected before the start of infusions and on day 4 of each infusion to obtain baseline and treatment carnitine concentrations. There was a dose × route interaction (P \u3c 0.05) and route effect (P \u3c 0.01) for increases in plasma carnitine above baseline, with increases above baseline being greater across all dose levels when infused abomasally compared to ruminally. Results demonstrated superior bioavailability of carnitine when ruminal exposure was physically bypassed

Shape It Better than Skip It: Mapping the Territory of Quantum Computing and Its Transformative Potential

Quantum Computing (QC) is an emerging and fast-growing research field that combines computer science with quantum mechanics such as quantum superposition and quantum entanglement. In order to contribute to a clarification of this field, the objective of this paper is twofold. Firstly, it aims to map the territory in which most relevant QC researches, scientific communities and related domains are stated and its relationship with classical computing. Secondly, it aims to examine the future research agenda according to different perspectives. We will do so by conducting a systematic literature review (SLR) based on the most important databases from 2010 to 2022. Our findings demonstrate that there is still room for understanding QC and how it transforms business, society and learning

A Hybrid Method Based on Quantum-enhanced RNN and Data Integration for the Prediction of COVID-19 Outbreak

Due to the continuous spread of COVID-19 worldwide, it is urgent, especially in the data science era, to develop accurate data driven decision-aided method to early detect its outbreak. Currently, Deep Learning and especially Recurrent Neural Networks (RNN) are one of the promising methods to accurately predict COVID-19 outbreak. However, designing an accurate RNN is always a challenging task because RNN often require big data and computational cost. To overcome these challenges, we propose in this paper a novel method to predict daily COVID-19 positive cases that consists of two steps: 1) data integration where medical data and weather data are integrated to improve both data quantity and quality especially when we deal with countries with less facilities of collecting data and 2) quantum improvement where quantum and classical RNN are integrated to provide super-calculator for the prediction. Experiments on six countries from Africa (Tunisia, Algeria, Senegal, Cameron, Niger, and Nigeria) indicate two main results. First, through data integration, a correlation between medical and weather data is detected where we note a real impact of the weather on COVID-19 outbreak. Second, compared with classical RNN, quantum-enhanced RNN trained on augmented data achieved the best results in terms of accuracy as well as root mean square error (RMSE) and it required the lowest time for training. Thus, our main contributions are i) to enrich medical data by weather data to improve data quality and quantity and ii) to enhance RNN by quantum layers to accurately and speedily forecast COVID-19 outbreak. All implementations and datasets are available online to the scientific community at https://github.com/nasriAhmed/Master_Covid.git

A Possibilistic Query Translation Approach for Cross-Language Information Retrieval

International audienceIn this paper, we explore several statistical methods to find solutions to the problem of query translation ambiguity. Indeed, we propose and compare a new possibilistic approach for query translation derived from a probabilistic one, by applying a classical probability-possibility transformation of probability distributions, which introduces a certain tolerance in the selection of word translations. Finally, the best words are selected based on a similarity measure. The experiments are performed on CLEF-2003 French-English CLIR collection, which allowed us to test the effectiveness of the possibilistic approach