On cosymplectic Lie Algebras

We give some properties of cosymplectic Lie algebras, we show, in particular, that they support a left symmetric product. We also give some constructions of cosymplectic Lie algebras, as well as a classification in three and five-dimensional cosymplectic Lie algebras.Comment: 20 page

Impact of Tube Voltage on Radiation Dose (CTDI) and Image Quality at Chest CT Examination

During Computed Tomography (CT) scan examinations, it is important to ensure a good diagnosis by providing the maximum information to detect pathologies and this can be done with a reduced dose. In this respect, several methods of dose reduction have been studied and evaluated. This work investigates the effect of tube voltage while varying the tube current on image quality and radiation dose at Chest CT examination. This study was conducted on HITACHI CT 16 slice Scanner using two phantoms for evaluating the dose and image quality; a PMMA phantom and a CATPHAN 500. Two tube voltages of 120 KVp and 100 KVp have been used for some variation of the tube currents (mAs) and recording the values of the measured quantities (CTDIv, spatial resolution, contrast to noise ratio CNR and noise). The scanning with 100 KVp at Chest CT examination led to a reduction in CTDIv until 45 %, an increase of noise from 17 % to 45 %, and the Spatial Resolution fell slightly (6 and 7 pl/cm) compared to the 120 KVp. The CNR shows a slight regression from 11 to 22 % for the 120 KVp and 100 KVp. This study has shown that despite the increase in the image noise at low tube voltage 100 KVp, it is possible to reduce the radiation dose by up to 45 % without degradation of image quality at Chest CT examination. Further works will evaluate the effect of acquisition parameters in other CT examinations

Quantification of ciprofloxacin in pharmaceutical products from various brands using FT-NIR: A comparative investigation of PLS and MCR-ALS.

peer reviewedThis study aims to quantify ciprofloxacin in commercial tablets with varying excipient compositions using Fourier Transform Near-Infrared Spectroscopy (FT-NIR) and chemometric models: Partial Least Squares (PLS) and Multivariate Curve Resolution - Alternating Least Squares (MCR-ALS). Matrix variation, arising from differences in excipient compositions among the tablets, can impact quantification accuracy. We discuss this phenomenon, emphasizing potential issues introduced by varying certain excipients and its importance in reliable ciprofloxacin quantification. We evaluated the performance of PLS and MCR-ALS models independently on two sets of tablets, each containing the same drug substance but different excipients. The statistical results revealed promising results with PLS prediction error of 0.38% w/w of the first set and 0.47% w/w of the second set, while MCR-ALS achieved prediction errors of 0.67% w/w of the first set and 1.76% w/w of the second set. To address the challenge of matrix variation, we developed single models for PLS and MCR-ALS using a dataset combining both first and second sets. The PLS single model demonstrated a prediction error of 4.3% w/w and a relative error of 6.41% w/w, while the MCR-ALS single model showed a prediction error of 1.88% w/w and a relative error of 1.29% w/w. We then assessed the performance of the single PLS and MCR-ALS models developed based on the combination of the first and the second set in quantifying ciprofloxacin in various commercial tablet brands containing new excipients. The PLS model achieved a prediction error ranging between 6.2% w/w and 8.39% w/w, with relative errors varied between 8.53% w/w and 12.82% w/w. On the other hand, the MCR-ALS model had a prediction error between 1.11% w/w and 2.66% w/w, and the relative errors ranging from 0.8% to 1.74% w/w

Modelling agricultural drought: a review of latest advances in big data technologies

Open Access Journal; Published online: 12 Oct 2022This article reviews the main recent applications of multi-sensor remote sensing and Artificial Intelligence techniques in multivariate modelling of agricultural drought. The study focused mainly on three fundamental aspects, namely descriptive modelling, predictive modelling, and spatial modelling of expected risks and vulnerability to drought. Thus, out of 417 articles across all studies on drought, 226 articles published from 2010 to 2022 were analyzed to provide a global overview of the current state of knowledge on multivariate drought modelling using the inclusion criteria. The main objective is to review the recent available scientific evidence regarding multivariate drought modelling based on the joint use of geospatial technologies and artificial intelligence. The analysis focused on the different methods used, the choice of algorithms and the most relevant variables depending on whether they are descriptive or predictive models. Criteria such as the skill score, the given game complexity used, and the nature of validation data were considered to draw the main conclusions. The results highlight the very heterogeneous nature of studies on multivariate modelling of agricultural drought, and the very original nature of studies on multivariate modelling of agricultural drought in the recent literature. For future studies, in addition to scientific advances in prospects, case studies and comparative studies appear necessary for an in-depth analysis of the reproducibility and operational applicability of the different approaches proposed for spatial and temporal modelling of agricultural drought

LANDSLIDE SUSCEPTIBILITY MAPPING IN THE MUNICIPALITY OF OUDKA, NORTHERN MOROCCO: A COMPARISON BETWEEN LOGISTIC REGRESSION AND ARTIFICIAL NEURAL NETWORKS MODELS

The Rif is among the areas of Morocco most susceptible to landslides, because of the existence of relatively young reliefs marked by a very important dynamics compared to other regions. These landslides are one of the most serious problems on many levels: social, economic and environmental. The increase in the frequency and impact of landslides over the past decade has demonstrated the need for an in-depth study of these phenomena, allowing the identification of areas susceptible to landslides. The main objective of this study is to identify the optimal method for the mapping of the area susceptible to landslides in municipality of Oudka. This area has been marked by the largest landslide in the region, caused by heavy rainfall in 2013. Two Statistical Methods i) Regression Logistics (LR) ii) Artificial Neural Networks (ANN), were used to create a landslide susceptibility map. The realization of this susceptibility map required, first, the mapping of old landslides by the aerial photography, the data of the geological map and by the data obtained using field surveys using GPS. A total of 105 landslides were mapped from these various sources. 50% of this database was used for model building and 50% for validation. Eight independent landslide factors are exploited to detect the most sensitive areas: altitude, slope, aspect, distance of faults, distance streams, distance from roads, lithology and vegetation index (NDVI). The results of the landslide susceptibility analysis were verified using success and prediction rates. The success rate (AUC&thinsp;=&thinsp;0.918) and the prediction rate (AUC&thinsp;=&thinsp;0.901) of the LR model is higher than that of the ANN model (success rate (AUC&thinsp;=&thinsp;0.886) and prediction rate (AUC&thinsp;=&thinsp;0.877). These results indicate that the Regression Logistic (LR) model is the best model for determining landslide susceptibility in the study area.</p

STUDY AND MODELING OF THE DISINTEGRATION KINETICS OF COATED PAPER

The disintegration of recovered paper is the first operation in the preparation of recycled pulp. It is known that the defibering process follows a first order kinetics from which it is possible to obtain the disintegration kinetic constant (KD) by means of different ways. The disintegration constant can be obtained from the Somerville index results (%ISV) and from the dissipated energy per volume unit (SS). The %ISV is related to the quantity of non-defibrated paper, as a measure of the non-disintegrated fiber residual (percentage of flakes), which is expressed in disintegration time units. In this work, disintegration kinetics from recycled coated paper has been evaluated, working at 20 rev/s rotor speed and for different fiber consistency (6, 8, 10, 12, and 14%). The results showed that the values of experimental disintegration kinetic constant, KD, through the analysis of Somerville index, as function of time, increased with the disintegration consistency. Therefore, as consistency increased, the disintegration time was drastically reduced. The calculation of the disintegration kinetic constant (modeled KD), extracted from the Rayleigh’s dissipation function, showed a good correlation with the experimental values using the evolution of the Somerville index or with the dissipated energy

Search based software engineering: Trends, techniques and applications

© ACM, 2012. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version is available from the link below.In the past five years there has been a dramatic increase in work on Search-Based Software Engineering (SBSE), an approach to Software Engineering (SE) in which Search-Based Optimization (SBO) algorithms are used to address problems in SE. SBSE has been applied to problems throughout the SE lifecycle, from requirements and project planning to maintenance and reengineering. The approach is attractive because it offers a suite of adaptive automated and semiautomated solutions in situations typified by large complex problem spaces with multiple competing and conflicting objectives. This article provides a review and classification of literature on SBSE. The work identifies research trends and relationships between the techniques applied and the applications to which they have been applied and highlights gaps in the literature and avenues for further research.EPSRC and E